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Smart meters for measuring electricity consumption are fast becoming prevalent in households. The meters measure consumption on a very fine scale, usually on a 15 min basis, and the data give unprecedented granularity of consumption patterns at household level. A multitude of papers have emerged utilizing smart meter data for deepening our knowledge of consumption patterns. This paper applies a modification of Okoli’s method for conducting structured literature reviews to generate an overview of research in electricity customer classification using smart meter data. The process assessed 2099 papers before identifying 34 significant papers, and highlights three key points: prominent methods, datasets and application. Three important findings are outlined. First, only a few papers contemplate future applications of the classification, rendering papers relevant only in a classification setting. Second; the encountered classification methods do not consider correlation or time series analysis when classifying. The identified papers fail to thoroughly analyze the statistical properties of the data, investigations that could potentially improve classification performance. Third, the description of the data utilized is of varying quality, with only 50% acknowledging missing values impact on the final sample size. A data description score for assessing the quality in data description has been developed and applied to all papers reviewed. [ABSTRACT FROM AUTHOR]

Energy saving is an important measure to promote social green transformation. The traditional energy-saving ideas usually only focus on a specific loss, and seldom consider the possible relationship and influence among various losses. In relatively complex energy-using systems, there are often many kinds of losses, and each loss may have many influencing factors. There may be some relationship between these losses and the influencing factors. To solve this problem, this paper presents an analysis method of the variable association in multi-variable systems. First, the basic relationships between variables and the representation methods are discussed. The basic concept of a path between variables is given, and the analysis method of variable association based on path statistics is provided. This paper focuses on the analysis of the influencing factors and paths of the observed variables, as well as which observed variables will be affected by a control variable. Then, based on the correlation matrix, the quantitative analysis method of the influence between variables is given. Variable correlation analysis is innovatively applied in the field of energy saving to determine the correlation of losses through variable associations, guiding the preliminary screening of energy-saving measures and analyzing the collateral effects of these measures. Based on the correlations between energy losses, a scientific process for formulating energy-saving measures is proposed. The variable correlation analysis method proposed in this paper is a generalized method, which can judge the correlation between variables from the perspective of theoretical analysis and avoid the dependence on data. In addition to good applications in the field of energy conservation, it can also be widely used in construction, transportation, climate change, and other fields. The proposed energy-saving ideas take into account the intensity of influencing factors on loss and the correlation between loss, which improves the effectiveness of energy saving measures. [ABSTRACT FROM AUTHOR]

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]

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]

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]

A collection of twelve papers published in Energies, in a Special Issue on "Building Energy Audits-Diagnosis and Retrofitting", was bound together and published in 2021, focusing on the built environment. The aim was to systematically collect and analyze relevant data for obtaining adequate knowledge on the energy use profile of buildings, and was extended for the sustainability assessment of the built environment. To date, all papers have been very well received, attaining a total of 97 citations and over 15,300 views. The papers addressed historic and various building types, baselines for non-residential buildings from energy performance audits and from in-situ measurements, monitoring and data analysis, assessment of indoor environmental quality, model calibration and verification of energy savings, along with an urban audit and rating method for assessing the sustainability of the built environment. Following on from the success of this Special Issue, the decision was made to reopen and extend it to include papers related to decarbonization and sustainability, at building, city, region, and national scales. This Editorial reviews the performance of the first Special Issue and outlines the second volume on Building Energy Audits-Diagnosis and Retrofitting Towards Decarbonization and Sustainable Cities, as a Special Issue in Energies. [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]

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]

The built environment is a significant contributor to global energy consumption and greenhouse gas emissions. Advancements in the adoption of environmentally friendly building technology have become crucial in promoting sustainable development. These advancements play a crucial role in conserving energy. The aim is to achieve an optimal design by balancing various interrelated factors. The emergence of innovative techniques to address energy conservation have been witnessed in the built environment. This review examines existing research articles that explore different metaheuristic optimization techniques (MOTs) for energy conservation in buildings. The focus is on evaluating the simplicity and stochastic nature of these optimization techniques. The findings of the review present theoretical and mathematical models for each algorithm and assess their effectiveness in problem solving. A systematic analysis of selected algorithms using MOT is conducted, considering factors that influence wellbeing, occupant health, and indoor environmental quality. The study examines the variations among swarm intelligence MOTs based on complexity, advantages, and disadvantages. The algorithms' performances are based on the concept of uncertainty in consistently providing optimal solutions. The paper highlights the application of each technique in achieving energy conservation in buildings. [ABSTRACT FROM AUTHOR]

Urban building energy modeling (UBEM) is a practical approach in large-scale building energy modeling for stakeholders in the energy industry to predict energy use in the building sector under different design and retrofit scenarios. UBEM is a relatively new large-scale building energy modeling (BEM) approach which raises different challenges and requires more in-depth study to facilitate its application. This paper performs a systematic literature review on physics-based modeling techniques, focusing on assessing energy conservation measures. Different UBEM case studies are examined based on the number and type of buildings, building systems, occupancy schedule modeling, archetype development, weather data type, and model calibration methods. Outcomes show that the existing tools and techniques can successfully simulate and assess different energy conservation measures for a large number of buildings. It is also concluded that standard UBEM data acquisition and model development, high-resolution energy use data for calibration, and open-access data, especially in heating and cooling systems and occupancy schedules, are among the biggest challenges in UBEM adoption. UBEM research studies focused on developing auto-calibration routines, adding feedback loops for real-time updates, future climate data, and sensitivity analysis on the most impactful modeling inputs should be prioritized for future research. [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]

Today, HVAC (heating, ventilation, and air conditioning) systems have become an integral part of modern buildings and are designed to provide comfortable indoor environments while conserving energy and reducing carbon emissions. With advancement in technology, HVAC systems have a variety of sensors that are used to detect the occupants within a controlled environment. Advancements in computer control systems and the use of smart technology have made HVAC systems even more sophisticated, allowing for approximate temperature control and energy management. This paper will review the historical development of technology and the current state of HVAC systems. With the proper data, development of artificial intelligence models can, in theory, improve the overall optimization and reduce energy consumption This paper will provide a review of HVAC history and the key concepts around the usefulness of using AI from previous research conducted in this field of study. [ABSTRACT FROM AUTHOR]

The development and utilization of marine renewable energy is an important measure for achieving energy conservation, emissions reduction and carbon neutrality. Ocean thermal energy is the most stable energy among all the types of marine renewable energy. This paper built a simulation model of an ocean thermal energy conversion system based on actual device specifications by Aspen and MATLAB and put forward a corresponding control strategy. The opening control signal of the control valve at the turbine inlet was the condenser inlet pressure in this paper, and the frequency control of the working fluid pump depended on the evaporating pressure and flow rate of the working fluid. This paper analyzed the key operating parameter changes of the system under different working conditions. According to the analysis results, the turbogenerator in this system was able to generate 50 kW power for about 8 months per year. The highest net output power of the Organic Rankine Cycle was 47.3 kW; the highest cycle thermal efficiency was 3.2%. [ABSTRACT FROM AUTHOR]

The electricity spot market plays a significant role in promoting the self-improvement of the overall resource utilization efficiency of the power system and advancing energy conservation and emission reduction. This paper analyzes and compares the potential impacts of spot market operations on system planning, considering the differences between planning methods in traditional and spot market environments through theoretical analysis and model comparison. Furthermore, we conduct research and analysis on grid planning methods under the spot market environment with the goal of maximizing social benefits. Unlike the pricing approach based on historical price data in traditional market simulation processes, a data-driven approach that combines experimental economics and machine learning is proposed, specifically using mixed empirical learning to simulate unit bidding strategies in market transactions. A simulation model for electricity spot market trading is constructed to analyze the performance of the planning results in the spot market environment. The case study results indicate that the proposed planning methods can enable the grid to operate well in the spot market environment, maintain relatively stable nodal prices, and ensure the integration of a high proportion of clean energy. [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]

This paper addresses the energy conservation problem in computing systems. The focus is on energy-efficient routing protocols. We formulated and solved a network-wide optimization problem for calculating energy-aware routing for the recommended network configuration. Considering the complexity of the mathematical models of data center networks and the limitations of calculating routing by solving large-scale optimization problems, and methods described in the literature, we propose an alternative solution. We designed and developed several efficient heuristics for equal-cost multipath (ECMP) and Valiant routing that reduce the energy consumption in the computer network interconnecting computing servers. Implementing these heuristics enables the selection of routing paths and relay nodes based on current and predicted internal network load. The utility and efficiency of our methods were verified by simulation. The test cases were carried out on several synthetic network topologies, giving encouraging results. Similar results of using our efficient heuristic algorithm and solving the optimization task confirmed the usability and effectiveness of our solution. Thus, we produced well-justified recommendations for energy-aware computing system design to conclude the paper. [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]

Due to its advantages of high acceleration, reusability, environmental protection, safety, energy conservation, and efficiency, electromagnetic energy has been considered as an inevitable choice for future space launch technology. This paper proposes a novel three-level orbital launch approach based on a combination of a traditional two-level orbital launch method and an electromagnetic boost (EMB), in which the traditional two-level orbital launch consists of a turbine-based combined cycle (TBCC) and a reusable rocket (RR). Firstly, a mathematical model of a multi-stage coil electromagnetic boost system is established to develop the proposed three-level EMB-TBCC-RR orbital launch approach, achieving a horizontal take-off–horizontal landing (HTHL) reusable launch. In order to optimize the fuel quality of the energy system, an artificial intelligence algorithm parameters-sensitivity-based adaptive quantum-inspired glowworm swarm optimization (AQGSO)is proposed to improve the performance of the electromagnetic boosting system. Simulation results show that the proposed AQGSO improves the global optimization precision and convergence speed. By using the proposed EMB-TBCC-RR orbital launch system and the optimization approach, the required fuel weight was reduced by about 13 tons for the same launch mission, and the energy efficiency and reusability of the spacecraft was greatly improved. The spacecraft can be launched with more cargo capacity and increased payload. The proposed novel three-level orbital launch approach can help engineers to design and optimize the orbital launch system in the field of electromagnetic energy conversion and management. [ABSTRACT FROM AUTHOR]

The energy consumption of the mushroom room air conditioning system accounts for 40% of the total energy consumption of the mushroom factory. Efficient and energy-efficient mushroom factories and mushroom houses are the development direction of the industry. Compared with maintenance structure transformation and air conditioning equipment upgrading, energy-saving technology based on regulation methods has the advantages of less investment and fast effectiveness, which has attracted attention. The current methods for regulating air conditioning in edible mushroom factories include simple on/off thermostat control or PID. In the field of energy efficiency in commercial building air conditioning, a large number of studies have shown that compared with traditional control algorithms such as classic on/off or PID control, model predictive control can significantly improve energy efficiency. However, there is little literature mentioning the application of MPC in factory mushroom production rooms. This paper proposes a data-driven MPC and PID combined energy-saving control method for mushroom room air conditioning. This method uses the CNN-GRU-Attention combination neural network as the prediction model, combined with prediction error compensation and dynamic update mechanism of the prediction model dataset, to achieve an accurate prediction of indoor temperature in mushroom houses. Establish an objective function for air conditioning control duration and temperature, use the non-dominated sorting genetic algorithm II (NSGA-II) to solve for the optimal control sequence of the air conditioning in the control time domain, and use the entropy weight method to determine the optimal decision quantity. Integrate rolling optimization, feedback mechanism, and PID to achieve precise and energy-saving control of the mushroom room environment. The experimental results show that compared with the on/off thermostat and PID controller, the designed controller reduces power consumption by 12% and 5%, respectively, and has good application and demonstration value in the field of industrial production of edible mushrooms. [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]

Reducing energy consumption while providing a high-quality environment for building occupants has become an important target worthy of consideration in the pre-design stage. A reasonable design can achieve both better performance and energy conservation. Parametric design tools show potential to integrate performance simulation and control elements into the early design stage. The large number of design scheme iterations, however, increases the computational load and simulation time, hampering the search for optimized solutions. This paper proposes an integration of parametric design and optimization methods with performance simulation, machine learning, and algorithmic generation. Architectural schemes were modeled parametrically, and numerous iterations were generated systematically and imported into neural networks. Generative Adversarial Networks (GANs) were used to predict environmental performance based on the simulation results. Then, multi-object optimization can be achieved through the fast evolution of the genetic algorithm binding with the database. The test case used in this paper demonstrates that this approach can solve the optimization problem with less time and computational cost, and it provides architects with a fast and easily implemented tool to optimize design strategies based on specific environmental objectives. [ABSTRACT FROM AUTHOR]

With the increase in population and the progress of industrialization, the rational use of energy in heating systems has become a research topic for many scholars. The accurate prediction of heat load in heating systems provides us with a scientific solution. Due to the complexity and difficulty of heat load forecasting in heating systems, this paper proposes a short-term heat load forecasting method based on a Bayesian algorithm-optimized long- and short-term memory network (BO-LSTM). The moving average data smoothing method is used to eliminate noise from the data. Pearson's correlation analysis is used to determine the inputs to the model. Finally, the outdoor temperature and heat load of the previous period are selected as inputs to the model. The root mean square error (RMSE) is used as the main evaluation index, and the mean absolute error (MAE), mean bias error (MBE), and coefficient of determination (R2) are used as auxiliary evaluation indexes. It was found that the RMSE of the asynchronous length model decreased, proving the general practicability of the method. In conclusion, the proposed prediction method is simple and universal. [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]

In this paper, we consider a general model and solution algorithm for the energy management of combined cooling, heating, and power micro energy grid (MEG) under the game theory framework. An innovative dynamic leader-follower game strategy is proposed in this paper to balance the interactions between MEG and user. We show that such game between MEG and user has a unique Nash equilibrium (NE), and in order to quantify the user's expenditure and dissatisfaction, we model them and adopt the fuzzy bi-objective algorithm. For more details in the proposed game model, the MEG leads the game by deciding energy sales prices and optimizing the power, cooling and heating outputs according to the user's load plan to maximize its own profit. With the prices being released by MEG, user's adjustment of energy consumption follows and is again fed to MEG. In practice, we initialize simulations with daily loads of a typical community. As the numerical results demonstrate, MEG is proficient in consumption capacity of renewable energy and energy optimization. It also shows that the user achieves his economic optimum with experience of energy usage taken into account. [ABSTRACT FROM AUTHOR]

The Kingdom of Saudi Arabia is experiencing a surge in electricity demand, with power generation increasing 4 times in 25 years from 1990 to 2014. Despite the abundant primary renewable energy sources, the country has overlooked them in the past in national energy policies. However, in recent years, renewable energy has become a part of the Kingdom of Saudi Arabia's energy conservation policy due to climate changes, technological progress, economies of scale, and increased competitiveness in supply chains. The Saudi government has created the King Abdullah City for Atomic and Renewable Energy (KACARE) to develop national strategies for effectively utilizing renewable and nuclear energy. This paper reviews the current state of the art of the renewable energy technologies available on the market and evaluates the installation of renewable energy plants near Saudi Arabia's East Coast for a new town, focusing on technical rather than economic aspects. The paper provides a wide review of the possible technical solutions to exploit the producibility of different renewable sources, considering the challenging climate conditions typical of desert areas. The analysis of a real case study shows a high availability of wind and solar irradiance that allow a net energy production of 354 and 129 GWh, respectively. In addition, the comparison between a typical ground-mounted photovoltaic (PV) system and an emerging floating PV reveals that for the same installed power, occupied area, and environmental conditions, the latter has a 4% greater performance ratio due to the cooling effect of water. [ABSTRACT FROM AUTHOR]

This paper aims to tap the potential of air conditioning loads (ACLs) for consuming photovoltaic power (PV) and wind power (WP). By fully considering different thermal comfort of different users, an ACL twice-clustered model based on different ACL parameters and users tolerance values (UTV) is built. Then, a two-stage ACL control method based on both temperature control (TC) and switch control (SC) is proposed, which achieves rapid control of ACLs as well as diminishing users' discomfort. Widely existent communication time delay in ACL control network causes obvious control error, which leads to ACL consumption deviation from the target. Therefore, on the basis of analyzing errors and impacts of ACLs caused by communication time delay, this paper proposes a time delay compensation method based on a network predictive control system. Applying the ACLs clustered model and the control method into consuming PV and WP, a dual-stage consumption model considering communication time delay is established. Simulations of the PV and WP consumption effects based on ACLs clusters are conducted, and the influence of SC cycle and outdoor temperature on the simulation results are analyzed. The simulation results demonstrate the validity of the model and the methods proposed in this paper, showing a strong adaptability in different circumstances. [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]

The use of supercapacitors (SCs) to store regenerative braking energy from urban rail trains is able to achieve a good energy saving effect. This paper analyzes the current balance method of stationary energy storage devices (ESDs). At the beginning of the paper, the mathematical model of the DC traction power system, which includes trains, ESDs and traction substations, is established. Next, based on this, the SC state-based control strategy (SCSCS) is proposed, which can adjust the charging voltage of the ESD according to the SC voltage and current, then the charging current of the ESD can be reasonably distributed under the voltage difference of ESDs, and the SC voltage and current stress can be reduced. In order to determine the optimal controlling parameters, the optimization model is proposed and solved by the genetic algorithm. The analysis of the case study also shows the effectiveness of the proposed control strategy and optimization algorithm. Finally, the rationality of the proposed strategy is verified by experiments. [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]

The European Union (EU) has committed to supporting the United Nations' efforts in line with the Paris Agreement for addressing climate change and has set ambitious targets to reduce primary energy consumption and emissions. Similar commitments have also been set by EU-27 member states. For this purpose, it is necessary to use a primary energy factor (PEF) for converting electricity use to primary energy units and for assessing energy conservation measures. Lower PEFs reflect efficiency improvements in power generation, an increased share of renewable energy sources in the fuel mix for electricity generation, and lower transmission and distribution losses. Over the past decades, there have been intensive efforts and notable progress in the EU-27 for increasing the use of renewables in the energy mix for electricity generation. However, the EU default PEF value for electricity was not regularly updated and remained at 2.5 for several years till it was finally recalculated at 2.1 in the 2018 recast of the Energy Efficiency Directive. This paper reviews different calculation options for estimating the PEF for electricity from official annual statistics, presents the historical evolution of the calculated conversion factors, and provides simple linear correlations for projecting the PEF values that can be used to facilitate more-realistic forward-looking calculations and assess national energy efficiency, climate change, or decarbonization plans in EU-27 member states. A more detailed analysis and case studies on the impacts of this work are illustrated for Greece and Poland. [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]

Based on the emission factor method and LMDI-LEAP model, this paper systematically studies the current situation, influencing factors and changing trend of carbon emissions from rural buildings in a typical village located in southern China. The results showed that (1) the per capita carbon emissions generated by the energy consumption of rural buildings is 2.58 tCO2/a. Carbon emissions from electricity consumption in buildings account for about 96.07%; (2) the per capita building area, building area energy intensity, population size, population structure and carbon emission coefficient affect rural building carbon emissions, with contribution rates of 70.13%, 31.27%, 0.61%, −1.21% and −0.80%, respectively; (3) from 2021 to 2060, the carbon emissions of rural buildings are expected to increase first and then decrease. In 2021, the base year, carbon emissions from buildings were 2755.49 tCO2. The carbon emissions will peak at 5275.5 tCO2. Measures such as controlling the scale of buildings and improving the utilization rate of clean energy can effectively reduce carbon emissions, in which case the peak can be reduced to 4830.06 tCO2. Finally, the countermeasures and suggestions about rural building energy saving and emission reduction are proposed, including improving the construction management, raising energy efficiency standards in buildings, increasing the proportion of clean energy and raising residents' awareness of energy conservation. [ABSTRACT FROM AUTHOR]

Nowadays, since large amounts of energy are consumed for a variety of applications, more and more emphasis is placed on the conservation of energy. Recent investigations have experienced the significant advantages of using metaheuristic algorithms. Given the importance of the thermal loads' analysis in energy-efficiency buildings, a new optimizer method, i.e., the teaching–learning based optimization (TLBO) approach, has been developed and compared with alternative techniques in the present paper to predict the heating loads (HLs). This model is applied to the adaptive neuro–fuzzy interface system (ANFIS) in order to overcome its computational deficiencies. A literature-based dataset acquired for residential buildings is used to feed these models. According to the results, all the applied models can appropriately predict and analyze the heating load pattern. Based on the value of R2 calculated for both testing and training (0.98933, 0.98931), teaching–learning-based optimization can help the adaptive neuro–fuzzy interface system to enhance the results' correlation. Also, the high R2 value means that the model has high accuracy in the HL prediction. In addition, according to the estimated RMSE, the training error of TLBO–ANFIS in the testing and training stages was 0.07794 and 0.07984, respectively. The low value of root–mean–square error (RMSE) indicates that the TLBO–ANFIS method acts favorably in the estimation of the heating load for residential buildings. [ABSTRACT FROM AUTHOR]

Keeping the global temperature rise below 2 degrees Celsius, as foreseen by the Paris Agreement, requires a new global roadmap for the energy transition. For this reason, the European Commission decided to directly involve local municipalities in reaching these objectives through multilevel, bottom-up actions for sustainable energy. The Covenant of Mayors is a very concrete demonstration of this trend of development and adoption of sustainable energy action plans (SEAP), rethinking the way cities operate and bringing them closer to energy self-sufficiency, with measures favouring local economic development and improving citizens' quality of life. The numerous RES/RUE actions included in SEAPs at the regional level have led both to the request for huge funding and to increased complexity for regional managers to identify the best projects to be financed. To manage the multitude of data (emissions, energy consumption, cost, etc.) present in the SEAPs at a regional level, a web-based platform called Lex-energetica was developed. In this context, this paper aims to present a participatory supportive framework for the decision-making process involved in financing the SEAPs' actions, considering the selection of sustainable Renewable Energy Sources (RES) and Rational Use of Energy (RUE) technologies. This study proposes a methodology based on two macro-phases: the first phase consists of a ranking evaluation of categories of areas of intervention based on the analytic hierarchy process, while the second identifies nine criteria, according to the domains corresponding to the three pillars of sustainability, to compare the most appropriate RES/RUE actions. [ABSTRACT FROM AUTHOR]

Smart grid plays a vital role in energy management systems. It helps to mitigate the demand side management of electricity by managing the microgrid. In the modern era, the concept of hybrid microgrids emerged which helps the smart grid management of electricity. Additionally, the Internet of Things (IoT) technology is used to integrate the hybrid microgrid. Thus, various policies and topologies are employed to perform the task meticulously. Pakistan being an energy deficient country has recently introduced some new policies such as Energy Wheeling Policy (EWP), Energy Import Policy (EIP), and Net Metering/Distributed Generation Policy (NMP) to manage the electricity demand effectively. In addition, the Energy Efficiency and Conservation Act (EECA) has also been introduced. In this paper, we present the overview and impact of these policies in the context of the local energy market and modern information and communication mechanisms proposed for smart grids. These new policies primarily focus on energy demand–supply for various types of consumers such as the demand for bulk energy for industrial ventures and the distributed production by consumers. The EWP deals with obtaining power from remote areas within the country to ease the energy situation in populated load centers and the EIP highlights energy import guidelines from foreign countries. The NMP deals with the integration of renewable energy resources and EECA is more focused on the measures and standardization for energy efficiency and conservation. The benefits and challenges related to EWP, NMP, and EIP have also been discussed concerning the present energy crisis in Pakistan. The generalized lessons learned and comparison of a few aspects of these policies with some other countries are also presented. [ABSTRACT FROM AUTHOR]

LVDC buildings use a low-voltage direct current (LVDC) distribution system for energy transmission and integrate photovoltaic (PV), battery energy storage (BES) and the utility grid as building energy resources. This technology can reduce energy loss in conversion to a certain extent and increase renewable energy compared with traditional AC distribution systems. Under the national goal of carbon peaking and carbon neutrality, LVDC buildings have been proven to be a new approach to energy conservation and emission reductions, and have been applied in engineering in China. However, the construction methods and integrated technologies of those projects are not clear, and technical barriers and policy constraints for the engineering application of LVDC buildings are not systematically discussed yet. This paper presents the latest study of LVDC building engineering applications the advantages and drawbacks of LVDC development in China. First, relevant policies and standards which support the development of LVDC building industries are summarized. More than 60 practical LVDC projects are investigated, and the application characteristics and the technology status of building types, and their capacity and design methods, etc., are analyzed. The attitudes of stakeholders toward LVDC buildings are surveyed to determine the policy direction according to the demands of this technology, with reference to building practitioners who intend to engage in LVDC projects. [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]

Urban metabolism has emerged over the past decades as an important new paradigm of regional and urban sustainability governance towards a Chinese national scheme of 'carbon neutrality' by 2060. Hebei province in China faces twin pressures related to its supply of water and energy resources, which has brought humans and nature into conflict. Overcoming this tension in the human-land relationship in Hebei and determining a suitable development path for the future has become a core issue for the achievement of coordinated development within the Beijing–Tianjin–Hebei region. This paper constructs a system to simulate the metabolism of water, energy, and human relationships, and uses this model to carry out simulations for Hebei province. The model establishes five scenarios: a natural development scenario, economic growth scenario, water conservation development scenario, energy conservation development scenario, and low carbon scenario. The simulation results show that, without intervention, the natural development scenario results in greater pressure on supply gaps and a greater demand for water and energy, with more production of industrial waste gas and domestic wastewater discharges. The economic growth, water conservation development, and energy conservation development scenarios focus on single economic, water conservation, and energy conservation measures by looking at core economic, water, and energy elements within the metabolic system; however, solving issues with individual elements merely leads to other, remaining problems. Under the low carbon scenario, issues with multiple elements in Hebei's metabolic system are considered more comprehensively, so the simulation results are better than those in the other scenarios, and it better fits the future orientation of sustainable development of Hebei province. [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]

In the context of energy conservation and the reduction of CO2 emissions, inconsistencies between the inevitable emission of CO2 in traditional hydrogen production methods and eco-friendly targets have become more apparent over time. The catalytic decomposition of methane (CDM) is a novel technology capable of producing hydrogen without releasing CO2. Since hydrogen produced via CDM is neither blue nor green, the term "turquoise" is selected to describe this technology. Notably, the by-products of methane cracking are simply carbon deposits with different structures, which can offset the cost of hydrogen production cost should they be harvested. However, the encapsulation of catalysts by such carbon deposits reduces the contact area between said catalysts and methane throughout the CDM process, thereby rendering the continuous production of hydrogen impossible. This paper mainly covers the CDM reaction mechanisms of the three common metal-based catalysts (Ni, Co, Fe) from experimental and modelling approaches. The by-products of carbon modality and the key parameters that affect the carbon formation mechanisms are also discussed. [ABSTRACT FROM AUTHOR]

The energy conservation achieved by utilizing waste heat in the energy and industrial sectors has became more and more important after the energy crisis in the 1970s because it plays a vital role in the potential energy-efficiency improvement. In this regard, cogeneration (combined heat and power, CHP) systems are thus becoming attractive due to the energy, economic, and environmental policies for pursuing stable electricity supply, sustainable development and environmental pollution mitigation in Taiwan. The objective of this paper is to present an updated analysis of CHP systems in Taiwan during the period from 1990 to 2010. The description in the paper is thus based on an analysis of electricity supply/consumption and its sources from CHP systems during the past two decades, and centered on two important regulations in compliance with CHP systems (i.e., Energy Management Law and Environmental Impact Assessment Act). Based on the total net power generation from CHP systems (i.e., 35,626 GWh) in 2011, it was found that the carbon dioxide reduction benefits were estimated to be around 20,000 Gg. [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]

The different locations of the equipment in urban distribution substations (DSSs) and the location of inlet holes and outlet holes usually result in different ventilation effect, which means the power consumed by any ventilating devices present is different. In this paper the temperature field distribution in an urban distribution substation with different locations of the equipment in the substation was calculated first, then factors influencing the temperature field distribution were investigated, and the influence of the different factors was analyzed. When the distance between the apparatus and walls exceeds 3 m, the change of the temperature in the DSS is very small. Therefore considering the floor area of the DSS, 3 m is the best value of the distance between the apparatus. With the change of the environment temperature or the velocity of the ventilation fans, the maximum temperature in the DSS or apparatus will change. Hence an energy saving ventilation strategy is proposed in the paper, and an intelligent cooling control system is developed, which can modify the velocity of the ventilation fans according to the environment temperature, and thus realize energy savings. [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]

Fault diagnosis is crucial for the operation of energy systems such as nuclear plants, and heavily relies on various types of sensors for temperature, pressure, concentration, etc. Due to the redundancy of sensors in each energy system, the sensor selection scheme can deeply influence the diagnostic efficiency. In this paper, a Boolean network (BN) with its linear representation is proposed for describing the fault propagation among sensors. Both the sufficient condition of fault detectability and that of fault discriminability are given. Then, a sensor selection method for fault detection and discrimination is proposed. Finally, the theoretic result is applied to realize the diagnosis oriented sensor selection for a nuclear steam supply system based on a modular high temperature gas-cooled reactor (MHTGR). The computation and simulation results verify the correctness of the theoretical results. [ABSTRACT FROM AUTHOR]