Sector-Specific Electricity Demand Forecasting in Sri Lanka Using Deep Learning and Hybrid Models

Abstract

Electricity demand forecasting is important in addressing the growing challenges in the energy sector. Traditional forecasting models often fail to capture the complex interplay of external factors, particularly in countries like Sri Lanka, where electricity demand in different sectors, such as the domestic and industrial sectors, which collectively account for over 65% of total electricity consumption, exhibit unique patterns. This study introduces a sector-specific approach to electricity demand forecasting in Sri Lanka's domestic and industrial sectors by employing Deep Learning models and integrating socioeconomic and weather variables to enhance accuracy. Key predictors for each sector were identified using Random Forest-based feature selection. A multivariate multi-step Long Short-Term Memory (LSTM) model, the Seasonal Autoregressive Integrated Moving Average (SARIMA) model, and a hybrid SARIMA–LSTM model were implemented to assess short-term and long-term forecasting capabilities. Results demonstrated that the 2-step LSTM model and the hybrid SARIMA–LSTM model outperformed the SARIMA model. Integrating diverse datasets enhances forecasting accuracy, providing actionable insights for sustainable energy planning and resource optimization. The study’s originality lies in its sector-specific focus, incorporating weather and socioeconomic features and the innovative use of hybrid models to address the unique electricity demand patterns in Sri Lanka.