A hybrid K-means and KNN approach for enhanced short-term load forecasting incorporating holiday effects

Accurate short-term load forecasting (STLF) is essential for efficient power system operations, particularly during holidays when demand patterns vary significantly. Traditional methods often struggle with these non-linear changes, leading to increased operational costs. This research introduces a hybrid model combining K-means clustering and K-nearest neighbors (KNN) classification to enhance forecast accuracy by accounting for holiday effects. Using historical electricity consumption data, the model applies K-means to classify load patterns and KNN to predict load clusters based on the day of the week, month, and holiday status. Advanced forecasting algorithms for each cluster, including linear regression, neural networks with Bayesian optimization, Support Vector Regression with Bayesian Optimization, and long short-term memory with Bayesian optimization, were evaluated using mean absolute percentage error (MAPE), ANOVA, and Tukey's HSD test. Results show significant improvements in forecast precision, especially during holidays, with the LSTM-BO model achieving the lowest MAPE values. The proposed hybrid model achieved an average 56.1 % improvement in forecasting accuracy across various scenarios, providing a reliable tool for power system planners and operators to optimize performance and reduce costs.