Urban Electricity Consumption Forecasts Under Abnormal Conditions

Due to the complexity and uncertainty of the power system, the scheduling and planning of the power grid has brought corresponding difficulties. In this paper, a long-term electricity consumption prediction model is developed for use under abnormal operating conditions. The fractional-order grey prediction model captures the shortfall of electricity consumption after the occurrence of abnormal working conditions, the restriction vector autoregressive model is used to achieve the prediction of electricity consumption, and the particle swarm optimization algorithm is used to optimize the parameters of the model to improve the prediction accuracy of the model. The results of the three prediction models are compared to verify the effectiveness of the model. The experimental results show that the model shows good performance in electricity consumption prediction and makes better use of all data information.