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Electricity consumption in buildings is one of the major causes of energy usage and knowledge of this can help building owners and users increase energy efficiency and conservation efforts. For Pacific Island countries, building electricity demand data is not readily accessible or available for constructing models to predict electricity demand. This paper starts to fill this gap by studying the case of schools in Fiji. The aim of the paper is to assess the factors affecting electricity demand for grid-connected Fijian schools and use this assessment to build mathematical models (multiple linear regression (MLR) and artificial neural network (ANN)) to predict electricity consumption. The average grid-connected electricity demand in kWh/year was 1411 for early childhood education schools, 5403 for primary schools, and 23,895 for secondary schools. For predicting electricity demand (ED) for all grid-connected schools, the stepwise MLR model shows that taking logarithm transformations on both the dependent variable and independent variables (number of students, lights, and air conditioning systems) yields statistically significant independent variables with an R2 value of 73.3% and RMSE of 0.2248. To improve the predicting performance, ANN models were constructed on both the natural form of variables and transformed variables. The optimum ANN model had an R2 value of 95.3% and an RMSE of 59.4 kWh/year. The findings of this study can assist schools in putting measures in place to reduce their electricity demand, associated costs, and carbon footprint, as well as help government ministries make better-informed policies. [ABSTRACT FROM AUTHOR]

Computational methods play a significant role in reducing energy consumption in cities. Many different sensor networks (e.g., traffic intensity sensors, intelligent cameras, air quality monitoring systems) generate data that can be useful for both efficient management (including planning) and reducing energy usage. Street lighting is one of the most significant contributors to urban power consumption. This paper presents a summary of recent attempts to use computational methods to reduce energy usage by lighting systems, with special focus on graph-based methods. Such algorithms require all the necessary data to be integrated, in order to function properly: this task is not trivial, and is very time-consuming; therefore, the second part of the paper proposes a novel approach to integrating urban datasets and automating the optimisation process. In two practical examples, we show how spatially triggered graph transformations (STGT) can be used to build a model based on the road network map, sensor locations and street lighting data, and to introduce semantic relations between the objects, including utilisation of existing infrastructure, and planning of development to maximise efficiency. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative cooperative model for energy efficiency retrofitting that incorporates carbon emission rights, addressing critical financial constraints in Energy Performance Contracting (EPC). By employing the fuzzy analytic hierarchy process (F-AHP) to evaluate risk assessment indicators and stakeholder contributions and utilizing the enhanced Shapley method for equitable benefit distribution, the model demonstrates significant improvements in financing and efficiency for energy conservation projects. The findings are as follows: (1) the energy efficiency retrofit model, which integrates carbon emission rights, effectively alleviates the financial constraints and fosters energy conservation and emission reduction in guaranteed-savings EPC projects; (2) the enhanced Shapley method is deemed appropriate for the equitable distribution of energy-saving benefits among stakeholders; (3) when compared with the traditional model and the benefit allocation-absent carbon rights, the energy-saving benefits of the energy efficiency retrofit model incorporating carbon emission rights are higher in individual and overall terms. The findings of this study offer a viable solution to financing challenges faced by stakeholders in such projects and delineate a pragmatic approach for enterprises to enhance energy efficiency and reduce emissions. [ABSTRACT FROM AUTHOR]

In advanced manufacturing, optimizing mixed-model synchronous assembly lines (MMALs) is crucial for enhancing productivity and adhering to sustainability principles, particularly in terms of energy consumption and energy-efficient sequencing. This paper introduces a novel approach by categorizing sequence-dependent setup times into bipartite categories: workpiece-independent and workpiece-dependent. This strategic division streamlines assembly processes, reduces idle times, and decreases energy consumption through more efficient machine usage. A new mathematical model is proposed to minimize the intervals at which workpieces are launched on an MMAL, aiming to reduce operational downtime that typically leads to excessive energy use. Given the Non-deterministic Polynomial-time hard (NP-hard) nature of this problem, a genetic algorithm (GA) is developed to efficiently find solutions, with performance compared against the traditional branch and bound technique (B&B). This method enhances the responsiveness of MMALs to variable production demands and contributes to energy conservation by optimizing the sequence of operations to align with energy-saving objectives. Computational experiments conducted on small and large-sized problems demonstrate that the proposed GA outperforms the conventional B&B method regarding solution quality, diversity level, and computational time, leading to energy reductions and enhanced cost-effectiveness in manufacturing settings. [ABSTRACT FROM AUTHOR]

Saving energy is an important strategy to address the current energy crisis and environmental degradation. Regarding the pilot policy of the energy quota trading as a quasi-natural experiment by employing a difference-in-differences method, the purpose of this paper is to investigate the effect of this pilot policy on energy saving and its mechanisms based on city-level data in China from 2006 to 2020. We find that the energy quota trading policy can reduce the total energy consumption and energy consumption intensity of pilot cities, and the effect of the policy can gradually strengthen over time. The market-oriented reform of energy factor allocation can effectively promote energy saving and economic growth. These results are convincing through a series of robustness checks. The heterogeneity test shows that the energy quota trading has a significant energy saving effect on economically developed cities, densely populated cities and southern cities, but not in economically underdeveloped cities, sparsely populated cities and northern cities. Further mechanism inspection suggests that the pilot policy of energy quota trading mainly achieves energy conservation through industrial structure upgrading and green technology innovation. Our findings provide a valuable insight for China to control energy consumption and promote the high-quality development of the energy economy. [ABSTRACT FROM AUTHOR]

This paper presents the findings of fifteen energy audits conducted on industrial sites in Canada's Greater Toronto Area (GTA). The audits covered a range of industries including food processing, packaged goods, and finishing processes (powder-coating). The primary focus of the audits was to analyze the natural gas consumption and the performance of major-gas-consuming equipment. The audits identified natural-gas-consuming equipment that could be optimized to yield energy and operational cost savings and greenhouse gas (GHG) reduction opportunities. Food production plants' energy intensity ranges from 5.59 m3/ft2 to 17.73 m3/ft2. Therefore, there is a significant opportunity to improve energy consumption through better technology integration. The results of the audits indicate a trend of an increase in the percentage of non-productive consumption with non-productive time. The proposed energy-saving measures include reducing non-productive natural gas consumption, gas-fired equipment tune-up, optimizing boiler loads, and reducing oven exhaust by using variable frequency drives (VFDs). The findings of this study could be used to develop a demand-side management program specifically for small- and medium-sized industrial facilities in the Greater Toronto Area and other parts of Canada. [ABSTRACT FROM AUTHOR]

This study provides a viable path to save energy by means of e-commerce development. Taking the national e-commerce demonstration cities (NEDC) pilots policy implemented in China as a quasi-natural experiment, based on the city panel data from 2006 to 2019, this study applies the multi-period difference-in-difference (DID) method to evaluate the effect of NEDC on energy saving in pilot cities. The empirical results suggest that the NEDC policy obviously contributes to energy conservation. The treated cities reduced energy consumption by 14.2% as a result of the implementation of NEDC, relative to the untreated cities. The conclusions remain valid after conducting robustness tests such as placebo test, instrumental variables regression, propensity score matching-difference-in-difference (PSM-DID), and synthetic difference-in-difference (SDID). The NEDC achieves energy-saving effects through technological innovation, industrial restructuring, and economic agglomeration. Furthermore, the heterogeneity analysis indicates that, in cities with high levels of human capital, well-developed information infrastructure, non-resource-based cities, and favorable business environments, the impact of NEDC on energy saving is more significant. Analysis of spatial effects shows that the implementation of NEDC has negative externalities, increasing energy consumption in the surrounding area. In the context of the digital economy, this paper presents new insights on the relationship between e-commerce and energy consumption and provides policy direction for countries looking for energy-saving solutions. [ABSTRACT FROM AUTHOR]

In this paper, the window-opening behavior in a three-star green building with an operation mark in Ningbo was investigated. Four single offices facing south were selected, and the measurement lasted 20–40 days in the cooling season. An analysis of the relationship between window use and environmental factors was assessed by monitoring the occupancy state, the window action and window state, the time, and the temperature and humidity. A statistical analysis method was conducted to reveal the similarities and differences in window use among different occupants. The main findings were as follows: The window adjustment behaviors of different people vary significantly. In terms of adjustment time, there are two modes of behaviors. In addition, the occupancy state had a great impact on window action. Moreover, some people prefer to open the windows in the morning and close the windows at departure time, However, some of them prefer to open the windows at departure time and close the windows at the arrival time. Some irrational window opening behaviors were found in this research. Through correlation analysis, it was shown that the longer time the occupants were in the office, the higher window-opening duration and window-opening frequency would be. If the window was closed at arrival time, the total opening duration of window was significantly reduced. Also, window opening duration and closing duration are proportional to each other. With the help of principal component analysis (PCA) of the 12 features that affected window adjustment, the correlation between the window opening behaviors and the features could be clearly illustrated. A stochastic model of window-opening behavior based on logistics regression and uncertainty analysis was used in this paper. The cooling energy consumption of the stochastic model is 99.9% higher than the energy consumption generated by a fixed number of air changes, and the average energy consumption exceeds the fixed value by more than 21%. The findings of this study have certain guiding significance for promoting energy conservation by occupants' behaviors in green buildings in this region. [ABSTRACT FROM AUTHOR]

The escalating issue of global climate change necessitates urgent measures to reduce carbon emissions globally. Within this context, the construction industry emerges as a critical sector to address given its high energy consumption, substantial CO2 emissions, and low utilization rate. Therefore, it is pivotal to foster energy conservation and reduce emissions in this sector. To this end, this paper delineates two primary objectives: (1) identifying optimal research methodologies and index parameters for evaluating carbon emission efficiency in the construction industry, and (2) assessing the variance in carbon emission efficiency at disparate stages and regions. Leveraging the Malmquist index, we scrutinize the carbon emission data from 30 Chinese provinces spanning from 2010 to 2019. Our findings indicate a geographical dichotomy in China's construction industry's carbon emission efficiency—lower in the west and higher in the east. Additionally, this study delves into the distinguishing features of emission efficiency alterations across regions, the main influencing factors, and avenues for enhancement. Subsequently, it proposes policy recommendations tailored to the unique attributes of various regions and the overarching framework. [ABSTRACT FROM AUTHOR]

As commercial transportation complexes, expressway service buildings have large passenger flow and a poor energy-saving effect, and have become the focus of energy conservation and emissions reduction efforts in the transportation field. At the same time, the particularity of the function determines that it is within the scope of no municipal supporting facilities, which renders them typical energy island-type buildings. This paper takes the expressway service buildings in a cold area as the research object, and carries out the correlation and partial correlation analysis of the factors influencing the operating energy consumption of the air-conditioning system. For the analysis of factors affecting the energy consumption of expressway service buildings during the operation period, considering that most of the service buildings are in the form of heating and cooling air conditioners, this paper chooses to represent the "refrigeration period" with a more obvious degree of influence. At the same time, during the operation period, because the ontological characteristics have been determined according to the analysis results, the outdoor meteorological characteristics are the main factors affecting the energy consumption of expressway service buildings. These include the dry bulb temperature and horizontal plane solar irradiance index, as well as the indoor comprehensive environment parameters: temperature, CO2 concentration index, indoor personnel density index. Based on the above analysis, a low energy consumption operation strategy for the air-conditioning system is proposed. The results of this article are of great significance for the construction of energy consumption models for expressway service buildings and the adoption of low energy consumption strategies. [ABSTRACT FROM AUTHOR]

The increasing price of and demand for energy have prompted several organizations to develop intelligent strategies for energy tracking, control, and conservation. Demand side management is a critical strategy for averting substantial supply disruptions and improving energy efficiency. A vital part of demand side management is a smart energy management system that can aid in cutting expenditures while still satisfying energy needs; produce customers' energy consumption patterns; and react to energy-saving algorithms and directives. The Internet of Things is an emerging technology that can be employed to effectively manage energy usage in industrial, commercial, and residential sectors in the smart environment. This paper presents a smart energy management system for smart environments that integrates the Energy Controller and IoT middleware module for efficient demand side management. Each device is connected to an energy controller, which is the inculcation of numerous sensors and actuators with an IoT object, collects the data of energy consumption from each smart device through various time-slots that are designed to optimize the energy consumption of air conditioning systems based on ambient temperature conditions and operational dynamics of buildings and then communicate it to a centralized middleware module (cloud server) for management, processing, and further analysis. Since air conditioning systems contribute more than 50% of the electricity consumption in Pakistan, for validation of the proposed system, the air conditioning units have been taken as a proof of concept. The presented approach offers several advantages over traditional controllers by leveraging real-time monitoring, advanced algorithms, and user-friendly interfaces. The evaluation process involves comparing electricity consumption before and after the installation of the SEMS. The proposed system is tested and implemented in four buildings. The results demonstrate significant energy savings ranging from 15% to 49% and highlight the significant benefits of the system. The smart energy management system offers real-time monitoring, better control over the air conditioning systems, cost savings, environmental benefits, and longer equipment life. The ultimate goal is to provide a practical solution for reducing energy consumption in buildings, which can contribute to sustainable and efficient use of energy resources and goes beyond simpler controllers to address the specific needs of energy management in buildings. [ABSTRACT FROM AUTHOR]

Future power systems will face more extreme operating condition scenarios, and system emergency dispatch will face more severe challenges. The use of distributed control is a well-designed way to handle this. It enables multi-energy complementation by means of autonomous communication, which greatly improves the flexibility of the grid. First, in the context of global energy conservation and emission reduction, this paper adopts the energy usage method of "renewable energy is the main source of energy, supplemented by thermal power and energy storage" to reduce the system abandoned wind (light) rate while supplementing the energy storage capacity. Second, a consensus algorithm is added to the system while considering the coordination between thermal units and energy storage. An "interface" for autonomous communication between thermal units and energy storage is created using the incremental cost of each agent. To address the recurring issue of power imbalance during emergency dispatch of the system, the consensus algorithm is enhanced so that the communication interval varies with the unit rate. This is based on the climbing characteristics of each thermal power unit. Finally, the effectiveness of the proposed method is verified in an IEEE-30 bus system. [ABSTRACT FROM AUTHOR]

In recent decades, significant changes have occurred in the consumer lifestyle and energy consumption structure. In order to foster a more holistic understanding of how residential consumers use energy in their everyday life and how it affects energy efficiency, individual data about energy-related behaviour in six primary areas, namely, housing, mobility, diet, consumption, leisure activities, and information, were studied. Specifically, the objective of this study is to investigate energy efficiency behaviour based on residential consumers' lifestyles. An exploratory pilot study with a total of 50 respondents was carried out in month of July. Data were collected from residential consumers from six states in Peninsular Malaysia and were analysed using the Partial Least Square Structural Equation Modelling approach. The result showed that consumption, housing, and information are the dominant direct contributors to energy efficiency. By understanding these lifestyles, researchers and policymakers can better understand consumer energy consumption behaviour and design targeted interventions to encourage energy efficiency and conservation. The paper highlights the need for further research on consumer lifestyles and their contribution to energy efficiency. It also provides findings from this study that was used to refine the research tools for future research. [ABSTRACT FROM AUTHOR]

Design priorities for tall and supertall buildings have for some time shifted to achieving more energy efficiency to address the energy needs of the increasing global population. Engineers and architects aim to achieve energy conservation through active and passive approaches, pursuing technological innovations and adopting climate-responsive design. Because of the green movement currently dominating the building industry, tall buildings that need a massive amount of energy to build and operate, and the practical desire to switch from non-renewable to clean renewable energy resources, intense attention has been given to the energy efficiency of tall buildings in the recent past. Due to the vast array of energy-efficient design features, equipment, and applications available now, it is timely to examine the pros and cons of these issues. This review paper is an attempt to comprehensively present and deliberate these issues. It illustrates and discusses the concepts and applications through a few case studies from several continents worldwide. The review shows that the design of tall buildings focusing on energy conservation is an evolutionary process and there is a need for further research about how to face the associated challenges to improve energy efficiency by developing creative solutions and strategies, as well as applying additional innovative technologies. [ABSTRACT FROM AUTHOR]

This editorial paper tackles the issue of the consumer attitudes toward energy reduction and changing energy consumption behaviors. This topic is of special relevance today as Europe faces an unprecedented energy crisis as a result of diverting from Russian supplies of oil and gas due to the war in Ukraine. For many years now, Europe has relied upon cheap and affordable Russian oil and gas (in fact, the European Green Deal and the strategy for the decarbonization of the economy by 2030 were indirectly based on it), but the transition to the renewable future now appears to be jeopardized. As energy prices are soaring globally, it is not yet clear whether this would have an effect on significantly changing consumer behavior and increasing energy efficiency and security as many consumers are reluctant to change their old habits and are used to having their energy on demand and for any possible occasion. However, changing energy consumption behaviors would be beneficial not only for handling the current energy crisis but also for setting the long-term trends with respect to energy saving, which is crucial for fighting global warming and climate change while sustaining economic growth. [ABSTRACT FROM AUTHOR]

Electricity is used in educational buildings, and there are now numerous attempts to reduce consumption, achieve sustainability, and protect the environment. This paper aims to study energy consumption, identify opportunities to rationalize energy consumption, and propose solutions at Sulaiman Al-Rajhi University in the Kingdom of Saudi Arabia in order for the university campus to become sustainable. The results showed that total annual electrical consumption totaled 13,859 MWh in 2021. The air conditioning system, other devices, and lighting have the percentage of consumption 79, 14, and 7% of the total, respectively. Electricity consumption intensity was approximately 145–155 kWh/m2, and the per capita intensity was approximately 12,987–16,351 kWh, in the period 2017–2021. The price of the electrical energy generated on the university campus ranged between 0.3 and 0.53 SR/kWh in 2021, while the electricity tariff from the grid for educational buildings was 0.18 SR/kWh. This means that the public grid was 50% cheaper than campus-generated electricity. At the price of energy generated by generators, the total simple payback time (SPBT) for the suggested energy conservation measures (ECMs) is 4.13 years, whereas the SPBT is 8.96 in the case of the consumption of the electricity supplied by the national grid. The environmental benefits of the proposed ECMs were also identified. [ABSTRACT FROM AUTHOR]

Automated fault detection and diagnostics analysis in commercial building systems using machine learning (ML) can improve the building's efficiency and conserve energy costs from inefficient equipment operation. However, ML can be challenging to implement in existing systems due to a lack of common data standards and because of a lack of building operators trained in ML techniques. Additionally, results from ML procedures can be complicated for untrained users to interpret. Boolean rule-based analysis is standard in current automated fault detection and diagnostics (AFDD) solutions but limits analysis to the rules defined and calibrated by energy engineers. Boolean rule-based analysis and ML can be combined to create an effective fault detection and diagnostics (FDD) tool. Three examples of ML's advantages over rule-based analysis are explored by analyzing functional building equipment. ML can detect long-term faults in the system caused by a lack of system maintenance. It can also detect faults in system components with incomplete sets of sensors by modeling expected system operations and by making comparisons to actual system operations. An example of ML detecting a failure in a building is shown along with a demonstration of the soft decision boundaries of ML-based FDD compared to Boolean rule-based FDD analysis. The results from the three examples are used to demonstrate the strengths and weaknesses of using ML for AFDD analysis. [ABSTRACT FROM AUTHOR]

The rational use of energy systems is one of the main discussions in sustainability in the 21st century. Water pumping systems are one of the most significant consumers of electricity in urban systems, whether for urban water supply, sewage, or use in vertical buildings. Thus, this work aims to present Industry 4.0 (I4.0) technologies applied in buildings' water pumping systems, focusing on energy efficiency, supervision, and control of the pumping system. The work involves four steps: (i) identifying the existing I4.0 technologies and (ii) mapping the possibilities of applying Industry 4.0 technologies in building pumping systems. The study includes the analysis of (16) articles published in journals between 2018 and June 2021 to identify I4.0 technologies cited in the publications. It identified and grouped eighteen (18) technologies based on twenty-two (22) terms observed in the papers. The study classified the identified technologies into three possible applications in a building water pumping system. The applications include: (i) directly applicable, (ii) partially applicable, and (iii) application not yet identified. Therefore, the study presents the advantages of I4.0 technologies developed primarily for the industry sector, also applicable in residential building water pumping systems. These technologies' benefits include energy efficiency, user control, a reduction from periods of failure of the pumping system (maintenance), water quality, and moving towards Intelligent Pumping or Pumping 4.0. [ABSTRACT FROM AUTHOR]

Energy efficiency represents a key resource for economic and social development, providing substantial benefits to different stakeholders, ranging from the entities which develop energy efficient measures to everyone in society. In addition to cost savings, multiple benefits can be achieved by supporting a better alignment between energy issues and strategic business priorities: e.g., improved competitiveness, profitability, quality, etc. Thus, energy efficiency can be a strategic advantage, not just a marginal issue, for companies. However, most firms, especially small and medium enterprises (SMEs), face many problems and, in some cases, hostility when trying to effectively implement energy efficiency actions. The most dominant barriers are the access to capital and the lack of awareness (especially in terms of life cycle cost effects). The supply chain viewpoint represents one of the main opportunities for overcoming those barriers and improving energy performance even for weaker companies. Since the current literature on energy efficiency and practical approaches to ensure energy efficiency mainly focus on energy performance on a single-firm basis, this paper aims to provide a systematic review of papers on the integration of energy efficiency in supply chain design and management published in academic journal, thereby defining potential research streams to close the gaps in the literature. A number of literature reviews have been published focusing on specific aspects of sustainable or on green supply chain management; however, to the best of our knowledge, no review has focused on the energy efficiency issue. Firstly, the present paper shows how considering energy consumption in supply chain management can contribute to more energy-efficient processes from a systemic point of view. Then, the review methodology used is defined and the sampled papers are analyzed and categorized based on the different approaches they propose. From these analyses, potential future research streams are outlined. [ABSTRACT FROM AUTHOR]

The increasing demand for applying energy performance contracting (EPC) for urban energy conservation has resulted in a significant amount of publications over the past decade. This study tries to identify future research trends in the subject of EPC through analyzing 127 journal papers published from 2008 to 2018. Based on the analysis and discussion of the EPC research, several main research trends were identified. The research results reveal an increasing research interest in EPC over the period. The findings imply that case study is the major research method and descriptive analysis and statistical analysis are primarily used for data analysis. In addition, EPC research in the past decade focused on five major research topics, which are 'implementation of EPC projects', 'EPC mechanism and business models', 'decision-making in EPC projects', 'Energy Service Companies (ESCOs) in EPC projects', and 'risk management in EPC projects'. Based on the five research topics, future research trends and directions in EPC were identified as well. The findings of this study can be informative and valuable for guiding future research in EPC, and are particularly helpful for researchers who are keen to open a new window of investigating EPC issues worldwide. [ABSTRACT FROM AUTHOR]

In the last few years, the application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from the research community. MPC is becoming more and more viable because of the increase in computational power of building automation systems and the availability of a significant amount of monitored building data. MPC has found successful implementation in building thermal regulation, fully exploiting the potential of building thermal mass. Moreover, MPC has been positively applied to active energy storage systems, as well as to the optimal management of on-site renewable energy sources. MPC also opens up several opportunities for enhancing energy efficiency in the operation of Heating Ventilation and Air Conditioning (HVAC) systems because of its ability to consider constraints, prediction of disturbances and multiple conflicting objectives, such as indoor thermal comfort and building energy demand. Despite the application of MPC algorithms in building control has been thoroughly investigated in various works, a unified framework that fully describes and formulates the implementation is still lacking. Firstly, this work introduces a common dictionary and taxonomy that gives a common ground to all the engineering disciplines involved in building design and control. Secondly the main scope of this paper is to define the MPC formulation framework and critically discuss the outcomes of different existing MPC algorithms for building and HVAC system management. The potential benefits of the application of MPC in improving energy efficiency in buildings were highlighted. [ABSTRACT FROM AUTHOR]

The increasing power consumption and related environmental implications currently generated by large data networks have become a major concern over the last decade. Given the drastic traffic increase expected in 5G dense environments, the energy consumption problem becomes even more concerning and challenging. In this context, Software-Defined Networks (SDN), a key technology enabler for 5G systems, can be seen as an attractive solution. In these programmable networks, an energy-aware solution could be easily implemented leveraging the capabilities provided by control and data plane separation. This paper investigates the impact of energy-aware routing on network performance. To that end, we propose a novel energy-aware mechanism that reduces the number of active links in SDN with multiple controllers, considering in-band control traffic. The proposed strategy exploits knowledge of the network topology combined with traffic engineering techniques to reduce the overall power consumption. Therefore, two heuristic algorithms are designed: a static network configuration and a dynamic energy-aware routing. Significant values of switched-off links are reached in the simulations where real topologies and demands data are used. Moreover, the obtained results confirm that crucial network parameters such as control traffic delay, data path latency, link utilization and Ternary Content Addressable Memory (TCAM) occupation are affected by the performance-agnostic energy-aware model. [ABSTRACT FROM AUTHOR]

Transaction-based energy (TE) management and control has become an increasingly relevant topic, attracting considerable attention from industry and the research community alike. As a result, new techniques are emerging for its development and actualization. This paper presents a comprehensive review of TE involving peer-to-peer (P2P) energy trading and also covering the concept, enabling technologies, frameworks, active research efforts and the prospects of TE. The formulation of a common approach for TE management modelling is challenging given the diversity of circumstances of prosumers in terms of capacity, profiles and objectives. This has resulted in divergent opinions in the literature. The idea of this paper is therefore to explore these viewpoints and provide some perspectives on this burgeoning topic on P2P TE systems. This study identified that most of the techniques in the literature exclusively formulate energy trade problems as a game, an optimization problem or a variational inequality problem. It was also observed that none of the existing works has considered a unified messaging framework. This is a potential area for further investigation. [ABSTRACT FROM AUTHOR]

Relieving the adverse effects of automobiles on the environment and natural resources has drawn the attention of numerous researchers. This paper seeks a new path to reach a target by focusing on the synergy of the vehicle and the environment. A real-time economical route planning method for a plug-in hybrid electric vehicle (PHEV) is proposed. Three main contributions have been made. Firstly, a real comparison test is performed to provide rudimentary understanding of the difference in energy usage and route planning between PHEVs and conventional vehicles. Secondly, an approach to obtain PHEV customized data is developed for road weight calculation, which is the essential step in route planning. This method incorporates traffic data from conventional vehicles with the PHEV simulation model, obtaining the required data. Thirdly, the travel expense estimation model (TEEM) is designed. The TEEM could be applied to calculate the road weight of each road segment considering the impact on energy consumption with respect to environmental factors, providing the grounds for route planning. The proposed method to plan an economical route is evaluated, and the results justify its validation and ability to improve fuel economy. [ABSTRACT FROM AUTHOR]

An injection-molding machine (IMM) is equipment that produces all kinds of plastic products. At present, the global production of IMMs amounts to more than 30 million units each year, and its total production accounts for 50% of all plastic molding equipment. Now, the main energy consumption equipment of plastic processing plants consists in IMMs. Therefore, energy conservation research on IMMs has become urgent. This paper initially introduces the current development of IMMs. The working principle and the distribution of energy consumption of IMMs are then analyzed in detail. In addition, the methods and characteristics of the energy conservation technology in hydraulic control circuits and electrical control circuits are developed. Meanwhile, the recovery and the reuse of braking energy of IMMs are proposed. Moreover, some control strategies are discussed in order to improve energy efficiency. Finally, challenges and prospects for hydraulic IMMs are carried out. This paper thus provides a comprehensive review on energy-saving technology of electric-hydraulic injection-molding equipment for researchers. [ABSTRACT FROM AUTHOR]

This paper investigates the relationships between energy efficiency improvements by producers, the ease of substitution between energy and other inputs and the size of the resulting "rebound effects". Fundamentally, easier substitution leads to larger rebounds. Focusing upon conceptual and methodological issues, the paper highlights the challenges of estimating and modeling rebound effects with the help of production and cost functions and questions the robustness of the evidence base in this area. It argues that the multiple definitions of "elasticities of substitution" are a source of confusion, the most commonly estimated elasticity is of little practical value, the empirical literature is contradictory, prone to bias and difficult to use and there are only tenuous links between this literature and the assumptions used within energy-economic models. While "energy-augmenting technical change" provides the natural choice of independent variable for an estimate of rebound effects, most empirical studies do not estimate this form of technical change, many modeling studies do not simulate it and others simulate it in such a way as to underestimate rebound effects. As a result, the paper argues that current econometric and modeling studies do not provide reliable guidance on the magnitude of rebound effects in different industrial sectors. [ABSTRACT FROM AUTHOR]

This paper presents the development of an energy efficient low power stepper converter. A prototype with a hydraulic output power of ≈600W was designed, manufactured, investigated and improved. The converter consists of a hydraulic cylinder piston unit controlled by a fast switching valve to displace a defined fluid quantum by the limited forward stroke of the piston in its cylinder. The displaced fluid generates a precise, incremental motion of a load cylinder which should be controlled. Energy saving is achieved by storing the pressure surplus intermediately in the kinetic energy of the piston to displace a part of the fluid quantum without hydraulic energy from the supply line. Energy recuperation can be done in a similar way. Simulations and experiments showed two main efficiency improvement measures of the first converter prototype. The weak points were the commercially available check valves and the used guidance system for the pistons. The second part of the paper reports about the development of a fast check valve and of a combined hydrostatic hydrodynamic bearing system based on the elastic deformation of plastics. The theoretical and experimental results show a significant improvement of the energy efficiency, the potential of this drive technology and further improvement potential. Expressed in terms of numbers an energy efficiency increase compared to a resistance control up to 30% and a maximum recuperation energy efficiency over 60% were measured. [ABSTRACT FROM AUTHOR]

A Virtual energy assessment (VEA) refers to the assessment of the energy flow in a building without physical data collection. It has been occasionally conducted before the COVID-19 pandemic to residential and commercial buildings. However, there is no established framework method for conducting this type of energy assessment. The COVID-19 pandemic has catalysed the implementation of remote energy assessments and remote facility management. In this paper, a novel framework for VEA is developed and tested on case study buildings at the University of Melbourne. The proposed method is a hybrid of top-down and bottom-up approaches: gathering the general information of the building and the historical data, in addition to investigating and modelling the electrical consumption with artificial neural network (ANN) with a projection of the future consumption. Through sensitivity analysis, the outdoor temperature was found to be the most sensitive (influential) parameter to electrical consumption. The lockdown of the buildings provided invaluable opportunities to assess electrical baseload with zero occupancies and usage of the building. Furthermore, comparison of the baseload with the consumption projection through ANN modelling accurately quantifies the energy consumption attributed to occupation and operational use, referred to as 'operational energy' in this paper. Differentiation and quantification of the baseload and operational energy may aid in energy conservation measures that specifically target to minimise these two distinct energy consumptions. [ABSTRACT FROM AUTHOR]

Microgrids (MGs) are presented as a cornerstone of smart grids. With the potential to integrate intermittent renewable energy sources (RES) in a flexible and environmental way, the MG concept has gained even more attention. Due to the randomness of RES, load, and electricity price in MG, the forecast errors of MGs will affect the performance of the power scheduling and the operating cost of an MG. In this paper, a combined stochastic programming and receding horizon control (SPRHC) strategy is proposed for microgrid energy management under uncertainty, which combines the advantages of two-stage stochastic programming (SP) and receding horizon control (RHC) strategy. With an SP strategy, a scheduling plan can be derived that minimizes the risk of uncertainty by involving the uncertainty of MG in the optimization model. With an RHC strategy, the uncertainty within the MG can be further compensated through a feedback mechanism with the lately updated forecast information. In our approach, a proper strategy is also proposed to maintain the SP model as a mixed integer linear constrained quadratic programming (MILCQP) problem, which is solvable without resorting to any heuristics algorithms. The results of numerical experiments explicitly demonstrate the superiority of the proposed strategy for both island and grid-connected operating modes of an MG. [ABSTRACT FROM AUTHOR]

This paper presents an optimal dispatch model of an ice storage air-conditioning system for participants to quickly and accurately perform energy saving and demand response, and to avoid the over contact with electricity price peak. The schedule planning for an ice storage air-conditioning system of demand response is mainly to transfer energy consumption from the peak load to the partial-peak or off-peak load. Least Squares Regression (LSR) is used to obtain the polynomial function for the cooling capacity and the cost of power consumption with a real ice storage air-conditioning system. Based on the dynamic electricity pricing, the requirements of cooling loads, and all technical constraints, the dispatch model of the ice-storage air-conditioning system is formulated to minimize the operation cost. The Improved Ripple Bee Swarm Optimization (IRBSO) algorithm is proposed to solve the dispatch model of the ice storage air-conditioning system in a daily schedule on summer. Simulation results indicate that reasonable solutions provide a practical and flexible framework allowing the demand response of ice storage air-conditioning systems to demonstrate the optimization of its energy savings and operational efficiency and offering greater energy efficiency. [ABSTRACT FROM AUTHOR]

Energy efficiency is widely recognized as the foundational and most critical strategy for decarbonizing the manufacturing sector. Misconceptions surrounding energy efficiency measures often hinder their widespread adoption. This article aims to debunk five common myths and provides data and resources to help implement efficiency projects faster and more effectively to achieve greater decarbonization. First, the article challenges the myth that organizations have exhausted all possible energy efficiency opportunities by achieving voluntary energy intensity goals or energy performance certification. Second, it also addresses the misconceptions that efficiency projects are capital-intensive, require many qualified specialists, and have long investment return periods. By presenting real-world case studies and referencing commonly found efficiency opportunities, the article illustrates that energy-savings opportunities are ubiquitous. Organizations can use various contracting mechanisms as well as financial and technical resources from utility companies and government programs to lessen their burden. The notion that efficiency measures can be implemented solely in proprietorship facilities is dispelled. This article emphasizes the importance of green leases and explains that aligning decarbonization goals between the lessor and lessee can help drive savings for both parties. Finally, using unbundled renewable energy certificates as the sole pathway to decarbonization is strongly discouraged. By debunking these prevalent myths, this article aims to foster a deeper understanding of energy efficiency's potential as a cornerstone of decarbonization efforts and to embrace it as a critical pathway toward a sustainable future. [ABSTRACT FROM AUTHOR]

In urban building management, accurate prediction of building energy consumption is significant in realizing energy conservation and improving energy efficiency. Due to the complexity and variability of energy consumption data, existing prediction models face the challenge of difficult parameter selection, which directly affects their accuracy and application. To solve this problem, this study proposes an improved particle swarm algorithm (IPSO) for optimizing the parameters of the least squares support vector machine (LSSVM) and constructing an energy consumption prediction model based on IPSO-LSSVM. The model fully combines the advantages of LSSVM in terms of nonlinear fitting and generalization ability and uses the IPSO algorithm to adjust the parameters precisely. By analyzing the sample data characteristics and validating them on two different types of building energy consumption datasets, the results of the study show that, compared with traditional baseline models such as back-propagation neural networks (BP) and support vector regression (SVR), the model proposed in this study is more accurate and efficient in parameter selection and significantly reduces the prediction error rate. This improved approach not only improves the accuracy of building energy consumption prediction but also enhances the robustness and adaptability of the model, which provides reliable methodological support for the development of more effective energy-saving strategies and optimization of energy use to achieve the goal of energy-saving and consumption reduction and provides a new solution for the future management of building energy consumption. [ABSTRACT FROM AUTHOR]

As energy demand and electricity costs continue to rise, consumers are increasingly adopting energy-efficient practices and appliances, underscoring the need for detailed metering options like appliance-level load monitoring. Non-intrusive load monitoring (NILM) is particularly favored for its minimal hardware requirements and enhanced customer experience, especially in residential settings. However, commercial power systems present significant challenges due to greater load diversity and imbalance. To address these challenges, we introduce a novel neural network architecture that combines sequence-to-sequence, WaveNet, and ensembling techniques to identify and classify single-phase and three-phase loads using appliance power signatures in commercial power systems. Our approach, validated over four months, achieved an overall accuracy exceeding 93% for nine devices, including six single-phase and four three-phase loads. The study also highlights the importance of incorporating nonlinear loads, such as two different inverter-type air conditioners, within NILM frameworks to ensure accurate energy monitoring. Additionally, we developed a web-based NILM energy dashboard application that enables users to monitor and evaluate load performance, recognize usage patterns, and receive real-time alerts for potential faults. Our findings demonstrate the significant potential of our approach to enhance energy management and conservation efforts in commercial buildings with diverse and complex load profiles, contributing to more efficient energy use and addressing climate change challenges. [ABSTRACT FROM AUTHOR]

In terms of power and energy consumption, DRAMs play a key role in a modern server system as well as processors. Although power-aware scheduling is based on the proportion of energy between DRAM and other components, when running memory-intensive applications, the energy consumption of the whole server system will be significantly affected by the non-energy proportion of DRAM. Furthermore, modern servers usually use NUMA architecture to replace the original SMP architecture to increase its memory bandwidth. It is of great significance to study the energy efficiency of these two different memory architectures. Therefore, in order to explore the power consumption characteristics of servers under memory-intensive workload, this paper evaluates the power consumption and performance of memory-intensive applications in different generations of real rack servers. Through analysis, we find that: (1) Workload intensity and concurrent execution threads affects server power consumption, but a fully utilized memory system may not necessarily bring good energy efficiency indicators. (2) Even if the memory system is not fully utilized, the memory capacity of each processor core has a significant impact on application performance and server power consumption. (3) When running memory-intensive applications, memory utilization is not always a good indicator of server power consumption. (4) The reasonable use of the NUMA architecture will improve the memory energy efficiency significantly. The experimental results show that reasonable use of NUMA architecture can improve memory efficiency by 16% compared with SMP architecture, while unreasonable use of NUMA architecture reduces memory efficiency by 13%. The findings we present in this paper provide useful insights and guidance for system designers and data center operators to help them in energy-efficiency-aware job scheduling and energy conservation. [ABSTRACT FROM AUTHOR]

Improved industrial energy efficiency is a cornerstone in climate change mitigation. Research results suggest that there is still major untapped potential for improved industrial energy efficiency. The major model used to explain the discrepancy between optimal level of energy efficiency and the current level is the barrier model, e.g., different barriers to energy efficiency inhibit adoption of cost-effective measures. The measures outlined in research and policy action plans are almost exclusively technology-oriented, but great potential for energy efficiency improvements is also found in operational measures. Both technology and operational measures are combined in successful energy management practices. Most research in the field of energy management is grounded in engineering science, and theoretical models on how energy management in industry is carried out are scarce. One way to further develop and improve energy management, both theoretically as well as practically, is to explore how a socio-technical perspective can contribute to this understanding. In this article we will further elaborate this potential of cross-pollinating these fields. The aim of this paper is to relate energy management to two theoretical models, situated action and transaction analysis. We conclude that the current model for energy management systems, the input-output model, is insufficient for understanding in-house industrial energy management practices. By the incorporation of situated action and transaction analysis to the currently used input-output model, an enhanced understanding of the complexity of energy management is gained. It is not possible to find a single energy management solution suitable for any industrial company, but rather the idea is to find a reflexive model that can be adjusted from time to time. An idea for such a reflexive model would contain the structural elements from energy management models with consideration for decisions being situated and impossible to predict. [ABSTRACT FROM AUTHOR]

The wheel driving torque on four-wheel-drive electric vehicles (4WDEVs) can be modulated precisely and continuously, therefore maneuverability and energy-saving control can be carried out at the same time. In this paper, a wheel torque distribution strategy is developed based on multi-objective optimization to improve vehicle maneuverability and reduce energy consumption. In the high-layer of the presented method, sliding mode control is used to calculate the desired yaw moment due to the model inaccuracy and parameter error. In the low-layer, mathematical programming with the penalty function consisting of the yaw moment control offset, the drive system energy loss and the slip ratio constraint is used for wheel torque control allocation. The programming is solved with the combination of off-line and on-line optimization to reduce the calculation cost, and the optimization results are sent to motor controllers as torque commands. Co-simulation based on MATLAB® and Carsim® proves that the developed strategy can both improve the vehicle maneuverability and reduce energy consumption. [ABSTRACT FROM AUTHOR]

The equipment energy efficiency improvement policy (EEEIP) is one of the important measures of energy conservation and emission reduction in various countries. However, due to the simultaneous implementation of variety policies, the effect of the single policy cannot be clearly reflected. In this paper, a method of identification and evaluation of EEEIP was proposed, and the application was verified by analyzing the example of EEEIP in Japan (Top Runner policy, TRP). Firstly, through the factor decomposition model, this paper studied the energy conservation and emission reduction potential of this policy area in Japan. Then, the TRP was identified by using moving windows and correlation analysis, and the impact of specific equipment in TRP was analyzed. Finally, through the calculation of the rebound effect of the carbon footprint (REC), this paper analyzed the energy consumption and emission reduction effects of TRP in the short-term and whole life cycle. It showed that the policy has a good effect in tertiary industry and transportation, while the effect in residential is poor. For life cycle, the TRP of air conditioning and passenger car can bring better CO2 emission reduction effect, but the emission reduction effect of lighting is basically offset. [ABSTRACT FROM AUTHOR]

In response to extreme climate change, China has set a goal of reaching emission peak by 2030 and achieving carbon neutrality by 2050. Energy conservation and emission reduction of building heating in northern China are key to achieving this goal. Based on building area prediction and energy consumption intensity, this paper establishes a model for calculating heating energy consumption and carbon emissions in northern residential buildings at the macro level, which provides a basis for formulating policies related to heating and emission reduction in northern buildings. Based on the research method of scenario projection, combining the carbon emission subsets and future heating energy mix projections in northern China, the heating energy consumption in northern China decreases to 175, 149 and 135 Mtce in 2050 under the baseline, medium control and strict control scenarios, respectively. The heating energy consumption in the northern region should be controlled at least under the medium control scheme. Under this scenario, building heating carbon emissions in the north could be reduced to 450 MtCO2 by 2050, or 280 MtCO2 if more stringent abatement technologies are adopted. In order to achieve this goal, a combination of energy-saving technologies must be used. The use of biomass and solar technologies should be emphasized in rural heating, while envelope renovation makes the greatest contribution to heating energy savings, with envelope renovation contributing up to 92 Mtce to urban heating. [ABSTRACT FROM AUTHOR]

The current European energy policies have an influence on the need to rehabilitate the housing stock in order to meet the objectives of the European Union. Most of this housing stock was built without any type of energy regulation in adverse technical and economic conditions and thus is now energetically obsolete. The major rehabilitation effort required must be approached through actions based on previous quantitative energy knowledge of the existing buildings in order to guarantee the efficiency of energy-retrofitted solutions. This assessment can be carried out through monitoring dwellings conditioned by use patterns; through simulation programs, which do not usually offer faithful representations of energy conditions; or by using test cells, which allow us to evaluate a controlled indoor environment without the influence of users. The objective of this paper is to present the design and performance of test cells as an experimental method for vertical facade analysis in order to tackle the problem of retrofitting residential buildings in a Mediterranean climate, taking into account energy and environment. With this equipment, efficiency and energy savings, as well as illumination and interior air quality, can be simultaneously and comprehensively evaluated. [ABSTRACT FROM AUTHOR]

Ongoing climate change and increasing energy consumption are becoming a serious threat to international development efforts. To ensure a secure and sustainable future, local, national and regional authorities, as well as energy companies, need to be involved in improving energy efficiency and promoting rational energy use. The consumer is always at the centre of interest of policy and energy reduction strategies makers. It is their behaviour, motives and beliefs that can lead to optimised and economical energy management. This paper responds to the need to identify the individual preferences of energy consumers. The presented research fills an existing gap in the literature by analysing the strength of the influence of different types of instruments and external stimuli shaping pro-environmental attitudes of consumers of different behavioural types. The analysis presented in this paper is the result of the next stage of the authors' research on energy consumers modelling, their segmentation and comprehensive characteristics. The analysis was conducted on a representative sample of N = 4332 respondents from 8 European countries (Czech Republic, France, Greece, Spain, Germany, Poland, Romania, and the United Kingdom). The study used multivariate statistical methods, such as: Correspondence Analysis, Factor Analysis and Kruskal-Wallis test. These methods are adequate to the assumptions of the research procedure and allowed for the identification of 4 latent factors that link the incentives into groups, where the motivation to save energy is based on: information ("Information and Knowledge"), social norms ("Social Influence"), investment funding ("Investments") and energy price changes ("Energy Price"). In addition, the level of effectiveness of the studied incentives and motivators depends on the behavioural type of energy consumers and increases with rising levels of their intrinsic pro-environmental motivation. [ABSTRACT FROM AUTHOR]

In addition, patterns in residential activities and energy consumption, as well as the causal relationship between residential energy consumption and work patterns, are analyzed. Energy behaviors have increasing importance in the design of policies and programs that promote transitions to a low-carbon and more sustainable future. [Extracted from the article]

Historical and heritage (especially UNESCO) buildings need a specific, peculiar approach regarding energy performance, energy behavior, and indoor microclimate. Comparing a new building with a historical (UNESCO) building, it is evident that the degrees of freedom for implementing energy efficiency in historical buildings are strongly limited. Several constraints about the materials, the geometry, and the structures do not allow a comprehensive enhancement of energy performance or microclimate parameters. In this paper, we describe an energy building performance criterion adopted in order to find out the energy behavior in the Malatestiana Library. The challenge consists of optimizing energy efficiency and microclimate as well as a full preservation of ancient manuscripts. The study adopts Google Sketchup software to model three-dimensional (3D) buildings, and IESVE software to simulate an indoor microclimate. Software building models allow for the evaluation of different types of natural ventilation and section forms, e.g., original, without attic, and without ground floor. The results of the software modeling allow for a comparison of several building use modality effects and the effect of the presence of an attic and ground floor on indoor microclimate parameters in order to conserve and preserve ancient manuscripts. [ABSTRACT FROM AUTHOR]

The increased global demand for metallic nanoparticles for an ever growing number of applications has given rise to a need for larger scale and more efficient nanoparticle (NP) production processes. In this paper one such process is evaluated from the viewpoints of scalability and energy efficiency. Multiple setups of different scale of an arc/spark process were evaluated for energy efficiency and scalability using exergy analysis, heat loss evaluation and life cycle impact assessment, based on data collected from EU FP7 project partners. The energy efficiency of the process is quite low, with e.g., a specific electricity consumption (SEC) of producing ~80 nm copper NP of 180 kWh/kg while the thermodynamic minimum energy need is 0.03 kWh/kg. This is due to thermal energy use characteristics of the system. During scale-up of the process the SEC remained similar to that of smaller setups. Loss of NP mass in the tubing of larger setups gives a lower material yield. The variation in material yield has a significant impact on the life cycle impact for the produced NP in both the Human Health and Ecosystem Quality categories while the impact is smaller in the Global Warming and Resource Depletion categories. [ABSTRACT FROM AUTHOR]

This article presents a study of optimal control strategies for an energy storage system connected to a network of electrified Rubber Tyre Gantry (RTG) cranes. The study aims to design optimal control strategies for the power flows associated with the energy storage device, considering the highly volatile nature of RTG crane demand and difficulties in prediction. Deterministic optimal energy management controller and a Model Predictive Controller (MPC) are proposed as potentially suitable approaches to minimise the electric energy costs associated with the real-time electricity price and maximise the peak demand reduction, under given energy storage system parameters and network specifications. A specific case study is presented in to test the proposed optimal strategies and compares them to a set-point controller. The proposed models used in the study are validated using data collected from an instrumented RTG crane at the Port of Felixstowe, UK and are compared to a standard set-point controller. The results of the proposed control strategies show a significant reduction in the potential electricity costs and peak power demand from the RTG cranes. [ABSTRACT FROM AUTHOR]

Among the different approaches for minimising the energy consumption of mass transit systems (MTSs), a common concern for MTS operators is the improvement of the electrical infrastructure. The traffic on the lines under analysis is one of the most important inputs to the studies devoted to improving MTS infrastructure, since it represents where and how frequently it is possible to save energy. However, on the one hand, MTS electrical studies usually simplify the traffic model, which may lead to a misrepresentation of the energy interactions between trains. On the other hand, if the stochastic traffic is rigorously modelled, the size of the simulation problem could grow excessively, which in turn could make the time to obtain results unmanageable. To cope with this issue, this paper presents a method to obtain a reduced-size set of representative scenarios. Firstly, a traffic model including the most representative stochastic traffic variables is developed. Secondly, a function highly correlated with energy savings is proposed to make it possible to properly characterise the traffic scenarios. Finally, this function is used to select the most representative scenarios. The representative scenario set obtained by the application of this method is shown to be sufficiently accurate with a limited number of scenarios. The traffic approach in this paper improves the accuracy with respect to the usual traffic approach used in the literature. [ABSTRACT FROM AUTHOR]

Motivated by the development of electric vehicles (EVs), this paper addresses the energy management problem for the PV-assisted charging station (PVCS) network. An hour-ahead optimization model for the operation of PVCS is proposed, considering the profit of the PVCS, the local consumption of the photovoltaic (PV) energy and the impacts on the grid. Moreover, a two-level feasible charging region (FCR) model is built to guarantee the service quality for EVs and learning-based decision-making is designed to assist the optimization of the PVCS in various scenarios. The multi-party energy management problem, including several kinds of energy flows of the PVCS network, is formulated as a non-cooperative game. Then, the strategies of the PVCSs are modeled as the demand response (DR) activities to achieve their own optimization goals and a two-level distributed heuristic algorithm is introduced to solve the problem. The simulation results show that the economic profit of the network is increased by 6.34% compared with the common time of use (TOU) prices approach. Besides, the percentage of the PV energy in total charging load (PPTCL) and load rate are promoted by 28.93% and 0.3125, respectively, which demonstrates the validity and practicability of the proposed method. [ABSTRACT FROM AUTHOR]

This paper presents a single-degree-of-freedom energy optimization strategy to solve the energy management problem existing in power-split hybrid electric vehicles (HEVs). The proposed strategy is based on a quadratic performance index, which is innovatively designed to simultaneously restrict the fluctuation of battery state of charge (SOC) and reduce fuel consumption. An extended quadratic optimal control problem is formulated by approximating the fuel consumption rate as a quadratic polynomial of engine power. The approximated optimal control law is obtained by utilizing the solution properties of the Riccati equation and adjoint equation. It is easy to implement in real-time and the engineering significance is explained in details. In order to validate the effectiveness of the proposed strategy, the forward-facing vehicle simulation model is established based on the ADVISOR software (Version 2002, National Renewable Energy Laboratory, Golden, CO, USA). The simulation results show that there is only a little fuel consumption difference between the proposed strategy and the Pontryagin's minimum principle (PMP)-based global optimal strategy, and the proposed strategy also exhibits good adaptability under different initial battery SOC, cargo mass and road slope conditions. [ABSTRACT FROM AUTHOR]

China building energy consumption (BEC) has been gradually derived and also sparked attention. However, something worth discussing is estimating the BEC based on the definition of life cycle or building operation. In addition, there exists discrepancy in the previous studies and the real status of BEC has not been known well. In order to settle the disputes over the calculation of BEC, this paper establishes an appropriate accounting method of building energy to present the BEC situation in China and lays the foundation for policymakers to develop appropriate energy saving policies. Meanwhile, this study attempts to clarify the BEC to help policymakers assess the effect of environmental policies. Using the method in this document, we find that the energy consumption of buildings just accounts for 15%–16% of the final total energy consumption in China; by contrast, the previous studies usually have double accounting through a top-down approach if central heat supply of buildings was taken into additional consideration. [ABSTRACT FROM AUTHOR]

The strategies of distribution network reconfiguration are applicable for minimizing power loss and saving electrical energy in the distribution system. Network reconfiguration is usually represented by constant load demand so ignoring the variability of load demand causes uncertainty and misleading results in the minimization of power loss. This paper consists of two parts: first, the reconfiguration was accomplished using an optimization framework based on constant load to find sets of optimal switches. The minimization of active power loss was taken as an objective function while bus voltage, branch current and system radiality were taken as system constraints. The study was applied to a 33-bus test distribution network, which is exceedingly used as test examples for solving reconfiguration problems. Second, lists of the configurations set obtained from the first part, as well as other different optimization methods proposed earlier under constant load demand were taken as test switches. Additionally, the network in the presence of distributed generators was taken to analyze the reconfiguration under an active network. Two types of load demands; the variable load and voltage-dependent load, are proposed to represent the practical load demands. This paper presents a new method for good analysis as it defines the effect of loading levels and loading patterns on a distribution system performance for passive and active networks. The proposed approach tries to find the actual power loss under different characteristics of loads. Therefore, the probable benefit of this approach is the contribution to providing more flexibility for electrical utilities in terms of distribution system operation, while also opening new prospects in the automation of smart distribution systems. [ABSTRACT FROM AUTHOR]

The primary objective of this paper is to present the use of a steady model that is able to qualify and quantify available natural ventilation flows applied to the energy retrofitting of urban residential districts. In terms of air quality, natural ventilation presents more efficient solutions compared to active systems. This method combines numeric simulations, through the utilization of Ansys Fluent R15.0® and Engineering Equation Solver EES®, with on-site pressurization tests. Testing consists of the application of the seasonal pressure gradient on the building's envelope and the calculation of the ventilation flows in three climatic representative conditions (summer, winter, and annual average). Through the implementation of this methodology to existing buildings it is possible to evaluate the influence of the built environment, as well as key parameters (relative height of the dwelling, number of vertical ventilation ducts, and airtightness of windows) of available natural ventilation. [ABSTRACT FROM AUTHOR]