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• This paper presents a comprehensive review of cross ventilation research in buildings. • The advantages and disadvantages of different research methods are compared. • The ventilation models of cross ventilation are reviewed. • The influencing parameters of cross ventilation are summarized. • The future perspectives of cross ventilation research and practical applications are proposed. There is a growing body of literature that recognizes that natural ventilation plays a vital role in indoor air quality, thermal comfort and building energy consumption. This paper systematically reviews the previously published research of the most efficient and typical natural ventilation type - cross ventilation, aiming to present the main research topics in contemporary research and provide an agenda for future studies. The methodologies, airflow pattern, ventilation models and influencing parameters of cross ventilation were comprehensively summarized and discussed. The chained analysis and data-driven methods are the potential approaches to study cross ventilation more efficiently. The comparisons of different ventilation models of cross ventilation help to better understand the basic mechanisms that drive the cross ventilation airflow. [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]

[Display omitted] • 33 worldwide open datasets of city-level building energy consumption are summarized. • Representative applications of 198 papers using the collected datasets are analyzed and compared. • Policy implications are presented for using the open datasets to support policymaking. • Useful non-energy datasets about weather, building features, urban forms, and occupant-related factors are recommended. • Solutions to privacy issues are explored, including anonymizing methods and proper data aggregation. Data related to building energy use fuels the research and applications on building energy efficiency, which is an essential measure to address global energy and environmental challenges. However, in most cities, there is a lack of comprehensive and publicly accessible building energy use datasets with necessary temporal and spatial granularities to support urban building energy modeling, regional energy planning, city-level building performance benchmarking, and policymaking on building energy efficiency. Data owners and governments are facing challenges in determining which and how building energy use data should be disclosed at the city level and how to protect data privacy. This review paper provides insights to answer these questions based on a comprehensive and critical review of worldwide open datasets and their applications in the built environment context. Detailed information about the collected 33 building energy datasets is summarized and categorized. Studies identified into 11 subdomains using these open datasets are critically analyzed and compared. Potential policy implications based on the studies are also proposed. Moreover, non-energy datasets that are frequently used in research relevant to urban building energy use are also introduced. Solutions to privacy issues are discussed to address concerns from data publishers. Finally, significant conclusions are made to support the proper disclosure of city-level building energy data. This review study is valuable to urban building energy data disclosure, urban building energy modeling, and data-driven energy policymaking. [ABSTRACT FROM AUTHOR]

To meet the goals established by the 2015 Paris Accord, scientists say that we will need to achieve zero emissions between 2030 and 2050, which will require a drastic departure from current practices in the building industry, as buildings are a primary contributor of carbon emissions and a major consumer of natural resources. However, building technology research is often driven by disciplinary 'experts' and end-users have little opportunity for involvement in the design process. As buildings become more technologically complex, users must rely on industry professionals with specialized knowledge to design, manufacture and manage these increasingly complex environments Instead, this paper provides an alternative attitude to approachable research methods that open up building technology innovation opportunities to untraditional innovators. When the user has control of the design/innovation process, it opens up a new realm of possibilities rich with lived experience. Understanding the movement of heat, or thermal transmittance , through materials and assemblies is critical in controlling energy consumption in buildings. Despite recent advances in computational technologies, physical testing is still the industry standard to determine a material's thermal properties. However, this process can require expensive equipment, a controlled environment for testing and deep technical knowledge to create a degree of accuracy that is not necessary for all phases of the innovation process and can even act as a barrier to the early stages of design. In this paper we propose the Approachable Hot Box (AHB), a low-cost, low-tech and accessible testing apparatus that can quickly and easily evaluate the impact of simple building materials and assemblies on ambient environmental conditions. By reducing unnecessary complexity for early-level design decisions, the AHB is consistent and reliable at an affordable price. Not only does the AHB rapidly and affordably test small material samples, it helps to articulate potentially complex concepts in a tangible and understandable way, which helps to lower the barrier to innovation. By creating a non-disciplinary, iterative design approach that uses conventional materials and accessible concepts, this work demonstrates that rapid experimentation can be used as a tool to study building assemblies or systems by non-traditional innovators. [ABSTRACT FROM AUTHOR]

[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]

Among the various factors that affect building performance, occupant behavior (OB) has received more and more attention and has become a research hotspot in building design and operation in recent years. However, due to the remarkable randomness and diversity of OB in buildings, it is difficult to accurately describe its characteristics using a fixed schedule, which is a normal approach of most energy simulation tools. DeST, a whole building energy simulation tool offers the function of stochastic OB simulation so that the simulation results can be more in line with the actual situation. In the newly released DeST 3.0, the OB modules are developed based on the actual observed OB data and are divided into two parts: occupancy and occupant action modules. The occupancy module is based on the Markov chain, which simulates the occupant movements among the spaces inside and outside the buildings. The occupant action modules are proposed with an environment and event-driven stochastic infrastructure based on a three-parameter Weibull distribution, such as occupant shading, window, and air-conditioning (AC)/heating control behavior modules. This paper presents the OB modules development for coupled simulation in DeST 3.0 and discusses the effects of integrated simulation of occupant behavior on building energy consumption. Firstly, an overview of the current modelling efforts of OB is given, from the trend to the existing foundation. Secondly, the development of OB modules for coupled simulation in DeST 3.0 is introduced in-depth, including co-simulation of AC/heating control behavior, window behavior and shading behavior. Lastly, two case studies that realize coupled simulation using the developed modules are shown, one is the comparison between existing occupancy schedules from GB standards and the developed occupancy simulation, and the other is the comparison between the result of simulating with or without using AC/heating control behavior modules in two bedrooms of the residential building. [ABSTRACT FROM AUTHOR]

[Display omitted] • Carbon emissions are explored in China's building operations from provincial perspective. • GDIM is employed to factorize ten affecting factors of CO 2 emissions in building sub-sectors. • CO 2 emissions of building sub-sectors are predicted with dynamic simulation. • The contribution of provinces to the change and peak of national emissions are discussed. Carbon emissions from building operations have a critical impact on achieving carbon peaking goal in China. However, the provincial emission performance in building operations are quite different and it is still unidentified how they affect the national emission change and peaking target, making national emission reduction target hard to implement at the provincial level. This paper explores the driving factors leading to emission changes and examines the prospective emission peaks in China and China's provinces by adopting generalized Divisia index method (GDIM) and the combination of scenario analysis and Monte Carlo simulation. Furthermore, the regional emission heterogeneity and the contribution of provinces to the change and peak of national emissions are discussed to identify regions and provinces that should be focused on emission monitoring. Results show that in China and most Chinese provinces, energy consumption of commercial buildings, energy consumption and floor space of urban residential buildings besides income improvement, and the per capita CO 2 emissions of rural residential buildings contributed greatly to emission growth. Under current policies, commercial and urban residential buildings in most provinces would fail to reach their emission peaks ahead of schedule. The urban residential buildings in the northern region, commercial and rural residential buildings in the eastern region caused higher CO 2 emissions. At the provincial level, Shandong, Shanxi and Hebei made the top contribution to total emissions of commercial, urban residential and rural residential buildings, respectively. Shandong and the provinces in north China of urban residential buildings and four provinces including Shandong, Guangdong, Hebei and Zhejiang of commercial and rural residential buildings had the significant impact on the uncertainty of the peaking time of CO 2 emissions. These findings provide a strong decision-making reference for Chinese government to formulate targeted regional emission reduction policies in building operations. [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]

• A heat transfer model was developed under the non-constant wall temperature condition. • Non-constant wall temperature was built based on field test results. • The developed model prediction accuracy is higher than the existing calculation model. • The developed model was extended for the hydropower tunnel ventilation performance. Underground tunnel ventilating has been used in many hydropower stations to reduce the building energy consumption of the underground space by precooling or preheating air. The tunnel wall temperature has an important influence on the cooling or preheating performance of the deeply buried underground tunnels. To solve the problem that the heat balance equation is difficult to solve due to the wall temperature variation along the tunnel, a simple heat transfer model under the non-constant wall temperature condition was developed. The developed model is validated against field tests which showed good agreement between the model results and test data. Field tests of underground traffic tunnels in 5 hydropower stations show that the maximum and minimum attenuations reach 13.0 °C and 2.2 °C, respectively. The comparison results show that the relative deviation of the prediction value of the developed model is 3.72% lower than that of the existing constant wall temperature model. Furthermore, the developed model is used for performance simulation of tunnel ventilation for real great hydropower stations, and the conclusions show that the maximum deviations between the calculated model and tested values range from 0.34 °C to 2.76 °C, and the maximum relative deviations range from 1.89% to 9.69%. In addition, the average relative deviations range from 1.13% to 2.40%. Data comparison verifies that the developed model established in this paper has accurate prediction performance, which is conducive to the design optimization of air conditioning and utilization of natural thermal for underground hydropower stations. [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]

A microalgae-based biofacade is a double-skin facade with integrated photobioreactors (PBRs) for solar cultivation of microalgae. One purpose of cultivating microalgae on building facades is to create an 'active' facade that can reduce the energy consumption required to maintain thermal comfort inside the building, by creating exchange loops between the building and the microalgae cultivation system. This paper describes the experimental validation of a thermal model of a building equipped with a biofacade PBR, and its use in analyzing the energy consumption of international-standard buildings in two different climate conditions (oceanic climate using the weather data from Nantes, France, and semi-arid climate using weather data from Los Angeles, USA). The results of this analysis show that the microalgae biofacade almost entirely removes the building's cooling demand, and the warmer the location the stronger the effect. In addition, the heating demand of the biofacade PBR is also greatly reduced (between 92% and 100% depending on the location). The microalgae biofacade is therefore a more sustainable solution to produce valuable biomass and a promising technology for the development of zero-emission buildings; to this end, suggestions for optimizing the process are put forward. [ABSTRACT FROM AUTHOR]

In recent years, with the rapid development of cities across the globe and global warming, Urban Heat Island (UHI) and building cooling energy consumption have reached a point of perilous concern. The use of shading systems on building fenestrations has long been known as an effective strategy to minimise façade heat gain as well as glare, resulting in lower cooling energy consumption and indoor comfort. Expending the potentials of shading systems beyond conventional use, the present paper focuses on the effects of shading systems on opaque facades that is currently unaccounted for in simplified Overall Thermal Transfer Value (OTTV) calculations in standards adopted by many countries. A field study was first conducted to compare the effects of various shading configurations on an opaque façade. Subsequently, a simulation study was then conducted to quantify the effects of shading systems on opaque facades with varying thermal transmittance value (U-Value). The results of this study revealed numerous benefits for the deployment of shading systems on opaque building facades, which includes, reduction to building conductive heat gain, building cooling loads, microclimate temperature near the façade, as well as potential reduction to carbon emissions over the building lifecycle. Moreover, the paper also provides considerations for the selection shading configurations, as well as the considerations that would be required for the formulation of a more accurate simplified OTTV equation that would take into account the effects of shading systems on opaque facades. [ABSTRACT FROM AUTHOR]

• The solar louver was proposed to increase direct heat gaining for building. • Effects of blades' angle and characteristics on heat gain of solar louver was quantified. • Indoor temperature was higher and temperature field was more uniform with coupled solar louver. • By comparison with non-coupled solar louver, solar louver in maximum light transmittance mode decreased active heating supply capacity by 33.8%. Promoting solar energy for heating is of great significance for achieving the national goals of "double carbon" goals. Increasing the heat gain of window space could improve the solar energy utilization in congested urban environments. The paper proposed a new heating type for active solar house by solar louver. Thermal effects validation on solar louver were studied by field-test and simulation analysis. A solar louver model was established on the platform of MATLAB and TRNSYS. The simulated results showing that heat gain of solar louver reached the largest when blades' angle was about 10°. Solar louver with high reflectivity and low thermal emissivity could capture more heat gain. The in-situ experiments of active solar house coupling solar louver were carried out in Ordos. From 24th to 29th Jan. 2022, all-day comparative experiments of thermal effects in actual winter-weather were conducted. The experimental results indicating that solar louver in maximum light transmission mode was preferred in active solar house. Compared with the condition of solar louver in overlapped light transmittance mode, maximum light transmission mode could obtain 17.7% higher heat gain. Indoor temperature was more higher and temperature field was more uniform. Moreover, by comparison with the non-coupled solar louver, solar louver in maximum light transmittance mode decreased active heating supply capacity by 33.8%, which could reduce energy consumption of building. The main work here provided a theoretical basis for performance optimization of active solar house. [ABSTRACT FROM AUTHOR]

The optimization of energy consumption in buildings' HVAC systems plays a crucial role in reducing greenhouse gas emissions worldwide. In new and deeply-renovated buildings, characterized by modulating terminal units and aiming at the maximum exploitation of renewables, an accurate hydraulic balance of the distribution network may become critical, resulting in a significant increase of pumping energy consumptions and a deterioration of indoor thermal comfort conditions. In the present paper, we compare the performance of traditional manual balancing valves and new pressure independent control valves (PICVs), used to balance the hydronic loop of HVAC systems. To perform this analysis, a new MATLAB-Simulink model has been specifically developed to simulate the behavior of PICVs and has been used in an application case study. The efficiency of manual and pressure independent control valves is evaluated numerically by simulating a multi-zone distribution network under variable operating conditions and considering different control strategies of the circulating pump. Results show that in off-design conditions, when some of the branches of the hydraulic network are closed, traditional manual balancing valves are not able to guarantee the nominal mass flow rate in the remaining loops. On the contrary, no significant variations of water mass flow rate are observed when PICVs are adopted, even in partial load conditions. Despite their additional cost, PICVs allow to ensure better indoor thermal comfort sensations for individuals, avoiding under/over-heating of rooms and, moreover, yield a decrease of the pump electric consumption with respect to traditional manual balancing valves. In addition, based on the obtained results, general rules concerning the adoption of PICVs are provided, depending on the extension of the hydraulic loop and the adopted pump control logic. The reported results highlight the role that PICVs can play to ensure energy savings in HVAC systems without penalizing users' indoor comfort conditions. [ABSTRACT FROM AUTHOR]

• Provided a platform to test various residential building control systems. • Used probability of occupancy to determine setback temperature when unoccupied. • Integrated optimizer with adaptive and occupancy control. • Generated dynamic electricity tariff based on wholesale electricity pricing. • Evaluated the peak load shaving potential with dynamic pricing and optimization. Energy consumption in buildings continues to rise with increased deployment of energy-consuming equipment such as Heating, Ventilation, and Air Conditioning (HVAC) amid a growing world economy. Renewable energy is projected to comprise a majority of the future electricity supply, but the intermittent nature of renewables means that consumption must respond to dynamic supply for optimal utilization. This paper proposes a novel HVAC control strategy for residential buildings using the adaptive comfort model, considering occupancy through probability and real-time information, and optimizing the HVAC schedule to reduce cost, maintain thermal comfort, and respond to the dynamic availability of renewable energy while being generalizable to different situations. To validate this approach, the Universal CPS Environment for Federation (UCEF) co-simulation platform is used to connect advanced building controls with the building energy simulation software EnergyPlus. Simulations are performed for a residential building in Sacramento, CA during a typical summer week. Economic impacts, energy consumption, and thermal comfort are analyzed for traditional, adaptive, and occupancy-based control strategies under demand-based, tiered, and fixed electric tariff systems. Simulation results show that occupancy consideration, adaptive thermal comfort, and optimization can reduce cost by 50.1 %, electricity consumption by 52.9 %, and discomfort by 56.2 % compared to traditional fixed setpoints. The ability of the proposed HVAC control strategy to shift energy consumption away from peak times under a demand-based tariff system is qualitatively analyzed and findings suggest that maximum load-shifting on a grid-scale is attained using occupancy consideration with optimized control and demand-based pricing. For individual residential buildings, similar economic benefits can be gained using the less-complex adaptive HVAC control strategy with existing tiered or simple electric tariff systems. [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]

This paper investigates factors associated with variation in daily total (electricity and gas) energy consumption in domestic buildings using linked pre-COVID-19 smart meter, weather, building thermal characteristics, and socio-technical survey data covering appliance ownership, demographics, behaviours, and attitudes for two nested sub-samples of 1418 and 682 British households selected from the Smart Energy Research Laboratory (SERL) Observatory panel. Linear mixed effects modelling resulted in adjusted R2 between 63% and 80% depending on sample size and combinations of contextual data used. Increased daily energy consumption was significantly associated (p -value < 0.05, VIF < 5) with: households living in buildings with more rooms and bedrooms, that are older, more detached, have air-conditioning, and experience colder (more heating degree days) or less sunny weather; households with more adult occupants, more children, older adult occupants, higher heating temperature setpoints, and that do not try to save energy. The results demonstrate the value of smart meter data linked with contextual data for improving understanding of energy demand in British housing. Accredited UK researchers are invited to apply to access the data, which has recently been updated to include over 13,000 households from across Great Britain. This paper provides guidance on appropriate methods to use when analysing the data. [ABSTRACT FROM AUTHOR]

Very little work has been done on the feasibility of Machine Learning (ML) for predicting buildings energy demand right at the design stage. This feasibility, if proven, would help to avoid the construction of inefficient buildings. This paper uses dataset from 7559 buildings, and estimates their energy consumption using nine ML models. Results show that deep neural network (DNN) is the most efficient ML model with MAE, MSD and RMSE of 0.93, 1.12 and 1.06 respectively achieved in less than 7 s despite the huge data size. Its r 2 is also the highest (0.96) which means that the DNN approach manages to explain 96% of the energy consumption in buildings and only 4% remains unexplained certainly due to the limitation of independent variables. Also, this result is not affected by building clusters nor by data from a particular climate zone. As an innovation, this study proposes a model that professionals could use in the design phase of a construction project. This model will allow them to take into account all crucial aspects of the design of an energy efficient building. The model will then serve as a decision-making tool to control and optimise the project and to anticipate energy consumption even before the building is constructed. [ABSTRACT FROM AUTHOR]

• Implementation and benchmarking of five advanced control methods. • Comparison of black-, gray-, and white-box MPC with RL and approximate MPC. • Introduction of qualitative KPIs to assess the applicability. • Application in a one-year simulation to evaluate adaptability. Advanced building control strategies like model predictive control and reinforcement learning can consider forecasts for weather, occupancy, and energy prices. Combined with system and domain knowledge, this makes them a promising approach to reduce buildings' energy consumption and CO 2 emissions. For this reason, model predictive control and reinforcement learning have recently gained more popularity in the scientific literature. Nevertheless, publications often lack comparability among different control algorithms. The studies in the literature mainly focus on the comparison of an advanced algorithm with a conventional alternative. At the same time, use cases and key performance indicators vary strongly. This paper extensively evaluates six advanced control algorithms based on quantitative and qualitative key performance indicators. The considered control algorithms are a state-of-the-art model-free reinforcement learning algorithm (Soft-Actor-Critic), three model predictive controllers based on white-box, gray-box, and black-box modeling, approximate model predictive control, and a well-designed rule-based controller for fair benchmarking. The controllers are applied to an exemplary multi–input–multi–output building energy system and evaluated using a one-year simulation to cover seasonal effects. The considered building energy system is an office room supplied with heat and cold by an air handling unit and a concrete core activation. We consider the violation of air temperature constraints as thermal discomfort, the yearly energy consumption, and the computational effort as quantitative key performance indicators. Compared to the well-tuned rule-based controller, all advanced controllers decrease thermal discomfort. The black-box model predictive controller achieves the highest energy savings with 8.4 %, followed by the white-box model predictive controller with 7.4 % and the gray-box controller with 7.2 %. The reinforcement learning algorithm reduces energy consumption by 7.1 % and the approximate model predictive controller by 4.8 %. Next to these quantitative key performance indicators, we introduce qualitative criteria like adaptability, interpretability, and required know-how. Furthermore, we discuss the shortcomings and potential improvements of each controller. [ABSTRACT FROM AUTHOR]

• The importance of 14 parameters for short-term heating load were determined by Relief. • 15 machine-learning models were compared from aspects of accuracy, stability and computation time. • GPR and SVM were recommended for small and large building load datasets, respectively. Short-term building energy consumption prediction is of great significance to the optimal operation of building energy systems and conservation. Machine-learning models are widely used due to their high prediction accuracy and efficiency in dealing with high-dimensional nonlinear problems. To compare the performance of different machine-learning models in building energy consumption prediction, this paper analyzes 15 machine-learning models, including a multi-layer perception, a radials basis function neural network, a generalized regression neural network, an extreme learning machine, a support vector machine, a least-square support vector machine, a Gaussian process regression, a regression tree, a model five tree, a random forest, a gradient boosting decision tree, an extreme gradient boosting tree, a light gradient boosting machine, a categorical gradient boosting tree and a multi-adaptive regression spline, from the aspects of model prediction accuracy, model stability (over-fitting) and calculation speed. The operation parameters determined by ReliefF algorithm were used as input parameters. The results showed that the prediction accuracy of all models is higher in the training phases, with R2 values greater than 0.90, while the prediction accuracy in testing phases was much lower. In terms of prediction accuracy and model stability, the Gaussian process regression model had the best overall performance among the 15 models, while the support vector machine had the faster calculation speed with acceptable prediction accuracy. For small datasets, the Gaussian process regression model is recommended, and the support vector machine should be preferred for large datasets. The results of this paper can provide a basis for model selection and the establishment of combined models for predicting building energy consumption. [ABSTRACT FROM AUTHOR]

[Display omitted] • The hysteresis is due to the difference of primary nucleation mechanism during heating and cooling. • The PCH is related to the properties of the material itself and the experimental measurement conditions. • The hysteresis temperature is positively correlated with the heating/cooling rate. • The hysteresis effect is more obvious in an incomplete phase transition. • Numerical models of the hysteresis effect of complete and incomplete phase transition are summarized. As an efficient energy storage material, phase change material can be combined with lightweight buildings to reduce the energy consumption of the building envelopes effectively, while it is often accompanied by phase change hysteresis(PCH) and supercoiling (SC) phenomena during the phase transition. SC means that the nucleation does not solidify at the theoretical solidification temperature and needs to be nucleated at a lower temperature. PCH indicates the difference between the melting temperature and the solidification temperature of the phase change materials (PCMs). So far, a lot of research on SC has been done and there is still a lack of research on PCH. On the one hand, the PCH is proportional to the energy loss of the system, the latent heat of PCMs cannot be exploited if PCH is beyond the operating temperature range of the system. On the other hand, when PCMs are combined with building envelopes, the greater the hysteresis temperature, the better the thermal performance of the wall. It's vital to use the hysteresis temperature to improve the performance of the system. Especially, PCMs are equipped in building envelopes, the hysteresis phenomenon of incomplete phase transition will be more obvious because of large temperature fluctuation outside. The hysteresis effect of PCMs in building envelopes is discussed in this paper, the numerical simulation methods and experimental research of PCH are reviewed and analyzed from two aspects of complete and incomplete phase transition. It showed that the PCH should be considered when simulating the thermal behavior of PCMs in building envelopes. The results would be more accurate if two enthalpy-temperature curves (H(T) curve) are used to represent the melting and solidification process, respectively. In addition, the nucleation mechanism of PCH has not been discussed in previous articles and there is no explanation about whether the hysteresis phenomenon is caused by the properties of the material itself or is affected by experimental measurement conditions. Therefore, the related issues are discussed in this paper, it is considered that the PCH is not only related to the properties of the materials but also affected by the experimental measurement conditions. [ABSTRACT FROM AUTHOR]

• Building codes require about 20 powered safety devices in new U.S. homes. • Health and safety devices consume about 1% of U.S. residential electricity. • Safety devices in the U.S. are typically exempted from efficiency regulations. • Per-unit consumption is small, but each home will have many units. • Home medical devices are a growing use of energy. Miscellaneous electrical loads (MELs) are responsible for a significant fraction of energy consumption in buildings. This paper addresses one category of MELs: Safety, Security, and Health Devices (SSHDs). Common SSHDs include: electrical life safety equipment, smoke alarms, radon mitigation fans, and home oxygen concentrators. The installation or use of these devices is dictated by building codes, health providers, insurance companies, and other entities—none of which would ordinarily consider energy efficiency a priority. For this project, the most important residential devices were explored in terms of their governing regulations, functions, technologies, and energy use. The power consumption of 41 life safety SSHDs were measured. Individual power consumption of life safety devices is very small but many are typically required in every home. Devices performing the same functions have wide ranges in power use. Opportunities for reducing SSHD energy use were investigated, the results are summarized, and recommendations are made for further actions that could be taken to reduce SSHD energy use. SSHDs identified in this paper are responsible for at least one percent of current U.S. residential electricity consumption, but that number will climb steadily as existing buildings are upgraded and new types of SSHDs appear. [ABSTRACT FROM AUTHOR]

In the tourism industry, hotels and homestay facilities account for considerable amounts of energy consumption and CO 2 emissions. Unlike other types of buildings, occupancy has significant fluctuations over time, while mostly being ignored or using a simple assumption based on energy use intensity or setting the occupancy levels. Due to the lack of occupancy rate information, great uncertainties exist in hotel operations, which leads to erroneous estimates of energy consumption in existing hotel buildings. This paper examines hotel operation practices in China with real occupancy rate data which is critical for reducing the uncertainty of estimating energy consumption patterns. Specifically, this study proposes a two-stage comprehensive and quantitative performance assessment approach for the homestay hotel building considering the system's energy and economic performance simultaneously. Regarding energy performance, there are two key facets to consider: renewable energy penetration and grid impact performance. Additionally, the economic performance (annual income and extra annual investment) of various energy portfolios is evaluated using break-even analysis. The impacts of large fluctuation in occupancy rate on electrical loads are considered and assessed in typical homestay hotels. The proposed assessment approach can comprehensively quantify the impacts of the photovoltaics (PV) areas and battery capacities on the system performance in terms of energy and economics. According to the results of the occupancy-based assessment, the ideal PV area and battery capacity are 400 m2 and 16 kWh, respectively, as opposed to the optimal energy portfolios (PV area 600 m2 and battery capacity 24 kWh) determined from conventional assessment results. [ABSTRACT FROM AUTHOR]

Because the building sector has become a huge energy consumer, providing useful approaches can help tackle this important issue. Windows are an important component of a building since most of the natural light inside the building is provided by passing this component. Improper design of windows and their associated components, such as shadings, overheats the space and increases energy consumption, and on the other hand, disturbs the visual and thermal comfort of the building's occupants. The present paper aims to present a new approach for multi-objective optimization of the architectural specifications and control parameters of a smart shadow curtain. The use of this proposed approach leads to a significant reduction in building energy consumption and improved thermal and visual comfort. Energy-related simulations are performed through EnergyPlus software. Afterward, 21 design variables including control strategies and setpoints, location and orientation of shading, slat dimensions and material specifications, as well as hourly slat angle are considered in JEPLUS software. These simulations are conducted in four geographical directions over seasonal and annual periods for the city of Tehran, Iran. JEPLUS + EA software is employed to perform sensitivity analysis on the outputs of JEPLUS software and data optimization. Morris sensitivity analysis is implemented to investigate the effects of input variables on the objective functions. The NSGA-II algorithm is then applied to optimize the data to extract the optimal points on a Pareto front. The optimization results indicate that the external blind performs better than the internal blind and also the smaller the angle of the blind slat is, the better the visual comfort is but increases the electricity consumption for lighting. A comparison of the results of multi-objective optimization of controlled blind characteristics with initial values demonstrates that based on geographical directions, the total energy consumption of the entire building decreases by a range of 40–50 % per year, while visual comfort and thermal comfort are improved by ranges of 70–100 % and 10–40 %, respectively. [ABSTRACT FROM AUTHOR]

Inter-building effect (IBE), which refers to the shading effect from surrounding buildings, plays an important role in building energy use in high-density cities. However, it is customary to neglect the IBE during energy simulation. Four issues are observed on previous research: 1) it has been controversial whether the IBE significantly affects energy consumption, 2) most studies used low-rise or medium-rise residential buildings as references, 3) most studies were conducted with hypothesised buildings, 4) most studies did not consider window blinds effect on IBE. These issues inhibit the theoretical understanding of high-rise building energy performance in cities and jeopardise the achievement of accurate simulation. This paper thus aims to investigate the IBE on the energy use of high-rise office buildings in Hong Kong as a typical high-density city. The IBE of ten selected real-life office buildings in their real-life communities was analysed. Four scenarios were compared, namely, simulation of building with both IBE and window blinds (base case), simulation of building with IBE but no window blinds, simulation of isolated building with window blinds, and simulation of isolated building without window blinds. The results show that the IBE influences the total energy use by up to 13.1%. Compared with the base case, heating, ventilation, air-conditioning (HVAC) and the lighting consumption deviation with different scenarios fluctuated largely, ranging from −11.8% to 18.8% in HVAC consumption, and from 0.1% to −27.6% in lighting consumption. An offset effect was found between the decreased lighting consumption and the increased HVAC consumption. From the floor-level analysis, with real communities, lighting energy use decreased with height among all the buildings generally. A multi-variant regression model was established to evaluate the relationship between IBE variables and building energy deviation caused by IBE. In addition, the results conclude that shading blinds cannot be overlooked when examining IBE on building energy use. These findings reveal the importance of considering IBE on building energy consumption in high-density contexts on a case-by-case basis. [ABSTRACT FROM AUTHOR]

Phase change materials (PCMs) reduce building energy consumption, but practical errors often occur due to material mismatches with the application environment. In this paper, taking Guangzhou as an example, to prepare PCMs with a phase transition temperature (T P) close to 26 °C, the Schrader equation is used, and therefore capric acid (CA) and stearic acid (SA) are selected as raw materials. The experimental results show that at a mass ratio (CA/SA) of 9:1, the PCMs can form a eutectic structure with T P and enthalpy of 24.9 °C and 157.3 J/g, respectively. Furthermore, the simulation results confirm that in the whole life cycle the energy-saving rate of proposed PCMs in Guangzhou is high at about 20 %. The study also provides recommendations for optimizing PCMs use in the hot summer and warm winter climates, i.e. on the outside of the envelope, especially on the west wall and roof, increasing the PCMs amount can better save energy. These results are a guideline on building envelope energy efficiency by using PCMs, which can be applied in the optimization of PCMs selection in different climatic zones. [ABSTRACT FROM AUTHOR]

Building Archetypes (BAs) are often used in modeling urban-scale building energy consumption profiles to represent the energy consumption behavior of typical buildings within the modeled urban environment. One current approach to the development of BAs involves the creation of Building Energy Models (BEMs) using surveyed data coupled with codes, standards, or engineering experience. Another approach is the modification of existing standardized BEMs using available data to produce a suitable BA for the representation of the building stock. The adoption of either of these methods has been treated as an axiom or a step that did not need much consideration. However, the results produced by BEMs created according to different methodologies present several differences. The discrepancy in the produced results is shown not only in the results of the BEMs developed according to different methodologies but also in the degree to which these results vary when changing input values. Limited studies analyzed the sensitivity of these models to uncertain inputs, even though it is essential for the development of BA libraries. This paper comparatively investigates the Region-Specific (RS) and Department of Energy (DOE)-based BEM creation methods for BA library development. The models are developed by following the two methodologies, and their predictions are compared to the metered data from a high-rise residential building in the Marina district of Lusail City, Qatar. A Global Sensitivity Analysis (GSA) method is used to identify the most influential parameters contributing to the energy consumption variation of these models. Further, a parametric simulation campaign is conducted by varying the inputs determined earlier to quantify the BEMs' output variability. According to the results, the baseline RS BEM and DOE BEM yield an Energy Use Intensity (EUI) of 211.03 kWh/m2 and 137.9 kWh/m2, respectively. The deviation of these values from the metered EUI of 185,37 kWh/m2 indicates that the RS BEM development methodology is preferable to the DOE BEM for the BA development. Based on the GSA results, regardless of the BEM development methodology used, the most important parameters contributing to output variation are the Cooling Setpoint, the Outdoor Air Requirements, the Equipment Power Density, and the Lighting Power Density. Further, the RS BEM's parametric simulations produce a mean cooling EUI of 324.83 kWh/m2, closer to the literature benchmarks than the mean cooling EUI of DOE BEM, 341.62 kWh/m2. The results obtained from the baseline model and the uncertainty analysis support the conclusion that a Region-Specific Model developed from the ground up serves to better represent high-rise residential buildings in regions with extreme climate conditions. The impact this work achieves is providing decision-making support for urban scale building energy modelers for future building stock representation efforts in such areas while also providing an expected uncertainty range for the developed high-rise residential archetypes. [ABSTRACT FROM AUTHOR]

• Synthetic thermal comfort profiles were generated from ASHRAE thermal comfort databases I and II used to evaluate occupant satisfaction and to calculate the HVAC setpoints. • Corrective Power of PCS was integrated into the Comfort Profiles. • PCS with maximum-satisfaction setpoints can improve occupant satisfaction by 18%. • ASHRAE requirement of 80% occupant satisfaction is met with little increase in energy consumption. Personal Comfort Systems (PCS) are energy-efficient devices that create a microenvironment around occupants and can be controlled individually to improve personal comfort. Most studies related to PCS either focus on improving occupants' thermal satisfaction or evaluate the energy-saving potential of PCS devices but do not consider the energy and comfort consequences simultaneously. This paper investigates the impact of PCS on improving occupants' thermal satisfaction and energy consumption across different climates in the U.S. using 16 representative cities. To account for the diversity in individual preferences, 400 comfort profiles for four climatic zones for summer and winter were generated utilizing comfort votes from ASHRAE global thermal comfort database I and II. PCS devices were used to improve occupant satisfaction by integrating the Corrective Power (CP) values into the comfort profiles. CP is defined as the temperature difference between two ambient temperatures that achieve the same thermal sensation one with PCS and another without PCS (reference condition). Then we compute the zone level setpoints that maximize the number of satisfied occupants and evaluate energy consumption of those zone level setpoints using EnergyPlus. Our study includes 3 cases; Case 1: without PCS (baseline), Case 2: only fans in summer and heaters in winter, and Case 3: fans and heaters in both summers and winters as required to improve satisfaction. In each of the three cases, we evaluated the comfort consequences using the comfort profiles, and the energy consumption implications. This accounts for the change in HVAC setpoints and the power requirements of PCS devices. The results show that incorporating PCS can increase occupants' satisfaction from 68% in Case 1 to 77.8% in Case 2 and 86.6% in Case 3. Moreover, energy consumption results in Case 2 are 0–1% higher, and 0–1.3% higher in Case 3 compared to without PCS, depending on the climate. Our results demonstrate that the adoption of PCS with maximum-satisfaction setpoints can improve occupant satisfaction by 18% and meet the ASHRAE requirement of 80% occupant satisfaction with little increase in energy consumption. The comfort profiles generated in this study are also released as a dataset to support future studies. [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]

• 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]

• The recycled polyethylene granule was proposed as drainage layer in green roof. • The technological solution consisted of a substrate made of local-sourced materials and a micro-drilled plastic. • The energy-efficient design was based on the thermo-physical parameters of the green roofs and of the building and on the thermal dynamic parameters. • The proposed green roof solution was proved to be effective in increase green roof thermal performance in Mediterranean climate. Green roofs were proposed to broaden nature-based solutions within the perspective of the ecological transition for the built environment. In this paper, an innovative and sustainable green roof was designed to optimize the energy performance in Mediterranean area during summer season by means of local-available and recycled materials, thus reducing the environmental impacts. This new green roof technology consisted of recycled polyethylene granules from the regeneration of disused agricultural plastic films as drainage layer and a substrate made of local-sourced materials with high percentage of organic matter and it was compared to two traditional green roofs and to the existing roof. The energy-efficient design was based on the thermo-physical parameters of the green roof (surface temperatures, heat flows and volumetric water content) and of the building (surface temperature of the ceiling) and on the thermal dynamic parameters (decrement factor, time lag and cooling potential). In addition, these thermo-physical parameters were correlated to the Mediterranean climate during summer season. The result demonstrated that although the proposed green roof reached higher surface temperatures than commercial solutions, it maintained an almost constant volumetric water content, it reduced daily fluctuations between minimum and maximum temperatures, it resulted in lower thermal flow through the cross-section, it reduced the surface temperatures inside the building by about 2 °C compared to the traditional roof, thus decreasing the energy consumption for building cooling and greenhouse gas emissions, and it had better dynamic thermal performance than commercial green roofs. Once the energy-efficient design of the proposed green roof technology was proved, the sustainability and the life cycle performance of the recycled polyethylene was discussed, also considering the costs. [ABSTRACT FROM AUTHOR]

• A top-down analysis of the non-residential building stock in Brazil is proposed. • The share of each building typology in the country energy consumption was assessed. • A statistical analysis showed variation of EUI according to type of building and region. • Reference values for the commercial building stock in Brazil is provided. This paper provides a pioneering, top-down analysis of the non-residential building stock in Brazil. A country-wide dataset was used, comprising 10,000 buildings divided into 12 typologies. Key indicators include floor area, energy use intensity, and operation patterns. In addition, predominant wall constructions, lighting systems, equipment, water heating, and HVAC systems for each typology were identified, including average density power for the systems. Data from the Brazilian Energy Balance report was correlated with the sample of buildings to determine the proportion of each building typology in the total energy consumption of the country, considering different energy sources. This study provides an innovative and up-to-date status of the characteristics of non-residential buildings in Brazil. A complementary statistical analysis addressed the correlation between energy use intensity and the building characteristics including an ANOVA test and a regression analysis in order to investigate the influence of the energy usage indicators (Lighting Power Density, Equipment Power Density, Cooling capacity) on the energy use intensity of each building typology in Brazil. This study is intended to serve as a reference for building characteristics and building energy performance in Brazil, and hence support diverse research regarding country-level building stock modeling, energy benchmarking, climate change impacts, and energy-efficiency related studies. [ABSTRACT FROM AUTHOR]

Central air conditioning accounts for more than 40 % of the energy consumption of social buildings. Ensuring the efficient operation of central air conditioning at full working conditions has a positive impact on reducing the total energy consumption of society. In this paper, the historical operation data of central air conditioning in a commercial building in Shenzhen is used as the source and the energy-saving strategy of this central air conditioning system is studied using 2019 data as the main research data. Firstly, the measured parameters are extracted as the Cooling factor, Delivery factor, and Load factor based on factor analysis, and the K-Means algorithm is used as input parameters for pattern recognition of the central air conditioning system. Then, according to the results of pattern recognition, C5.0 and the CHAID decision tree algorithm are used to obtain the energy-saving strategy model. Finally, the 2020 operational data of the building's central air conditioning system was used as the validation data set for energy efficiency verification. The energy-saving strategy based on the C5.0 decision tree algorithm is calculated to save 78,183 kW·h, which is 32.4 % of the total energy consumption, and the energy-saving strategy based on the CHDIA decision tree algorithm is calculated to save 73,182 kW·h, which is 30.3 % of the total energy consumption. [ABSTRACT FROM AUTHOR]

• A new methodology to investigate the impact of thermal mass is defined. • The use of unrepresentative boundary conditions could lead to inaccurate conclusions. • High thermal mass helps to decrease and regulate the internal temperature. • High thermal mass could increase energy use in the summer and winter seasons. • Isolating thermal mass through internal insulation results in better energy performance than external insulation. Contradictory conclusions have been reported on whether thermal mass reduces or increases energy demand in hot and cold climates. A novel approach to measuring the impact of thermal mass on the building's energy consumption is presented. The method aims to better represent reality and applies two different control conditions simultaneously in a numerical model: the first condition, which has been used in past studies, adjusts the internal boundary condition based on the time duration of heating/cooling activation (simulating an occupancy pattern) and the second, which has not been used in past studies, adjusts the internal boundary condition based on both the time duration and the internal temperature (simulating an occupancy pattern and the thermostatic control system). When investigating the impact of thermal mass on the internal temperature in a scenario where no heat is transferred into the building, the high thermal mass outperforms the low thermal mass, showing a time-shifting effect, long time lag, and small decrement factor. However, when modelled using the method presented in this paper, high thermal mass results in greater energy consumption during summer and winter periods in Saudi Arabia. During transition seasons where temperature swings occur on either side of the indoor set-point temperatures, thermal mass reduces thermal transmission. The impact of thermal mass is also investigated for several climatic conditions in Saudi Arabia. Simulation results show that an internally exposed thermal massive wall section results in greater energy consumption when compared to a similar building with isolated thermal mass (i.e. insulated internally). This is due to the fact that even though the internal temperature inside the building is being cooled/warmed up to the set-point, the thermal mass continues to transfer heat that is absorbed by the building's fabric from/to the interior, placing a greater demand on the cooling/heating system. Over the course of a year, however, the impact of thermal mass is negligible when compared to other factors, such as the insulative properties of the building's fabric. [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]

• Implementation of a model predictive control scheme for HVAC systems is presented. • The hardware/software solution is generalisable to most building management systems. • Cloud solutions can offer low-cost, scalable control in commercial buildings. • Economic MPC reduced energy usage by 1.7% compared to baseline control. • Building thermal performance strongly affects viability of MPC schemes. Many businesses are looking for ways to improve the energy and carbon usage of their buildings, particularly through enhanced data collection and control schemes. In this context, this paper presents a case study of a food-retail building in the UK, detailing the design, installation and cost of a generalisable model predictive control (MPC) framework for its Heating, Ventilation and Air Conditioning (HVAC) system. The hardware/software solution to collect relevant data, as well as the formulation of the MPC scheme, is presented. By utilising cloud-based microservices, this approach can be applied to all modern building management systems with little upfront capital, and an ongoing monthly cost as low as $6.39/month. The MPC scheme calculates the optimal temperature setpoint required for each Air-Handling Unit (AHU) to minimise its overall cost or carbon usage, while ensuring thermal comfort of occupants. Its performance is then compared to the existing legacy controller using a simulation of the building's thermal behaviour. When simulated across two months the MPC approach performed better and achieved the same thermal comfort for a lower overall cost. The economic optimisation resulted in an energy saving of 650 kWh, with an associated cost savings of $240 (an improvement of 1.7% compared to the baseline), while the carbon optimisation gave negligible CO 2 savings due to the inability of the building to shift heating to low-carbon periods. Findings from this study indicate the potential for improving building performance via MPC strategies, however the level of impact will depend on specific building attributes. [ABSTRACT FROM AUTHOR]

It has been a pressing challenge for human beings to reduce the energy consumption of buildings and decrease carbon emission. The application of photovoltaic (PV) system in buildings is identified as a renewable energy and carbon reduction approach. As a kind of typical building, the gymnasium usually has a large non-occupation roof area, which shows great application potential to cover the building demand by available photovoltaic source. This paper presents the performance of a grid-connected system integrated photovoltaic, battery storage, and electric vehicles (EV) applied in gymnasium buildings. The main building demand of gymnasium buildings can be easily covered by adopting an appropriate size of PV installation and battery capacity. The load cover ratio can achieve 0.95 when PV generation equals the energy consumption of the building and the battery with 2.7 times capacity of average daily building load is equipped. The proposed charging strategy for EVs can significantly decrease the peak power of the system compared with the constant power charging method. The peak grid power of the system adopted with the proposed charging strategy can be decreased by 51.7% of the rated load power on a typical day when PV generation is 1.4 times the energy consumption of the building and the battery capacity is 66% of the average daily building load. The results of this study show a reference for the configuration and operation scheme of PV system and electric vehicles in gymnasium buildings for renewable energy development and zero-carbon activity. [ABSTRACT FROM AUTHOR]

[Display omitted] • The proposed method can be applied to all window types and does not require the manual adjustment of threshold values according to different weather conditions. • The prediction accuracy of the proposed method in the case study reaches 97.79%. • The features of window opening behavior for a teaching building in severe cold regions are explored. • Occupants of teaching buildings in severe cold regions in the transition season prefer a large opening angle (45°∼180°). The building industry plays an important role in the effort to achieve carbon neutrality, and window opening behavior is a major contributor to building energy consumption. In severe cold regions, opening windows during the heating period causes energy waste. If the window opening behavior can be monitored in real time, then the occupants' energy use behavior can be guided to reduce energy waste. This paper proposes a window opening behavior monitoring method using a deep learning-based image recognition model that can identify the window state based on real-time video and was validated in a case study based on outdoor thermal and wind data. A teaching building with casement windows was used as a case study. The results showed that the occupants preferred a large window opening angle (45°∼180°), which is a choice that significantly affects the building energy consumption. The prediction accuracy of the proposed method can reach 97.79%, which also serves to avoid the difficulty of manually calibrating the pixel threshold and realize real-time feedback. This method has a wide range of application scenarios that enable us to obtain window opening behavior to improve the building performance simulation accuracy and guide occupants' energy consumption behavior. [ABSTRACT FROM AUTHOR]

• A kNN-BS method is proposed to repair lighting and plug energy data. • Using cross-validation method to select the optimal k. • Using 2-step data classification method to decide day type. • Achieved higher accuracy than traditional k NN. The public building energy consumption monitoring platforms (BECMPs) play an important role in building energy conservation and energy-related decision-making. However, problems such as data missing and data mutation are quite common that they impede the effectiveness of the BECMPs. Based on the k NN algorithm, this paper proposes a k NN-BS data repair algorithm with the characteristics of electricity consumption trends taken into consideration. After data classification and cleaning, the data of hourly lighting and plug power consumption of an office building of Dalian University of Technology is repaired using the k NN-BS algorithm. The cross-validation method is used to determine the optimal k value. The relative repair error of the total daily electricity consumption of k NN-BS is less than 4% for working days and less than 6% for non-working days. Compared with k NN, the average CVRMSE of the k NN-BS algorithm is reduced by 1.36%∼1.59% for working days and 5.49%∼8.17% for non-working days. The k NN-BS algorithm shows excellent stability and robustness compared with k NN, polynomial and BPNN, which makes it very suitable for practical use, especially in efficiency-demanding scenarios. The k NN-BS algorithm can effectively improve the quality of public building energy consumption monitoring data and provide a theoretical basis and technical means for solving data quality problems in the BECMPs. [ABSTRACT FROM AUTHOR]

• The outdoor environment indexes are integrated by simulation and empirical method. • Quantitatively study the interaction between green space and water bodies. • The proper layout of landscape planning can reduce the building carbon emissions. The rapid urban construction has made urban residential buildings become the main source of greenhouse gas emissions. So far, the existing studies on the impact of urban micro-climate on building energy carbon emissions have shown that green space (GS) and water bodies (WB) play positive roles in regulating outdoor thermal environment and reducing building energy consumption. Accordingly, this paper quantitatively explores the influence of the interaction between GS and WB on reducing building carbon emissions. First, the building's micro-climate is simulated by Computational Fluid Dynamics (CFD) to obtain outdoor thermal environment indexes, which are used as inputting data to calculate carbon emissions by software HDY-SMAD. Then, we utilize Geographic Information System (GIS) and Remote Sensing (RS) techniques to explore the natural surroundings of buildings, such as GS and WB distribution within the administrative scope of Dujiangyan. Next, the linear regression method is applied to analyze the relationship between GS, WB, and residential building carbon emissions, in which the residential carbon emissions data is obtained by social survey. Finally, the simulation results are verified by empirical research method. The findings demonstrate that the GS and WB have area, accessibility and seasonal differences in regulating local thermal environment and reducing building carbon emissions, and the overall regulation ability of WB for micro-climate is more significant than that of GS. In addition, the ratio area between GS and WB has the most significant influence on reducing building carbon emissions when it is 3:1 in winter, but this optimal ratio is 1:2 in summer. These conclusions can provide benchmark information for landscape planning and building energy conservation in the future. [ABSTRACT FROM AUTHOR]

Occupancy-Centric Controls (OCC) are specialized control sequences which modulate building operation as a function of the sensed occupancy status, thereby leading to increased energy savings, while maintaining thermal comfort. However, occupancy sensor nonidealities, such as bias, latency, random noise, or misdetection, can degrade the performance of OCCs. In this paper, we perform a systematic simulation-based investigation to understand the impacts of nonidealities from both occupancy counting and presence sensors on OCCs. We use a prototype medium office EnergyPlus-based building model, with a detailed zoning layout, as our evaluation testbed. Subsequently, we evaluate the effectiveness of rule-based, occupancy-centric HVAC, lighting and equipment control strategies on an annualized basis. Furthermore, we systematically characterize occupancy sensor nonidealities. Our results indicate that for counting sensors, although the effects of random noise on energy consumption and thermal comfort are minimal, sensor bias and latency can increase HVAC and whole building energy consumption by up to 3.3 % and 4.9 %, respectively. Additionally, noting that an enumeration approach to understand the sensor impact evaluation map for all considered nonidealities may be resource- and time-intensive, we propose a Bayesian Optimization (BO)-based smart sampling approach to efficiently identify the most impactful sensor nonideality sets. Our results indicate that the proposed BO-based approach is able to increase the computational efficiency by 60-fold, when compared to an exhaustive grid-search (enumeration) method. For presence sensors, we observed that higher false positive rates could have a direct impact on HVAC and whole building energy consumption. A low-grade presence sensor could lead to the increase of HVAC and whole building energy consumption by 12.5 % and 8.1 % on average. [ABSTRACT FROM AUTHOR]

[Display omitted] • Thermal potential estimation of water source and their energy systems are reviewed. • Practical potential mainly depends on the geographical and temporal potential. • Pumping-well system requires high installation costs but can use high temperature. • Critical parameters for improving performance of entire system were investigated. In recent years, the utilization of a water source heat pump system (WSHP) has been proposed in South Korea as a solution to reduce energy consumption of buildings. To practically utilize the thermal energy of a water source, it is necessary to estimate the thermal potential of water sources and apply suitable energy conversion systems. However, there are extremely limited studies that conduct in-depth analyses to estimate the thermal potential of water sources and the energy conversion systems of water source heat pumps. In this context, this paper focuses on the review of the main issues in thermal potential estimation and energy conversion systems of water source heat pump systems. The estimation of thermal potential was reviewed in terms of theoretical, geographical, environmental, technical, and temporal potentials. The components constituting the system were investigated by dividing into heat pumps, water intake systems, and air handling units. In addition, the critical parameters for improving the performance of the entire system, such as building type, water source, and water intake distance, were investigated. It is expected that this study will be a valuable supplement to enable the thermal energy utilization of water sources to meet the growing energy demands. [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]

• Two-stage clustering was developed to recognize multiple energy load patterns. • Proposed approach can depict the load fluctuations at different time scales. • A series of exploratory studies on distribution of load patterns were launched. • Uncertainty of multiple energy load patterns was quantified by entropy. • Multi-energy consumption data from 122 buildings were used for method validation. The energy consumption data of buildings contain plenty of knowledge that can be used to enhance demand side management (DSM). However, existing research rarely conducts comprehensive analysis on the multiple energy characteristics for the same building group simultaneously. Additionally, DSM requires the load side to meet the flexibility demands at multiple time scales. To address these problems, the paper presents a strategy with two-stage clustering to recognize multiple energy load patterns of higher education buildings at different time scales. In this work, the low frequency component (LFC) and high frequency component (HFC) are obtained from daily load curve by wavelet decomposition, which are clustered by two-stage clustering to construct typical patterns of load at different time scales. Based on these patterns, a series of exploratory studies on their characteristics are launched. The influence characteristic of factors on multiple energy load patterns are revealed. The results show that different functional buildings have obvious differences in the distribution of their electrical LFC patterns on working days and nonworking days, and the shapes of the dominant patterns of power load and cooling load are more consistent with the change of the ambient temperature throughout the day. In addition, the uncertainty of the load pattern is quantified by entropy, which is used to reflect the order degree of the load pattern change over time for each building. The results indicate that probability of a pattern remaining unchanged is higher than transitioning to other patterns, and the uncertainty of HFC pattern for power load is generally higher than that of LFC. The results obtained from this study could be potentially used to enhance DSM. [ABSTRACT FROM AUTHOR]

• Review identified 29 unintended impacts post residential energy retrofits. • Moisture and dampness are common issues reported in post retrofitted housing. • The indoor environment is significantly affected by airtightness and over-insulation. • Pre retrofit assessment is essential to avoid hygrothermal failure. Improving energy efficiency of existing buildings is currently among the most diverse and extensive mitigation opportunities to reduce energy consumption and CO 2 emissions worldwide. However, the implementation of energy-saving measures has caused unintended impacts, often correlated with dampness and mold growth connected to poor hygrothermal behavior in residential buildings. The focus of this paper is research on the impacts of energy efficiency measures (EEMs) in regard to the hygrothermal behavior resulting from the interaction of building's envelope, indoor environment, and occupants. The results show that dampness and mold growth are by no means exclusive to neglected houses, since the occurrence of these pathologies actually depends upon a complex set of conditions, including indoor and outdoor conditions, occupancy, maintenance, ventilation, mechanical systems, and quality of the envelope. We found that building envelope post-retrofit may suffer from higher levels of moisture and dampness, higher condensations risks, and a faster structural degradation caused by higher humidity levels. We also found that measuring hygrothermal behavior may play a role in more accurately predicting both overall energy consumption and occupant comfort. While hygrothermal behavior may be problematic, we found evidence that retrofits may moderately improve thermal comfort. [ABSTRACT FROM AUTHOR]

Data quality assurance of building energy consumption monitoring platform plays an important role in building energy consumption management. However, Data collected and transferred to the cloud platform is affected by many factors and shows some missing values and outliers in the time series. Combining with the correlation of smart meters on branches and regularity of building energy consumption characteristics, this paper proposes a synchronous prediction method for predicting building energy consumption in the secondary branch. In this model, synchronous data feature similarity (SDFS) model is used to find a similar energy consumption feature, Extreme gradient boosting (XGBoost) model is used to train and produce accurate prediction results which are compared with back propagation neural network (BPNN) and adaptive boosting (AdaBoost) and maximum distance outlier correction (MDOC) model can further correct the prediction results. Taking the daily energy consumption of the primary branch with a smart meter in the building energy consumption monitoring platform as the test object, the predition results of VRF energy consumption show that the MAE, MAPE, RMSE, and CVRMSE are 1.150, 0.142, 1.511, and 0.132 respectively, which is much lower than BPNN and AdaBoost. This study explores a novel feature mining method for historical data and an integrated model for outlier recognition and correction which significantly improves the accuracy of prediction results. Moreover, after correlation verification, the prediction model can be widely applied in building distribution system with sub-meter system which improves the data utilization rate of building energy management system. [ABSTRACT FROM AUTHOR]

In recent years, a large number of building energy consumption prediction models, with various intended uses, have been proposed. The majority of these models have either taken a data-driven or a physical modeling approach, with each approach having its own strengths and limitations. Towards leveraging the strengths and reducing the limitations of each approach for improved prediction performance, this paper presents a hybrid machine-learning approach for occupant-behavior-sensitive energy consumption prediction. The proposed approach is composed of three constituent models: (1) a machine-learning model that learns the impact of outdoor weather conditions from simulation-generated data, (2) a machine-learning model that learns the impact of occupant behavior from real data, and (3) an ensemble model that predicts cooling energy consumption based on the outputs of the first two models. The simulation-generated data were created through simulating a set of reference buildings in EnergyPlus. The real data were collected from an office building in Pennsylvania. The proposed hybrid model was validated on an unseen real dataset. It achieved 0.73 kWh RMSE and 9.07% CV in hourly cooling energy consumption prediction, which indicates that the proposed approach is promising. [ABSTRACT FROM AUTHOR]

In this paper, a value-engineering based integrated design approach is presented to improve energy efficiency of residential buildings. The design approach is based on reducing construction costs from oversizing structural systems of the building to allow investing in energy efficiency measures. The integrated design utilizes optimized selection procedure to identify set of measures that can minimize life-cycle costs while maximizing energy consumption for the buildings without exceeding budget thresholds defined by the value engineering analysis. The integrated design approach is demonstrated and applied to design a prototypical villa in Kuwait considering both homeowner and government perspectives to optimize the selection of energy efficiency measures. The results indicate that significant reductions in construction costs can be achieved due to commonly practiced oversizing of the villa's structural systems. These cost reductions are more than sufficient to cover the incremental costs of implementing optimally identified energy efficiency measures with the benefits of lowering annual energy demand by 70% compared to the current design practices in Kuwait. [ABSTRACT FROM AUTHOR]

[Display omitted] • Review on application of PCM in trombe wall was performed. • Incorporation of PCMs results in minimizing energy consumption of buildings. • The optimum PCM location into buildings depends on the climate. • Immersion and direct incorporation causes leakage due to heat cycles. • The encapsulation techniques have successfully overcome all of drawbacks. Energy crises and environmental pollution are two of the cardinal obstacles that affect human life today. As part of the efforts to address the excessive energy demands in the last few decades is using renewable energy like solar. As the building sector continues to grow with the growing population, correspondingly, the demand for energy consumption required to heat and cool buildings for thermal comfort augments. To tackle this problem, techniques such as designing green buildings for sustainable architecture have been employed. One of these technologies is the Trombe wall which has been utilized as an avenue for heating buildings using renewable solar energy. Meanwhile, the practice of latent heat storage systems with phase change materials (PCM) is an efficient method to store energy. Substantial research has been carried out to assess the contribution of incorporation of latent heat storage and Trombe walls in buildings on their thermal energy performance. The present review paper communicates a summary of the recent advancements in this topic. The various designs of Trombe walls and different phase change materials types are presented. Research concerning the incorporation of PCM incorporation techniques is scrutinized. Finally, hitherto published literature regarding the applications of PCM in the Trombe wall is underscored and discussed. [ABSTRACT FROM AUTHOR]