The “average occupant” methodology is widely used in energy consumption simulations of residential buildings; however, it fails to consider the differences in energy use behavior among family members. Based on a field survey on the Central Shaanxi Plain, to identify the energy use behavior patterns of typical families, a stochastic energy use behavior model considering differences in energy use behavior among family members was proposed, to improve the accuracy of energy consumption simulations of residential buildings. The results indicated that the surveyed rural families could be classified into the following four types depending on specific energy use behavior patterns: families of one elderly couple, families of one middle-aged couple, families of one elderly couple and one child, and families of one couple and one child. Moreover, on typical summer days, the results of daily building energy consumption simulation obtained by the “average occupant” methodology were 25.39% and 28% lower than the simulation results obtained by the model proposed in this study for families of one elderly couple and families of one middle-aged couple, and 13.05% and 23.05% higher for families of one elderly couple and one child, and families of one couple and one child. On typical winter days, for the four types of families, the results of daily building energy consumption simulation obtained by the “average occupant” methodology were 21.69%, 10.84%, 1.21%, and 8.39% lower than the simulation results obtained by the model proposed in this study, respectively.

While the grid-connected capacity of rural household photovoltaics is increasing rapidly, achieving dynamic supply-demand matching despite fluctuations in solar energy is challenging. With the rapid development of rural electrification, battery-powered technologies, such as electric vehicles and electric agricultural machinery, are becoming increasingly popular in rural areas. In this context, utilizing idle mobile batteries to assist in energy storage for rural residential buildings offers a new way to solve the problem of dynamic supply-demand matching. In this study, a field survey was conducted on several typical fruit-growing villages in the Central Shaanxi Plain in Shaanxi Province of China. Typical rural households were selected to calculate the electricity loads of the residential buildings, with due consideration to the intervention of mobile batteries. Under the premise of installing 3 kW household photovoltaic systems in rural households, an economical efficiency-oriented model was built for the optimal regulation of flexible loads. The results were compared in the context of two patterns of electricity consumption, i.e., unidirectional charging of mobile batteries from buildings and bidirectional charging and discharging between mobile batteries and buildings. The bidirectional pattern significantly increased the photovoltaic consumption of typical rural households on various typical days. Specifically, during both scenarios of not implementing time-of-use and implementing time-of-use, the typical day of the winter slack farming season exhibited the best photovoltaic consumption effect among all types of typical days. Additionally, the bidirectional pattern also result in a significant increase in the annual electricity sales revenues for typical rural households.

The energy consumption of a teaching building can be effectively reduced by timetable optimization. However, in most studies that explore methods to reduce building energy consumption by course timetable optimization, self-study activities are not considered. In this study, an MATLAB-EnergyPlus joint simulation model was constructed based on the Building Controls Virtual Test Bed platform to reduce building energy consumption by optimizing the course schedule and opening strategy of self-study rooms in a holistic way. The following results were obtained by taking a university in Xi'an as an example: (1) The energy saving percentages obtained by timetabling optimization during the heating season examination week, heating season non-examination week, cooling season examination week, and cooling season non-examination week are 35%, 29.4%, 13.4%, and 13.4%, respectively. (2) Regarding the temporal arrangement, most courses are scheduled in the morning during the cooling season and afternoon during the heating season. Regarding the spatial arrangement, most courses are arranged in the central section of the middle floors of the building. (3) During the heating season, the additional building energy consumption incurred by the opening of self-study rooms decreases when duty heating temperature increases.

Northwest China has abundant solar energy resources and a large demand for winter heating. Using solar energy for centralized heating is a clean and effective way to solve local heating problems. While present studies usually decoupled solar heating stations and the heating network in the optimization design of centralized solar heating systems, this study developed a joint multi-objective optimization model for the equipment capacity and the diameters of the heating network pipes of a centralized solar district heating system, using minimum total life cycle cost and CO₂ emission of the system as the optimization objectives. Three typical cities in northwest China with different solar resource conditions (Lhasa, Xining, and Xi'an) were selected as cases for analysis. According to the results, the solar heating system designed using the method proposed in this study presents lower economic cost and higher environmental protection in comparison to separately optimizing the design of the solar heating station and the heating network. Furthermore, the solar fraction of the optimal systems are 90%, 70%, and 31% for Lhasa, Xining, and Xi'an, and the minimum water supply temperatures are 55 ℃, 50 ℃, and 65 ℃ for an optimal economy and 55 ℃, 45 ℃, and 45 ℃ for optimal environmental protection, respectively. It was also established that the solar collector price has a greater impact on the equipment capacity of the solar heating station than the gas boiler price.