Abstract
In order to improve the predict precision of GM(1,1) when it is applied to the short term load forecasting (STLF) problem, the meteorological information is taken into consideration. Firstly, an improved multi-strategy is used to organize the origin load data. Secondly, this chapter proposes meteorological analyzing and correcting algorithm to recognize the weather sensitive data and amend them. Then GM(1,1) is taken as the basic method to do the prediction. Finally, nearby trend extrapolation amending and similar-day replacing method is proposed to adjust the result and clear the mutation in it. Through the test, it is found that such method has a far more better precision than origin GM(1,1) when there is weather mutation in history days or predict days. The highest variety of accuracy can be up to 7 % and there is an average increase in predicting accuracy by almost 2 %. It can be concluded that such methods can not only take care of the social and climate affect but also considers the weakness of GM itself.
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Acknowledgment
This research is supported by the Natural Science Foundation of HeBei(F2011502038) and the Fundamental Research Funds for the Central Universities.
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Jiao, Rh., Su, Cj., Lin, By., Mo, Rf. (2014). Short Term-Load Forecasting Based on Meteorological Correcting Grey Model. In: Xing, S., Chen, S., Wei, Z., Xia, J. (eds) Unifying Electrical Engineering and Electronics Engineering. Lecture Notes in Electrical Engineering, vol 238. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-4981-2_23
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DOI: https://doi.org/10.1007/978-1-4614-4981-2_23
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