Study of Short-Term Photovoltaic Power Forecast Based on Error Calibration under Typical Climate Categories

摘要： With the increasing permeability of photovoltaic (PV) power production, the uncertainties and randomness of PV power have played a critical role in the operation and dispatch of the power grid and ampli?ed the abandon rate of PV power. Consequently, the accuracy of PV power forecast urgently needs to be improved. Based on the amplitude and ?uctuation characteristics of the PV power forecast error, a short-term PV output forecast method that considers the error calibration is proposed. Firstly, typical climate categories are de?ned to classify the historical PV power data. On the one hand, due to the non-negligible diversity of error amplitudes in different categories, the probability density distributions of relative error (RE) are generated for each category. Distribution ?tting is performed to simulate probability density function (PDF) curves, and the RE samples are drawn from the ?tted curves to obtain the sampling values of the RE. On the other hand, based on the ?uctuation characteristic of RE, the recent RE data are utilized to analyze the error ?uctuation conditions of the forecast points so as to obtain the compensation values of the RE. The compensation values are adopted to sequence the sampling values by choosing the sampling values closest to the compensation ones to be the ?tted values of the RE. On this basis, the ?tted values of the RE are employed to correct the forecast values of PV power and improve the forecast accuracy.