Implementation of the panel data regression analysis in PCM integrated buildings located in a humid subtropical climate

Various researchers have investigated the performance of PCM in buildings. However, the following issues still need more elaboration: a) How the climate related factors influence the performance of PCM integrated building in subtropical climate region? b) Can generalized equations be proposed for estimation of energy demand based on annual and seasonal data sets? c) Is PCM integration in buildings economically and environmentally feasible? Hence, the impact of climate-related factors on the energy demand of PCM integrated buildings in eight cities of Cfa climate was evaluated by using panel data regression analysis. The results showed that PCM 24 and PCM 27 performed the best and achieved annual energy savings up to 12,635 kWh and energy consumption reduction up to 19.9 %. The greatest impact on the energy demand was caused by temperature-derived functions -heating and cooling degree days. The wind speed, solar azimuth, and atmospheric pressure had a negative impact on energy demand. PCM integration might be the most economically feasible in Brazil and the USA, while in Paraguay it was infeasible. Furthermore, the application of PCM was more environmentally pronounced in the USA and China. The developed equations can be used as energy forecast tools for buildings incorporated with PCM. (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

The study focuses on the performance of PCM in buildings and its impact factors, indicating that temperature-derived functions have a significant impact on energy demand, while wind speed, solar azimuth, and atmospheric pressure have negative impacts. PCM integration may be most economically feasible in Brazil and the USA, while it is infeasible in Paraguay. Furthermore, the environmental impact of PCM application is more pronounced in the USA and China.