Load profile analysis and forecasting for rural mini grids in Uganda

Abstract

Accurate load forecasting is essential for the reliable and cost-effective operation of rural mini grids, where constrained generation capacity and high penetration of renewable energy resources require well-informed operational decisions. This study examines electricity demand characteristics and forecasting performance for the Buzaami and Ssenyondo mini grids in Uganda, with particular focus on diurnal load profiles, peak demand behavior, and seasonal variability. 2022 operational data show extended peak demand from early morning to late evening, driven by socio-economic activities that strain resource scheduling and reliability management. To address these challenges, the study evaluates and compares Long Short-Term Memory (LSTM) networks, fuzzy logic models, and a hybrid LSTM–fuzzy forecasting framework using standard accuracy metrics, namely Mean Absolute Percentage Error (MAPE) and Root Mean Square Error (RMSE). The hybrid LSTM–fuzzy model integrates data-driven temporal learning with knowledge-based reasoning, achieving consistently lower MAPE and RMSE values than the standalone fuzzy logic model, while offering greater robustness and interpretability than the pure LSTM approach. These results demonstrate that hybrid forecasting can effectively support optimized dispatch of solar photovoltaic generation and battery energy storage systems, reduce reliance on costly backup generation, and enhance overall system reliability during critical peak-demand periods in rural mini-grid environments.