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Title |
Combined Extension Neural Network and Multi-Regression Analysis Method for Yearly Load Forecasting |
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Author |
Meng-Hui Wang, Hung-Cheng Chen, Kuei-Hsiang Chao, Kuo-Hua Huang |
| Citation |
Vol. 6 No. 12 pp. 109-114 |
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Abstract |
In this paper, forecasting models of yearly peak load and electricity demand are built according to the gross domestic product (GDP) and economic growth rates. First, a novel clustering method based on the extension neural network is introduced to recognize the load types of yearly peak load (YPL) and yearly electricity demand (YED). Second, according to the load data of every load type, we use the multi-regression analysis method (MRAM) to build the load forecasting models of every load type, and then the forecasting models can be used to forecast the values of YPL and YED at the forecasting time. To verify the proposed forecasting method, the statistics of YPL and YED in Taiwan have been tested. The compared forecasting results with the grey GM (1,1) model show that the proposed method has better accuracy for both YPL and YED forecasting. |
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Keywords |
yearly peak load, yearly electricity demand, ENN, multi-regression analysis method, grey model. |
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