There is growing interest in zero-energy and low-energy buildings, which have a net energy consumption (on an annual basis) of almost zero. Because they can generate both electricity and thermal energy through the use of solar photovoltaic (PV) and solar thermal collectors, and with the help of reduced building thermal demand, low-energy buildings can not only make a significant contribution to energy conservation on an annual basis, but also reduce energy consumption and peak demand. This study focused on electricity consumption during the on-peak period in a low-energy residential solar building and considers the use of a building’s thermal mass and thermal storage to reduce electricity consumption in summer and winter by modulation of temperature setpoints for heat pump and indoor thermostats in summer and additional use of a solar heating loop in winter. Experiments were performed at a low-energy solar demonstration house that has solar collectors, hot water storage, a ground-coupled heat pump, and a thermal storage tank. It was assumed that the on-peak periods were from 2 pm to 5 pm on hot summer days and from 5 pm to 8 pm on cold winter days. To evaluate the potential for utilizing the building’s thermal storage capacity in space cooling and heating, the use of simple control strategies on three test days in summer and two test days in the early spring were compared in terms of net electricity consumption and peak demand, which also considered the electricity generation from solar PV modules on the roof of the house.

The aim of this study is to develop a model that can accurately calculate building loads and demand for predictive control. Thus, the building energy model needs to be combined with weather prediction models operated by a model predictive controller to forecast indoor temperatures for specified rates of supplied energy. In this study, a resistance–capacitance (RC) building model is proposed where the parameters of the models are determined by learning. Particle swarm optimization is used as a learning scheme to search for the optimal parameters. Weather prediction models are proposed that use a limited amount of forecasting information fed by local meteorological centers. Assuming that weather forecasting was perfect, hourly outdoor temperatures were accurately predicted; meanwhile, differences were observed in the predicted solar irradiances values. In investigations to verify the proposed method, a seven-resistance, five-capacitance (7R5C) model was tested against a reference model in EnergyPlus using the predicted weather data. The root-mean-square errors of the 7R5C model in the prediction of indoor temperatures on all the specified days were within 0.5 °C when learning was performed using reference data obtained from the previous five days and weather prediction was included. This level of deviation in predictive control is acceptable considering the magnitudes of the loads and demand of the tested building.

In this study, a convergence technology that can be linked with photovoltaic thermal (PVT) modules was derived to increase the performance and efficiency of air source heat pumps that are currently most widely used as cooling, heating, and domestic hot water supply equipment in public buildings. The solar energy and air source heat pump convergence technology developed through this study was an indirect supply type outdoor unit heating system that supplied heat energy acquired through a PVT module by increasing the temperature of the air flowing into the outdoor unit when the heat pump heating mode was operating. A bench-scale test device was designed and manufactured to verify the performance of the outdoor unit heating system of an air source heat pump using a PVT module. The performance evaluation confirmed that the heating performance of the air source heat pump was improved compared with that of the conventional air source heat pump without the heating system under the same test conditions. In addition, the heating demonstration was performed by applying the air source heat pump outdoor unit heating system using a PVT module to the air source heat pump installed at the Seodaemun Fire Station Hongeun 119 Safety Center, where an actual public building was remodeled. Consequently, the air source heat pump was heated. The analysis revealed that COP increased by more than 0.5.

Renewable energy sources are among the most promising alternatives to fossil fuels, and solar thermal energy stands out due to its high conversion efficiency and direct thermal utilization. The performance of solar collectors is evaluated under standardized procedures, including ISO 9806:2025. In the Republic of Korea, KS B 8295:2023 is applied for certification; however, it lacks clear guidance on the selection of the working fluid mass flow rate during experimental testing. This study experimentally investigates the thermal performance of a double-glass evacuated tube solar collector under varying flow rates, tested in accordance with both KS B 8295:2023 and ISO 9806:2025 standards. Three flow rates (0.042, 0.067, 0.092 kg/s) were tested at four inlet temperature levels. Unlike most previous studies, which were primarily based on simulations and lacked standardized experimental validation, this work provides empirical results obtained under fully standard testing conditions, thereby filling an important research gap. Instantaneous efficiency curves were derived, showing that increasing the flow rate enhanced the average thermal output by approximately 6%. These results highlight the necessity of defining optimal flow rate conditions in KS B 8295:2023, and the empirical correction factor proposed herein can support future standard revisions and promote international harmonization.