Short-term Load Forecasting Based on K-medoids Clustering and XGBTideModel

Introduction: Electric power load is significantly influenced by weather conditions, making accurate load prediction under varying weather scenarios essential for effective planning and stable operation of the power system. This paper introduces a short-term load forecasting method that combines k-Medoids clustering and XGB-TiDE. Initially, k-Medoids clusters the original load data into categories such as sunny, high temperature, and rain/snow days. Subsequently, XGBoost identifies critical features within these subsequences. The combined forecast model, XGB-TiDE, is then tailored for each subsequence. Here, the TiDE model's predictions are refined point-by-point using the XGBoost results to derive the final short-term load forecasts. An empirical analysis using real power load data from a specific region demonstrates that our proposed model achieves superior accuracy, especially under extreme weather conditions such as high temperatures and precipitation. Background: Electric power load is significantly influenced by weather conditions, making accurate load prediction under varying weather scenarios essential for effective planning and stable operation of the power system. Objective: Addressing the limitations of short-term load forecasting under extreme weather conditions, this paper introduces a novel approach that leverages k-Medoids clustering and the XGBTiDE model to enhance forecasting accuracy. This method strategically segments power load data into meaningful clusters before applying the robust predictive capabilities of XGB-TiDE, aiming for a significant improvement in forecast precision. Method: Initially, k-Medoids clusters the original load data into categories such as sunny, high temperature, and rain/snow days. Subsequently, XGBoost identifies critical features within these subsequences. The combined forecast model, XGB-TiDE, is then tailored for each subsequence. Here, the TiDE model's predictions are refined point-by-point using the XGBoost results to derive the final short-term load forecasts. Results: The model presented in this study demonstrates outstanding performance across all weather conditions, consistently achieving lower mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) compared to other models. For instance, on a sunny day, this model records an MAE of 8.49, RMSE of 10.29, and MAPE of 2.55%, markedly surpassing the Autoformer, which shows an MAE of 18.29, RMSE of 22.33, and MAPE of 5.50%. These results underscore the superior accuracy of our proposed forecasting approach. Conclusion: An empirical analysis using real power load data from a specific region demonstrates that our proposed model achieves superior accuracy, especially under extreme weather conditions such as high temperatures and precipitation.