Search

Precise prediction of load profiles—most notably the accurate identification of peak demand—remains difficult owing to the highly variable and unpredictable patterns of energy use. This work evaluates three forecasting approaches: Long Short-Term Memory (LSTM), Support Vector Regression (SVR), and a hybrid ensemble combining SVR, Gated Recurrent Units (GRU), and Linear Regression (LR) to generate 24-hour-ahead load forecasts. Individual load categories—household (HH), heat pump (HP), and electric vehicle (EV)—were treated together using full one-year consumption records. The research focuses on improving the reliability of load-profile predictions by comparing LSTM, SVR, and the proposed ensemble across HH, HP, and EV demand patterns. An innovative correction procedure is presented that refreshes the forecast every 8 hours, thereby improving forecast trustworthiness. Results underscore the value of deep learning techniques for better anticipating electricity demand, with particular strength in recognizing peak events, which in turn supports smoother and more cost-effective power system management. Both graphical assessments and quantitative validation metrics were analyzed, covering model behavior during off-peak hours, peak hours, and across the complete daily cycle. Across the tested approaches, LSTM delivered marginally stronger overall metrics in the majority of cases and demonstrated especially good performance at detecting peaks. That said, the hybrid model slightly outperformed LSTM when predicting EV-specific power demand, achieving an on-peak mean absolute percentage error (MAPE) of 21.45%, compared with 29.24% (SVR) and 22.02% (LSTM). Even so, graphical inspection clearly illustrated LSTM’s superior peak-capturing ability. This advantage was also evident in the on-peak mean absolute percentage error (MAPE) and symmetric mean absolute percentage error (SMAPE) values, where LSTM improved by roughly 3–5% relative to both SVR and the hybrid model. In the final application, LSTM was used to forecast next-day load profiles based on real measurements collected from four separate grid areas and consistently demonstrated excellent peak-detection capability, with MAPE below 10% across most of those datasets.