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Short-Term Load Forecasting With Exponentially Weighted Methods
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DOI：10.1109/TPWRS.2011.2161780.png)
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摘要
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Short-term load forecasts are needed for the efficient management of power systems. Although weather-based modeling is common, univariate models can be useful when the lead time of interest is less than one day. A class of univariate methods that has performed well with intraday data is exponential smoothing. This paper considers five recently developed exponentially weighted methods that have not previously been used for load forecasting. These methods include several exponential smoothing formulations, as well as methods using discount weighted regression, cubic splines, and singular value decomposition (SVD). In addition, this paper presents a new SVD-based exponential smoothing formulation. Using British and French half-hourly load data, these methods are compared for point forecasting up to one day ahead. Although the new SVD-based approach showed some potential, the best performing method was a previously developed exponential smoothing method. A second empirical study showed the better of the univariate methods outperforming a weather-based method up to about five hours ahead, with a combination of these methods producing the best results overall.
Keyword:
Discount weighted regression
exponential smoothing
load forecasting
singular value decomposition
spline functions
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IF:
7.2
论文数:
1.1W
被引数:
5.0W
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