A novel algorithm for multi-node load forecasting based on big data of distribution network

Abstract

Abstract. Effective load forecasting for different scales of loads is essential for the planning and operation of distribution network. The extending scale of the network, the diversity of load types and the rapid increase of data volume are some of the facing problems. In this paper, we propose a novel method for multi-node load forecasting, AR-ANN, which takes those problems into consideration. This new algorithm makes a combination of AR method and BP neural network method while eliminating their disadvantages. Comparing to traditional bottom-up method, a top-down method is more applicable when considering the limitation of measurement equipment in distribution network. Both top-down method and bottom-up method are tested in this paper by using AR-ANN algorithm. The data processing speed and the forecasting accuracy of AR-ANN is validated by several tests: an ordinary single-node load forecast, two multi-node load forecasts by traditional bottom-up method and by the new top-down method.