Short-Term Load Forecasting Based on Improved TCN and DenseNet

Abstract

With the grid-connected application of renewable energy sources such as wind and photovoltaic power, the nonlinearity and fluctuation of load data makes load forecasting more difficult than ever before. In order to extract the implicit relationship between multiple features and power load to construct a long-term sequence dependency, this paper proposes a short-term load forecasting based on improved temporal convolutional network (TCN) and densely connected convolutional network (DenseNet). Firstly, multiple features are reconstructed by using a fixed-length sliding window, and then the high-dimensional features reflecting the complex and non-stationary characteristics of power load are extracted by the DenseNet to construct a feature matrix. Secondly, we innovatively improve the TCN and introduce a parallel pooling into the traditional TCN to mine the features of time sequences. Finally, the self-attention mechanism (SAM) is used to further enhance the weight of key features to eliminate the influences of interference signals. Experiments were performed on Southern China and ISO-NE (New England) public datasets to verify the effectiveness and generalization of the proposed model. Compared with the traditional TCN, the mean average percentage error (MAPE) of the improved TCN on the two datasets decreases by 23.38% and 8.14%, respectively. Furthermore, when compared to the TCN-SAM hybrid model, the MAPE of the proposed model is significantly reduced by 42.41% and 26.89%, respectively.

Keywords

• Short-term load forecasting
• improved temporal convolutional network
• densely connected convolutional network
• self-attention mechanism