Toward Cost-Oriented Forecasting of Wind Power Generation

Forecasting is considered to be one of the most cost-efficient solutions to integrating wind power into existing power systems. In some applications, unbiased forecasting is necessary, while in others, the forecasting value can be biased for optimal decision making. In this paper, we study optimal point forecasting problems under cost-oriented loss functions, which can lead to a forecasting process that is far more sensitive to the actual cost associated with forecasting errors. Theoretical points of optimal forecasting under different loss functions are illustrated, then a cost-oriented, boosted regression tree method is presented to formulate the optimal forecasting problem under study. Case studies using real wind farm data are conducted. A comparison between cost-oriented forecasting and traditional unbiased forecasting demonstrates the efficiency of the proposed method in maximizing benefits for the decision-making process.

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