Analyzing the impact of weather conditions on energy efficiency in residential buildings using machine learning techniques with explainable artificial intelligence

This study utilizes advanced machine learning (ML) algorithms to assess the influence of external weather-related factors on the energy efficiency (EE) of residential buildings. Two deep neural network (DNN) models were developed: a standard DNN and an enhanced one incorporating residual layers and attention mechanisms. The models were trained using a dataset comprising historical meteorological data and energy performance data from residential homes in York, UK. The investigation indicated that energy consumption is substantially influenced by meteorological factors including temperature (denoted as heating degree days (HDD) and cooling degree days (CDD)), humidity, wind velocity, and sun radiation. The random forest model surpassed previous models, attaining root mean square error (RMSE) of 0.0819 and a (Formula presented.) score of 1.0000, underscoring its exceptional capacity to represent the intricate interactions between meteorological factors and energy usage.The advanced deep neural network demonstrated favourable outcomes with an RMSE of 4.12 and a (Formula presented.) of 0.878, underscoring the significance of employing complicated architectures to represent intricate relationships. The results underscore the need of integrating ML for EE forecasting and identify critical domains for enhancing household energy consumption. The study compared multiple ML models, highlighting their benefits and weaknesses for prediction accuracy and resilience. This is followed by the application of SHAP (SHapley Additive exPlanations) to interpret model outputs and obtain an understanding of the significance of these weather features.