• Energy Research
• 2025-2025close
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The mismatch between the actual heat production of an air source heat pump (ASHP) and the instantaneous load of the building it heats is a critical cause of low energy efficiency. Research on the measured heating effect provides an effective approach to improve the energy efficiency of an ASHP. This study therefore focused on two small office buildings to explore the operating data describing the ASHP system source and load sides during the heating season. A sensitivity analysis was employed to explore the actual operational effects of the ASHP. Common problems associated with system operation were comprehensively identified, including the operation characteristics, energy efficiency coefficients, load characteristics, and energy consumption characteristics of the transmission and distribution systems. The effects of these problems were evaluated from three perspectives: integrated part load value (IPLV), system performance loss, and indoor environmental parameters. The results shows that the highest coefficient of performance (COP) of the ASHP in Building A was 3.27 while the IPLV was 2.68, operating status reached the standard. The COP value of the ASHP in Building B was 1.55 while the IPLV was 2.45. Poor value shows the low performance in operating phase, which can be further optimised by matching the building instantaneous load to the heat production of the ASHP, improves the operating effect of two buildings comprehensively.

Hourly global solar radiation data is an important factor for solar energy utilization. Due to the lack of solar radiation observation stations in many areas, some hourly solar radiation models are proposed to predict hourly solar radiation. However, the existing models perform poorly in heavy fog-haze areas because the weakening effect of fog-haze on solar radiation is not considered. Thus, in this paper, hourly global solar radiation prediction models are developed considering air quality index (AQI) using XGBoost algorithm. The results show a general improvement in the accuracy of models with AQI as an additional input (Model B1-B6) compared to models that do not consider AQI (Model A1-A6). Compared to Model A, Model B have an increase in R value from 0.927 to 0.948, a decrease in RMSE value from 0.300 to 0.282 and a decrease in MAPE value from 0.159 to 0.145. In addition, for hourly solar radiation prediction, the six most important inputs are the day of the year, air temperature difference, surface temperature difference, hour, AQI, and total cloud cover.

The energy storage is an effective solution for the current imbalance between energy supply and demand. In particular, the cascaded storage method can enhance the heat exchange temperature difference and heat storage efficiency. Previous research mainly focused on the combination of different phase change materials, while there was rare research on efficient cascaded conversion pathways for electrothermal direct conversion coupled thermal storage devices. This study investigated the influence of sensible and latent heat storage materials on the thermal performance, and identified the optimal volume ratios and materials types. When the volume share of Mg-Al:PW-EG=1:1, the heat storage performance was the optimal with a quantity/efficiency of heat stored as 7328.7 kJ/97.3 %, leading to an increase of 458.5 kJ/6.6 % than the sensible heat storage condition (Mg-Al:PW-EG=1:0) and 630.18 kJ/8.5 % than the latent heat storage condition (Mg-Al:PW-EG=0:1). When the melting point and latent heat of phase change materials increased from 68.9∼79.1 °C and 224.8 kJ/kg to 118.0 °C and 344.9 kJ/kg respectively, the heat storage temperature rose by 162.7 °C, quantity of heat stored rose by 7535.5 kJ. While materials with large subcooling were not recommended for short-term heat storage, as approximately 25.6 % (3309.3 kJ) of stored heat and 22.4 % (2505.2 kJ) of exergy were wasted when the subcooling degree was 70 °C. The findings provided solutions to support the synergistic enhancement of heat storage/release performance of the composite energy storage heat sink.

Liquid leakage of PCM and thermophysical performance defects seriously affect the application prospect of PCMs. Aerogels provide an excellent solution for packaging and performance improvement of PCMs with its ultra-high specific surface area and low density and give PCMs other functions besides energy storage, such as energy conversion (photothermal/electrothermal conversion, magnetic thermal/acoustic thermal conversion), thermal management (battery thermal management, electronic thermal management), thermal infrared stealth, building materials, etc. In this paper, firstly, the preparation method and multifunctional response mechanism of aerogel-based PCMs are systematically described, and the improvement of thermophysical and mechanical properties of various aerogel-based PCMs is reviewed from the perspective of aerogel preparation. Then, according to the different application scenarios of aerogel-based PCMs, the advanced functions of aerogel-based PCMs are reviewed, and the multifunctional effects of different materials in aerogel-based PCMs are compared. Finally, some insightful guidance and suggestions for the research and development of aerogel-based PCMs are put forward.

The growing global demand for clean and sustainable energy sources has sparked interest in hybrid energy systems that combine multiple renewable energy technologies. This review paper explores the integration of cow dung biogas, solar thermal, and kinetic energy for power production. The synergistic utilization of these energy sources holds significant potential for addressing the energy challenges faced by various communities. This paper provides an overview of each technology, discusses the benefits and challenges of integration, and highlights successful case studies. Furthermore, it discusses this hybrid energy generation system's potential future developments and implications.

The prediction of heat pump system has more complicated characteristics, and the prediction accuracy of the existing single model is not ideal. From the perspective of energy efficiency and energy consumption, it is necessary to improve the accuracy of prediction. A sewage source heat pump system in Shenyang, China, was used as the research object in this paper. The ARIMA model, the BP neural network model, and the ARIMA-BP integrated model, were built. The accuracy of the predicted values of heat supply obtained by the models was verified. The prediction accuracy of the model was verified in extreme weather. The completeness of the model validation was improved. Three prediction models had been applied to the water source heat pump system and the soil source heat pump system. The adaptability and generalization of the model were verified. The number of training sets for heat supply prediction was divided. The number of training sets at the beginning of the heating season was analyzed. The results showed that the mean absolute percentage errors of the ARIMA model, BP neural network model and ARIMA-BP integrated model were 5.37 %, 5.97 % and 3.21 %, respectively. The root mean square errors were 177.31, 186.98, 139.44, respectively. The ARIMA-BP integrated model had a prediction accuracy that improved by 2.16 % compared to the ARIMA model. The ARIMA-BP integrated model had a prediction accuracy that improved by 2.76 % compared to the BP model. In extreme weather, the mean absolute percentage error was 7.83 %, the root mean square error was 296.42. The overall error was also within a reasonable range. The ARIMA-BP integrated model had high prediction accuracy and good applicability and generalization. At the beginning of the heating season, the heat supply can be better predicted when the number of training sets is 4 days.

The COVID-19 pandemic threatened the world. As an important transportation hub connecting countries and regions, airports have played a critical role in COVID-19 prevention and control. This study developed an infection risk-human comfort-energy consumption model to calculate the COVID-19 transmission and energy consumption for epidemic prevention and control in seven different functional areas of an airport during different seasons (winter, summer, and transition season) and actual passenger movement. When considering dynamic passenger flow, the energy consumption needed to prevent and control transmission of the epidemic in each area of the airport could be reduced by 71–85 %. The waiting, dining, and shopping areas were the areas with the highest energy consumption, accounting for 25–47 %, 15–32 %, and 11–38 % of the total energy consumption of epidemic prevention at the airport, respectively. The dining area had the highest energy consumption per square meter, reaching 14.2 kWh/m2 at its highest. After closing the dining area, energy consumption was reduced by 14–20 %. Compared with strict epidemic prevention and control, energy consumption in the airport was reduced by 70–85 % considering both optimized intervention and dynamic passenger flow. The results of this study provide a scientific basis for energy-saving and emission reduction in airports under an epidemic situation.