WANG Feng1, SHANG GUAN An qi1, XIA Jun li2

 

(1. State Grid Hubei Electric Power Company, Wuhan 430077, China;

 

2. School of Electrical Engineering, Wuhan University, Wuhan 430072, China)

 

 

Abstract: Aiming at the shortage of electricity Hubei power grid may face in the future, the present power supply situation and the future construction plan of were analyzed. A kind of midlong term load forecasting method based on improved support vector machine(SVM) was put forward. Genetic algorithm (GA) was used to improve SVM, so that the optimal parameters can be automatically selected. The maximum load of Hubei grid during the “Thirteenth Five Year Plan” was predicted using the method proposed. On this basis, the power balance of Hubei grid was analyzed. The results indicate that the prediction accuracy of this method is 3.66% higher compared with BP neural network. And there will be a yearround power gap in 2017 in Hubei power grid and the gap will reach to a serious cases of 1.533×107kW in 2020. Therefore, the construction of UHV must be accelerated during the period of “Thirteenth Five Year Plan”.

 

Key words: support vector machine (SVM); genetic algorithm (GA); midlong term load forecasting; power balance; UHV planning