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• This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency. • A demand response program integrated with home energy storage systems is presented to optimize energy consumption. • To modify the consumption pattern of household consumers, a real-time pricing algorithm is proposed. • A self-healing system reconfiguration program integrated with distributed energy resources is presented. • Real data of California households are considered to model the home appliances and home energy storage systems. This paper proposes a comprehensive optimization program to increase economic efficiency and improve the resiliency of the Distribution Network (DN). A Demand Response Program (DRP) integrated with Home Energy Storage Systems (HESSs) is presented to optimize the energy consumption of household consumers. Each consumer implements a Smart Home Energy Management System (SHEMS) to optimize their energy consumption according to their desired comfort and preferences. To modify the consumption pattern of household consumers, a Real-Time Pricing (RTP) algorithm is proposed to reflect the energy price of the wholesale market to the retail market and consumers. In addition, a Self-Healing System Reconfiguration (SHSR) program integrated with Distributed Energy Resources (DER), reactive power compensation equipment, and Energy Storage Systems (ESSs) is presented to manage the DN energy and restore the network loads in disruptive events. The reconfiguration operation is performed by converting the isolated part of the DN from the upstream network to several self-sufficient networked virtual microgrids without executing any switching process. Real data of California households are considered to model the home appliances and HESSs. The proposed comprehensive program is validated on the modified IEEE 123-bus feeder in normal and emergency operating conditions. [ABSTRACT FROM AUTHOR]

In this paper, a real-time chemi-thermomechanical pulping (CTMP) process was investigated, where opportunities were explored to increase energy consumption efficiency with a guarantee of expected pulp properties as a priority. Due to the process instrumentation limitations, such as the lack of advanced regulatory controllers and optimization units, the advanced process controllers and real-time optimization strategies can hardly be implemented in the existing CTMP processes. Therefore, a novel visualization-based process evaluation methodology is proposed to aid the mill operators in making further decisions to improve the process operations. Additionally, to determine the desirable pulp properties for production, an inferential sensor model is developed that estimates the handsheet properties from pulp properties. Also, to assess the model prediction accuracy in real-time, a reliability index model is designed. The proposed methodologies have been validated on a practical CTMP process, and the results show the satisfactory performance of the system. • Developed a strategy to evaluate the CTMP performance using visualization methodology. • A novel inferential sensor model is implemented to determine the desirable pulp properties. • A reliability index model classifying reliability levels and assessing model accuracy is developed. • The proposed model adapts to model updates, uncertainties, and computation resource limitations. [ABSTRACT FROM AUTHOR]

Due to the mobile and random nature of services in cyber–physical–social systems (CPSSs), developing service composition approaches that ensure high availability, minimal energy consumption, and high quality of service (QoS) remains a complex challenge. Over the last two decades, several service composition approaches have been proposed in the literature to deal with this challenge. Nevertheless, the existing approaches have certain limitations, particularly in situations where services may move from one location to another, become unavailable due to intensive battery usage, encounter failures, or undergo a decline in quality. These limitations often arise because these approaches do not simultaneously integrate mobility, energy, and QoS constraints while defining the user's movement in a random manner. In this paper, the learning-based swarm optimization-aware service composition algorithm (LS-SCA) is proposed to overcome the aforementioned shortcoming. This approach surpasses existing ones by accounting simultaneously for the user's mobility, energy, and QoS criteria during the service composition process. First, the Small World in Motion (SWIM) mobility model is employed in this study to determine the user's mobility traces, avoiding the random generation of users' traces. Second, an energy consumption model is proposed to increase the energy efficiency by avoiding the overuse of the devices' batteries that can reduce the availability of services and lead to the composition failure. Third, the two-phase learning-based swarm optimizer (TPLSO) method is used in the composition process to find the sub-optimal composition that satisfies the global QoS constraints with the highest utility in terms of mobility, energy, and QoS. Unlike the most existing metaheuristic-based service composition approaches where the overall composition population is improved over a given number of iterations, the TPLSO method is exploited in this paper to improve only a subset of compositions, which reduces the composition time and increases the QoS utility of the composition. The simulation scenarios using two real datasets demonstrate that the LS-SCA approach outperforms six baselines in terms of energy consumption, QoS utility, and availability of composition. This notable performance makes the proposed approach more suitable for real-world applications where energy efficiency, QoS, and availability are crucial factors to consider. [ABSTRACT FROM AUTHOR]

Currently, building energy consumption prediction models are usually based on a large amount of historical operational data in high demands of building operating hours and monitoring systems. However, many buildings may lack operational data due to relatively limited monitoring systems, causing the failure to use data-driven methods to characterize the energy profile. In this context, transfer learning is a promising method to establish the knowledge transfer between many high-quality building operation datasets and a small amount of target building data, and to help predict energy consumption in the target building. This paper studies the possibility of employing transfer learning to achieve both short and long-term building energy consumption prediction. Firstly, a similarity analysis method, based on variable modal decomposition and dynamic time warping, is proposed for identifying the source buildings with the most similar energy features to the target building. Then, transfer learning for long-term prediction air-conditioning energy consumption is developed with weather parameters generated by the Morphing method as inputs. For the short-term single-step prediction, the proposed model CV-RMSE improves 81.3% (AEC) and 77.4% (EEC), respectively, compared to the prediction model that does not implement the transfer learning strategy and directly uses the target BEC data. As for the short-term multi-step prediction, the proposed model CV-RMSE improves 62.0% (AEC) and 65.5% (EEC), respectively. For the long-term prediction, the average CV-RMSE for the whole year is 6.62% and 11.15% for the proposed and directly target domain-based model, respectively. The proposed method explores the practicality of transfer learning in building energy forecasting, contributing to the use of existing building operation data for energy management at different timespan. • A similarity analysis method for building energy consumption data based on VMD-DTW is proposed. • The Seq2Seq-Transformer model is applied to short-term multi-step prediction of building energy consumption. • Transfer learning idea is first introduced to the long-term building energy consumption prediction. [ABSTRACT FROM AUTHOR]

Regional data center clusters have flourished in recent years to serve customers in a major city with low latency. The optimal coordination of data centers in a regional cluster has become a pressing issue because of their rising energy consumptions. In this paper, an online algorithm based on Lyapunov optimization framework is developed for the combined computing workload and energy coordination of data centers in a regional cluster. The proposed online algorithm is prediction-free and easy to implement. We prove that the workload queues and battery energy level will be within their physical limits, though their related time-coupling constraints are not considered explicitly in the proposed algorithm. The previous online algorithms do not have such a guarantee. A theoretical upper bound on the optimality gap between the online and offline results is derived to provide a performance guarantee for the proposed algorithm. To enable distributed implementation, an accelerated distributed coordination algorithm is developed based on the alternating direction method of multipliers (ADMM) with iteration truncation and follow-up well-designed adjustments, whereby a nearly optimal solution is attained with much enhanced computational efficiency. Case studies show that the proposed algorithm reduces the operational costs and saves computation time compared to online benchmarks. • The combined computing workload and energy coordination of data centers is studied. • A prediction-free online coordination algorithm is built with proven properties. • An accelerated ADMM algorithm is developed for distributed implementation. • The proposed coordination method greatly reduces the total cost of data centers. • The computation time is largely reduced compared to the traditional ADMM. [ABSTRACT FROM AUTHOR]

The ever-intensifying threat of climate change renders the electric power system undergoing a profound transition toward net-zero emissions. Energy efficiency measures, such as demand response, facilitate the transformation to jointly relieve consumers' financial burden and improve the operability of the electric power grid, in a carbon-free way. In this paper, we design a cluster-based appliance-level-of-use demand response program, based on the massive volume of appliance consumption data, to expand the role demand response can play in the power grid's low-carbon transition. We systematically model the appliance-level utility function to distinguish consumers' distinct consumption patterns. We then develop a bi-level optimization model to capture the interactions between individual consumers and a distribution system operator (DSO) and enable appliance-level-of-use demand response functions. To further improve the efficiency and scalability of the proposed mechanism, we propose a cluster-based approach to capture the heterogeneity of users based on their energy consumption behaviors. Simulation results show that by capturing the detailed appliance-level response patterns, the proposed approach can systematically improve overall social welfare compared with conventional demand response mechanisms. • Data Empowered Appliance-level Utility Modeling. • Demand Response Design to Offer Appliance-level Incentive. • Heterogeneity Characterization to Improve Scalability and Efficiency. [ABSTRACT FROM AUTHOR]

With the fragmentation of global production, the share of intermediate trade in international trade has increased significantly. Nevertheless, more research must be done on how intermediate trade factors influence energy consumption. This paper utilized the multi-regional input-output (MRIO) model to categorize China's energy utilization into six parts based on its position within the global production chain. Additionally, it examined the impact of intermediate factors on China's energy consumption through index decomposition analysis (IDA). The results revealed that: (1) from 2000 to 2014, intermediate factors led to an increase of 1380.96Mtces in China's energy consumption, accounting for around 37% of the national total; (2) the reduction in efficiency is the primary cause of the surge in domestic intermediate energy consumption, while the increase in market proportion is the dominant driver promoting international intermediate energy consumption growth; (3) the international intermediate proportion contributed the most among all intermediate factors. Drawing upon the findings above, this paper proposed policy recommendations to regulate China's rising energy consumption, precisely the escalation of intermediate process energy usage, focusing on the global production chain. [Display omitted] • Energy consumption is divided into six parts based on the intermediate destination. • Fifteen factors is decomposed based on index decomposition analysis. • The contributions of the structure, proportion, and efficiency changes is considered. • This paper shows a way of identifying the composition of energy consumption. [ABSTRACT FROM AUTHOR]

With the rapid expansion of computational infrastructure, the electricity consumption of geographically dispersed Internet Data Centers (IDCs) has imposed a substantial influence on the power grid. Geographically distributed IDCs, which present a versatile electricity demand with significant operational flexibility, hold potential for mitigating cost issues and balancing regional supply and load demand. To exploit this flexibility, this paper has developed a dual-drive coordinated operational strategy, which integrates locational electricity prices and unit carbon emission costs based on the dynamic carbon emission intensity (DCEI) of various regions as interaction signals to improve coordination effectiveness. The upper-level determines the consumption behaviour of IDCs, the lower-level provides feedback on dual-drive signals factoring in IDCs' involvement in grid operation, whilst an iterative algorithm is suggested to solve the bi-level model. Case studies based on real-world datasets demonstrate that the proposed operation strategy can achieve arbitrage through multi-regional carbon-electricity markets, significantly reduce operating costs, and support the rapid development of IDCs. Additionally, this strategy facilitates optimal matching of power load in IDCs with renewable energy sources, thereby mitigating imbalances in regional renewable energy supply and demand. • Analysing the necessity of integrating IDCs into the carbon market. • Proposing a dual-drive coordination to activate spatiotemporal flexibility of IDCs. • An iterative algorithm for solving the bi-level model is designed. • Comparing impacts of carbon policies in IDC costs and renewable energy accommodation. [ABSTRACT FROM AUTHOR]

Multi-energy systems with carbon capture system (CCS) and power to gas (P2G) can reduce carbon emissions while increasing the consumption of renewable energy. This paper proposed a long-term equilibrium including electricity, gas and carbon markets to study the price effect of the system and its impact on the carbon-gas- electricity sector. The simulation results indicate that the main impact of the system on the electricity sector is to reduce energy curtailment and provide flexible resource for the electric system. P2G used an excess of 22.21 TWh of renewable energy, while CCS used 2.65 TWh of electricity to operate the capture system. The multi-energy system will reduce the market clearing price in the gas market (the annual average gas price decreased from 276.90 to 276.30 yuan/MWh). However, the partial transfer of flexibility to the electricity sector may increase cost in the gas industry, which in turn will be transferred back to the electricity sector again. In addition, through the operation of multi-energy system, carbon elements will circulate in the three sectors. CCS will become a carbon price-maker when electricity scarcity and carbon quota tightening, and increase carbon prices, which will increase the utilization hours of CCS and P2G and their profitability. • A carbon-gas-electricity market coupling model is proposed. • The multi-energy system will reduce the market clearing price in the gas market. • The partial transfer of flexibility may increase cost in the gas industry • CCS will become a carbon price-maker when electricity scarcity. [ABSTRACT FROM AUTHOR]

Battery electric industrial vehicles (BEIVs) offer significant environmental protection and decarbonization potential for rapidly growing logistics and transportation sectors in China. However, the limited driving range remains a major obstacle to the widespread adoption of BEIVs. Eco-driving is an effective solution that can improve energy efficiency and extend the driving range of vehicles by controlling driving behavior and speed. Therefore, this paper introduces an energy efficient speed planning method for BEIVs, in which data-driven models are developed to estimate energy consumption. Real-world driving data collected from a BEIV are used to model the energy consumption by curve fitting, random forest, and LSTM. The speed planning process is considered a Markov Decision Process, which is solved using Dynamic Programming and Q-learning, respectively. The case study of a battery electric forklift shows that speed planning can reduce energy consumption by 13%–27% compared to time-optimal speed, while only increasing the driving time by 10%–20%. • Energy efficient speed planning methods for electric industrial vehicles are proposed. • The speed planning method uses data-driven vehicle energy consumption models. • The energy consumption models are developed using random forest and LSTM. • The speed planning problem is formulated as a Markov decision process. • The speed planning problem is solved by Dynamic Programming and Q-learning. [ABSTRACT FROM AUTHOR]

• Uses stochastic demand for green/non-green cars with known/unknown distribution. • Government intervenes in car supply chain using subsidy taxation and customer loans. • Two-way demand leakage applied for green/non-green cars. • Analyzes impact of fuel-efficient technology on fuel consumption in internal market. Transportation is one of the most influential factors in greenhouse gas emissions and global warming. This paper studies a network with an automotive supply chain that includes one supplier and one manufacturer next to a fuel supply chain consisting of a fuel manufacturer, operating under government intervention. The car manufacturer sells its green and non-green products under the dual-channel system with stochastic demand and demand leakage. To decrease greenhouse gas emissions, the car manufacturer invests in fuel-efficient technology for green products to reduce car fuel consumption. Moreover, due to increasing public awareness of environmental issues, the supplier invests in greening efforts to provide raw materials for fuel-efficient cars. The fuel manufacturer determines the fuel price under the government supervision and the government considers subsidies to support it. The fuel manufacturer has the opportunity to export the surplus fuel to other markets at a higher price. The government supports the production of fuel-efficient cars with two policies of subsidy-taxation and granting loans to customers. By doing so, the car manufacturer improves the level of fuel-efficient technology and the greening efforts of the supplier are increased. Additionally, the amount of fuel consumption in the internal market is reduced and the fuel manufacturer can export more fuel to the external market. As a result, the profit of all members increases simultaneously and environmental conditions improve. This model is studied under two scenarios for the distribution function of stochastic part of demand: (1) the Stackelberg scenario with complete information as a uniform distribution function and (2) the Nash scenario with partial information on a distribution-free approach. Some numerical examples based on real cases are provided to examine the capability of the proposed models. Finally, some sensitivity analyses are applied to the main parameters to extract several in-depth managerial concepts. [ABSTRACT FROM AUTHOR]

Abstract Most of the sustainable scheduling research deals with some environmental issues such as electricity consumption or carbon emissions although there are many other environmental and social indicators. Whereas, more and more manufactures are executing many solutions not only via implementation of environmentally-oriented but also socially responsible economically sound and management program. This proposed research has an objective to get a comprehensive view of the state and progress of sustainable scheduling by considering more sustainable indicators to assist many manufacturers to apply environmentally sound technological solutions. To achieve this objective, we, first, create a pool of sustainable manufacturing indicators that refers to some previous research that focus on applicable-proven indicators. Then, we employ an iterative process that is adapted from the general guidelines for literature review. We conceptualize, search, evaluate, analyze, and synthesize all 50 relevant papers resulting a completely new research framework to characterize the research. We use mathematical model components, i.e. manufacturing model, the system of objectives, objective function, constraints, model type, and optimization method, as the dimensions to classify the papers that were included in our analysis. We also apply the triple bottom line pillars – economic, environmental, and social to specify attributes and categories for the objective function and constraints. In literature classification step, the framework seems very sensitive since it can identify 49 different sustainable scheduling configurations from 50 reviewed papers. It means the framework can easily grasp the contributions of each paper. Then, in the analyses phase, we use 'sustainable link' to indicate if one research can be classified as sustainable scheduling research. The results show that energy cost and greenhouse gas indicators become the most frequently used indicators in sustainable scheduling. On the other hand, the mapping shows that some links have been rarely or never included, indicating potential areas for further research. We also list four main directions for future research which are: implementing other optimization methods; adding sustainability indicators; extending the model to a larger scale of manufacturing system; and loosening some assumptions. [ABSTRACT FROM AUTHOR]

• The paper reviews 33 on-field residential feedback studies. Only occupied homes where feedback was provided and energy consumption was monitored were considered for the review. • Our study proposes a taxonomy for energy feedback based on different characteristics of feedback such as frequency, type, presentation style and mode of access. • Understand the different ways in which field studies were conducted and assess a few parameters to establish their relationship with energy savings achieved. Residential energy feedback is about providing personalized information on household energy use to consumers to encourage energy savings. This paper conducts a review of field-based studies that have evaluated the impact of energy feedback on residential energy consumption. The review includes studies in real occupied homes that have deployed feedback intervention(s) and measured energy savings. Our study builds a taxonomy for energy feedback based on different characteristics of feedback such as frequency, type, presentation style, and methods of access. Energy savings from similar feedback types were found to differ depending on how the study was conducted. The reviewed studies deployed a range of feedback information including energy units, energy cost and tailored information conducted across diverse audiences (ethnicity, geographical positioning), varying experimental types (longitudinal, Randomized Control Trial) and, size and duration of the studies. The duration of studies varied widely, ranging from one month to three years and demonstrated energy savings between 5% and 20%. While most studies achieved energy savings due to energy feedback, a few of them reported an increase in energy consumption which could be due to rebound effect. Most of the studies in literature adopted a longitudinal design where the current household energy consumption was compared with the past consumption to arrive at the savings. Others used Randomized Control Trial (RCT) design where energy consumption of intervention group was compared with control group. Most of the studies were conducted in developed countries with cold climates.. There are fewer studies done on large-scale residential energy feedback especially in emerging economies where there is a rise in air conditioning use. We propose future studies to also consider the human behavior and cultural influences while evaluating impact of energy feedback. Our recommendation is that the academic and policy community address this gap since energy feedback is likely to stimulate positive behavior change amongst householders leading to energy savings. [ABSTRACT FROM AUTHOR]

This paper studies the price-based demand response problem in a deregulated retail electricity trading, aiming to coordinate the energy consumption behavior of end-users under dynamic retail prices. The challenge here is that in addition to the hierarchical decision-making process between utility company and end-users considered in existing works, the non-cooperative and competitive interdependence among end-users cannot be ignored. To address this issue, we first construct a novel Stackelberg–Nash game, in which the Stackelberg game is used to capture the hierarchical decision-making process between utility company and end-users, while the Nash game is dedicated to describing the interdependence among end-users. Then the existence and uniqueness of the Stackelberg–Nash equilibrium is provided along with theoretical analysis. On the basis of the analysis of equilibrium, we propose a distributed iterative algorithm with an adaptive step size, which is benchmarked with a fixed step-size algorithm. The comparison results on a real-life residential retail electricity market show that our proposed algorithm has better performance in terms of effectiveness and scalability. • Develop a Stackelberg—Nash game model to characterize the interactive relationship. • Proof the existence and uniqueness of Stackelberg–Nash equilibrium. • Propose a distributed iterative algorithm with an adaptive step size. [ABSTRACT FROM AUTHOR]

This paper investigates the causal effect of COVID-19 quarantine measures on the electricity consumption of different types of regulated customers. In particular, the average energy consumption of a group of customers in Chile is examined, exploiting the weekly variations in the application of the stages of the Step by Step Plan as a natural experiment. Step by Step Plan was the Chile COVID containment strategy, which involved implementing partial lockdowns based on the COVID incidence in the municipalities, and adjusted on a weekly basis. An econometric model is specified, and estimated using the technique Difference-in-Difference-in-Difference (DDD) to quantify the causal impact of these measures. After applying the model, it is found that the quarantine measures have had a negative effect on the trend weekly consumption of customers located in municipalities where some of the phases of the Plan have been applied. This effect is significantly different depending on each stage of the plan analysis, and statistically significant. It is also observed that, on average, the savings for a representative customer in the sample range from $ 8 to $ 6. 15 for some municipalities, and the sample costs range from $ 0. 02 to $ 0. 26 for others, depending on the municipality analyzed. The results can be expressed in a total of 2.95 tons less CO 2 emitted on a monthly basis. This shows a great impact in terms of sustainability and planning of energy demand and supply. • Quarantines by Covid 19 affected energy consumption, like a "Natural Experiment". • Using Panel Data of 400 thousand observations with geographic, temporal and Daylight Saving Time controls. • Difference-in-Difference-in-Difference method to quantify causal impact of quarantines on electric consumption. • Applying quarantines generated Energetic Savings from 8 cents to 6.15 dollars depending on client type. • An Event Study was designed to check the estimation results. [ABSTRACT FROM AUTHOR]

Traffic energy consumption estimation is significant for the sustainable transportation. However, it is difficult to directly employ macro traffic flow data to accurately estimate the traffic energy consumption due to many traffic energy consumption models need second-by-second vehicle trajectory. To solve this problem, this paper proposes a traffic energy consumption model based on the macro-micro data, which the macro data derived from the fixed-location sensors and sparse micro data derived from the Connected and Automated Vehicles (CAVs). The completed vehicle trajectories are constructed by the nonparametric kernel smoothing algorithm and variational theory. To test the performance of the proposed method, the Next Generation Simulation micro (NGSIM) dataset and Caltrans Performance Measurement System macro dataset obtained from the same road and time are used. The results indicate that the proposed method not only can reflect the characteristics of traffic kinematic waves caused by traffic congestion, but also minimize the errors generated by the macro-micro transformation. In addition, it can significantly improve the accuracy of energy consumption estimation. • Estimation of fuel consumption and emissions is closer to the real values than other methods. • Reconstructing second-by-second vehicle trajectories based on macroscopic traffic data. • Introduces CAVs trajectory data as a reference in the process of estimating the road spatio-temporal speed evolution. • Proposes a spatio-temporal consistency validation framework by using macro and micro data. • Experimental tests the effect of cell size and probe vehicle penetration on reconstruction accuracy. [ABSTRACT FROM AUTHOR]

In the 21st century, urbanization has caused rapid growth in the global construction area, resulting in high building resource consumption and long-term environmental impacts. Analyzing the resource consumption of public buildings and identifying the key factors that affect the consumption of building resources is important for developing of targeted policies. In this paper, we analyzed each branch of the People's Bank of China based on energy, water, and land resource consumption as well as the structure of energy consumption. Based on resource-consumption indicators, we divided 31 provincial-level administrative regions into four resource consumption levels; 65% were in the low-to-medium resource consumption levels. Medium-sized buildings had the highest energy consumption level and a more balanced energy consumption structure. The energy consumption per capita, water consumption, and utilization rate of small-sized buildings were low. To reduce resource consumption by public buildings, the construction of large-sized buildings should be limited. • The comprehensive evaluation method is used to calculate the resource consumption of the People's Bank of China. • Regression equations were established for energy consumption and resource consumption. • Medium-sized institutions had the highest energy consumption level and a more balanced energy consumption structure. [ABSTRACT FROM AUTHOR]

During the operation of a power press, the movement of moving parts consumes large amounts of energy. Minimizing the mass of moving parts through lightweight design is considered an effective strategy to reduce this energy consumption. The slider has the largest mass among moving parts and is the best object for lightweight design. Therefore, this paper proposes a lightweight design and structural optimization method for the slider to reduce the energy consumption of the power press. The reduction in mass of the slider is achieved through the application of a novel steel-aluminum composite (steel face sheets, high-strength aluminum alloy core) bionic sandwich structure (SACBSS). Afterward, the performance of the SACBSS is evaluated, and the SACBSS slider is designed. The energy consumption model and the finite element model of the slider are established, respectively. Finally, the response surface method is used to establish the optimization model by comprehensively considering energy consumption and structural performance. The results show that compared to the referenced steel slider, the optimized SACBSS slider reduces the mass by 18.9% and the energy consumption by 6.1%. The proposed design and optimization method of the slider can effectively reduce energy consumption while ensuring performance, which is important for the realization of green production in factories. [Display omitted] • A slider of steel-aluminum composite bionic sandwich structure (SACBSS) is proposed. • An energy model is built for analyzing energy saving potential of the SACBSS slider. • Effect of structural parameters on energy consumption and performance is analyzed. • The SACBSS slider reduces mass by 18.6% and energy consumption by 6.1%. [ABSTRACT FROM AUTHOR]

Multiple geographically dispersed data center buildings have been increasingly positioned in renewable-rich areas due to their high energy consumption. This paper proposes an optimal multi-energy portfolio for zero carbon data center buildings in the presence of proactive demand response programs. In the data center buildings, the complementarities of hydro-solar-wind hybrid renewable energy sources (RESs) are fully exploited via multi-energy conversion and storage devices, which are modeled as 100% renewable energy hubs for zero carbon multi-energy supplies. In order to exploit the hub-internal operational dispatchability and flexibility, a comprehensive energy consumption model of data center buildings is proposed to facilitate the demand response in terms of available serves, while the variable speed pumped storage (VSPS) is modeled to capture its dynamic operational characteristics. The energy‑carbon production, conversion, storage, and consumption are formulated as an energy‑carbon matrix, which are solved via a two-stage method to obtain the optimal portfolio. Case studies on southwest China data center buildings are implemented to demonstrate the effectiveness and superiority of the proposed methodology on cost-effective accommodation of RESs. Simulations results show that the system portfolio cost can be reduced by at most 17.8% with a higher operational flexibility. • A cost-effective data center building is proposed to accommodate zero carbon targets. • Hydro-solar-wind 100% renewables are formulated for multi-energy supplies. • VSPS and data center energy consumption are modeled to exploit inherent flexibility. • The energy-carbon production, conversion, storage, and consumption are optimized. [ABSTRACT FROM AUTHOR]

• A nominal distributed consensus controller for homogeneous vehicle platoons that considers the spacing errors with both the predecessor and the follower. • Input-to-state stability of the closed-loop platoon dynamics is proven through a Lyapunov approach. • String stability properties of the nominal controller are analyzed. • The nominal distributed controller is fed into an online optimization problem derived from control barrier functions to calculate the constrained control input that satisfies state and input constraints that take into account the constraints of the vehicles. Vehicle platooning is deemed a promising solution to improve traffic safety, reduce fuel consumption and increase traffic throughput and road capacity. Road throughput can be increased by driving at small inter-vehicle distances. Adjusting the spacing with both the preceding and the succeeding vehicles using bidirectional communication can potentially improve the platoon cohesiveness and is crucial when considering the synchronized merging scenario. In this paper, a distributed consensus controller is presented which relies on bidirectional platoon communication. To take into account the constraints of the system state and input constraints are implemented using control barrier functions via linear quadratic programming. Along with the consensus-based controller synthesis, platoon stability proof and string stability analysis, effectiveness of the controller is verified in a simulation study. [ABSTRACT FROM AUTHOR]

The increasing effects of global warming and energy depletion have raised concerns about the pollution caused by traditional oil and fossil energy usage. Distributed energy resources (DERs) have emerged as promising techniques to address these issues. However, the growing proportion of power injection from DERs presents technical challenges such as power quality decline and power flow reversal, which may destabilize the grid. Non-intrusive load monitoring (NILM) is a promising and cost-effective approach that can provide residential power information to improve grid scheduling, dispatching, and optimize residential power consumption behavior. This paper proposes a novel NILM method that can decompose the power of both household appliances and DERs integrated with residential houses. The proposed approach employs a data segment method that utilizes adaptive window lengths to identify the behavior of DERs based on the operating periods and characteristics of different equipment. A non-intrusive load monitoring method is proposed based on the ResNet-seq2seq framework to address the problem of model degradation and gradient vanishing. Post-processing techniques are applied to the output results using a feature list of the target equipment. Experiments are conducted on a dataset organized based on the REDD and Pecan Street datasets to demonstrate the accuracy and effectiveness of the proposed method. The results show that compared to the start-of-the-art CNN-seq2seq model, the proposed method achieved 54.95%, 69.18%, 56.95%, and 84.98% reduction in MAE for PV, EV, refrigerators, and microwaves, respectively. • A novel ResNet-seq2seq-based NILM approach is proposed for load disaggregation of residential houses integrating DERs. • Wavelet denoising and adaptive window length data segments are used for data pre-processing and accurate disaggregation. • Residual connections are employed to solve model degradation and vanishing gradient issues as network depth increases. • A post-processing technique is applied to modify the output of the network and improve disaggregation accuracy. [ABSTRACT FROM AUTHOR]

Accurate building energy consumption forecasting is crucial for developing efficient building energy management systems, improving energy efficiency, and local building energy supervision and management. However, short-term building energy consumption forecasting is challenging due to highly non-smooth and volatile trends. In this paper, we present a novel methodology that combines interpretable decomposition methods with an interpretable forecasting model. We first illustrate a daily energy consumption pattern recognition (DECPR) method, which decomposes daily energy consumption patterns into interpretable energy consumption subsequences. To achieve satisfactory forecasting performance, we design the vector representation of each subsequence as a static input to the temporal fusion transformers (TFT) model. This vector representation integrates the DECPR method into the TFT model. The TFT model produces interpretable outputs, such as the attention analysis of different step lengths and the visualization of the importance ranking of exogenous variables, including meteorological data, calendar information, and the vector representation. Empirical studies demonstrate that our proposed DECPR-TFT system outperforms comparable models with a mean absolute percentage error (MAPE) of 6.11%, which is significantly lower than other models. These interpretable outputs provide valuable insights for researchers seeking to develop energy-saving operation strategies in buildings. Overall, our methodology offers a promising solution for short-term building energy consumption forecasting that can contribute to more efficient building energy management and energy-saving operation strategies. • This study proposes DECPR, an interpretable decomposition method, explaining decomposed subsequences. • TFT, a deep learning-based approach with interpretability, predicts hourly energy consumption for the next day. • The interpretability of TFT model are used to analyze the effects of exogenous variables on energy consumption • The proposed DECPR-TFT model outperforms others, with 6.11% MAPE, significantly lower than competitors. [ABSTRACT FROM AUTHOR]

Iron & Steel, Pulp & Paper, Non-Metallic Minerals and Chemical & Petrochemical are the most energy intensive subsectors, even though they utilise only a limited range of production processes compared to other sectors like Machinery or Food & Beverages. To support future efforts for decarbonising the European industry, this study aims to develop a methodology to correctly and dynamically depict all relevant production processes of the mentioned subsectors and to generate synthetic load profiles (LP) 1 1 LP ... Load Profile. based upon their consumption and generation behaviour. In a first step, the energy intensive subsectors and their main production processes are identified. A standardised research approach is used to correctly depict their characteristics e.g. runtime, energy consumption and generation, unit sizes etc. Next, a methodology for modelling the timely behaviour of these production processes and for generating synthetic LPs is developed. This method is based upon the bottom-up approach of discrete-event simulation combined with stochastics. The developed methodology is then implemented into the simulation software Ganymed. Finally, the results of this methodology are validated via a case study, modelling the primary steel production route of an Austrian steel mill. In overall, the synthetic electricity LP shows good approximations to the measured one with a mean absolute percentage error of 6.08% for the simulated five days in total. However, a stronger deviation of the generated LP compared to the measured counterpart can be noted at the last two days. This deviation results from a reduction of the capacity during the real life production. This, however, can be taken into account in the synthetic generation given a more extensive data basis. Consequently, Ganymed can be deemed as a suitable software for generating energy consumption and generation behaviour of processes and production chains of energy intensive industries. • Production processes of energy intensive industries are classified and structured. • A methodology for generating load profiles of these industries is presented. • Discrete-event simulation is enhanced extensively to accurately depict industries. • Load profiles of various energy carriers for these industries can be generated. • Various system dimensions from single process to overall plant can be depicted. [ABSTRACT FROM AUTHOR]

Additive manufacturing (AM) has been recognized as a promising technology for achieving more secure and resilient supply chains to support social transitions to sustainable post-fossil carbon societies. AM is considered to be efficient and sustainable and its environmental impact is mainly from energy consumption. Therefore, it is of great significance to accurate predict and thus reduce the energy consumption of AM. However, current models to predict AM energy consumption are not sufficiently accurate. In this paper, a hybrid mechanism-based and data-driven approach is proposed to forecast the AM energy consumption. This approach accurately modelled the working time and power of each energy-consuming component of the FDM machine. Firstly, the physical model was used to establish the energy consumption prediction framework which documents the total energy into energy consumed by each component. Secondly, the data-driven method was leveraged via data fitting and deep learning to obtain the power and temporal model of the nozzle and the platform, considering their working states and interactions between them and the cooling fan. Thirdly, experimental measurements and G-code parsing were utilized to obtain data of the power model and the temporal model of the remaining components. In this work, the different types of parts were fabricated by the fused deposition modelling (FDM) printer and the energy consumption was measured and compared with the predicted values to verify the accuracy and effectiveness of the proposed method. Finally, the proposed method was compared with other methods to document the benefits of the proposed method. The results showed that the prediction accuracy of the proposed method for these four experiments was more than 94% accurate and it outperformed other methods. The proposed method can be used to guide process design to more accurately predict and thus reduce the energy consumption of AM parts during their manufacturing processes. [ABSTRACT FROM AUTHOR]

The design requirements for the propellers of vertical take-off and landing (VTOL) UAVs are contradictory in the cruise phase and VTOL phase. To alleviate the contradiction, the concept of passive variable-pitch propeller (PVPP) using the aerodynamic moment to change the pitch was proposed by previous researchers, which can adaptively adjust the pitch as the advance ratio changes. However, this type of PVPP has not been widely used due to its complex balance module. This paper constructs a new framework to design such PVPP to simplify its balance module and increase its utility. We proposed new methods to parameterize the shape of PVPP and established mathematical models for each optimization in the design process. Subsequently, a case study verified the feasibility of the design method to simplify the trim module. By optimizing the position of the blade-pivot axis, the design results can work efficiently in multiple working conditions and can also achieve the highest efficiency at a specific advance ratio, which is of practical significance for VTOL UAVs to reduce energy consumption and increase payload in mission profiles. We conducted additional analysis on the characteristics of PVPP based on two aspects: (A) the influence of the blade airfoil on the pitching moment characteristics of PVPP; (B) the comparison between the PVPP with normal layout and the PVPP with canard layout. Overall, the methodology and general rules proposed in this paper provide a theoretical basis and technical support for designing PVPP for UTOL UAVs. [ABSTRACT FROM AUTHOR]

In machining processes, there are contradictions between high efficiency and environmentally-friendly machining. This indicated that there is a major potential for sustainable machining to both increase efficiency and protect ecological balance. For this, the employment of effective methods has become imperative to reduce resource use. In this way, the use of analysis and optimization tools to select the tool material, cutting parameters, cooling/lubrication conditions, etc., will be a benefit for minimizing waste without sacrificing efficiency. In the literature, researchers have used various methods to reveal the requirements of sustainability. However, reaching the concept of sustainable machining by using one more than technique confuses the readers. Therefore, it was deemed necessary to present and discuss the current methods, models and analyzes in a comprehensive review paper. In this context, this paper reviewed the previously published works, especially focusing on sustainability and energy consumption modeling methods in machining operations. At the end of the study, it was seen that LCA provides a systematic and quantitative view of the system and thus can act as a decision support tool. Moreover, it can give an idea to the manufacturers about the improvement of the process and the areas of innovation. However, the method has several disadvantages such as relatively expensive and specific software, obtaining inventory data, impractical to use, time-consuming and requires large amounts of data. Various mathematical models and algorithms have been developed to deal with the complex situations and offer a more practical use compared to LCA. However, it is difficult to state that clear results have been achieved thanks to these models. It was seen that in complex machining processes, only approximate results can be found with models, which leads to questioning the reliability of the models. [ABSTRACT FROM AUTHOR]

• A mathematical model to calculate the LCOS for reversible fuel cells and Li-ion batteries. • Techno-economic model for a standalone PV solar system in green energy buildings. • Uncertainty and simulation cost analyses of energy storage systems used in green buildings. Energy storage systems (ESSs) were introduced to overcome the risks posed by energy curtailment. In this paper, we compare the Levelized cost of storage (LCOS) for PEM Reversible Fuel Cells, Solid Oxide Fuel Cells, and Lithium-Ion Batteries in three different locations in the United States (Tucson, Seattle, and Rochester) that are characterized by different energy consumption profiles. This research examines three commercial buildings of different sizes: small, medium, and large, with three occupancy profiles: low, medium, and high energy consumption in each of the three locations. The objective is to quantify the impact of uncertain demand and uncertain financial parameters on the LCOS. The results show that all ESSs can represent an attractive alternative to energy curtailment regardless of the building size or the occupancy profile. Further, the results show that fuel cells are a better fit for demand uncertainty as they are more resilient to changes and remain attractive despite that. The uncertainty analysis showed that the minimum LCOS was $0.049/kWh, and it was for a PEM-RFC in a small building in Tucson under high energy consumption behavior. Oppositely, the maximum LCOS was $0.679/kWh, and that was for a large building in Seattle under high energy usage occupancy as well. [ABSTRACT FROM AUTHOR]

Due to the excellent energy-saving and environmental protection features, electric vehicles (EVs) are gaining significant market penetration, especially in densely populated urban areas with systemic air quality problems. What follows is that we will face one of the biggest challenges: how to accurately predict the energy consumption (EC) of electric vehicles to alleviate the 'range anxiety' problem. To address this problem, this paper proposes a hybrid approach using short-trip segment division (S-TSD) algorithm and deep neural network (DNN) for predicting the energy consumption of battery electric vehicles (BEVs) by using real-world driving datasets. In this study, we aim to accurately predict energy consumption at the short-trip level, so we propose a novel S-TSD algorithm to divide the driving process of BEVs into several driving segments and conduct feature extraction for each segment. Then, using the extracted features, we develop a series of DNN models with varying hidden layers to investigate the respective performance for EC predictions. The results show that the optimal number of hidden layers is identified as 5, and the neuron structure for each hidden layer is determined as 13 × 256 , 256 × 512 , 512 × 256 , 256 × 128 , and 128 × 1 , respectively. Two state-of-art models are selected as benchmark methods to compare with the proposed model for the EC prediction tasks. The comparative analyses suggest that the proposed model outperforms the benchmark methods in terms of the four index values of MAE, MAPE, MSE, and RMSE, indicating that the proposed model has a good performance for predicting the energy consumption of BEVs at a short-trip level. • A novel S-TSD algorithm is proposed for feature extraction at a short trip level. • An EC prediction model combined with S-TSD approach is proposed for the first time. • The optimal structure of the DNN-based model is determined for the EC prediction. • The DNN-based proposed model has a good performance for EC prediction of BEVs. [ABSTRACT FROM AUTHOR]

While energy consumption is the primary concern for the design of real-time embedded systems, reliability and Quality of Service (QoS) are becoming increasingly important in the development of today’s pervasive computing systems. In this paper, we present a reliability-aware energy management (RAEM) scheme for reducing the energy consumption for weakly hard real-time systems with ( m, k )-constraints, which requires that at least m out of any k consecutive jobs of a task meet their deadlines. In order to ensure the ( m, k )-constraints while preserving the system reliability, we propose to partition the real-time jobs into mandatory and optional ones as well as to reserve recovery space for mandatory ones in an adaptive way. Moreover, efficient on-line scheduling techniques are proposed to reduce the system-wide energy, which is consumed not only by the processor but also by other peripheral devices at run-time. Through extensive simulations, our experiment results demonstrate that the proposed techniques can significantly outperform the previous research in reducing system-wide energy consumption for weakly hard real-time systems while preserving the system reliability. [ABSTRACT FROM AUTHOR]

• Unbalanced residential energy consumption data classification problem is tackled. • Urban vs rural energy consumption is classified in original and reduced feature space. • Monte Carlo under-sampling is used to obtain distribution of classification performance metrics. • Six family of classifiers were compared using 6 classification metrics for unbalanced data. • Hyperparameter tuning and optimal classifier pipeline were obtained using the genetic programming optimizer. Energy consumer locations are required for framing effective energy policies. However, due to privacy concerns, it is becoming increasingly difficult to obtain the locational data of the consumers. Machine learning (ML) based classification strategies can be used to find the locational information of the consumers based on their historical energy consumption patterns. The ML methods in this paper are applied to the Residential Energy Consumption Survey 2009 dataset. In this dataset, the number of consumers in the urban area is higher than the rural area, thus making the classification problem unbalanced. The unbalanced classification problem has been solved in original and transformed or reduced feature space using Monte Carlo based under-sampling of the majority class datapoints. The hyperparameters for each classification algorithm family is represented as an optimized pipeline, obtained using the genetic programming (GP) optimizer. The classification performance metrics are then obtained for different algorithm families on the original and transformed feature spaces. Performance comparisons have been reported using univariate and bivariate distributions of the classification metrics viz. accuracy, geometric mean score (GMS), F 1 score, precision, area under the curve (AUC) of receiver operator characteristics (ROC). The energy policy aspects for the urban and rural residential consumers based on the classification results have also been discussed. [ABSTRACT FROM AUTHOR]

• The proposed residential power scheduling strategy in smart grid is capable to achieve the optimal energy consumption profile. • The electricity payment and discomfort cost are coupled to constraint the DR to influence on users' consumption efficiency. • A novel DR algorithm based on cost-coupling constraint with distributed generation is developed to reduce consumers' expenditure while improving their comfort. • A day-ahead bidding price mechanism including real-time charging management is introduced to establish different household consumption patterns to guide the consumer to enjoy convenience while saving costs. • Various simulation experiments are performed to confirm that the renewable energy penetration is higher, consumers' consumption utility is higher, and the corresponding cost-efficiency is higher. Power scheduling is a major task for demand response (DR) to cope with the electricity consumption during the peak period in smart grid. In the residential sector, consumers trend to trigger their electrical appliances when the electric price is low. However, power scheduling including load curtailment, time delay and equipment power adjustment will change consumer's comfort level to produce discomfort cost. Thus, electricity payment and discomfort cost are coupled to constraint the DR. In this paper, we focus on renewable energy use by smart homes and consider cost-coupling constraints, taking an approach whereby we decouple the primary problem into several independent subproblems. A novel DR algorithm based on cost-coupling constraint with distributed generation is developed to reduce consumers' expenditure while improving their comfort. To that end, a day-ahead bidding price mechanism including real-time charging management is introduced, and a cost efficiency algorithm is designed based on electricity payments, discomfort and distributed generation. Simulation results demonstrate that the proposed method is effective and feasible when tested in real application scenarios. [ABSTRACT FROM AUTHOR]

• Examines the effect of banking performance on renewable energy consumption (REC). • Improved banking performance enhances renewable energy consumption globally. • Heterogenous effect of the relationship between banking performance and REC across income groups. • Market capitalization, asset quality and managerial inefficiency explain REC in HI countries. • Return on asset, market capitalization, asset quality and z-score explain REC in MI and LC countries. • Heterogenous effect of the relationship between banking performance and REC types is observed. To secure future universal access to modern energy, large investments in renewable energy technology are required. This paper estimates the impact of five banking sector performance indicators (return on asset, market capitalisation, asset quality, managerial efficiency and financial stability) on renewable energy consumption for a global panel consisting of 124 countries. The study used two-step system-GMM panel model to handle potential endogeneity and serial correlation. The paper considers three homogenous subpanels which are constructed based on the income group classification (high-, middle-, and low-income countries). Generally, our results show that improved banking sector performance enhances renewable energy consumption, with heterogenous effect across income group classification. For high -income (HI) countries, an increase in bank size together with improved asset quality and managerial efficiency have positive effects on renewable energy consumption. For middle-income (MI) and low-income (LI) countries, a high return on asset, an increase in bank size and financial stability are positive determinants of renewable energy consumption. We also find heterogenous effect of banking performance indicators across various renewable energy consumption types. The results highlight the importance of a well-functioning bank sector to achieve the investment in renewable energy needed to meet future energy demand and simultaneous decrease CO 2 emissions. [ABSTRACT FROM AUTHOR]

Much theoretical and empirical research has verified the non-linear and uncertain relationships between carbon emissions and economic growth. To forecast the carbon emissions from fossil energy consumption, this paper introduces the power exponential term of the relevant variables as exogenous variables into a multivariable grey model. Under the target of minimisation of the mean absolute percentage error, two non-linear programming models are constructed to solve the unknown parameters of the non-linear grey multivariable model. In addition, to enhance the adaptability of the grey model to large sample sizes, we divide the data of Chinese gross domestic product and carbon emissions from fossil energy consumption of 1953–2013 into 15 stages. The empirical results show that the non-linear grey multivariable model can reflect the mechanism of the non-linear effects of gross domestic product on carbon emissions from fossil energy consumption, and has higher forecast accuracy than the traditional grey model and the autoregressive integrated moving average models. In three schemes – economic growth at low, medium, and high, speeds – we use the non-linear grey model to quantify future Chinese carbon emissions from fossil energy consumption from 2014 to 2020, and the predicted results can provide the basis for energy planning and the formulation of environmental policy. [ABSTRACT FROM AUTHOR]

Reducing the cutting energy consumption in machining is important to improve the sustainability of cryogenic machining. Currently, the analysis of cutting energy for cryogenic machining mostly relies on equipment testing, while less research has been conducted on energy analysis based on material removal mechanisms. In this paper, the chip formation mechanism of cryogenic machining is studied on the basis of liquid nitrogen orthogonal milling experiments with Ti–6Al–4V alloy, and the corresponding specific cutting energy model is established. First, the effects of cooling conditions and process parameters on chip microscopic morphology were analyzed based on orthogonal milling experiments. Next, a specific cutting energy model was established according to the chip formation mechanism. Finally, the reliability of the model was verified by cutting forces. The results show that in cryogenic machining, when the cutting speed is less than 200 m/min, adiabatic shear dominates the formation of serrated chips, and the cutting energy consumption is higher than that of dry cutting. As the cutting speed exceeds 200 m/min, adiabatic shear and periodic fracture together dominate the formation of serrated chips, and the energy consumption of cutting is significantly reduced at this time. This research provides a reference and guidance for the sustainable development of cryogenic machining. • This research reveals the chip formation mechanism of cryogenic machining of TC4 alloy. • In this study, an energy consumption model for cryogenic machining was established. • Cryogenic machining is an energy saving and environmental protection method. [ABSTRACT FROM AUTHOR]

This paper is based on the concept of dissonance, that is, gaps or conflicts existing in a specific knowledge base or among different knowledge bases. It presents a rule-based system that assists human operators in dissonance discovery and control by taking into account two kinds of dissonance, i.e., affordance to study conflicts of use, and inconsistencies to study conflicts of intention and action, through the analysis of cognitive behavior implemented in knowledge bases. This system elaborates the knowledge base composed of rules, and analyzes the knowledge content to discover new knowledge by creating additional rules, or to identify inconsistencies when conflicts between rules occur. The affordance discovery control process uses a deductive and an inductive reasoning algorithm of which the aim is to establish new rules using existing ones. The inconsistency discovery control process applies an abductive reasoning algorithm in order to determine contradictory rules when existing rules may result in opposite intentions being accomplished. Two groups of inconsistencies are addressed: interferences involving several decision makers, and contradictions involving the same decision maker. A knowledge acquisition control process facilitates the creation of the initial rules that contain parameters such as intentions relating to the goals to be achieved, actions to be performed to achieve these intentions, objects used to carry out these actions and the decision makers who execute these actions using the corresponding objects. A feasibility study taking into account five rule bases relating to the manual use of an Automated Speed Control System (ASCS), the automated control of the car speed by the ASCS, the manual control of aquaplaning, the manual control of the car speed, and the manual control of car fuel consumption is proposed to validate the rule-based support system. [ABSTRACT FROM AUTHOR]

In the highly competitive modern-day industrial landscape, characterized by globalization and resource scarcity, manufacturers are striving to improve economic and environmental performance. Innovation that enables self-adjustment, control and optimization of the energy consumption of individual machines continues. However, more research is needed if such systems are to be deployed successfully, especially considering the complex characteristics of the energy flows in the factory. In this paper we propose a novel approach to the coordination of information, processing and sensing systems for energy and resource efficient production systems. By leveraging on a recently-developed framework focusing on physical flows of energy, materials and waste we propose a solution based on specific energy efficiency KPIs and an online data acquisition/processing system, that enables real-time monitoring of the current status of the machining process and lagging assessment of system energy efficiency. The proposed solution allows the identification of abnormal energy consumption during the operational machine cycle, caused by incorrect part dimensioning or erroneous cutting conditions programmed by the process engineer, enabling identification of potential disruptions with different gravity levels, and delivery of meaningful alarms for the operator. Adaptive control of the machine cutting conditions or even trajectory re-programming is then possible, by correlating the energy-consumption data with other data, such as head temperature. Furthermore, by analysing the energy consumption of value and non value adding activities over complete production cycles (such as a shift or day), it is possible to monitor the progress of production systems toward achieving energy efficiency targets and to conduct root-cause analysis of inefficient energy usage for continuous improvement programs. We tested the proposed solution, modeling, index system ad online data acquisition/processing platform, through an industrial case study by deploying the developed hardware and software modules on a Nicolás Correa S.A. VERSA milling machine. [ABSTRACT FROM AUTHOR]

Daylight control systems, which automatically adjust the artificial light levels depending on the daylight penetration, can result in substantial energy savings. However, their energy saving potential cannot be estimated accurately because it depends on several building and system parameters, climate conditions, occupant behaviour and type and commissioning of the daylight control system. The objective of this paper is to compare the energy saving potential and operation of different daylight control systems in school buildings. One year monitoring has been carried out simultaneously in 3 neighbouring classrooms, equipped with a different type of control system. The active power and the electric energy consumption of the artificial lighting were measured continuously on a minute-by-minute basis, as well as the occupancy of the classrooms and the global irradiance outside the building under an unobstructed horizon. Momentary visual comfort assessments were carried out in the classrooms. Although all classrooms have comparable occupancy and identical building characteristics, differences between the annual energy savings of the different daylight control systems are found to be significant: the total annual energy savings varied from 18% to 46%. Under the given conditions, the open loop system with the outward facing daylight sensor was noticed to yield the largest while the closed loop system with centrally positioned sensor produced the smallest savings. However, it has to be made sure that the energy savings are not at the expense of the visual comfort. The performance of the systems regarding both energy savings and visual comfort is related to the operation and the initial commissioning. An in-depth analysis of the monitoring campaign is discussed to explain the differences in energy savings and visual comfort. The energy savings due to the implementation of a daylight control system are divided into on the one hand dimming due to daylight penetration and on the other hand initial dimming to compensate for the over dimensioning of the lighting system and to take into account constant illuminance control. [ABSTRACT FROM AUTHOR]

Under China's current Renewable Portfolio Standards (RPS) policy system, the provincial power grid is the assessment subject, and then the assessment is conducted on the user side. However, in the centralized clearing power market, renewable energy (RE) consumption on the user side cannot be accurately identified, so accurate value transmission cannot be formed to guide users to optimize their electricity consumption behavior. To improve this problem, this paper identifies the irreplaceable role of users in RE consumption through time series simulation. We construct a three-layer RPS assessment value transmission model of "power grid company - retailers - users" based on the identified unique contributions of each user. The equilibrium state of multi-market participants is obtained based on the decentralized training framework of reinforcement learning. The following conclusions are drawn through simulation analysis:(1) the user-side RE contribution identification method can effectively identify the user's contribution to RE consumption, which can reduce about 8% or increase about 7.48% of RPS cost allocation according to different contribution degrees; (2) the RPS value allocation mode can optimize users' power consumption behavior, achieve peak shaving and valley filling, promote RE consumption, and reduce about 0.6% of the abandoned wind power; (3) Under the RPS system, the effect of improving the response-ability of the user-side is better than increasing the RPS target and the peak shaving capacity. • Accurately identify the RE consumption of users under the unified clearing mode. • Construct a transmission mode of RPS assessment among grid, retailers, and users. • Construct a distributed training framework of multi-agent. • The research is helpful in promoting the consumption of new energy. [ABSTRACT FROM AUTHOR]

Under China's current Renewable Portfolio Standards (RPS) policy system, the provincial power grid is the assessment subject, and then the assessment is conducted on the user side. However, in the centralized clearing power market, renewable energy (RE) consumption on the user side cannot be accurately identified, so accurate value transmission cannot be formed to guide users to optimize their electricity consumption behavior. To improve this problem, this paper identifies the irreplaceable role of users in RE consumption through time series simulation. We construct a three-layer RPS assessment value transmission model of "power grid company - retailers - users" based on the identified unique contributions of each user. The equilibrium state of multi-market participants is obtained based on the decentralized training framework of reinforcement learning. The following conclusions are drawn through simulation analysis:(1) the user-side RE contribution identification method can effectively identify the user's contribution to RE consumption, which can reduce about 8% or increase about 7.48% of RPS cost allocation according to different contribution degrees; (2) the RPS value allocation mode can optimize users' power consumption behavior, achieve peak shaving and valley filling, promote RE consumption, and reduce about 0.6% of the abandoned wind power; (3) Under the RPS system, the effect of improving the response-ability of the user-side is better than increasing the RPS target and the peak shaving capacity. • Accurately identify the RE consumption of users under the unified clearing mode. • Construct a transmission mode of RPS assessment among grid, retailers, and users. • Construct a distributed training framework of multi-agent. • The research is helpful in promoting the consumption of new energy. [ABSTRACT FROM AUTHOR]

[Display omitted] • Various elements influence micro-climate around building and impact its energy use. • Micro-climatic impact on buildings in sub-tropical/tropical zones is rarely studied. • Passivhaus building performance in sub-tropical/tropical zones is under examined. • Passivhaus buildings are sensitive to surrounding micro-climates in temperate zones. • Digitised Eco-feedback is effective in increasing energy consumption awareness. The earth's climate differs enormously across its breadth with numerous small-scale variations to surrounding atmospheric conditions (micro-climates) existing side by side. These differences impact indoor environments of residential buildings and, depending on occupants' preferences, building energy use, and can mean that home designs are unfit for the local context. Although considered leaders in producing energy efficient designs which are equally comfortable for occupants and the environment, even Passivhaus (PH) homes have been demonstrated to be sensitive to localised outdoor conditions. Meanwhile, occupants have little understanding of their own domestic energy use, how the situation of their property might impact their comfort, and of more sustainable alternatives open to them. To boost understanding of household energy use and to encourage energy conservation, instantaneous computerised feedback on household's energy consumption (Eco-feedback) has been widely used. Along with improving household energy use understanding, Eco-feedback technologies may assist in comprehending local micro-climatic conditions around a building, and in learning how to manipulate these can help to establish and sustain preferred indoor conditions with minimal energy use. This can be achieved through producing personalised and contextualised feedback and responses to update and renovate users' homes by suggesting short-term actions and long-term design recommendations. This systematic literature review paper explores the breadth of Eco-feedback techniques, their relative benefits and shows how these could be deployed in the context of understanding building performance in the home. [ABSTRACT FROM AUTHOR]

The impact of village morphology on habitats' daily spatiotemporal behavior and building energy consumption is still vague. This research aims to propose a method to reveal how and to what extent village morphology and residents' demographic structure influence farmhouses' occupancy rate and energy consumption. For this purpose, a combined time geography and building energy consumption method was proposed to disclose the relationship among residents' family roles, village morphology and household energy consumption. To explore the occupancy rate of farm-houses with different family members in different village morphology, resident types based on field research (retrospective interviews and aggregation behavior observation) were formulated. Residents with similar time-space behavior seemed as an "agent" and then a specified number of agents are combined into different families for ideal experiments. By placing different households in different villages for multi-factor cross-experiment, the occupancy rates of different households are obtained based on individual time-space behavior patterns, and then the energy consumption of each household under different scenarios is obtained by combining building energy consumption simulation. In addition, the relationship between resident's disposable personal time, local public space' quality, and the house occupancy rate was analyzed. Combined with a case study in Shanghai with 417 valid questionnaires, it can be seen that the method proposed in this paper is effective and villages with high quality public space have the potential to reduce household energy consumption from 2.17% to 38.82% for diverse families. The quantitative result indicated that the occupancy rate is mainly influenced by residents having more disposable personal time, and such space-time behavior is sensitive to village morphology. This study proposed a combined method and case evidence to systematically analyze the complex relationship between resident behavior, the village morphology, and household energy consumption, thus contributing to the sustainable plan and design of the community spatial system. [Display omitted] • Proposed systematic approach to reveal the relationship between morphology and energy. • Energy-saving effect of public space is revealed from villagers' behavior chain. • The influence of the village morphology on energy consumption is between 2.17% and 38.82%. • Alternative mode of villager's space usage in disposable personal time was proved. [ABSTRACT FROM AUTHOR]

Due to the increasingly severe resource scarcity and environmental pollution, appropriate recovery of end-of-life (EOL) products has gained significant importance in recent years. The disassembly lines are one of the vital steps during the entire recovery process. However, considering the disadvantages of manual disassembly lines such as high labor cost, low efficiency, and harm to workers' health, the transition to robotic disassembly lines is underway. Therefore, in this paper, a sustainable robotic disassembly line balancing problem (RDLBP) is proposed to improve the disassembly efficiency (cycle time) and environmental friendliness (total energy consumption) simultaneously. To narrow the gap between theory and practice, uncertain processing time, sequence-based tool changeover, and robots with different efficiencies and energy consumption rates are considered, which requires adding the steps of resequencing the tasks and allocating the most suitable robots within each workstation on the basis of assigning the tasks to workstations as evenly as possible. To solve this problem, the epsilon constraint algorithm is used to obtain the exact solutions for small scale cases and verify the validity of the proposed model. Simultaneously, due to the NP-hard nature, a modified bi-objective Harris Hawks optimization (MBOHHO) algorithm is developed to solve the line balancing problem for the first time, which combines opposition-based learning, differential evolution, and Gaussian mutation mechanisms, and modifies the renewal strategy of two vital parameters based on bioenergy consumption pattern, contributing to improve the diversity and help jump out of local optimum. Finally, computational experiments are performed to evaluate the performance of the MBOHHO algorithm by comparing it with four other outstanding optimization algorithms, and the results reveal its effectiveness and superiority in various metrics. [ABSTRACT FROM AUTHOR]

Abstract: The Y architecture has recently received much attention due to its many potential advantages, such as substantially reduced wirelength, power consumption and significantly improved throughput. To fully utilize the virtues of Y architecture, several hexagon/triangle placement (HTP) algorithms suitable for the Y architecture were presented, however the wirelength optimization is not included in the algorithms. Wirelength estimation is fundamental to guide the wirelength optimization process in early design stages. In this paper, we present an accurate and efficient wirelength estimation technique called APWL-Y appropriate for the Y architecture, and especially for HTP floorplanner and placer. The average error of APWL-Y is 4.41% for 1.57 million nets from industrial circuits. When developing APWL-Y, we find out that 3-SMT wirelength is a power function of aspect ratio of bounding box of the given n-pin nets. The time complexity of APWL-Y is . APWL-Y is very effective to guide the wirelength optimization in a HTP placer. Moreover, we develop an efficient HTP algorithm with wirelength optimization driven by APWL-Y estimator. The placement results by our placer subject to different optimization objectives are presented. Compared to the HTP placer with only area optimization, our placer can reduce the wirelength by 54.3% with a small area overhead of 9.07% on average. In addition, we explore the HPWL technique in the Y architecture. To the best of our knowledge, this paper is the first in-depth study on wirelength estimation technique in Y architecture and HTP floorplanning optimization with consideration of interconnects. [Copyright &y& Elsevier]

Energy consumption has become one of the main costs of the stone processing industry and results in substantial stress on the environment. Consequently, the energy-saving issue has drawn increasing attention in the stone processing industry. Cutting power modeling is the crucial technology of energy prediction of stone machining process, being the foundation of energy-saving optimization in this sector. The cutting power changes dynamically during the variable material removal rate process (V-MRR process). Therefore, the power characteristic of the V-MRR process is more complicated than that of the constant material removal rate process (C-MRR process). In the stone processing industry, various machining processes belong to the V-MRR process, such as block sawing, slab chamfering, curved surface milling and grinding. In this paper, a modeling methodology of cutting energy consumption for variable-MRR process is proposed. The dynamic power characteristic can be reflected in this methodology, and the influences of cutting parameters on stone-cutting energy consumption are also fully considered. Based on the specially designed test system, experimental studies were conducted to obtain the fitting coefficients of the proposed energy consumption model. Then, energy calculations of four stone blocks sawing processes were performed. The results show that the predictive accuracy of all tested stone block sawing cases is above 90%. Finally, a case application is elaborated to demonstrate that the proposed method can help stone manufacturers determine the energy-optimal process plan. The detailed results of energy-saving & benefits-improving with using the proposed method are also proved in the case study. • This paper proposes a new modeling method of cutting energy consumption for V-MRR process. • The dynamic power characteristic can be fully considered in the I -model. • The power simulation using I -model is more in accordance with the actual situation. • The I -model can help stone manufacturers determine the energy-optimal process plan. • It shows that the effects of energy-saving & benefit-improving using the I -model are remarkable. [ABSTRACT FROM AUTHOR]

• A data set of 4.3 million bicycle records are used as dynamic input for the method. • Propose a dynamic electric fence planning algorithm base on real demand. • Propose an agent-based model to simulate massive bicycle-sharing trips. • Evaluation of potential energy consumption for electric fence implementation. As a newly-emerging option of shared transportation, Internet-enabled dockless bicycle sharing is well accepted by the public. The implementation of electric fences has great potential to tackle the problem of random parking in bicycle sharing services. However, the deployment of electric fences would have a negative impact on the convenience of bicycle sharing services, which might lead to an increase in energy consumption among customers who switch their methods of transportation. This paper proposes a dynamic electric fence planning method with an assessment of resource-saving and potential energy consumption increasement. An agent-based model is proposed to simulate the trips and evaluated the performance of static and dynamic electric fences. The results show that dynamic electric fences require significantly shorter walking distances than static electric fences. The implementation of electric fences in the city center can significantly avoid random parking and improve the parking tidiness of bicycles. The implementation of dynamic and static electric fences can averagely save 25.31% and 27.76% bicycle resources. By estimating travel mode shifting, dynamic electric fence can reduce energy consumption by 5.79% per day compared to the static electric fence situation. [ABSTRACT FROM AUTHOR]

• Meter-level anomaly detection and diagnosis is performed through CMP algorithm. • CMP parameters are automatically set using supervised and unsupervised methods. • A severity score, based on 4 statistical methods, is used to recognize anomalies. • 55 anomalous energy consumption patterns are discovered in a one-year timeseries. • Anomalies at meter-level are diagnosed by exploiting information at sub-load level. Recently, the spread of IoT technologies has led to an unprecedented acquisition of energy-related data providing accessible knowledge on the actual performance of buildings during their operation. A proper analysis of such data supports energy and facility managers in spotting valuable energy saving opportunities. In this context, anomaly detection and diagnosis (ADD) tools allow a prompt and automatic recognition of abnormal and non-optimal energy performance patterns enabling a better decision-making to reduce energy wastes and system inefficiencies. To this aim, this paper introduces a novel meter-level ADD process capable to identify energy consumption anomalies at meter-level and perform diagnosis by exploiting information at sub-load level. The process leverages supervised and unsupervised analytics techniques coupled with the distance-based contextual matrix profile (CMP) algorithm to discover infrequent subsequences in energy consumption timeseries considering specific boundary conditions. The proposed process has self-tuning capabilities and can rank anomalies at both meter and sub-load level by means of robust severity score. The methodology is tested on one-year energy consumption timeseries of a medium/low voltage transformation cabin of the university campus of Politecnico di Torino leading to the detection of 55 anomalous subsequences that are diagnosed by analysing a group of 8 different sub-loads. [ABSTRACT FROM AUTHOR]

It is important for Chinese cities to advance sustainable energy consumption and ensure that they achieve their carbon emission peak and carbon neutrality. This paper develops a comprehensive assessment of the spatial variability and influencing factors of the quantity of household energy consumption per capita in mega-cities. Based on microlevel household survey data from Guangzhou, Guangdong Province, China (N = 1082), we analyze the spatial effects and factors of urban residents' energy consumption. A spatial econometric model with four spatial weight matrices is employed, and the nested spatial weight matrix, which holds practical economic and geographical significance, is set. We find that (1) the quantity of energy consumption per capita of all households in Guangzhou is 189.57 kgce/year and shows a spatial difference, with 177.5 kgce/year in the central urban area and 203.47 kgce/year in the outskirts urban area. In particular, households in the outskirts of urban areas present a higher percentage of "high" and "medium-high" energy consumption per capita than those in central urban areas. (2) The quantity of household energy consumption per capita of urban residents is characterized by significant spatial correlation and agglomeration, with "high-high" and "low-low" agglomeration in central urban areas. (3) The household labor force, the housing area and household income are the core factors that drive the quantity of household energy consumption per capita, while the region and educational level are also main factors. We observe the diversity of the energy transition by comparing the patterns of the quantity of household energy consumption per capita within the city. A comprehensive picture of the diversity underlying the energy transition applied across cases can be distilled. [ABSTRACT FROM AUTHOR]

• Detailed energy consumption models for different appliance categories are developed. • Modular models can be used as building blocks enhancing their flexibility. • MILP models of appliance operation can be incorporated in energy management systems. • Models allow for the integrated optimization of all energy resources. • Models enable the participation in demand response programs. Demand response programs are essential to enable accommodating larger shares of variable power generation based on renewable sources, the deployment of which is imperative for decarbonizing the economy and mitigating global warming. Consumers/prosumers are increasingly exposed to and may benefit from time-differentiated energy prices aimed to induce changes in regular consumption patterns. These changes are also beneficial for retailers and grid operators in face of the variability of wholesale market prices, renewable energy availability and grid conditions. The optimization models to be implemented in autonomous home energy management systems require a rigorous modeling of appliance operation to generate effective load scheduling solutions, respecting their physical operation principles and use patterns in everyday life. A balance should be sought between the detail level of optimization models and the computational requirements to generate usable solutions having in mind their implementation in low-cost processors. This paper presents a comprehensive and modular set of mixed-integer linear programming models aimed at enabling their seamless incorporation in home energy management systems, allowing for the integrated optimization of all energy resources (exchanges with the grid, load management, electric vehicle and stationary battery, local microgeneration). Detailed energy consumption optimization models for shiftable, interruptible and thermostatic loads are presented, also including the power cost component and ways of dealing with user's discomfort. The modular models are presented in a building block manner enhancing the flexibility of their utilization in overall models with different objective functions encompassing the economic and comfort dimensions. Computational results are presented for a case study using actual data, which considers a time-of-use tariff with six periods. In addition to comparing with a plain tariff scheme, different consumer profiles are simulated to assess the impact of comfort requirements on cost. These results show that whenever consumers have the flexibility to change their consumption patterns, they are able to lower the net electricity bill by having an energy management system endowed with the models herein proposed to make optimized decisions on their behalf. [ABSTRACT FROM AUTHOR]

Based on household-level survey data collected from Qinghai Province of China, we estimated the greenhouse gas (GHG) and air pollutant emissions from spatial perspectives, including household energy consumption in pastoral, agropastoral, and agricultural zones. The findings suggest that the total annual GHG and pollutant emissions per capita in the area was 2296.32 kg per year. The highest amount of pollutants was emitted from the pastoral zones, followed by the agropastoral and agricultural zones. CO 2 is the primary GHG emitted by household energy consumption. Dung burning was the cause of the high PM 2.5 emissions in the pastoral areas, while the use of coal was the primary cause of GHG and pollutant emissions in the agropastoral and agricultural zones. • We used empirical data from rural households in Qinghai, China. • This paper indicates the reliance of local energy consumption on non-commercial energy. • We estimate greenhouse gas and pollutant emissions resulting from solid-fuel combustion. • We compare indoor pollutant emissions from pastoral, agropastoral, and agricultural zones spatially. • This paper provides policy recommendations for energy transition towards clean energy. Energy consumption in the household sector has become an important issue in China's energy consumption and an important unit of China's clean energy transformation. Currently, the potential air pollution, carbon emissions and health risks caused by energy consumption in many areas cannot be ignored, and refined and regionalized index-based research data necessary to support decision making are lacking. Based on household-level survey data collected from Qinghai Province, China, we estimated greenhouse gas (GHG) and air pollutant emissions from spatial perspectives, including household energy consumption in pastoral, agropastoral, and agricultural zones. The findings suggest that the total annual GHG and pollutant emissions per capita in the area was 2296.32 kg per year. The highest amount of pollutants was emitted from the pastoral zones, followed by the agropastoral and agricultural zones. CO 2 is the primary GHG emitted by household energy consumption. Dung burning was the cause of the high PM 2.5 emissions in the pastoral areas, while the use of coal was the primary cause of GHG and pollutant emissions in the agropastoral and agricultural zones. These findings highlight the need to integrate household energy policies with rural development to enable a complete transition towards cleaner fuels. [ABSTRACT FROM AUTHOR]

• Household indirect energy consumption is decomposed by input-output subsystem method. • Indirect consumption is 1.3 and 1.65 times of direct in 2012 and 2017 respectively. • Coal indirect consumption accounts for 50.2%, 54.6% in 2012 and 2017 respectively. • Spillover effect is the most one among the four components. • Ferrous metal smelting and rolling sector is most affected by household activities. In recent years, China's rapid economic development and steady growth of household consumption have contributed to the continuous growth of energy consumption. In addition to considering direct household energy consumption, the indirect effects of people's consumption activities on energy consumption in other productive sectors can't be ignored. According to the input-output analysis framework and consumer lifestyle analysis, this paper mainly studies the direct and indirect consumption of energy by Chinese household consumption ways and the relationship between energy structure and lifestyle. From the perspective of structural analysis, the input-output subsystem model (scale component, own internal component, feed-back component and spillover component) is used to analyze the energy supply and demand relations among 36 sectors of the national economy related to the lives of household in 2012 and 2017, revealing the change trend and transmission mechanism. This paper also focuses on the impact of household consumption ways on energy consumption of industrial sector and changing consumer lifestyles on energy saving, meanwhile it also forecasts China's future household energy consumption under four scenarios. The results show that in 2012 and 2017, total consumption of energy which is influenced by household consumption ways in China is 28461.82 PJ and 44759.96 PJ, respectively, accounting for 26.9% and 34.1% of total consumption of final energy. The indirect energy consumption is 1.3 times and 1.65 times of direct, respectively. The coal consumption accounts for the majority of indirect energy consumption. The smelting and pressing of ferrous metals is the industrial sector which is most affected by the household consumption ways. The scale component, own internal component, feed-back component and spillover component has accounted for 18.5%, 1.3%, 0.1% and 80.0%, from 17.5%, 2.0%, 0.2% and 80.3% of energy consumption in total, respectively. Under the four scenario development modes (NN, NA, LN and LA), it is predicted that the indirect energy consumption in household life in 2025 is 43740.56PJ, 34922.93PJ, 39149.37PJ and 31257.28PJ, respectively. The implementation of low-carbon consumption mode and the accelerated decline of energy intensity will help to reduce energy consumption. [ABSTRACT FROM AUTHOR]