Optimizing key parameters with energy consumption as the control target can minimize the heating and cooling needs of buildings. In this paper we focus on the optimization of performance parameters design and the prediction of energy consumption for nearly Zero Energy Buildings (nZEB). The optimal combination of various performance parameters and the Energy Saving Ratio (ESR)are studied by using a large volume of simulation data. Artificial neural networks (ANNs) are applied for the prediction of annual electrical energy consumption in a nearly Zero Energy Building designs located in Shenyang (China). The data of the energy demand for our test is obtained by using building simulation techniques. The results demonstrate that the heating energy demand for our test nearly Zero Energy Building is 17.42 KW·h/(m²·a). The Energy Saving Ratio of window-to-wall ratios optimization is the most obvious, followed by thermal performance parameters of the window, and finally the insulation thickness. The maximum relative error of building energy consumption prediction is 6.46% when using the artificial neural network model to predict energy consumption. The establishment of this prediction method enables architects to easily and accurately obtain the energy consumption of buildings during the design phase.

With the rapid development of ground source heat pump (GSHP) system, energy saving measures are of special interest for practice. In order to meet heating demand, capacity control of GSHP system can be carried out by regulating either part load ratio (PLR) or supply water temperature. A data-driven optimization approach was developed and applied on a school building in heating mode, which aims at minimizing energy consumption without compromising thermal comfort. An artificial neural network (ANN) model of the GSHP system was proposed and trained with experimental data as well as simulated data of a validated physics-based model, which was employed for data supplement to cover more data variations. The multi-objective optimization problem was then solved using genetic algorithm. The results suggest the optimal operation strategy for either continuous or staged capacity control regarding heating demand variation. With the proposed optimal control strategy, energy savings as compared to existing strategy can be up to 22% for a single month and 14% for the whole heating season.

Severely cold weather reduces the willingness of residents to open windows while cooking. This results in an insufficient replenishment of makeup air and a reduction in the range hood discharge capacity. For an effective trade-off between indoor air temperature maintenance and air quality aggravation in winter, a new makeup air supply method (ceiling makeup air) was proposed and established both experimentally and numerically. The improvements in the kitchen air environment during cooking were studied through experimental tests and CFD simulations, considering different makeup air arrangements. The results reveal that the ceiling makeup air scheme can significantly reduce the concentration of PM2.5 compared with the cracks makeup air scheme (wherein the kitchen window and door are closed). Moreover, it increased the indoor temperature by over 11.9 °C compared with the open window makeup air scheme. The average relative error between the experimental and simulated data was within 6.1%. Among the considered factors, the size of the air inlet had the largest impact. This was followed by the layout, size, and shape of the ceiling inlets. The ceiling makeup air scheme demonstrated the potential for improving residential kitchen air environments in severely cold regions.

The negative impact of indoor formaldehyde pollution has become a growing interest, especially in severe cold areas, since most residential buildings do not have enough ventilation and people are unwilling to open windows. In order to explore the status and the influencing factors of indoor formaldehyde pollution in severe cold areas and predict the formaldehyde concentrations in these areas, a study of 60 residential buildings in Liaoning, China, was carried out using the method of phenol reagent spectrophotometry. While testing the formaldehyde concentration, the infiltration air change rate of the room was also tested using CO₂ as a tracer gas. The correlation between formaldehyde concentration and its influencing factors was analyzed by SPSS software. Multiple linear regression equations were established for the linear regression analysis. The measured data were used to assess the formaldehyde cancer risk of residents in Liaoning. The test results showed that the most serious rates of average formaldehyde pollution occurred in summer with a concentration of 0.097 mg/m³, and the bedroom was the room most seriously polluted by formaldehyde in autumn with a concentration of 0.104 mg/m³. According to the correlation analysis, the formaldehyde concentration was significantly correlated with the indoor temperature, years of decoration, and the infiltration ventilation rate. The linear regression equation for predicting the formaldehyde concentration was established. According to the risk assessment of the test results, residents in Liaoning are already at risk of cancer caused by formaldehyde.

In the practical operation of air-source heat pump heating systems coupled with phase-change energy storage tanks, wide fluctuations in outdoor temperatures often cause issues such as excessive heating, frequent unit start–stops, and low operational efficiency. Traditional start–stop control strategies struggle to balance heating quality with system energy savings. To enhance the system’s energy efficiency across all operating conditions and improve the stability of indoor temperatures, this study introduces a straightforward and easy-to-implement return water temperature zone control strategy. Using physical reference points, a three-zone control approach for return water temperature was created, which integrates outdoor temperature feedback along with combined indoor temperature adjustments. The proposed strategy’s effectiveness was confirmed through comparative experiments that split the heating season into two parts: one employing traditional control and the other using the zone control method. The results show that, compared to empirical start–stop control, the segmented control strategy increased the system’s average coefficient of performance (COP) from 3.06 to 3.11, representing a 1.63% improvement; reduced indoor temperature deviation from 1.4 °C to 1.2 °C, a 14.2% decrease; and narrowed the amplitude of extreme temperature deviations from 7.9 °C to 3.9 °C, a 50.6% reduction. Total electricity consumption for the entire heating season was approximately 4191 kWh. These findings indicate that the proposed control strategy effectively improves system energy efficiency and indoor temperature stability while meeting heating demands. It significantly suppresses excessive heating during transitional seasons and enhances heating reliability under extreme low-temperature conditions. This study involves low retrofitting costs and balances both energy-saving and comfort objectives, providing a practical, engineering-ready solution for the intelligent control of air-source heat pump heating systems.