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Building energy baseline models, particularly machine learning-based models, are a core aspect in the evaluation of building energy performance to identify inefficient energy consumption behavior. In smart city design, energy planners and decision makers require comprehensive information on energy consumption across diverse building types as well as comparisons between different types of buildings. However, there is no comprehensive study of baseline modeling across the main building types to help identify factors that influence the performance of different machine learning algorithms for baseline modeling. Therefore, the goal of this paper is to review and analyze energy consumption behavior and evaluate the prediction performance and interpretability of machine learning-based baseline modeling techniques across major building types. The results have shown that the Extreme Gradient Boosting Machine (XGBoost) model is the most accurate baseline modeling method for all building types. Time-related factors, especially the week of the year and the day of the week, have the most impact on energy consumption across all building types. This study is presented as a useful resource for smart city energy managers to help in choosing and setting up appropriate methodologies for better operational effectiveness and efficiencies when designing and planning smart energy systems. [ABSTRACT FROM AUTHOR]

Accurate and effective building energy consumption prediction is an important basis for carrying out energy-saving evaluation and the main basis for building energy-saving optimization design. However, due to the influence of environmental and human factors, energy consumption prediction is often inaccurate. Therefore, this paper presents a building energy consumption prediction model based on an attention mechanism, time convolutional neural (TCN) network fusion, and a bidirectional gated cycle unit (BIGRU). First, t-distributed stochastic neighbor embedding (T-SNE) was used to preprocess the data and extract the key features, and then a BIGRU was employed to acquire past and future data while capturing immediate connections. Then, to catch the long-term dependence, the dataset was partitioned into the TCN network, and the extended sequence was transformed into several short sequences. Consequently, the gradient explosion or vanishing problem is mitigated when the BIGRU handles lengthy sequences while reducing the spatial complexity. Second, the self-attention mechanism was introduced to enhance the model's capability to address data periodicity. The proposed model is superior to the other four models in accuracy, with an mean absolute error of 0.023, an mean-square error of 0.029, and an coefficient of determination of 0.979. Experimental results indicate that T-SNE can significantly improve the model performance, and the accuracy of predictions can be improved by the attention mechanism and the TCN network. [ABSTRACT FROM AUTHOR]

The current practice of building thermal retrofitting is based on the outcomes of energy audits that make use of standardized tabulated information mapped on a structure or structural elements under inherently very specific conditions. Therefore, it provides very limited outcomes for further analysis, especially when decision making procedures are required to particularize the retrofitting strategy. This paper introduces a novel fuzzy logic approach for predicting the thermal performance of building walls that can be used in practice to partially substitute time-consuming and costly energy audits or complex computational analyses. The objective of this concept is to forecast the annual heating energy demands of buildings and to identify the potential energy savings that could be achieved by applying thermal retrofitting measures based on limited resources and information obtained from maps, blueprints, and/or a simple site inspection. For this purpose, a sample knowledge base was created using a validated computational model of the heat and moisture transport in the multilayered wall assemblies. Then, the fuzzy logic model was introduced to predict the thermal performance of selected walls. Our comparison of the fuzzy model outputs with simulated data proved the potential of using the proposed concept as an efficient and straightforward tool for predicting the seasonal thermal performance of building envelopes and to partially replace the current practice, which requires the utilization of building energy audits. [ABSTRACT FROM AUTHOR]

As the only renewable building material, wood has a strong carbon sequestration effect. Thus, timber structures have the natural advantages of saving energy and reducing emissions. Currently, the research objects of building energy consumption are rarely timber structures. To further control the operational energy consumption of timber structures, this paper takes six-story glued laminated timber beam-column frame and light woodframe shear wall structures as the research objects. Building energy consumption research is conducted through testing the heat transfer coefficient of the envelope structure, air circulation ratio, and Building Information Modeling (BIM) technology on-site. The results show that the energy consumption of the building is consistent with the current energy consumption of small- and medium-sized office buildings, and the heat gain and loss of the building are mainly due to solar radiation and heat conduction of the envelope, respectively. The airtightness of the building has the greatest influence on the energy consumption of the building, and the type of building structure and window-to-wall ratio have little influence on the energy consumption of the building. [ABSTRACT FROM AUTHOR]

[Display omitted] Two significant manifestations of the passive effect of building photovoltaic (PV) systems include the enhancement of the thermal performance of the envelope and the reduction of building energy consumption through the installation of PV systems on the building envelope's surface. This paper presents a review of the passive impact of PV roof systems, PV wall systems, and PV window systems, as well as the effects of the external environment, building ontology conditions, and PV system configuration on the passive effects. The study concludes that in regions with a hot climate, the shading effect of PV roof and PV wall systems during the summer can reduce the building's cooling load by 20% to 70%. In frigid climate regions, heating energy consumption during the winter can be reduced by 10% to 20% through the PV wall system's ability to recycle hot air in the cavity. Indoor thermal comfort can be improved while reducing lighting energy consumption by 3% to 14% with PV windows. A comprehensive analysis of the existing literature on the influencing factors reveals that the influencing factors of the passive effect have complex uncertainties and non-linear coupling characteristics under various conditions. To provide a theoretical foundation for the scientific popularization of photovoltaic systems, this paper identifies current research problems and prospective research directions for building photovoltaic systems based on the current status of research in the literature. [ABSTRACT FROM AUTHOR]

Various studies have assessed the energy performance alterations affected by the novel technology of Building-Integrated Photovoltaic in Double-Skin Facades (BIPV-DSF), while lighting performance tied to the BIPV-DSF has not received much attention. This paper provides numerical modelling to assess the effect of BIPV-DSF on both indoor visual condition and energy consumption for an office module under a typical climate in the United Kingdom. The proposed study was focused on the comparisons between a reference case (a DSF office module with both layers using clear double glazing) and a design case of the same office module with BIPV-DSF using semi-transparent Amorphous Silicon PV glazing. Results show a significant drop in maximum daylight illuminance of 73% by configuring the BIPV-DSF with reference to the regular DSF. It was also reported the resultant average and minimum daylight factors (0.65% and 0.00%) were not able to meet indoor visual comfort requirements for office environments. Furthermore, it was found that the use of BIPV-DSF resulted in a net increase of 8% in building energy consumption over the reference DSF. Therefore, it is concluded that in the present context the BIPV-DSF is not viable for a commercial installation under the UK's climate conditions. [ABSTRACT FROM AUTHOR]

The decarbonisation of the building sector is crucial for Portugal's goal of achieving economy-wide carbon neutrality by 2050. To mobilize communities towards energy efficiency measures, it is important to understand the primary drivers and barriers that must be overcome through policymaking. This paper aims to review existing Energy Policies and Actions (EPA) in Portugal and assess their effectiveness in improving Energy Efficiency (EE) and reducing CO2 emissions in the building sector. The Local Energy Planning Assistant (LEPA) tool was used to model, test, validate and compare the implementation of current and alternative EPAs in the North of Portugal, including the national EE plan. The results indicate that electrification of heating and cooling, EE measures, and the proliferation of Renewable Energy Sources (RES) are crucial for achieving climate neutrality. The study found that the modelling of alternative EPAs can be improved to reduce investment costs and increase Greenhouse Gas (GHG) emissions reduction. Among the alternatives assessed, the proposed one (Alternative 4) presents the best returns on investment in terms of cost savings and emissions reduction. It allows for 52% investment cost savings in the residential sector and 13% in the service sector when compared to the current national roadmap to carbon neutrality (Alternative 2). The estimated emission reduction in 2050 for Alternative 4 is 0.64% for the residential sector and 3.2% for the service sector when compared to Alternative 2. [ABSTRACT FROM AUTHOR]

At present, the construction energy consumption accounts for 30% of the total energy consumption of the city, and this data also has the tendency to rise, therefore, the building energy saving becomes the object that the relevant scholar focuses on. There are many ways of building energy saving, is one of the most critical to building the scientific management of energy, and in this paper, the research content is the application of Internet of things technology of intelligent building energy management, the purpose is to provide a good reference for energy saving. Building energy management system is the basis of precise metering, energy consumption is the key management scheme design, the core is the analysis of the measurement data, based on energy consumption measuring, management solution and to study the way of accessing data processing platform, analyses the power component measurement, clarification of water supply and drainage, HVAC itemized metering and renewable energy metering, designed by computer room air conditioning monitoring and BAS energy management solutions of solar photovoltaic power generation system, it is concluded that the EMS integration of overall scheme, and based on this, advances the scheme of BAS and the integration of EMS. The implementation of the scheme effectively, in order to make the management of intelligent building energy management system is also energy consumption data of Internet access was studied, in order to lay a foundation for the realization of the Internet of things technology. [ABSTRACT FROM AUTHOR]

In this paper, a CdTe photovoltaic double-skin ventilated window integrated with vacuum glazing (VPV-DSV) is proposed. The mathematical model of VPV-DSV was developed and validated through tests in Hefei, China. Annual energy performance and the economic analysis of the VPV-DSV was evaluated and compared with the other two PV-DSVs and an insulating glazed window (IG) under climate conditions of five selected cities, including Changchun, Hefei, Kunming, Shenzhen, and Shijiazhuang. Moreover, effects of window design factors, including the window-to-wall ratio, PV coverage, and pillar spacing of the VG, were explored. Results indicated that: (1) the VPV-DSV produced the best energy saving results and the least energy consumption. (2) NECs of the VPV-DSV in the five cities above was 955.05 kW•h, 464.31 kW•h, 110.66 kW•h, 53.71 kW•h, and 443.14 kW•h, respectively. (3) Due to high cost of the VG, the VPV-DSV only had the best economic performance in Shenzhen and the NPV was 3931.47 CNY. (4) The WWR and PV coverage had obvious impacts on the overall performance of the VPV-DSV, while the pillar spacing only had a more significant impact on the air conditioning energy consumption. [ABSTRACT FROM AUTHOR]

Nearly 30% of global energy is consumed in buildings for heating and cooling. Many researches have been undertaken at finding novel techniques to reduce buildings' energy consumption without using any conventional mechanical devices. Earth-air heat exchanger (EAHE) is such a passive cooling technique that is seen to significantly reduce energy consumption. This paper investigates the cooling performance of a vertical EAHE for a subtropical climatic zone through a parametric analysis. A thermal transient model for the vertical EAHE has been developed using ANSYS Fluent to analyze the parametric study. The influence of the key parameters: air velocity, thickness, diameter, depth, length, and material of the pipes on the EAHE cooling performance has been conducted and presented here. This parametric study is viewed as new in a subtropical climate since no such study is found in the literature and its outcome will provide a deeper understanding of the passive cooling system. The cooling performance is considerably affected by pipe diameter, air velocity, and pipe length while no noticeable impact is found for pipe material. The optimal performance contributed to reducing the outlet air temperature up to 8.21 °C. • VEAHE performance is assessed in a subtropical climate through a parametric study. • A thermal model is developed in Ansys Fluent to analyze the parametric study. • VEAHE performance is significantly affected by pipe diameter, air velocity, and pipe length. • No noticeable impact is found for pipe material on VEAHE cooling performance. • The VEAHE has the potential to reduce the outlet air temperature up to 8.21 °C. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a comprehensive review of the significant studies exploited Artificial Neural Networks (ANNs) in BEA (Building Energy Analysis). To achieve a full coverage of the relevant studies to the scope of the research, a three-decade time span of the publishing date of the existing studies was taken into account. The review focuses on the studies utilized ANN to analyze the energy-related issues associated with buildings in major areas, including modeling of water heating and cooling systems, heating and cooling loads prediction, modeling heating ventilation air conditioning systems, indoor air temperature prediction, and building energy consumption prediction. Moreover, the findings of the abundant reviewed studies along with the potential future research to be carried out are discussed elaborately. Regarding the comprehensive review conducted, it is found out that the majority of studies focused on building energy consumption and indoor air temperature prediction. Additionally, it is observed that there has been a growing interest in the application of newly-developed ANNs to BEA areas, such as general regression neural network and recurrent neural network, due to their abilities in improving the modeling and prediction of buildings energy analysis. It is believed that this thorough review paper is useful for the researchers and scientific engineers working on the application of AI-based techniques to the building-energy-related areas to find out the relevant references and current state of the field. [ABSTRACT FROM AUTHOR]

Reliable spatio-temporal distribution analysis of building energy consumption is a crucial basis of bottom-up regional energy models, especially when faced with uncertain information at the planning stage. In existing statistical models, the regional consumption levels were mostly identified based on a large amount of samples with multi-dimensional parameters, which are usually not available for developing countries. Pointing to this, the distribution features of regional energy consumption are explored in this paper based on the whole procedure of data mining, which consists of three parts namely pre-processing, information mining, and validation & application. In this process, 212 samples of classified public buildings in Beijing and 66 samples in Hangzhou are included. Firstly, the pre-processing is conducted stepwise aiming at processing the missing data and the abnormal data. Afterwards, the interdisciplinary Lorenz curve is introduced to transfer the scatters into regular curves with satisfied fitting goodness. Thus, empirical formulae are extracted to quantify the nonlinear distribution principles of individual EUIs along with the accumulative building area. Finally, the achieved empirical formulae of different building types are validated, and the application potential of the identified patterns is discussed aiming at the planning stage. Through data mining of the limited datasets, this paper attempts to identify the hidden distribution patterns of regional energy consumption, which enables the regional modeling. [ABSTRACT FROM AUTHOR]

Green roofs can decrease the energy consumption and improve air quality and durability associated with a roofing material. Previous studies have conducted simulations of the energy performance of green roofs over a limited period, and important parameters such as leaf area index (LAI) was set at a constant value. To develop an energy performance model for a green roof with a variable LAI which is suitable for annual simulation, this paper proposes a method to integrate a seasonally variable LAI equation into a green roof model based on an improved Dickinson's equation. After verification with measured data, this equation has a more accurate temperature setting for calculating seasonally variable LAI, the average error and average relative error are improved from 0.34 to 0.09 and 39.3 to 7.1%, respectively. Thus, it can be applied in different climatic regions. To determine the differences of annual energy performance of green roof, this paper compares simulation results of constant and variable LAI values for a two-story building in Shanghai, China. The simulation is conducted on the EnergyPlus platform. The results show that the simulations of temperature, heat flux, and heat load of the building for green roof modeling differ considerably for a constant and a variable LAI. In summer, the calculated latent heat flux is usually underestimated and the sensible heat flux is usually overestimated. Moreover, if the selected LAI value is higher than that of actual data in winter, the calculated latent heat flux is overestimated and the sensible heat flux is underestimated. The simulation results are more relevant for a seasonally variable LAI. [ABSTRACT FROM AUTHOR]

[Display omitted] • A split-degree days index is proposed for building energy use/climate classification. • Split-degree days index is shown to better estimate solar radiation and humidity data. • The estimation of building energy end-uses is improved using the split-degree days. • Split-degree days also showed improved results for high thermal mass and 24-hr models. • Split-degree days index maintains a high accuracy for varying base temperatures. Degree days have been widely used in different applications in buildings, such as estimating building energy use and climate classification for building energy standards. However, there are limitations with the use of conventional degree days that result in inaccuracies in estimating building energy consumption using degree day-based methods. This paper proposes a new method, the split-degree day method, that shows substantially improved results in the accuracy of the building energy use estimation compared to the conventional degree day methods. The analysis in 801 locations in the U.S. using regression models showed that the new, proposed split-degree day method in this paper, compared to the conventional degree day method, better accounts for the weather parameters and more accurately estimates end-uses. The split-degree days showed an improvement of over 5% in the accuracy of the total annual energy use prediction, 8% for predicting the heating energy use, 0.3% for predicting the cooling energy use, and 33% for predicting the fan energy use. Additionally, the analysis showed improved results for the model with higher thermal mass and the model with a 24-hour operating schedule. These improvements were due to the fact that the split-degree day method includes more information related to the weather characteristics of a location, which is missing in a conventional degree day calculation procedure that relies on aggregated data at daily intervals. Furthermore, as opposed to the conventional degree days, the accuracy of the estimations using the split-degree days are shown to maintain their high accuracy when the base temperature deviates from the optimal base temperature. The proposed method can be used as a more accurate alternative to the conventional degree days in different applications, including but not limited to building climate classification, building energy estimation, and weather normalization for building energy savings calculation. [ABSTRACT FROM AUTHOR]

• Physical and hybrid EAHE modelling techniques used in buildings have been reviewed. • The prospects, benefits, progress, and challenges of these modelling techniques are analyzed. • The hybrid modelling technique is found more effective based on overall performance. • Physical models offer improved performance in terms of generalization capability. Noteworthy advancements are seen in developing the earth-air heat exchanger (EAHE) models in the past several decades to reduce building energy consumption. However, it is still an ongoing challenge in selecting and implementing the most suitable and appropriate EAHE modelling technique in buildings based on the climates, performance, and limitations of the techniques. Therefore, this paper aims to review the published research related to the physical, and hybrid EAHE modelling techniques used in buildings, and highlight the prospects, benefits, progress, and challenges of these techniques. This is the first study that comprehensively evidences the prospects and technical challenges caused by unmeasured disturbances, assumptions, or the uncertainties generated in experimental and numerical works of all EAHE modelling techniques. Nevertheless, this study found that hybrid modelling is more effective than physical models for accurate prediction. On the contrary, the hybrid models suffer from high complexity if EAHE operating conditions and all key parameters are considered during the model development. Regarding the generalization capability, the physical models offer improved performance followed by the hybrid models. A minimum number of training data is needed for developing physical models, whereas medium training data is required for the hybrid models. The outcome of this study also provides valuable information regarding the physical and hybrid EAHE modelling techniques to the scientists, researchers, and so on in adopting the most appropriate EAHE modelling technique for their climates. [ABSTRACT FROM AUTHOR]

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

• A multistage recursive procedure is proposed for building frequency-domain analysis. • The proposed recursive procedure accounts for dual setpoint thermostatic control. • The proposed multistage procedure accounts for time-varying window transmittance. • Energy savings for thermochromic glazing are assessed using the proposed method. • Different critical transition temperatures and transmissivity ratios were analyzed. The frequency-domain analysis is an effective approach to analyze the steady-periodic heat transfer in buildings. Auxiliary load in buildings can be calculated using the indoor temperature in a frequency-domain analysis. However, both auxiliary load and indoor temperature are usually unknown in building analysis, which makes computing the indoor temperature and auxiliary load with the presence of heating and cooling setpoints are challenging in a frequency-domain analysis. A similar problem applies to the calculation of the auxiliary loads of buildings with dynamic materials, such as thermochromic materials. This paper proposes and implements a recursive approach in time- and frequency-domain to calculate the indoor temperature in an office space with dual setpoint and thermochromic glazing systems with different critical transition temperatures and tinted-to-clear transmissivity ratios. This method allows for capturing the free-floating fluctuations of the indoor temperature within the deadband as well as determining the points-in-time when the indoor temperature equals heating or cooling setpoint temperatures. A multistage procedure is defined to account for the varying window transmittance. The proposed method accounts for the time-dependent response of a thermostat and thermochromic glazing within a finite number of adjustments without requiring to carry out time-domain temperature or load calculations. Results revealed reductions in the annual cooling energy use and increases in the annual heating energy consumption using thermochromic glazing systems with the total annual energy savings of up to 1.22 kWh/m2-yr for the τ-ratio of 0.65, 3.50 kWh/m2-yr for the τ -ratio of 0.30, and 5.22 kWh/m2-yr for the hypothetical case with zero τ-ratio. [ABSTRACT FROM AUTHOR]

Buildings - especially the residential buildings - are among the high priority areas in climate change due to their large share of CO2 emissions, their significant energy saving potentials and the increased comfort expectations of their occupants. A systematic and numerical investigation of how building performance will be affected by climate change in the future is crucial for both retrofitting the existing buildings and making long-term strategical decisions. This paper presents the simulation-based results of the climate change impact assessment for a typical multi-storey residential building in Istanbul. In this study, RCP4.5 and RCP8.5 scenarios - representing the low/medium and high CO2 emission scenarios - defined by the Intergovernmental Panel on Climate Change (IPCC) are used to generate two different climate data sets for 2060. Three cooling scenarios (natural ventilation, hybrid ventilation and air-conditioned) have been developed to represent different building usage profiles for the building simulations. Investigated performance metrics were selected as building heating/cooling energy consumption, CO2 emission and occupant thermal comfort. Analysis results analysis show that the predicted temperature rise will have a strong impact on the building cooling energy consumption and/or occupant thermal comfort. The results indicate the necessity of taking measures for adaptation of buildings to the changing climate and the importance of decarbonization of electricity generation to mitigate the effects of climate change. This study will also serve as a basis for future retrofit studies for the changing climate. [ABSTRACT FROM AUTHOR]

This paper analyzes the final results of residential and public buildings energy renovation on the area of the Osijek-Baranja County as well as the beginning of energy renovation of residential and public buildings in the five counties of Eastern Croatia based on of signed contracts for energy renovation. The structure of purpose of public buildings by cities and settlements is presented an overview of the contracted projects with the number of funds for energy renovation of multi-residential buildings is given. The conclusion emphasizes the importance of energy renovation of buildings in terms of reducing energy consumption and reducing energy imports and emphasizes that energy distributors in the five counties of Eastern Croatia need to coordinate their business procurement plans for energy delivery. [ABSTRACT FROM AUTHOR]

• Obtaining the optimal interpolation method for adjacent sites. • Proposing the method for revising meteorological data at city scale. • Verifying the accuracy of the method by energy consumption simulation. In the development process of building energy conservation design, the correct provision of local Typical Meteorological Year (TMY) data is a prerequisite and basic work to evaluate building energy consumption, which directly affects the fine use of outdoor data for building energy conservation. Based on the recorded and observed data of meteorological stations in China, the acquisition and correction methods of TMY were studied by mathematical statistics and target searching, and the building energy consumption simulation of urban office buildings was conducted to verify the accuracy of the method, including:(1) Reviewed the generation method for TMY and the acquisition of data.(2) Based on the existing TMY data, the best spatial interpolation method of adjacent stations is studied by using the standard deviation method, and the selection method of the best number of stations on a large regional scale is given.(3) An improved method of urban environment TMY is proposed. The dry bulb temperature and relative humidity of the building site are corrected to generate TMY under Urban Heat Island (UHI) effect. The research in this paper improves the coverage and accuracy of urban TMY data and supplies basic research support for building energy-saving designs. [ABSTRACT FROM AUTHOR]

In the early stage of building design, typical meteorological year (TMY) used for building energy consumption simulation which mainly representing average conditions, cannot reflect the building energy consumption under extreme weather conditions. Intensified climate change leads to global warming and frequent extreme events, therefore, generating meteorological years for simulating building energy consumption under extreme weather conditions is desperately needed. In this paper, a new method for generating extreme weather years—An extreme building energy year (EBEY) is proposed, which not only considers the influence of various meteorological parameters on building energy consumption, but also calculates the extreme intensity and occurrence time of multiple parameters using dynamic thresholds. The results show that the accuracy of using EBEY to simulate building energy consumption under extreme weather conditions is better than that of temperature-only extreme years, radiation-only extreme years, extreme meteorological years (EMY), typical cold years (TCY) and typical hot years (THY). In addition, this study verifies that combining TMY and EBEY data reduces the number of simulations without losing the accuracy of the simulation results. • The intensity and duration of extreme weather and many parameters are considered. • Use dynamic thresholds in determining the occurrence of extreme weather. • The combination of TMY and EBEY improves the accuracy of simulation results. [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]

[Display omitted] • A novel approach to control CO 2 and H 2 O composition in an enclosed environment. • A cyclic CO 2 adsorption process and dehumidication units are considered. • The mathematical model shows superior performance of the proposed system. • Carbon footprint of the system is discussed with scale-up study. • Influence of parametric uncertainties in the model is discussed. Rapid growth in global energy consumption has raised concern on the environmental impacts such as ozone layer depletion and climate change. Enclosed space, such as commercial buildings, accounts for about 40% of global energy consumption and the demand is constantly increasing due to increasing population, urbanization, and economic development. The energy demands in the building sector calls for strategic measures to develop energy efficient technologies. This paper presents a strategy to decrease energy demands inside buildings by proposing a ventilation system which regulates the enclosed air quality resulting in reduced air conditioning. The system consists of multiple adsorption beds with zeolite 13X monoliths for CO 2 removal, and silica gel for humidity control, inside the enclosed space. The air conditioning system results in decrease in energy requirement and improvement in economics by 55% as compared with conventional ventilation system. The model is scaled up to the size comparable with total office inventory of New York City, and the reduction in carbon emissions by introducing the air composition control system for New York City is equivalent to replacing 57 million incandescent light bulbs by LEDs. This paper concludes that the air conditioning system proposed in this study results in the improvement in performance as compared to a conventional ventilation system and could reduce energy consumption inside commercial buildings. [ABSTRACT FROM AUTHOR]

• A roof is divided into several. • Roof is designed as On-top Sunspace. • Obtain the appropriate the ratio of glass-to-roof and angle of the roof. • Installing a solar hot water system inside the north wall. • The daylight and economy of the building are analyzed simply. In this paper, a novel passive heating design approach—On-top Sunspace (OS)—is proposed to solve the problem of rural heating in the severely cold regions of the northern China. Passive strategies for heating should be applied before considering mechanical equipment. The framework of "Passive–Simulation–Optimization–Active" was adopted in the study. The OS was first built on the roof of a building. The simulation was completed using the Design Builder software. Based on this foundation, a solar hot water system was installed at the northern wall to further reduce the building energy consumption. The results revealed that the lowest building energy consumption can be achieved when the angles of the roofs are 28° and the glass-to-roof ratios of the front and rear roofs are 0.5 and 0.6, respectively. The daylight could also satisfy two criteria based on the useful-daylight-illuminance (UDI 100-2000 ≥ 80%) and median daylight factor (DF ≥ 2.1%). Moreover, the ultimate average temperature of the building reached 14.75 °C after installation of the solar hot water system, which may have a significant impact on the studies of the indoor thermal environment. [ABSTRACT FROM AUTHOR]

The ambitious goal of limiting end-of-century temperature rise relative to pre-industry level below 1.5 °C will offer both great challenges and opportunities in terms of curbing energy use and CO2 emissions for the building sector of China. This paper simulates the long-term trend (2010-2100) of energy consumption and CO2 emissions of the building sector in China, within the framework of 1.5 °C climate target, with application of GCAM-TU model. The impacts of energy efficiency improvement and carbon policy on energy savings and emission reductions are also explored. The results show that direct CO2 emissions from the building sector would have to peak around 2030, and cumulative emissions during 2015-2100 would be limited to 48.2 Gt CO2 in the 1.5 °C-consistent scenario. The share of electricity in building's energy consumption by the mid-century would be 8.9% higher than that under 2 °C scenario. Accelerating efficiency improvement would create significant energy savings relative to the reference scenario, especially in space heating and cooling. However, the emission reductions required by the 1.5 °C temperature increase limit cannot be effectively achieved without the collaboration of economic-wide carbon policy, which would play important role in the deep decarbonization of the energy system. Finally, it is suggested that policy makers should further strengthen technological efficiency improvement in the building sector to help enhance energy performance, while carefully devising prospective carbon policies to facilitate fast and early mitigation. [ABSTRACT FROM AUTHOR]

Highlights • A highly interactive web tool for the exploration of NILM results is presented. • Data cube allows insightful integration of NILM components with raw consumption data. • Hospital total demand, 7 cooling machines and temperature each minute for a year. • Fluid feedback by live coordinated views allows to interpret the NILM decomposition. • NMF exploits positivity of energy consumptions for an interpretable decomposition. Abstract Non-intrusive load monitoring (NILM) techniques have recently attracted much interest, since they allow to obtain latent patterns from power demand data in buildings, revealing useful information to the expert user. Unsupervised methods are specially attractive, since they do not require labeled datasets. Particularly, non-negative matrix factorization (NMF) methods decompose a single power demand measurement over a certain time period into a set of components or “parts” that are sparse, non-negative and sum up the original measured quantity. Such components reveal hidden temporal patterns which may be difficult to interpret in complex systems such as large buildings. We suggest to integrate the knowledge of the user into the analysis in order to recognize the real events inside the electric network behind the learnt patterns. In this paper, we integrate the available domain knowledge of the user by means of a visual analytics web application in which an expert user can interact in a fluid way with the NMF outcome through visual approaches such as barcharts, heatmaps or calendars. Our approach is tested with real electric power demand data from a hospital complex, showing how the interpretation of the decomposition is improved by means of interactive data cube visualizations, in which the user can insightfully relate the NMF components to characteristic demand patterns of the hospital such as those derived from human activity, as well as to inefficient behaviors of the largest systems in the hospital. [ABSTRACT FROM AUTHOR]

Building energy consumption forecasting is crucial for energy managers and policymakers. However, acutely predicting the energy consumption of any building is more important in energy efficiency benchmarking of buildings. Energy benchmarking is a precise technique for measuring, tracking, and reducing end-use energy usage of buildings by employing comparative scenarios. Bottom-Up energy consumption assessment has been used in several studies to evaluate a collection of buildings' energy performance to a standard. Traditionally, researchers have used a single approach to predict the energy use of buildings and to conduct benchmarking. However, it has been found that doing so frequently compromises the accuracy of the predicted energy use of buildings. It also has an adverse effect on the buildings' energy benchmarking framework results. This paper will provide an energy benchmarking framework for residential buildings based on the blending techniques of multiple approaches. Over 3000 datasets were gathered from the Indian city of Jaipur. In order to analyse the trends in energy consumption, the data were divided into categories based on economic levels. The data is then subjected to some fundamental statistical analysis, along with several approaches are blended to forecast the energy consumption of the buildings with the highest accuracy. These benchmark levels set guidelines for assessing and recognising strong performance while also identifying underperforming buildings and giving them more priority for energy efficiency improvement. The technique may be used for different metropolitan levels and climatic conditions. [ABSTRACT FROM AUTHOR]

The energy consumption of buildings, particularly heating, ventilation, and air conditioning systems, exceeds that of transportation and industry combined. Future projections show a concerning increase in building energy consumption, prompting researchers to investigate the potential of smart window technology to mitigate this issue. However, current research on smart windows has primarily focused on individual functionalities that do not meet consumer demands. Therefore, researchers must exploit the inherent properties of materials to develop multifunctional smart windows that can cater to consumer needs. The integration of smart windows into the construction of green buildings shows great promise, but mutual working mechanisms must be achieved to produce a synergistic effect toward high-performance smart window technology. Multifunctional smart windows can be achieved by incorporating applications, such as electrochromic, thermochromic, photochromic, and supercapacitor technologies, into one device. Implementing multifunctional smart windows is possible if the mechanisms and properties of composite materials meet both criteria for feasible applications. In evaluating the feasibility of integrating electrochromic and energy conversion devices, this review assesses the state-of-the-art smart window technology, limitations and roadblocks to commercialization, and potential materials for smart window technology. Notably, there is no previous review paper on the feasibility of integrating energy storage and energy harvesting applications to produce smart windows technology and its potential for net-zero buildings. In the future, multifunctional smart windows technology could be applied to more buildings and retrofits to reduce energy consumption and increase human comfort. • Buildings consume more energy than transportation and industry combined. • Smart windows can mitigate building energy consumption. • Current research on smart windows must cater to consumer needs. • Multifunctional smart windows can be achieved by integrating technologies. • Feasibility of integrating energy storage and harvesting applications for net-zero buildings is unexplored. [ABSTRACT FROM AUTHOR]

In order to resolve the contradiction between the lack of building energy data and the need of more detailed information about energy performance, this paper presents a simplified building energy performance diagnosis method which can assess the energy performance from building level to system level with only limited information. Based on multiple-parameter regression between the whole building energy bills and outdoor weather data, the proposed diagnosis method uses the regression coefficients to identify the energy use of main systems (i.e., lighting-plug system and cooling/heating system), as well as other detailed information about physical properties of the building and cooling/heating system (i.e., cooling or heating load, the operation of cooling/heating system and the efficiency of cooling/heating system). One case study was conducted in an office building in China to test the application of this diagnosis method. In addition, the regression result of simulated energy consumption served as benchmark data to further diagnose the performance gap between the operation stage and the design stage and help locate the poor performance and key points of energy-saving. Finally, all diagnosis results have been verified by the performance data from advanced energy consumption monitoring system together with field surveys and measurements. [ABSTRACT FROM AUTHOR]

This paper presents a case study of a recently built high-performance Canadian social housing building with the aim of comparing the expected and measured energy consumptions and to identify the parameters affecting the most the energy need. A monitoring system compiles at a 10-min frequency information related to the energy use and the thermal conditions observed in the building and its HVAC system. The building has the particularity of comprising two symmetric sections made of different timber structure systems. No significant differences of energy consumption were detected between the two parts of the buildings. However, a large variance was observed when comparing each dwelling individually regardless of their structures. The orientation of the dwelling also exhibited a minimal influence compared to these variations, suggesting that occupant behavior is the dominant factor explaining dwelling-to-dwelling variability and is thus critical for understanding energy use in residential buildings. Regression analysis showed that specific occupant actions, such as opening windows in winter or using electrical appliances, have a great impact on the energy balance of the apartments. In 2016, the performance gap between measured and expected total energy demand of the building was 74%. With the use of the large dataset coming from the building, it was possible to determine the causes behind this large gap for the reference building. [ABSTRACT FROM AUTHOR]

Urban microclimate studies are gaining popularity due to rapid urbanization. Many studies documented that urban microclimate can affect building energy performance, human morbidity and mortality and thermal comfort. Historically, urban microclimate studies were conducted with observational methods such as field measurements. In the last decades, with the advances in computational resources, numerical simulation approaches have become increasingly popular. Nowadays, especially simulations with Computational Fluid Dynamics (CFD) is frequently used to assess urban microclimate. CFD can resolve the transfer of heat and mass and their interaction with individual obstacles such as buildings. Considering the rapid increase in CFD studies of urban microclimate, this paper provides a review of research reported in journal publications on this topic till the end of 2015. The studies are categorized based on the following characteristics: morphology of the urban area (generic versus real) and methodology (with or without validation study). In addition, the studies are categorized by specifying the considered urban settings/locations, simulation equations and models, target parameters and keywords. This review documents the increasing popularity of the research area over the years. Based on the data obtained concerning the urban location, target parameters and keywords, the historical development of the studies is discussed and future perspectives are provided. According to the results, early CFD microclimate studies were conducted for model development and later studies considered CFD approach as a predictive methodology. Later, with the established simulation setups, research efforts shifted to case studies. Recently, an increasing amount of studies focus on urban scale adaptation measures. The review hints a possible change in this trend as the results from CFD simulations can be linked up with different aspects (e.g. economy) and with different scales (e.g. buildings), and thus, CFD can play an important role in transferring urban climate knowledge into engineering and design practice. [ABSTRACT FROM AUTHOR]

Over the past 15 years, the evaluation of energy demand and use in buildings has become increasingly acute due to growing scientific and political pressure around the world in response to climate change. The estimation of the use of energy in buildings is therefore a critical process during the design stage. This paper presents a review of the literature published in leading journals through Science Direct and Scopus databases within this research domain to establish research trends, and importantly, to identify research gaps for future investigation. It has been widely acknowledged in the literature that there is an alarming performance gap between the predicted and actual energy consumption of buildings (sometimes this has been up to 300% difference). Analysis of the impact of occupants’ behaviour has been largely overlooked in building energy performance analysis. In short, energy simulation tools utilise climatic data and physical/ thermal properties of building elements in their calculations, and the impact of occupants is only considered through means of fixed and scheduled patterns of behaviour. This research review identified a number of areas for future research including: larger scale analysis (e.g. urban analysis); interior design, in terms of space layout, and fixtures and fittings on occupants’ behaviour; psychological cognitive behavioural methods; and the integration of quantitative and qualitative research findings in energy simulation tools to name but a few. [ABSTRACT FROM AUTHOR]

Building energy consumption prediction plays an important role in improving the energy utilization rate through helping building managers to make better decisions. However, as a result of randomness and noisy disturbance, it is not an easy task to realize accurate prediction of the building energy consumption. In order to obtain better building energy consumption prediction accuracy, an extreme deep learning approach is presented in this paper. The proposed approach combines stacked autoencoders (SAEs) with the extreme learning machine (ELM) to take advantage of their respective characteristics. In this proposed approach, the SAE is used to extract the building energy consumption features, while the ELM is utilized as a predictor to obtain accurate prediction results. To determine the input variables of the extreme deep learning model, the partial autocorrelation analysis method is adopted. Additionally, in order to examine the performances of the proposed approach, it is compared with some popular machine learning methods, such as the backward propagation neural network (BPNN), support vector regression (SVR), the generalized radial basis function neural network (GRBFNN) and multiple linear regression (MLR). Experimental results demonstrate that the proposed method has the best prediction performance in different cases of the building energy consumption. [ABSTRACT FROM AUTHOR]

In this paper, the internal energy consumption on the Internet of Things (IoT) has been studied. The purpose of this paper is to predict the factors affecting energy consumption in buildings by considering machine learning algorithms such as k-nearest neighbors (KNN), Ada boost, random forest and neural network. These algorithms are implemented in the Orange tool. Also, the univariate regression algorithm is used to select the best feature. This algorithm determines the most important factors affecting energy consumption and their impact. Then, with the help of Gephi tool, these data are simulated in the IoT environment as a complex network. The simulated network in the Internet of things is also a small world network. This network shows the relationships between the features. The results of this paper show that the overall height, roof area, surface area and relative compaction have the greatest impact on the energy consumption of buildings. It can be seen that the predicted error percentages for these data for cooling loads and heating loads are 0.911 and 0.292, respectively. It should be noted that the best algorithm for cooling load and heating load is Ada boost algorithm. [ABSTRACT FROM AUTHOR]

Deep anomaly detection (DAD) is essential in optimizing building energy management. Nonetheless, most existing works concerning this field consider unsupervised learning and involve the analysis of sensor readings through a one-dimensional (1D) energy time series, which limits the options for detecting anomalies within the building's energy consumption. To the best of the authors' knowledge, this paper presents the first study that explores using two-dimensional (2D) image representations as features of a supervised deep transfer learning (DTL) approach. Specifically, using 2D image representations allows taking advantage of any underlying data within the feature set, providing more possibilities to encode data and detect pertinent features which may not be considered in standard 1D time-series. Furthermore, the effects of using CNN activations as machine learning (ML) model features are also investigated to combine the advantages of both techniques. Additionally, the concept of layer and hyperparameter variation for the CNN model is also studied, with the objective of reducing the overall time computation and resource requirements of the proposed system. Hence, this makes our approach a candidate for edge-based applications. As per the conducted experiments, the top methodology rests at the F1-scores of 93.63% and 99.89% under simulated and real-world energy datasets, respectively. This involves using grayscale 2D image representations that combine CNN activations extracted from AlexNet and GoogleNet pre-trained models as features to a linear support vector machine (SVM) classifier. Finally, the comparison analysis with the state-of-the-art has shown the superiority of the proposed method in various assessment criteria. • Deep anomaly detection of energy consumption using energy time-series images. • Converting 1D energy time series into 2D representations. • 2D energy representations as features of a neural network-based transfer learning. • Using neural network activations as machine learning model features. • Combining neural network activations extracted from AlexNet and GoogleNet. [ABSTRACT FROM AUTHOR]

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

The building sector accounts for approximately 40% of total global energy usage. Energy consumption for space heating and cooling makes up 60% of the total consumed energy in buildings. This paper presents a comprehensive technical review of passive wall systems in building envelopes while discussing their respective capabilities in optimizing energy efficiency. Different types of energy efficient walls such as Trombe Walls, Autoclaved Aerated Concrete Walls, Double Skin Walls, and Green Walls are explored. Furthermore, novel concepts for optimizing energy efficiency in building envelopes are also introduced. Finally the utilization of passive wall systems to save energy while improving the building environmental impacts is discussed. [ABSTRACT FROM AUTHOR]

This paper provides a novel and efficient approach for the simulation-based multi-criteria optimization of the building energy performance. In the current investigation, a multi-objective non-dominated sorting genetic algorithm (NSGA-II) is coupled with EnergyPlus building energy simulation program to obtain optimal solutions leads to increase the building energy performance. The main aim of this work is to explore the effect of some architectural parameters, including the building orientation, the window size and the overhang specifications on the building energy consumption in four major climatic regions of Iran, e.g. cold, mild, warm-dry, and warm-humid. In the optimization section, mono-criterion and bi-criteria optimization analyses of the annual cooling and lighting building energy demands are studied with the purpose of understanding the interactions between these objective functions. The results point out that the bi-criteria optimization of the test case model brings down the annual cooling energy consumption 55.8–76.4% in various climates. However, the annual lighting electricity demand increases 1–4.8%. As a result, the final optimum configuration leads to 23.8–42.2% decrease in the annual total building energy consumption. The results depict that the climate and the appropriate selection of the architectural parameters are very important and critical in reducing the building energy consumption. The proposed optimization methodology is a powerful and useful tool to achieve this goal and to facilitate decision-making in early phases of a building design in order to enhance its energy efficiency. [ABSTRACT FROM AUTHOR]

The large public building energy consumption (BEC) is the focus of Building Energy Saving, therefore, it is necessary to study the characteristics of BEC to find out the important factors affecting BEC. For this purpose, 27 office buildings in the west of Inner Mongolia Autonomous Region were studied, based on statistical analysis of the researched basic information and energy consumption bill of these buildings. This paper focused on the determination of the significant factors affecting the total and subentry energy consumption intensity (ECI) of office building, as well as the establishment of standardized linear regression models between these selected factors and total and subentry ECI. Firstly, eleven continuous variables, three independent categorical variables, and the climate factor were selected and analyzed the impact on total and subentry ECI by statistical software SPSS20.0, in order to find out the significant influencing factors. Then based on the results of curve fitting, standardized models of total and subentry ECI and their respective significant impact factors were established using multiple linear regression analysis. The regression results showed that the electricity use percentage of the total equivalent electricity consumption was an important factor affecting the total ECI of office building in the west of Inner Mongolia Autonomous Region. Finally, univariate analysis of variance (ANOVA) was conducted between the three independent categorical variables and the total and subentry ECI, and the results showed that these factors had no significant effect on the ECI of office building. Process of the regressive model establishment and the results of analysis of variance could both guide us to propose more targeted energy saving measures. [ABSTRACT FROM AUTHOR]

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

• This paper determines the relationship between urban form and building energy use. • The surface-to-volume ratio and obstruction angle of buildings are important. • Consideration of the heating energy type and building use can yield reliable result. • The urban heat island effect exerts an impact on the electricity use in some months. • Correlation variation is caused by the building permit date. In the climate change era, urban geometry serves to ensure sustainability for urban planners since it notably affects building energy consumption. However, previous studies have mainly conducted experimental investigations under hypothetical conditions. Without empirical evidence, it is difficult to implement these findings under real conditions. This paper examined empirical data to determine the urban geometry effect on building energy consumption in a large, complicated urban area. Urban geometric parameters, including the surface-to-volume ratio, obstruction angle, and building orientation, were measured across Seoul, and their correlations with building energy use were analyzed. The results show that the surface-to-volume ratio and obstruction angle are important, despite the empirical data complexity. Additional data analysis indicates that the heating energy type and building use consideration yields more reliable correlation coefficient trends. The potential impacts of natural ventilation and the urban heat island (UHI) effect on building energy consumption were examined. The UHI effect, rather than shadows on buildings, predominantly impacts the building electricity use in August and September. The correlation variation caused by the building permit date was investigated. The results in this paper could help urban planners and designers develop blueprints to control urban geometry and establish relevant design guidelines. [ABSTRACT FROM AUTHOR]

In this paper, a novel adaptive enclosure structure (AES) combining the building wall and heat pipe is proposed for building wall energy saving. The theoretical heat transfer process of the structure is analyzed, and its heat transfer performance is obtained. The theoretical thermal resistance of the structure is about 0.165 K/W. The one-dimensional steady-state heat transfer experiment has been carried out. Under the temperature difference of 35 °C, the surface temperature of the AES is much higher than that of the traditional wall on the heating demand side (about 9.5 °C). The heat transfer experiment under the influence of multiple factors has been carried out. Under heating demand, the indoor side surface temperature of the AES is higher than that of the traditional wall (about 1.5 °C). Under cooling demand, the indoor side surface temperature of the structure is lower than that of the traditional wall (about 0.8 °C). In addition, it is realizable to switch the performance of the AES between efficient heat transfer or good thermal insulation according to requirements. The proposed structure has great potential for reducing the indoor energy consumption and improving thermal comfort, and could provide a new solution for passive energy-saving technology based on building envelope. • An adaptive structure based on heat pipes is proposed for building energy saving. • A method combining heat pipe and wall with less impact on the structure is proposed. • The thermal resistance of the structure is 0.165 K/W, 2% of the traditional structure. • The change of heat transfer performance based on the environment can be realized. • The switching schemes of the heat transfer in four seasons is proposed and analyzed. [ABSTRACT FROM AUTHOR]

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

A growing attention has been paid to building integrated photovoltaics (BIPV) from both architectural and engineering favorability. There are various computational tools developed to provide computations to optimize BIPVs and often simulations for predicting their performance. This provides a great potential for designers to have different helpful tools to be utilized. This paper introduces a comparative analysis of the most common computational tools that compute or simulate main parameters of BIPVs: building energy consumption, solar exposure radiation flux and PV system performance. These computational tools are classified based the method of processing the inputs and compared using evaluation criteria. Also, optimization algorithms that can be used in optimizing BIPVs have been compared. This comparative analysis helps designers to determine better tool/s and algorithm/s for their design cases and required optimization for BIPV. The main findings of this study are the capabilities, limitations, advantages and disadvantages of each computational tool and optimization algorithm presented, in addition to the best selections among them via a comparative analysis to be used for different design cases. [ABSTRACT FROM AUTHOR]

In the field of building constructions, there is undeniably a growing need to optimize the energy systems which are a key target in new modern constructions and industrial buildings. In this sense, energy systems are being traced for the development of energy distribution networks that are increasingly smart, efficient, and sustainable. Modern generation and distribution energy systems, such as microgrids control systems, are being affected by the presence of linear and nonlinear loads, resulting a distorted voltage and current waveforms. Thus, it is stated that industrial and residential building heating and cooling loads behave essentially like sources of harmonics. This paper presents a new framework based on geometric algebra (GA) to the definition of a multivectorial distortion power concept, which is represented by a bivector that is geometrically interpreted to distinguish the rotated distortion and distortion power bivectors in these kinds of loads. Both bivectors, and their relations to the phase angles of distorted voltage are the main subject of this paper to interpret an optimal control of building energy. Numerical examples are used to illustrate of the suggested distortion power concept, as well as the information it provides for energy control in new buildings in a more sustainable way. [ABSTRACT FROM AUTHOR]

The relationship between urban form and building energy use has received increasing attention in recent literature. However, findings from existing studies are diverse and sometimes contradictory, and this issue has not been extensively discussed in previous reviews. This paper provides a systematic review of studies on the relationship between urban form and building energy use based on the proposed classification framework. The classification framework categorizes existing studies by their measure definitions, mechanism assumptions, and methodologies. Aside from these conceptual differences, the differences in site contexts of the selected cases are also reviewed. On the basis of the review results, this paper summarizes the consideration of mechanisms by pathway maps and provides an updated understanding of the three main debates in this field. These debates concern the magnitude of influence, impact of densification, and preference of typology. This review also reveals many challenges in this field, including the limited measures of urban form and building energy use, lack of understanding of pathway contributions, and uncertainty and validity issues of tools and models. In addition, future research directions are provided to inform urban planning and policy making decisions for sustainable urban development. • The urban form–building energy use relationship is systematically reviewed. • A classification framework is proposed to analyze the reviewed studies. • Highly diverse measures, mechanisms and methodologies are found in the literature. • Knowledge gaps exist in definitions of measures and pathways and assessment tools. • Future studies should be more interdisciplinary and address sustainability issues. [ABSTRACT FROM AUTHOR]

• Miscellaneous loads are a major opportunity to save electricity in vacant buildings. • A method for inferring vacancy was developed. • The model generates a continuously updated likelihood of vacancy. • The vacancy inference model requires less ground truth data. • The sensor fusion approach is robust and enables flexibility. Recent events have forced building managers to examine energy use during vacant periods and revealed miscellaneous electrical loads (MELs) as an opportunity for savings. This paper addresses a key step in unlocking these savings, specifically the reliable identification of when a building is vacant. A Vacancy Inference Engine (VIE), using sensor fusion, was developed to identify vacant periods based on outputs from common sensors, historical building vacancy patterns, and expert knowledge. The VIE calculates the confidence that a building is vacant, allowing building managers to balance the capture of energy savings with the possibility of complaints due to powering down MELs. The VIE has the advantage over logistic regression and other models in that it does not require a full set of ground truth for the training process. The VIE successfully predicted vacancy in an office building using input data streams of instantaneous electricity demand, indoor carbon dioxide concentrations, and the number of active Wi-Fi connections. The VIE's ability to predict vacancy was compared to that of logistic regression using a metric based on the Complaint Opportunity Rate and found to be nearly identical (0.94 versus 0.95, respectively). [ABSTRACT FROM AUTHOR]

Currently, there is no clear and unified understanding about the status quo of China's energy consumption in the building sector. In addition, a considerable underestimation of energy associated with buildings has impeded the effective implementation of measures to improve building energy efficiency of China. Thus, in this paper, we seek to identify the building sector's energy consumption of China by establishing an estimation model of building energy consumption from a life cycle perspective. On the basis of macro-level statistical data and relevant literature, we analyze the activities in each phase and calculate associated energy consumptions throughout buildings’ whole life cycle in China from 2001 to 2013. The results show that China's energy consumption associated with buildings has reached 1.66 billion tons coal equivalent in 2013, with a stable growth rate of 7% annually since 2001. Buildings’ life-cycle energy has approximately accounted for 43% of China's total energy consumption for recent three years (2011–2013). What's more, energy consumption in buildings’ operation phase has been salient, accounting for over 20% of China's total energy consumption. More focus should be drawn on energy efficiency in building material production phase and energy consumed in China's rural residential buildings as both have been significantly neglected. [ABSTRACT FROM AUTHOR]

This paper investigates the influence of solar radiation on thermal comfort inside an indoor environment and its effect on the building energy consumptions. Furthermore, it draws up a procedure which allows the rating of the thermal comfort quality of indoor environments in the presence of solar radiation, to be used in correlation with the energy classification of building in order to refer the energy performance to the indoor environmental conditions. Mean Radiant Temperatures (MRT) for a subject exposed to solar radiation in different positions of the environment were calculated, with an hourly time step and for a whole year. These values were utilized to assess the Predicted Percentage of Dissatisfied (PPD) and its variation with time and space, so that long term thermal comfort evaluations were able to be carried out and comparisons among irradiated and not irradiated positions were able to be made. [ABSTRACT FROM AUTHOR]

China is currently experiencing rapid urbanization. The building sector plays an important role in China's energy conservation and emission reduction. An increasing amount of energy will be steadily consumed in the building sector as the urbanization process accelerates. China's government attaches great importance to building energy efficiency (BEE). How does the urbanization process affect building energy consumption (BEC)? How much energy can be saved in the building sector through implementation of energy polices? These questions are critical for government development strategies and planning. Thus, in this paper, an econometric model (co-integration technique) is developed to analyze how China's BEC is affected by macroeconomic variables in the context of urbanization, and also forecast the BEC and BEE potential until 2020 under different scenarios. The research result shows that occupants’ lifestyles, residents’ living standards, and energy price have significant influences on BEC. The building sector is likely to contribute approximately 20% to China's energy conservation through electricity pricing reform. [ABSTRACT FROM AUTHOR]