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Nonparametric regression based short-term load forecasting
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DOI：10.1109/59.708572.png)
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This paper presents a novel approach to short-time load forecasting by the application of nonparametric regression. The method is derived from a load model in the form of a probability density function of load and load affecting factors. A load forecast is a conditional expectation of load given the time, weather conditions and other explanatory variables. This forecast can be calculated directly from historical data as a local average of observed past loads with the size of the local neighborhood and the specific weights on the loads defined by a multivariate product kernel. The method accuracy relies on the adequate representation of possible future conditions by historical data, but a measure to detect any unreliable forecast can be easily constructed. The proposed procedure requires few parameters that can be easily calculated from historical data by applying the cross-validation technique.
Keyword:
short-term load forecasting
nonparametric regression
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7.2
论文数:
1.1W
被引数:
5.0W
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