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[Display omitted] • Present a comprehensive review of building electricity profile models. • Distinguish the differences of building electricity profile and prediction models. • Categorize the statistical and bottom-up models for electricity use profiles. • Investigate the validation metrics in accordance with the modeling methods. • Discuss the key future perspectives of building electricity profile analysis. The building sector contributes significantly to overall energy consumption and carbon emissions. Improving renewable energy utilization in buildings is of considerable importance for their carbon neutrality. Electricity is expected to become the major form of final energy consumption in buildings; as such, understanding the features of building electricity use profiles is pivotal for building energy system design and city-scale electricity distribution network planning. This study reviews the current status and future challenges of building electricity use profile models. The paper distinguishes between electricity use profile models and electricity use prediction models. Then it comprehensively reviews current research papers, focusing on four aspects, including the temporal and spatial distribution of datasets, the distinction between statistical and bottom-up modeling methods, mathematical and engineering domain validation metrics, and applications in building energy system design and energy policy making. Key perspectives for future research are discussed in the paper, including the temporal scales of models, the aggregation of electricity use profiles, occupant-driven environment-interactive model structures, as well as the fit-for-purpose model evaluation. This is intended to inform and inspire future research on building electricity use profiles, improve the in-depth understanding and quantitative descriptions of building electricity use, and support the design of future building systems and energy distribution networks. [ABSTRACT FROM AUTHOR]

Facing the challage of energy conservation and emission reduction in the building sector, most energy saving strategies of central heating only focus on the performance improvement of building envelope, ignoring the impact of energy use behavior. This paper proposed a building energy conservation strategy based on energy use behavior analysis, which provides a perspective for in-depth exploration of building energy conservation. After investigating a typical university in the northern China, the energy use behaviors were characterized and two different DeST energy consumption models before or after considering energy use behavior were established. By comparing the results calculated by these two models, the impact of energy use behavior analysis on building energy consumption was quantitatively analyzed. The results show that the heating energy consumption and carbon emission can be reduced by 23.28% and 23% respectively through the proposed strategy based on energy use behavior. Therefore, the analysis of energy use behavior can provide a theoretical basis for heating operators to make decisions on energy supply regulation. With central cooling and heating further popularizing in the future, the research methodology of this paper has more extensive reference value for energy conservation and emission reduction strategies in the building sector. [ABSTRACT FROM AUTHOR]

• The data requirements of quota methods are summarized, and a city-scale database containing 670 credible government office buildings is established. • By comparing the processes, the results and applicability, the selection basis of quota methods under different conditions is refined. • A flow chart of the formulation process and method selection of city-level building energy consumption quotas is constructed. • It is proposed that special attention should be paid to the outliers, and the characteristics of outliers in Chongqing are summarized. Energy consumption quota policies are effective management measures to promote energy conservation and emission reduction in China. Cities need to choose an appropriate method to formulate local energy consumption quota standards. This paper is based on the data of 670 government office buildings in Chongqing and the selected quantile method (QM), quota-level method (QLM) and multiple linear regression (MLR) method, which are widely used in the formulation of city-level energy quotas. By comparing their interpretability, evaluation, consistency and stability, the application conditions and applicability of each method are summarized. MLR has the strongest explanatory power but the highest requirements for the technology and information dimensions. The QLM is more explanatory and stable than the QM and is more suitable for the initial stage of energy quota management. When using the QLM, we should consider scientific classifications and reasonable quota levels. Special attention should be given to "outliers" with abnormal evaluations of the quota results. The outliers in Chongqing are characterized as buildings that have been used for more than 10 years and have an area of less than 5000 m2. Finally, this paper constructed a flow chart of the formulation process and method selection to help policymakers better understand how to choose the most appropriate method and identify inefficient buildings. [ABSTRACT FROM AUTHOR]

• A new urban building energy modeling tool-AutoBPS was developed. • AutoBPS calculated urban building energy demands, analyzed energy retrofit and rooftop PV potential via EnergyPlus. • AutoBPS automatically generated multi-zone models, considering shading from surrounding buildings. • A downtown district of 3633 buildings in Changsha, China, was simulated to demonstrate the tool's features. • All three energy conservation measures and PV installation could save 38.6% of total energy demand. China has promised to peak carbon emissions by 2030 and become carbon neutral before 2060. Achieving this target requires actions from all sectors, especially the building sector, which accounts for 33 % of the total energy consumption in China. Within this scenario, this paper introduces a new urban building energy modeling tool, Automated Building Performance Simulation (AutoBPS), to calculate urban building energy demands and to analyze energy retrofit and rooftop photovoltaic (PV) potential. AutoBPS uses a building-by-building approach to automatically generate multi-zone (one zone per floor) models based on the city building dataset, considering the shading effect by surrounding buildings. The tool simulates building energy use and PV generation via EnergyPlus. A downtown district of 3633 buildings in Changsha, China, was selected as a case study to demonstrate the features of the tool. Firstly, annual baseline energy uses of 1793 GWh were simulated by inputting Geographic Information System (GIS) data. Then, different energy conservation measures (covering envelope, lighting, cooling system upgrade, etc.) and roof PV generation systems were adopted to explore energy saving potential. The results showed that the window upgrade was the most energy-efficient individual measure for residential buildings, while the lighting upgrade was that one for commercial buildings. Altogether three measures could save 2.7 ∼ 30.7 % of annual site energy use intensity per building. The total energy demand could be reduced by 18.5 % by combining all measures and saved by 38.6 % when PV installation was added. AutoBPS can be easily applied to other cities, supporting urban energy efficiency plans for carbon reduction goal implementation. [ABSTRACT FROM AUTHOR]

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

• POE was carried out to compare the design target and operational effect after use. • A Large Irregular Green Commercial Building was chosen for in-depth analysis. • We recommend the building energy saving and carbon emission reduction strategy. Commercial buildings, especially large irregular commercial buildings with complex space and dense occupation, have high energy consumption and carbon emissions. Moreover, the actual operating performance differs significantly from its design, in terms of energy conservation and carbon emissions. This study selected an irregular large-scale green commercial building in Dalian, a coastal city in the cold zone of China. The running energy consumption and indoor physical environment were investigated and analyzed, and a satisfaction survey was conducted using the Post-occupancy evaluation (POE) methods. The promotion strategies of holistic design were proposed by comparing the operational building performance with the design target. The research shows that the energy consumption of the building was 68.02 kWh/m2, which failed to meet the standard requirements. The carbon emission was 74.76 kgCO 2 /m2. Less than 10 % of humidity in summer fell in the expected range, and the lowest value of PM 2.5 concentration in winter was 28.3 %. The score of indoor environmental quality and service satisfaction was low. This paper identifies the problems and analyzes the differences between the operation stage and design phase. It provides recommendations and guidelines for optimizing the carbon emission and indoor environment based on the performance improvement of building operational conditions. [ABSTRACT FROM AUTHOR]

• Two model buildings are specially built for comparative study. • Dynamic monitoring of external insulation for improving thermal environment and energy saving during heating. • The time for EPS building to approach the air temperature set value has no obvious advantage. • The energy saving effect of short-time heating operation of EPS building is very weak. • A dynamic index is proposed to evaluate the improvement effect of external insulation on indoor thermal environment. In this paper, two identical model buildings were built, one of which adopted 38 mm Expanded Polystyrene (EPS) board as the external insulation. Under the same heating set-point temperature, the dynamic change process of indoor thermal environment and heating energy consumption of EPS insulation building and non-insulation Reference building was compared and monitored. Results show that under the same heating set-point temperature, the indoor temperature of the two buildings can quickly approach the set value in about 20 min. However, the temperature rise of the internal surface of the wall lags behind. After several hours, the advantage of the temperature rise of the internal surface of the EPS building gradually appeared. The energy consumption of the two buildings is similar in the initial heating period of 20 min, and the energy saving rate of EPS building increases slowly with the increase of the heating duration. The energy saving rate is about 1%–26% in the range of 0.5–8 h intermittent heating in hot summer and cold winter zone. The change curve of cumulative temperature rise ratio with heating time is proposed to evaluate the dynamic improvement effect of external insulation on indoor thermal environment. This research provides a reference basis for the development of energy conservation. [ABSTRACT FROM AUTHOR]

• Residential sector accounts for a quarter of consumption and a fifth of CO 2 emissions. • HVAC accounts for a third of residential consumption, equivalent to 8% of final energy. • Collecting and reporting information is crucial to target residential energy policies. • Retrofitting of residential stock and adopting conservation behaviours are mandatory. • Residential electrification is far away and needs to be accompanied by the decarbonisation of the electricity sector. The residential sector is a major contributor to climate change, accounting for almost a quarter of global energy consumption and a fifth of CO 2 emissions in 2019. Since 2000, residential consumption has grown at a sustained rate of 1%/year, driven by the development of emerging economies, despite stagnation in developed countries. The increasing demand for living space, energy services and comfort levels seems difficult to curb, especially in the developing world on its fair attempt to reduce inequality. To understand these trends, this paper analyses the trajectories of key indicators of activity and efficiency in this sector, for emerging and developed regions, as well as for major consuming nations, mainly China, United States, European Union, Russia, India, Japan and Brazil. Despite data limitations, meaningful cross-country comparisons are presented for fuel mixes, energy services and dwelling types. Heating, ventilation and air conditioning (HVAC) systems account for a third of residential consumption and will grow rapidly as increasing wealth in emerging economies allows for satisfying the thermal comfort demand. Economic development will naturally increase housing size and equipment level and reduce household size, and could close the per capita consumption gap between developing and developed regions. Efficiency improvements could reduce the energy use intensity to around 10 koe/m2 but will not be enough to curb residential consumption. International cooperation, policy support and funding are essential to accelerate development and efficiency gains in developing countries without compromising environmental targets. In the meantime, politicians should focus on decarbonising the energy mix and promoting energy efficiency, while citizens focus on energy conservation to avoid irreversible environmental damage. [ABSTRACT FROM AUTHOR]

[Display omitted] • The influencing factors on energy efficiency of data center are reviewed. • Energy efficiency metrics are classified according to different priorities. • Appropriate metrics should be selected according to data center function for optimization. • Metrics are analyzed by hierarchy according to the degree of sustainable development. • Challenges and gaps are highlighted and suggestions are given for future works. The rapid development of the Internet requires data center infrastructures to be extended to a large scale with a higher power capacity and a reduction in energy consumption and carbon emissions. It is of great significance for this application to reduce energy consumption, capital cost and carbon emissions while ensuring a safe and reliable data center operation. Evaluation metrics have been considered an effective method to assess the energy efficiency of data centers and have been widely used in various data centers for many years. This work reviews the latest progress of data center evaluation metrics development in recent 20 years. Energy efficiency metrics are summarized based on energy conservation, eco-design and data center security, with the advantages and disadvantages and their correlations discussed. The applicability of each metric is analyzed. The influencing factors of energy efficiency of data centers discussed include site selection, IT equipment, power supply and distribution systems, cooling systems, building structure, and temperature control. The results show that the current evaluation metrics are valuable for the assessment of energy saving of data centers. However, more comprehensive and multi-scale evaluation metrics combining energy consumption, eco-design and data center security are needed to accelerate the progress towards carbon neutrality. In this paper, the hierarchy analysis of metrics is carried out according to sustainability, and metrics in line with sustainable development should be used preferentially. [ABSTRACT FROM AUTHOR]

Using energy conservation measures (ECMs) for building retrofit can significantly reduce building operational carbon emissions (BOCEs) and contribute to Chinese carbon neutrality by 2060. This paper involves 11 feasible ECMs for commercial buildings in hot-summer and cold-winter zone, China, and proposes a quick assessment approach to BOCEs reduction. Prototypical models are simulated for ECMs using EnergyPlus and the emission intensity is corrected by the building shape factor and window-to-wall ratio. Based on pre-simulated results, effects on reducing emissions of individual measures and packages of them are studied to develop regression models in SPSS. The results show that decreasing the U-factor of external walls, lighting power density (LPD), or shading coefficient can reduce BOCEs linearly. BOCEs are most sensitive to LPD and using daylighting control can reduce emissions by 5–15%. In addition, increasing the boilers' efficiency reduces emissions linearly, while performance improvement of chillers or heat pumps reduces emissions logarithmically. Furthermore, utilizing variable speed pumps, heat recovery ventilation, and air-side economizers can reduce emissions by 3.8%, 6.6%, and 15.2%, respectively. Finally, significant interactions exist among measures of enhancing glazing U-factor, adding external shading, using energy-saving lamps, and improving cooling/heating system efficiencies. The developed regression models are validated in a retrofit case in Shanghai. Through assessment, the case can reduce BOCEs by 15.9 kgCO 2 /m2, with a reduction rate of 20.8%. Compared with recorded data, the relative error of assessing emission reduction is −13.0% and that of assessing reduction rate is 0.5%, which are acceptable in the decision-making stage of retrofit strategies. [ABSTRACT FROM AUTHOR]

• Occupants on-demand heating control behavior in a rural residential building is investigated. • Independently controlled split-type heating equipment were used to elucidate actual demand patterns. • Data collection over three years under an interference avoidance scheme. • Five representative intermittent heating patterns were obtained from a real-world case study. A better understanding of actual heating demands is crucial for optimizing operational heating systems, because disparities between expected load and actual demand are key factors in inefficient heating system design, implementation, and corresponding energy loss. Additionally, the discrete, energy consumption-based billing systems applied to freestanding rural residential buildings encourages reductions in energy waste but produces spatially and temporally distinct heating patterns. Conventional heating systems have only basic controls and slow response times that cannot reflect actual user demand. Hence, this paper presents an experimental scheme for measuring room-wise heating demands, and thus, the actual heating demand patterns of rural residential buildings. A case study was conducted on a typical rural residential building in Beijing to validate the method's feasibility. Real-world usage patterns were obtained using indoor environment sensors and independently-controlled heating devices that collected the operational parameters of each room over three years, in a long-term non-intervention study design. Clustering analysis and various indicators were used to characterize heating demand for each room: heating patterns, usage intensity, and temporal variation. In most case, the occupants heated only a few rooms, rather than the whole building. These results have potential real-word implications for predicting occupant heating behavior and energy conservation. [ABSTRACT FROM AUTHOR]

Interest in urban building energy modeling as a tool to evaluate effective management of urban building energy and carbon reduction policies is increasing. Since it takes a lot of time to model all buildings in the city with the lack of information about buildings, archetypes have been used. However, the deterministic method using archetypes does not reflect uncertainty with the disadvantage of not presenting design alternatives. To overcome such problems, we proposed an urban building energy modeling method using a stochastic-deterministic-coupled approach. In this paper, the identifiability of unknown parameters in a building stock was verified by analyzing the effect of the meta-model accuracy on the estimation of unknown parameters in the proposed urban building energy model. In addition, the possibility of using energy conservation measure analysis of the proposed model was evaluated. As a result, the posterior distribution of unknown input parameters was distorted when using the meta-model instead of the original building energy model in Bayesian calibration for building stock. However, the distorted posterior distribution could evaluate the effect of energy conservation measures within 0.71% errors. [ABSTRACT FROM AUTHOR]

• End-use of electricity consumption in residential buildings in Gulf Cooperation Council countries was presented. • Discrepancies in certain indicators have been observed by the end-use of electricity consumption. • Improving the efficiency of air conditioners and thermal insulation of the walls are the best measures to conserve electricity in residential buildings. • The results can be used in areas with a similar climate, living standard, buildings types, and the inhabitants' behavior. Energy conservation and improvement in energy efficiency yield multiple technical, economic, environmental, and social benefits and realize comfort and sustainability of residential buildings (RBs). This review paper presents the end-use of electricity consumption in RBs and the results of economic analysis. In addition, a description and discussion of studies on energy conservation measures, Net Zero Energy Building, and renewable energy in RBs are discussed. The percentage of end-used energy consumption for different purposes obtained in many studies are often not consistent. Moreover, discrepancies in certain indicators have been observed, necessitating the need to clarify the factors in the consumption of electricity in RBs. Further, a few studies used the term energy consumption in buildings (total or electrical); however, specifying the type of buildings to identify and use the necessary indicators of energy consumption in each type of building are desirable. Furthermore, most studies emphasized improving the efficiency of air conditioners and thermal insulation of the walls as these are the best measures to conserve electricity. [ABSTRACT FROM AUTHOR]

Buildings account for almost 40% of global energy consumption. Due to the high energy consumption of chilled water plants, various studies have optimized chiller loading in plants with multiple chillers for energy conservation. However, few studies have optimized dual-temperature chiller plants, even though better energy efficiency could be achieved than that of typical single-temperature chiller plants. This paper proposes two optimal control strategies for dual-temperature chilled water plants, strategy B and strategy C. Strategy B optimizes the cooling load distribution of the chillers in each group by adjusting the cooling load ratio of each chiller. Under this strategy, the energy consumption of the chiller plant for the entire cooling season was reduced by 10.1%. Meanwhile, strategy C optimizes the cooling load distribution among chillers in the same chiller group and between two chiller groups, by simultaneously adjusting the temperature setpoint of the air leaving the primary cooling coils and the partial load ratio of each chiller. By considering both the impact of the chilled water loop and the air handling process, strategy C achieved greater energy saving (16.4%) for the entire cooling season. In hot summer months, the energy savings arise mainly from optimization of the cooling load distribution among chillers in each chiller group, as this optimization accounts for 63–68% of the total savings. In moderate months, optimizing the cooling load distribution among chillers in the same group and optimizing the distribution between two chiller groups account for nearly the same proportion of the total energy savings. [ABSTRACT FROM AUTHOR]

In Energy Performance Contracts (EPC), savings resulting from building and energy system retrofit are estimated as the difference between the actual consumption an prediction from a baseline model. This paper proposes an automated method to select the most relevant baseline model. In addition to commonly used cooling degree days (CDD), climatic variables mixing temperature and humidity are introduced, such as dew point or moist air enthalpy. In terms of prediction algorithm, 11 algorithms, linear and nonlinear, are compared. The novel methodology enables an energy analyst to easily find the most relevant model for a specific building, with a focus on both precision and seasonal bias. By applying the novel methodology on 11 buildings under real EPCs, the result shows a decrease of prediction error by 23.5% with respect to models referenced in the contracts. Lastly, the proposed method is generalized to address the case where contracts involve multiple buildings of different types or consumption ranges, with the ability to identify a common best model by using new dimensionless indicators, and reach an 11% improvement in prediction error. [ABSTRACT FROM AUTHOR]

[Display omitted] Improving the performance of building envelope is essential to energy conservation and emission reduction. Adaptive building envelope (ABE) has the potential to effectively reduce building energy consumption at a lower cost. Movable window insulation is an instantiation of the ABE with great application potential in buildings, but it has not been systematically studied. Trying to fill this gap, this paper proposes an automatic control system and a manual control rule for movable window insulation. The energy consumption of double-glazed windows, triple-glazed windows, movable insulation with automatic/manual control, and electrochromic windows is compared in different scenarios using building energy simulation (BES). In some scenarios, the optimal solar heat gain coefficient (SHGC) for windows with movable insulation is higher than that required by the building code. Triple-glazed windows are only effective in reducing the heating energy, while electrochromic windows are only effective in reducing the cooling energy. Infiltration has a great impact on the performance of movable insulation. The net preset value (NPV) analysis shows that movable insulation has a great application potential in Minneapolis, Atlanta, and Phoenix, while triple-glazed windows are only viable in some cases in Minneapolis. Electrochromic windows are not a profitable investment at their current prices. [ABSTRACT FROM AUTHOR]

• Novel energy conservation measure yields up to 12% of energy savings. • Grey-box data-driven model efficiently models monozone building. • Building energy consumption optimized while satisfying thermal comfort constraints. • Energy consumption of a commercial building is optimized during winter and summer. This paper proposes a novel energy conservation measure that optimizes the planning of heating and cooling systems for tertiary sector buildings. It consists of a model-based predictive control approach that employs a grey-box model built from the building and weather data that predicts the building heat load and indoor temperature. Different from classical optimization approaches where the discretized differential algebraic equations are integrated into the optimization formulation, our model is calibrated using black-box multiobjective optimization, which allows for decoupling the predictive model from the optimization problem, thus having more flexibility and reducing the total computational time. Moreover, rather than requiring the angle of solar radiation, solar orientation and solar masks to calculate the radiation data, our approach requires only a simple model of the solar irradiance. The calibrated model is then used by heating and cooling optimization strategies that aim at reducing the energy consumption of the building in the next day while satisfying the indoor thermal constraints. The proposed approach was applied in a case study of a commercial building during heating and cooling seasons and the results show that it was able to yield up to 12% of energy savings while having a mean power forecast error of 8%. [ABSTRACT FROM AUTHOR]