Finding the optimal balance between end-user’s comfort, lifestyle preferences and the cost of the heating, ventilation and air conditioning (HVAC) system, which requires intelligent decision making and control. This paper proposes a heating control method for HVAC based on dynamic programming. The method first selects the most suitable modeling approach for the controlled building among three machine learning modeling techniques by means of statistical performance metrics, after which the control of the HVAC system is described as a constrained optimization problem, and the action of the controller is given by solving the optimization problem through dynamic programming. In this paper, the variable ‘thermal energy storage in building’ is introduced to solve the problem that dynamic programming is difficult to obtain the historical state of the building due to the requirement of no aftereffect, while the room temperature and the remaining start hours of the Primary Air Unit are selected to describe the system state through theoretical analysis and trial and error. The results of the TRNSYS/Python co-simulation show that the proposed method can maintain better indoor thermal environment with less energy consumption compared to carefully reviewed expert rules. Compared with expert rule set ‘baseline-20 °C’, which keeps the room temperature at the minimum comfort level, the proposed control algorithm can save energy and reduce emissions by 35.1% with acceptable comfort violation.

Heating, ventilation and air conditioning (HVAC) systems account for approximately 50% of the total energy consumption in buildings. Advanced control and optimal operation, seen as key technologies in reducing the energy consumption of HVAC systems, indispensably rely on an accurate prediction of the building’s heating/cooling load. Therefore, the goal of this research is to develop a model capable of making such accurate predictions. To streamline the process, this study employs sensitivity and correlation analysis for feature selection, thereby eliminating redundant parameters, and addressing distortion problems caused by multicollinearity among input parameters. Four model identification methods including multivariate polynomial regression (MPR), support vector regression (SVR), multilayer perceptron (MLP), and extreme gradient boosting (XGBoost) are implemented in parallel to extract value from diverse building datasets. These models are trained and selected autonomously based on statistical performance criteria. The prediction models were deployed in a nearly zero-energy office building, and the impacts of feature selection, training set size, and real-world uncertainty factors were analyzed and compared. The results showed that feature selection considerably improved prediction accuracy while reducing model dimensionality. The research also recognized that prediction accuracy during model deployment can be influenced significantly by factors like personnel mobility during holidays and weather forecast uncertainties. Additionally, for nearly zero-energy buildings, the thermal inertia of the building itself can considerably impact prediction accuracy in certain scenarios.

In recent years, the promotion of nearly zero-energy buildings (NZEBs) in China has emerged as a crucial step for the building industry in its shift towards a green and low-carbon future. Starting from scratch, the development of NZEBs in China has included the application and fundamental research, the compilation of national and local standards, and the rapid emergence of projects encompassing 23.89 million square meters nationwide. These efforts have yielded preliminary results in national standards, technology systems, technical roadmaps, and large-scale project demonstrations. However, challenges remain, including further reducing incremental costs, developing industries for retrofitting existing buildings into NZEBs, and expanding the range of policy incentives. This article provides a comprehensive overview of the last decade’s progress in NZEBs in China, covering key technological research, policy development, engineering demonstrations, and the current industrial development status. Finally, the future prospects of the NZEB industry are discussed.

Considering the comprehensive effect of building carbon emissions, cost savings is of great significance in nearly-zero-energy buildings (NZEBs). Previous research mostly focused on studying the impact of technical measures in pilot projects. The characteristics of different cities or climate zones have only been considered in a few studies, and the selection of cities is often limited. At times, only one city is considered in each climate zone. Therefore, this study selected 15 cities to better cover climate zone characteristics according to the variation in weather and solar radiation conditions. A pilot NZEB project was chosen as the research subject, in which the energy consumption was monitored and compared across different categories using simulated values by EnergyPlus software. Various NZEB technologies were considered, such as the high-performance building envelope, the fresh air heat recovery unit (FAHRU), demand-controlled ventilation (DCV), a high-efficiency HVAC and lighting system, daylighting, and photovoltaic (PV). The simulated carbon emission intensities in severe cold, cold, and hot summer and cold winter (HSCW) climate zones were 21.97 kgCO₂/m², 19.60 kgCO₂/m², and 15.40 kgCO₂/m², respectively. The combined use of various NZEB technologies resulted in incremental costs of 998.86 CNY/m², 870.61 CNY/m², and 656.58 CNY/m². The results indicated that the HSCW region had the best carbon emission reduction potential and cost-effectiveness when adopting NZEB strategies. Although the incremental cost of passive strategies produced by the envelope system is higher than active strategies produced by the HVAC system and lighting system, the effect of reducing the building’s heating load is a primary and urgent concern. The findings may provide a reference for similar buildings in different climate zones worldwide.

Building air tightness increased quickly in recent years as nearly zero energy buildings concept gradually drawn more attentions from the industry. Ventilation system plays an important role for the indoor air quality control in residential buildings with good air tightness. The energy consumption of the ventilation system is a significant part of the overall energy consumption of low energy residential building. The influence of the building air tightness on the energy consumption of ventilation system was not addressed sufficiently in previous studies. This paper analyses the quantitative relations between building air tightness, energy recovery efficiency and ventilation system control strategy. A mathematical model of the heating and cooling energy consumption in residential buildings is proposed, which takes building air tightness, energy recovery efficiency and control strategy of ventilation system as major input parameters. Equivalent COP of ventilation energy recovery system is proposed as an energy efficiency index of the ventilation system. It can be used as a criterion to decide the optimal design parameters of nearly zero residential buildings in different climate conditions.