Abstract
The uncertainty of PV output power brings a series of scheduling operation problems, and accurate prediction of PV output power is an effective means to reduce the influence of uncertainty. A short-term PV power prediction method based on time-phased and error correction is proposed. The representative meteorological factors selected by GRA are used to classify the days to be predicted, and the improved sparrow search algorithm (CMSSA) is used to optimize the Elman model, and Elman, CMSSA-Elman, and CMSSA-Elman error correction models are established to select different prediction models for power prediction for different weather types. The results show that the method proposed in this paper has obvious improvement in the accuracy of PV system output power prediction for different weather conditions and has some practical value.
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