A Novel Short-Term Power-Load Forecasting Method Based on High-Dimensional Meteorological Data Dimensionality Reduction and Hybrid Deep Neural Network

Abstract

Accurate power load forecasting could provide a scientific basis for the rapid response and stable operation of a modern power system. To take advantage of the meteorological big data to improve short-term forecasting accuracy, while considering the nonlinear and spatiotemporal correlation characteristics of the power load data, this paper proposes a short-term power load forecasting method based on meteorological data dimensionality reduction and a hybrid deep neural network. First, the elastic network is used to reduce the dimensions of high-dimensional meteorological big data, eliminate irrelevant meteorological factors, and improve the quality of input data. Then, taking the dimension-reduced meteorological data and historical load data as input, a load forecasting model based on a novel deep neural network is established. This model uses a convolution neural network (CNN) and a bi-directional long short-term memory (BiLSTM) neural network to extract the spatial and temporal correlation features of power load related data, and combines the attention mechanism to enhance the learning weight of the load series in important periods, and adopts residual connection (RC) to optimize the network training speed and alleviate the overfitting problem. Finally, taking the open data set of the New York Independent System Operating Agency (NYISO) as an example, single-step and multi-step advance prediction experiments are carried out to verify the advantages of the proposed method.