Short-term Time Series Data Prediction of Power Consumption Based on Deep Neural Network

Abstract

Under the background of the rapid development of Internet technology and the popularity of smart grids, the analysis and prediction of short-term time series data of users’ power consumption has important guiding significance for grid planning, management decision of economic sector and optimization and allocation of power resources. Considering that the traditional statistical-based time series analysis method is weak in generality and can not handle the complex linear problem in prediction, the long-term dependence of the ordinary cyclic neural network model is insufficient, and the time series data has multidimensional problems, a deep neural network is proposed. The PCA-LSTM model is used for time series data prediction. The model firstly uses the PCA (principal component analysis) method to reduce the dimensionality of the electricity consumption time series data, optimizes the number of input variables, and inputs the data into the long- and short-term memory network LSTM for training prediction. The experimental results show that the LSTM network prediction based on PCA improves the accuracy of short-term time series data prediction, and also improves the convergence speed of LSTM network. It proves that the method has better prediction performance and versatility.