Enhanced short-term load forecasting with hybrid machine learning models: CatBoost and XGBoost approaches

Abstract

The focus of this paper is to improve short-term load forecasting for electric power. To achieve this goal, the study explores and evaluates hybrid models, specifically using the CatBoost and XGBoost algorithms, which are optimized with different optimizers. The study incorporates hourly electricity load data and also includes temperature data to enhance the precision of the forecasting models. Statistical metrics are then used to assess the performance of these models. The study evaluates the performance of the hybrid models on both training and testing datasets. It finds that the CatBoost-Arithmetic Optimization Algorithm hybrid model outperforms the other models in the training dataset. However, in the testing dataset, the XGBoost- Arithmetic Optimization Algorithm hybrid model demonstrates superior performance compared to the CatBoost models. The study conducts an importance and sensitivity analysis to understand which variables have the most significant impact on the target variable, which is likely electricity load. The results of this analysis reveal that temperature is the most influential variable affecting the target variable. Additionally, the month variable is identified as having a notable impact on load forecasting. These findings suggest that employing hybrid models, particularly those optimized with appropriate algorithms, can significantly improve the accuracy of short-term load forecasting. Moreover, the study highlights the importance of incorporating temperature data into these models, as temperature is a key driver of electricity load patterns.