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Probability Analysis for Multiple-time Renewable Energy Forecast Error Using Conditional D-Vine

Probabilistic information of the forecast has become more important due to the ever-increasing penetration of renewable energy. However, most forecast methods are based on the point forecast that cannot provide valuable probability information. Meanwhile, the existing probability analysis methods are basically based on one-time intervals and only, which omits vital temporal correlation information. Thus, this paper proposes a novel conditional D-vine based forecast error analysis method that considers various dependence relations among sequential time intervals. Then, a Monte Carlo based method is proposed to compute the forecast error interval under different confidence levels. The case study on the actual wind farms’ forecast and actual output data in Ningxia, China, demonstrates the effectiveness of the proposed D-vine based conditional forecast error analysis method. The result shows that the proposed method is much more robust and provides more probability information than the Gaussian copula-based forecast error analysis method.