Residential Electrical Load Forecasting Based on a Real-Time Evidential Time Series Prediction Method

Load forecasting is essential for efficient microgrid management, providing key advantages in operational efficiency, cost control, and grid reliability. As microgrids become increasingly critical in the global transition toward decentralized renewable energy systems, accurately predicting load demand is vital for optimizing performance and ensuring a stable, resilient, and sustainable power supply. This study introduces a novel short-term load forecasting approach based on Belief Functions Theory (BFT). The proposed method employs information fusion techniques to combine multiple predictors, each with its own forecasting mechanism. Using lagged power values and weather data, the predictors generate estimated power values along with corresponding uncertainty or error levels. A mass function is assigned to each predictor, taking into account both prediction and error data, even when some information is missing. These mass functions are then merged to produce a final, reliable prediction. Application of this method to publicly available load datasets demonstrates its effectiveness, achieving a 12% reduction in forecasting error compared to state-of-the-art methods and delivering substantial improvements in computational efficiency.