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Short-Term Load Forecasting Using Comprehensive Combination Based on Multimeteorological Information
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DOI：10.1109/TIA.2009.2023571.png)
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摘要
En 中文
Short-term load forecasting is always a popular topic in the electric power industry because of its essentiality in energy system planning and operation. In the deregulated power system, an improvement of a few percentages in the prediction accuracy would bring benefits worth of millions of dollars, which makes load forecasting become more important than ever before. This paper focuses on the short-term load forecasting for a power system in the U. S., where several alternative meteorological forecasts are available from different commercial weather services. To effectively take advantage of the alternative meteorological predictions in the load forecasting system, a new comprehensive forecasting methodology has been proposed in this paper. Specifically, combining forecasting using adaptive coefficients is applied to share the strength of the different temperature forecasts in the first stage, and then, ensemble neural networks have been used to improve the model's generalization performance based on bagging. The proposed load forecasting system has been verified by using the real data from the utility. A range of comparisons with different forecasting models have been conducted. The forecasting results demonstrate the superiority of the proposed methodology.
Keyword:
Artificial neural network (ANN)
bagging
combining forecasting
ensemble learning
load forecasting
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期刊
IF:
4.5
论文数:
1.1W
被引数:
3.5W
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