Reactive Load Prediction Based on a Long Short-Term Memory Neural Network

Abstract

Accurate reactive load prediction can improve the accuracy and process of reactive power optimization for power grids and improve the control effect. The changes in the bus reactive load and active load are not synchronous, the base of the reactive load is small, nonlinear changes are abundant, and it is difficult to mine the inherent data trends. In view of the above problems, this paper proposes a method for predicting bus reactive loads based on deep learning. A bus reactive load prediction model is constructed based on a dual-input long short-term memory neural network to mine the detailed characteristics of active and reactive load data. Active and reactive loads are used as input and output data for the dynamic modeling of load time series data to form integrated forecasts of bus active and reactive loads. The experimental results show that this method can accurately predict the reactive power load of buses, and the prediction accuracy is better than that of time series and general long short-term memory neural network prediction models.

Keywords

• Reactive power optimization
• reactive load prediction
• long short-term memory neural network