Energy consumption forecasting of neighborhood living facilities: A panel regression approach

Energy consumption forecasting plays a crucial role in establishing a plan for Green Remodeling (GR) to achieve carbon neutrality in the building sector. This study aims to forecast greenhouse gas emissions from daycare centers and medical facilities among neighborhood living facilities, which are the primary targets of GR. We use panel regression models to alleviate endogeneity concerns with annual panel data of 66 buildings over a 2-year period. We evaluate the performance of each model by comparing the predicted annual energy consumption with the actual values. The empirical analysis results show that forecasting using panel Generalized Least Squares (GLS), while taking into account the heteroscedasticity observed in our data, resulted in a lower Root Mean Square Error (RMSE = 17,687) compared to other regression models. Furthermore, the GLS model showed comparable performance to AI methods, accurately predicting energy consumption within a ± 30 % error margin in 57.1 % of test cases. Therefore, when predicting building energy consumption, it is considered that analysis through an appropriate regression model not only allows for the inference of causal relationships but also aids in efficient prediction by saving time and costs. This study can be used to assess the effectiveness of GR in achieving the greenhouse gas reduction goal and can contribute to developing an efficient carbon-neutral strategy through GR.