Power short-term load forecasting based on big data and optimization neural network - Xin JIN; Long-wei LI; Jia-nan JI; Zhi-qi LI; Yu HU; Yong-bin ZHAO (Tongxin xuebao)
With the reduction of the cost of power data acquisition and the interconnection of large scale power systems,the types of data available in the power network are becoming more and more abundant.In the past,the centralized fore-casting method was limited to the analysis of the massive power data.Therefore,a short-term power load forecasting based on large data and particle swarm optimization BP neural network was proposed,and short-term power load fore-casting model was established.The actual load data of the national grid,using the method of prediction,compared with the actual load data and centralized load forecasting results prove that this method is accurate enough,reduce the load forecasting time with feasibility in practical application.