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| Application of PCA-GRNN in Integrated Meteorological Short-term Load Forecasting |
| ZHANG Shu-qing1,REN Shuang1,JIANG An-qi2,HU Yong-tao1,ZHANG Hang-fei1,QIAO Yong-jing1 |
1. Institute of Electrical Engineering, Yanshan University, Qinhuangdao, Hebei 066004, China
2. College of Information Science and Engineering, Central South University, Changsha, Hunan 410006, China |
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Abstract In order to avoid the shortcomings such as redundancy inputs, long prediction time and low prediction accuracy, which caused by more weather factors and information embedded each other, principal component analysis (PCA) is adopted to extract characteristics of weather factors which are taken as the modeling objects, together with the dates of historical load. Simultaneously, against the shortcoming of BP neural network under dynamic performance, short-term load forecasting model based on generalized regression neural network (GRNN) is established. Comparing with the traditional network model, the forecast to the actual power system load proved that, this method can improve the prediction accuracy and speed significantly and was more practical and effective.
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Received: 29 June 2015
Published: 19 April 2017
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2017, Vol. 38 
