Short-term Electric Load Forecasting Using Least Square Support Vector Machines
نویسندگان
چکیده
This paper presents a model for short-term load forecasting using least square support vector machines. Available data are analyzed and appropriate features are selected for the model. Last 24 hours load demands are used for features in combination with day in week and hour in day. It is shown that temperature is not always a very good feature for the model. Appropriate data set is used for the model training, and then forecasting of day ahead hourly load demands is performed. Experimental results, obtained from real life benchmark, show that the proposed model is effective and accurate.
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تاریخ انتشار 2011