Short-Term Load Forecasting Based on a Hybrid Neural Network and Phase Space Reconstruction

Abstract

Most current short-term load forecasting models have difficulty in simultaneously taking into account the time-series nature of load data, the non-linear characteristics, and the ineffectiveness of extracting potential high-dimensional features from historical series. To solve this problem, we propose a hybrid neural algorithm model (DPCL). In the DPCL, we use convolutional neural networks to obtain the high-dimensional spatial features of the phase space reconstruction of the load time series. Then, we combine the obtained high-dimensional spatial features with the external influence features extracted in the Pearson correlation analysis. Long and short-term memory networks retrieve Spatio-temporal features through the connection layer and obtain prediction results. In addition, there are problems of network gradient degradation and overfitting during the training process, We use an improved differential evolutionary algorithm to optimize the topology and time step of the hybrid neural network. We use the public dataset of a European utility and the loaded dataset of a Chinese mathematical competition as practical arithmetic examples. Experiments have higher prediction accuracy and faster prediction speed compared with other traditional algorithms.

Keywords

• Phase space reconstruction
• convolutional neural network
• long short-term memory
• improved differential evolution