Exploring attitudes and behavioral patterns in residential energy consumption: Data-driven by a machine learning approach

Abstract

The present study focuses on two main objectives: firstly, to clarify the mechanisms by which attitudes impact behavioral changes related to household energy consumption, and secondly, to offer valuable insights to enhance the understanding of residential energy usage through a novel technique called Support Vector Regression (SVR). This method employs several feature space transformations to convert nNar relationships into linear ones. The results highlight the crucial role of psychological factors in determining energy consumption behaviors, demonstrating that cognitive factors significantly influence attitudes and behavioral patterns. The findings show that psychological variables have a major role in determining how people consume energy, with cognitive variables having a particularly large impact on attitudes and behavior patterns. Our findings demonstrate the superior performance of Support Vector Regression (SVR) with radial basis function kernels over traditional predictive models, with a prediction accuracy of 93.7 % for changes in behavior patterns (CHP) and 94.4 % for changes in attitudes (CHA). These results highlight the value of applying cutting-edge machine-learning approaches to create precise models for comprehending and directing energy-saving actions. The policy implications suggest that reducing cognitive barriers can significantly encourage energy-saving behaviors and contribute to a comprehensive approach for energy-efficiency initiatives