Predicting the short-term electricity demand based on the weather variables using a hybrid CatBoost-PPSO model

Abstract

In this study, by using the capabilities of the CatBoost model and meta-heuristic algorithms, as well as the hybridization technique, an attempt was made to improve the prediction of electricity demand on a short-term scale. For this purpose, the hybrid CatBoost-PPSO model was suggested in this study to predict electricity demand based on weather variables. Finally, by conducting a case study and comparing the results of the proposed model with five other hybrid models, the results were evaluated and compared using various statistical indexes. The general approach used in this study is that the hyper-parameters of the Catboost were optimized using a meta-heuristic algorithm, and the best of them were used during the forecasting process. Also, during the network training, the K-Fold cross-validation algorithm is used to avoid over-fitting. The evaluation results of the models based on the test data showed that the hybrid CatBoost-PPSO model has high capabilities in short-term electricity demand forecasting. The indices obtained from this model show better values than other hybrid models. For example, the RMSE value of this model is equal to 42.3, which shows an improvement of almost 9.5% compared to the hybrid CatBoost-ALO model.