Short-Term Electricity Load Forecasting Based on T-CFSFDP Clustering and Stacking-BiGRU-CBAM

To fully explore the potential features contained in power load data, an innovative short-term power load forecasting method that integrates data mining and deep learning techniques is proposed. Firstly, a density peak fast search algorithm optimized by time series weighting factors is used to cluster and analyze load data, accurately dividing subsets of data into different categories. Secondly, introducing convolutional block attention mechanism into the bidirectional gated recurrent unit (BiGRU) structure significantly enhances its ability to extract key features. On this basis, in order to make the model more accurately adapt to the dynamic changes in power load data, subsets of different categories of data were used for BiGRU training based on attention mechanism, and extreme gradient boosting was selected as the meta model to effectively integrate multiple sets of historical training information. To further optimize the parameter configuration of the meta model, Bayesian optimization techniques are used to achieve automated adjustment of hyperparameters. Multiple sets of comparative experiments were designed, and the results showed that the average absolute error of the method in this paper was reduced by about 8.33% and 4.28%, respectively, compared with the single model and the combined model, and the determination coefficient reached the highest of 95.99, which proved that the proposed method has a better prediction effect.