Abstract

This paper addresses a research gap in predicting residential energy consumption by proposing a data-driven approach utilizing features from non-electric data for training the machine-learning models. Specifically, forecasting models are trained to predict the aggregated household demand for one day, seven days, and one month. Comparisons are made to forecasting models trained on historical consumption data. By employing machine learning algorithms and exploring two distinct approaches – utilizing dwelling and occupants’ data with seasonal factors (non-electric data) and historical time-series consumption data – the study provides valuable insights into energy consumption prediction based on household characteristics. Results using data from 20 households in the UK indicate that while utilizing historical consumption data yields superior performance, the proposed approach remains a viable alternative in cases where historical consumption time series are unavailable, demonstrating promising results for forecasting household energy demand.