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.

在这项研究中，通过使用 CatBoost 模型和元启发式算法的能力，以及混合技术，尝试改进短期规模的电力需求预测。为此，本研究提出了混合 CatBoost-PPSO 模型，以根据天气变量预测电力需求。最后，通过案例研究并将所提出模型的结果与其他五个混合模型的结果进行比较，使用各种统计指标对结果进行评估和比较。本研究中使用的一般方法是使用元启发式算法优化 Catboost 的超参数，并在预测过程中使用其中最好的参数。此外，在网络训练过程中，使用 K-Fold 交叉验证算法来避免过拟合。基于测试数据的模型评估结果表明，混合CatBoost-PPSO模型具有较高的短期电力需求预测能力。从该模型获得的指数显示出比其他混合模型更好的值。例如，该模型的 RMSE 值为 42.3，与混合 CatBoost-ALO 模型相比提高了近 9.5%。