Abstract：Aimed at the low precision of general GM(1, 1) grey forecasting model in the uncertain conditions of original data sequence fluctuation, mutation or transition, the paper proposes a short-term electrical load forecasting on optimized combination model of grey correlation-segmentation. GM(1, 1) presents good forecasting result within the smoothly upward or downward segment, while daily power load can be divided into several segments with peaks or valleys. Therefore, grey correlation segmentation and optimized combination are carried out in order to avoid the risk in which errors are introduced into the model and then gradually amplified for the inappropriate choice of original condition. Meanwhile, several GM(1, 1) models from different views are combined to prevent the influence of uncertain multi-factors on power load. In the application of the proposed model on the project in Guigang city of Guangxi province, the average forecasting error is only about 3%, which illustrates that the forecasting precision is greatly improved by the model and it can completely satisfy the practical requirement of short-term power load forecasting in this area.
张志明, 金敏. 基于灰关联分段优选组合模型的 短期电力负荷预测研究[J]. 电工技术学报, 2009, 24(6): 115-120.
Zhang Zhiming, Jin Min. Research on Short-Term Electrical Load Forecasting Based on Optimized Combination Model of Grey Correlation Segmentation. Transactions of China Electrotechnical Society, 2009, 24(6): 115-120.
2009, Vol. 24 
