Power load forecasting based on grey neural network

Abstract

To improve the accuracy of power load forecasting, this paper analyzes the defects as well as merits of artificial neural network (ANN) and grey prediction method, and it combines the two methods to propose a novel forecasting method called grey neural network (GNN). GNN utilizes the accumulation generation operation (AGO) of grey prediction to transform the original load data to first order AGO data which has better regularity, making it easier for ANN to model and forecast. At the same time the theoretical error of traditional grey prediction method is avoided. GNN is suitable for middle and long term load forecasting, and case study shows that its forecasting accuracy is better than that of ANN and grey prediction method. The paper also reveals the importance of data transformation in load forecasting process, and it further investigates the effect of inverse transformation on forecasting error.