Research on Power Grid Short-Term Traffic Prediction Model Based on Regression Decomposition

Accurate short-term traffic forecasting of power grid can effectively manage personnel, balance personnel costs and improve the quality of electricity customer service. Therefore, this paper proposes a short-term traffic forecasting method based on regression decomposition. Firstly, statistical tools are used to model the periodicity of short-term traffic, the correlation of temperature and the correlation of power outage events. Then, the number of components of the regression-based seasonal trend decomposition framework is customized accordingly. Then, the traffic volume is decomposed by regression decomposition method, the periodic component and trend component are predicted by LSTM, and the temperature component and random component are predicted by WLSSVM. Finally, the effectiveness and superiority of the proposed method are verified by practical examples.