Comparative Analysis of Load Profile Forecasting: LSTM, SVR, and Ensemble Approaches for Singular and Cumulative Load Categories

Highlights: What are the main findings? A comparative study is performed to find the most optimum model for load profile forecasting. A model is developed to forecast single and cumulative load profiles containing EV, HH, and HP. What is the implication of the main finding? The model is validated on both synthetic and measured data for some grids in Austria. The selected model demonstrates high robustness in forecasting all profiles’ categories based on a ROC-like curve for peak catching and other validation metrics such as MAPE, MAE, and SMAPE. Energy forecasting and correction are applied to control the peaks and make the forecast even more optimal and realistic. Accurately forecasting load profiles, especially peak catching, is a challenge due to the stochastic nature of consumption. In this paper, we applied the following three models for forecasting: Long Short-Term Memory (LSTM); Support Vector Regression (SVR); and the combined model, which is a blend of SVR, Gated Recurrent Units (GRU), and Linear Regression (LR) to forecast 24 h-ahead load profiles. Household (HH), heat pump (HP), and electric vehicle (EV) loads are singular, and these were collectively considered with one-year load profiles. This study tackles the issue of accurately forecasting load profiles by evaluating LSTM, SVR, and an ensemble model for predicting energy consumption in HH, HP, and EV loads. A novel forecast correction mechanism is introduced, adjusting forecasts every eight hours to increase reliability. The findings highlight the potential of deep learning in enhancing energy demand forecasting, especially in identifying peak loads, which contributes to more stable and efficient grid operations. Visual and validation data were investigated, along with the models’ performances at different levels, such as off-peak, on-peak, and entirely. Among all models, LSTM performed slightly better in most of the factors, particularly in peak capturing. However, the blended model showed slightly better performance than LSTM for EV power load forecasting, with an on-peak mean absolute percentage error (MAPE) of 21.45%, compared to 29.24% and 22.02% for SVR and LSTM, respectively. Nevertheless, visual analysis clearly showed the strong ability of LSTM to capture peaks. This LSTM potential was also shown by the mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) during the on-peak period, with around 3–5% improvement compared to SVR and the blended model. Finally, LSTM was employed in predicting day-ahead load profiles using measured data from four grids and showed high potential in capturing peaks with MAPE values less than 10% for most of the grids.