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In deep learning, the load data with non-temporal factors are difficult toprocess by sequence models. This problem results in insufficient precision ofthe prediction. Therefore, a short-term load forecasting method based onconvolutional neural network (CNN), self-attention encoder-decoder network(SAEDN) and residual-refinement (Res) is proposed. In this method, featureextraction module is composed of a two-dimensional convolutional neuralnetwork, which is used to mine the local correlation between data and obtainhigh-dimensional data features. The initial load fore-casting module consistsof a self-attention encoder-decoder network and a feedforward neural network(FFN). The module utilizes self-attention mechanisms to encode high-dimensionalfeatures. This operation can obtain the global correlation between data.Therefore, the model is able to retain important information based on thecoupling relationship between the data in data mixed with non-time seriesfactors. Then, self-attention decoding is per-formed and the feedforward neuralnetwork is used to regression initial load. This paper introduces the residualmechanism to build the load optimization module. The module generates residualload values to optimize the initial load. The simulation results show that theproposed load forecasting method has advantages in terms of prediction accuracyand prediction stability.

Short-term power load forecasting method based on CNN-SAEDN-Res