Short-term power load forecasting method based on CNN-SAEDN-Res
Yang Cui Han Zhu Yijian Wang Lu Zhang Yang Li
In deep learning, the load data with non-temporal factors are difficult to
process by sequence models. This problem results in insufficient precision of
the prediction. Therefore, a short-term load forecasting method based on
convolutional neural network (CNN), self-attention encoder-decoder network
(SAEDN) and residual-refinement (Res) is proposed. In this method, feature
extraction module is composed of a two-dimensional convolutional neural
network, which is used to mine the local correlation between data and obtain
high-dimensional data features. The initial load fore-casting module consists
of a self-attention encoder-decoder network and a feedforward neural network
(FFN). The module utilizes self-attention mechanisms to encode high-dimensional
features. This operation can obtain the global correlation between data.
Therefore, the model is able to retain important information based on the
coupling relationship between the data in data mixed with non-time series
factors. Then, self-attention decoding is per-formed and the feedforward neural
network is used to regression initial load. This paper introduces the residual
mechanism to build the load optimization module. The module generates residual
load values to optimize the initial load. The simulation results show that the
proposed load forecasting method has advantages in terms of prediction accuracy
and prediction stability.