A Comparative Evaluation of AI Models for Electricity Load Forecasting in Smart Homes

Abstract

The short-term residential electricity demand forecasting is very important for sustainable electricity use management in smart cities. The purpose of this study is to compare the artificial intelligence models for short-term electricity load forecasting in a realistic residential condition in a rigorous comparison. The study uses half-hourly smart-meter data from 10 households for two years to assess the forecasting performance. The leakage-free chronological data splitting approach was used to maintain temporal causality and reliable out-of-sample evaluation. Three machine learning models, standalone XGBoost, Long Short-Term Memory (LSTM), and a hybrid model combining LSTM and XGBoost, were systematically compared to two naive models: persistence and seasonal benchmark models. The outcomes illustrate that XGBoost can always deliver the highest prediction accuracy in most homes and the hybrid model can boost the performance of LSTM, but does not always outperform XGBoost. The errors in predictions were larger when the electric demand was higher, thus practical evaluation under realistic operating conditions is needed. The results emphasize the need for selecting the most appropriate model based on data to accurately predict energy consumption in smart homes. Moreover, predicting energy demand for individual households is difficult because of its nonlinear, stochastic and human behavior characteristics. There is, however, a limitation in the number of households and the results obtained may not be directly transferable to a larger-scale smart city setting.