Enhancing Short-Term Electrical Load Forecasting Using SARIMA, XGBoost, LSTM, and VMD-Based Signal Decomposition
Short-term electric load forecasting is essential for stable power system management, yet remains inherently uncertain due to volatile demand patterns, weather variability, and irregular consumption behaviour. The proliferation of smart metering infrastructure has made high-resolution consumption data widely available, enabling machine learning and deep learning methods to model complex non-linear temporal patterns that conventional statistical approaches cannot capture. In this context this work proposes a framework for a comparative evaluation of four representative forecasting methods: the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, Extreme Gradient Boosting (XGBoost), the Long Short-Term Memory (LSTM) neural network, and a hybrid Variational Mode Decomposition–LSTM (VMD-LSTM) model. In the hybrid approach, VMD is first adopted to decompose the load time series into several intrinsic mode functions, which are then modelled individually with LSTM to improve forecasting accuracy. RMSE, MAE, R 2 , and MAPE are used to evaluate the effect of model selection on forecasting uncertainty. The results indicate that the VMD-LSTM model exhibits the most favourable performance for the considered dataset (RMSE: 2731.95, MAE: 2190.34, R²: 0.9641, MAPE: 5.15%). However, forecasting performance may vary depending on data characteristics and modelling conditions; therefore, SARIMA and XGBoost can also provide effective results in different scenarios of short-term electricity load forecasting