Prediction method for weekly electricity consumption based on LSSVM algorithm
Huadian Technology ›› 2020, Vol. 42 ›› Issue (1): 35-40.
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Abstract: With the deepening of the reform of electric power system and the vigorous development of electric power market, a more refined electricity consumption forecast is required by power generation enterprises in order to make a reasonable powergeneration plan and a market bidding strategy. A prediction method for weekly electric power consumption is proposed based on Least Squares Support Vector Machine (LSSVM) and shortterm electricity consumption forecast.Fully considering the periodicity and continuity of the electricity demand, weekly meteorological characteristics are added into the model input,which makes up for the shortcomings of traditional power forecasting models that only consider historical electricity quantity and cannot accurately predict shortterm power trends during great weather changes. The model proposed can meet the demand of a more refined electric quantity prediction in commercialized electric power market.It is of great practicability.
Key words: electricity market, power generation enterprises, load forecast, LSSVM algorithm, weekly meteorological characteristics, weekly electricity consumption
CHEN Tao1,LYU Song1,REN Tinglin1,XUE Xiaocen2,LUO Xingxiang1,LIU ming1.
Prediction method for weekly electricity consumption based on LSSVM algorithm[J]. Huadian Technology, 2020, 42(1): 35-40.
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https://www.hdpower.net/EN/Y2020/V42/I1/35
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