A Comparison of Top-down Load Forecasting and Bottom-up Load Forecasting in Distribution Network

Abstract

Abstract In this paper, the electricity load of a certain region is taken as the research object. Firstly, the back propagation neural network (BP) model is used to predict the macro load of the region from top to bottom. Then, on the premise of comprehensive consideration of the region’s meteorological, economic, large user registration and other causes, the power grid of the region is divided. On this basis the differential integration moving average autoregressive (ARIMA) model is used to carry out the bottom-up micro load forecasting: First, the load forecasting of each cell is carried out, and then the prediction results are summarized in proportion to obtain the overall prediction results of the region. Through the comparative analysis of the two load forecasting results, it is found that although the two forecasting errors are fewer than 9%, the bottom-up load forecasting method which combines grid partition and fine classification has a prediction error of fewer than 5 %. The results show that the bottom-up load forecasting method is better.