Temporal clustering for accurate short-term load forecasting using Bayesian multiple linear regression

Effective short-term load forecasting (STLF) is essential for optimizing electricity grid operations. This study focuses on refining STLF for day-ahead predictions using Bayesian multiple linear regression (BMLR). This study’s originality lies in its innovative use of BMLR combined with data clustering techniques to improve prediction accuracy, a method not previously explored in existing literature. We address the critical issue of input data clustering, highlighting its impact on prediction accuracy. Four clustering methods based on temporality were examined, with clustering by weekday and hour proving most effective for BMLR-based STLF. Predictors included historical load, temperature, season, weekday, and hour, selected using the Akaike information criterion (AIC). Linear regression assumptions were verified, and solutions were proposed for deviations, notably addressing heteroscedasticity. Autocorrelation in residuals was addressed to improve forecasting efficiency. Time-cross validation and performance metrics demonstrated model effectiveness. Second-degree polynomial terms are included for better fitting. Clustering by weekday and hour is optimal for BMLR-based STLF, aiding accurate load forecasts. The main objectives of this research are to determine the optimal clustering method for BMLR in STLF and to provide practical insights into the application of Bayesian techniques in load forecasting. This research significantly contributes to the field of STLF by providing practical insights into data clustering and model refinement, offering valuable perspectives for enhanced energy management and grid stability.