Building energy simulation analysis plays an important supporting role in the conservation of building energy. Since the early 1980s, researchers have focused on the development and validation of building energy modeling programs (BEMPs) and have basically formed a set of systematic validation methods for BEMPs, mainly including analytical, comparative, and empirical methods. Based on related papers in this field, this study systematically analyzed the application status of validation methods for BEMPs from three aspects, namely, sources of validation cases, comparison parameters, and evaluation indicators. The applicability and characteristics of the three methods in different validation fields and different development stages of BEMPs were summarized. Guidance were proposed for researchers to choose more suitable validation methods and evaluation indicators. In addition, the current development trend of BEMPs and the challenges faced by validation methods were investigated, as well as the existing progress of current validation methods under this trend was analyzed. Subsequently, the development direction of the validation method was clarified.

Building energy modeling (BEM) has become increasingly used in building energy conservation research. Prototype building models are developed to represent the typical urban building characteristics of a specific building type, meteorological conditions, and construction year. This study included four residential buildings and 11 commercial buildings to represent nationwide building types in China. With consideration of five climate zones and different construction years corresponding to national standards, a total of 151 prototype building models were developed. The building envelope properties, occupancy and energy-related behaviors, and heating, ventilation, and air-conditioning (HVAC) system characteristics were defined according to the corresponding building energy efficiency design standards, HVAC design standards, and through other sources, such as questionnaire surveys, on-site measurements, and literature, which reflect the real situation of existing buildings in China. Based on the developed prototype buildings, a large database of 9225 models in 270 cities was further developed to facilitate users to simulate building energy in different cities. In conclusion, the developed prototype building models can represent realistic building characteristics and construction practices of the most common residential and commercial buildings in China, serving as an important foundation for BEM. The models can be used for analyses related to building energy conservation research on typical individual buildings, including energy-saving technologies, advanced controls, and new policies, and providing a reference for the development of building energy codes and standards.

The outdoor climate condition is one of the deterministic factors influencing building energy consumption. Building performance simulation (BPS) tools usually adopt typical meteorological year (TMY) as the outdoor climate input. Despite that many scholars and institutes have developed TMY datasets, these datasets are usually based on distinct data sources and methods. Considering the increase of international cooperation construction projects, compatible TMY dataset for different countries is in urgent need. This paper presents a global typical meteorological year (TMY) database covering 38,947 stations worldwide based on the fifth-generation atmospheric reanalysis product released by the European Center (ERA5). The data is created with Chinese Standard Weather Database (CSWD) method to reflect the average level of historical weather. The dataset is saved in a 55 GB database of compressed CSV files and a website is established where users can download their required TMY data for certain cities according to the longitude and latitude information. A systematic validation is conducted to confirm the feasibility of ERA5 as data source and validity of generated TMY data. This TMY-ERA5 dataset is fundamental and essential in building system designs of international construction projects, building performance simulation, especially for some countries lacking ground meteorological stations or missing meteorological year data in the building sector. It can be used as references for other meteorological climate studies.

The building sector is one of the largest energy user and carbon emitter globally. To achieve China's national carbon target, the building sector in China needs to achieve carbon peaking and neutrality targets by 2030 and 2060, respectively. However, data deficiency on building energy and emissions become barriers for tracking the status of building energy and emissions, and identify potential opportunities for achieving dual carbon targets. To address these shortcomings, this study established an integrated China Building Energy and Emission Model (CBEEM). With CBEEM, this study evaluated the building-construction and building-operation energy and emissions in China, and revealed the status quo and potential challenge and opportunities. According to modelling results, building operation energy use of China was 1.06 billion tce in 2020, accounting for 21% of China's total primary energy consumption. Building construction energy consumption was 0.52 billion tce in 2020, accounting for another 10% of total primary energy consumption. Key messages found on building carbon emissions are: building construction embodied emissions were 1.5 billion tCO₂ in 2020 and are declining slowly, building operational carbon emissions were 2.2 billion tCO₂ in 2020 and are still increasing. International comparisons between China and other countries on building stock, energy use intensity and carbon emission intensity were conducted as well, and help shed a light on the challenges for decarbonization of China's building sector. Finally, technology perspectives to achieve carbon neutrality target were discussed and related policy suggestions were provided.

Buildings contribute to almost 30% of total energy consumption worldwide. Developing building energy modeling programs is of great significance for lifecycle building performance assessment and optimization. Advances in novel building technologies, the requirements of high-performance computation, and the demands for multi-objective models have brought new challenges for building energy modeling software and platforms. To meet the increasing simulation demands, DeST 3.0, a new-generation building performance simulation platform, was developed and released. The structure of DeST 3.0 incorporates four simulation engines, including building analysis and simulation (BAS) engine, HVAC system engine, combined plant simulation (CPS) engine, and energy system (ES) engine, connected by air loop and water loop balancing iterations. DeST 3.0 offers numerous new simulation features, such as advanced simulation modules for building envelopes, occupant behavior and energy systems, cross-platform and compatible simulation kernel, FMI/FMU-based co-simulation functionalities, and high-performance parallel simulation architecture. DeST 3.0 has been thoroughly evaluated and validated using code verification, inter-program comparison, and case-study calibration. DeST 3.0 has been applied in various aspects throughout the building lifecycle, supporting building design, operation, retrofit analysis, code appliance, technology adaptability evaluation as well as research and education. The new generation building simulation platform DeST 3.0 provides an efficient tool and comprehensive simulation platform for lifecycle building performance analysis and optimization.

Buildings have a significant impact on global sustainability. During the past decades, a wide variety of studies have been conducted throughout the building lifecycle for improving the building performance. Data-driven approach has been widely adopted owing to less detailed building information required and high computational efficiency for online applications. Recent advances in information technologies and data science have enabled convenient access, storage, and analysis of massive on-site measurements, bringing about a new big-data-driven research paradigm. This paper presents a critical review of data-driven methods, particularly those methods based on larger datasets, for building energy modeling and their practical applications for improving building performances. This paper is organized based on the four essential phases of big-data-driven modeling, i.e., data preprocessing, model development, knowledge post-processing, and practical applications throughout the building lifecycle. Typical data analysis and application methods have been summarized and compared at each stage, based upon which in-depth discussions and future research directions have been presented. This review demonstrates that the insights obtained from big building data can be extremely helpful for enriching the existing knowledge repository regarding building energy modeling. Furthermore, considering the ever-increasing development of smart buildings and IoT-driven smart cities, the big data-driven research paradigm will become an essential supplement to existing scientific research methods in the building sector.

Electricity is now the major form of energy used in residential buildings and has seen a significant increase in usage over the past decades. One of the main features of electricity use in residential buildings is the diversity of total electricity consumption and use patterns among households. Current models may not be able to simulate and generate electricity use curves or reflect the variations accurately. To fill this gap, this research simulates electricity use curves in residential buildings with a clustering-based probability distribution model. The model extracts feature parameters to represent the electricity use level and patterns and then conducts a two-step cluster analysis to identify the distinctions of both electricity use levels and patterns. Based on the clustering results, probability distributions are fitted for all feature parameters within each sub-cluster. The model is then validated with three validation approaches. Monthly electricity consumption in households of the Jiangsu Province, China, was studied to test the performance of the model. Lastly, this paper discusses the application of this model under different spatial resolutions and analyzes the temporal-relevant model features.

Space cooling energy consumption is a significant component of building energy consumption, and in recent years it has attracted much attention worldwide owing to its significantly increasing usage. The variable refrigerant flow (VRF) system is one common type of cooling equipment for buildings in China and is applied extensively to residential and office buildings. The performance of VRF systems significantly influences the cooling energy consumption of buildings. The system energy efficiency and electricity consumption are the main indicators employed to evaluate the performance of VRF systems. It is hard to obtain the actual energy efficiency and electricity consumption of VRF systems in buildings because of the high cost of the required complicated measurements. This study proposes a virtual sensor modeling method to determine the actual energy efficiency and electricity consumption of 344 VRF systems in residential buildings. Statistical and clustering analyses are conducted to determine the energy efficiency and electricity consumption to obtain distributions and typical operation load patterns of VRF systems in residential buildings in China. The main findings are as follows: the main range of the Seasonal Energy Efficiency Ratio (SEER) for the cooling season is from 2.9 to 4.4; the median SEER in the Hot Summer and Cold Winter zone is lower than in another climate zones; the longer cooling duration may lead to greater electricity consumption, and the electricity load for VRF systems electricity load is periodic for each day. The oversizing issue is common for VRF systems in the dataset, which also led to the lower energy efficiency of VRF systems. The high usage of VRF systems appeared from July 27th to August 26th. The findings provide recommendations for designing VRF systems in residential buildings.

Aside from the consumption for domestic cooling and heating, the energy consumption of appliances in a household is a large amount. This energy use by appliances is highly dependent on the activities of the occupants in a building. Occupant behavior in residential buildings possesses the characteristic of randomness, variety, and complexity. More specifically, the profiles of energy consumption and appliance use are highly dependent on the timing of residents’ activities. Thus, it is necessary to model the domestic energy use profile with high temporal resolution. For example, in the context of energy demand response analysis, it is crucial to take occupant behavior into consideration. This article presents a thorough and detailed method for generating an appliance use pattern based on measured power data in real households, taking the television as a case study. The study develops a stochastic model based on appliance use data at one-minute temporal resolution. The proposed model firstly extracts feature parameters to depict the use behavior of the appliance. Then, one stochastic simulation model is established with the input of feature parameters. An evaluation method for this appliance behavior model is also proposed. As a result of one case study simulating appliance consumption at a variety of scales of households, it was concluded that the model can enable applicability to further household energy profile simulation.

The lighting system accounts for 8% of the total electricity consumption in commercial buildings in the United States and 12% of the total electricity consumption in public buildings globally. This consumption share can be effectively reduced using the demand-response control. The traditional lighting system control method commonly depends on the real-time occupancy data collected using the passive infrared (PIR) sensor. However, the detection inaccuracy of the PIR sensor usually results in false-offs. To diminish the false-error frequency, the existing lighting system control simply deploys a delayed reaction period (e.g., 5 to 20 min), which is not sufficiently accurate for the demand-response operation. Therefore, in this research, a novel data-driven model predictive control (MPC) method that is based on the temporal sequential-based artificial neural network (TS-ANN) is proposed to overcome this challenge using an updated historical occupancy status. Using an office as case study, the proposed model is also compared with the traditional lighting system control method. In the proposed model, the occupancy data was trained to predict the occupancy pattern to improve the control. It was found that the occupancy prediction mainly correlates with the historical occupancy ratio and the time sequential feature. The simulation results indicated that the proposed method achieved higher accuracy (97.4%) and fewer false-offs (from 79.5 with traditional time delay method to 0.6 times per day) are achieved by the MPC model. The proposed TS-ANN-MPC method integrates the analysis of the occupant behavior routine into on-site control and has the potential to further enhance the control performance practice for maximum energy conservation.