Short-Long Term Load Prediction Based on GM-BP Model

With the continuous development of the smart grid, rational power planning can greatly increase economic benefits. The power load is affected by temperature, humidity, and other factors, which presents typical nonlinear and random characteristics. Therefore, a combined prediction model based on grey model GM (1, N) and BP neural networks is proposed, where GM is improved by weighting the original data, selecting appropriate initial conditions and self-adaptive optimizing model parameters, while PSO algorithm is used to automatically optimize the global optimization. Finally, an experiment is carried out based on the actual power load data of a certain area, and the results show that the proposed method realizes higher accuracy in load prediction, and has a stronger generalization ability for small samples and low SNR conditions.