Short‐Term Electricity Load Forecasting Based on <scp>PCA</scp> ‐ <scp>PSO</scp> ‐ <scp>Kmeans</scp> ++ Clustering and Improved <scp>DSC</scp>

ABSTRACT Electricity load data is usually significantly trending, cyclical, and stochastic in nature; it is also influenced by external factors such as weather, holidays, and socioeconomic activities. In addition, electricity loads usually have long‐time dependencies, such as daily, weekly, and yearly periodicity. To address these challenges, we propose an electricity load forecasting method that combines PCA‐PSO‐Kmeans++ clustering with improved depthwise separable convolution. This article consists of two parts: data processing and electricity load forecasting. The data processing part of this includes seasonal decomposition of raw data, the Pearson correlation coefficient to select suitable exogenous variables, manual feature processing of raw electricity load data, and cluster analysis of feature‐processed data. The electricity load forecasting part of the model is trained using an improved depthwise separable convolution that incorporates an attention mechanism and residual connection. Electricity load forecasting on datasets from the US and Nordic region. Experimental results show that clustering combined with improved depthwise separable convolution is more accurate and reliable in electricity load forecasting. Based on experimental results, we quantify the performance gain contributed by clustering relative to the strongest non‐clustered baseline and demonstrate that clustering combined with the improved DSC further enhances accuracy.