Short-term Photovoltaic Power Prediction Based on Typical Climate Types and Stochastic Prediction Error

doi:10.11930/j.issn.1004-9649.2016.05.157.06

Abstract

Abstract: Photovoltaic(PV) power output is directly related to the climate types and the prediction accuracy of PV power decreases for cloudy and rainy weathers. Based on the categories of typical climate types, a PV power forecast model is proposed with consideration of the predictive relative tolerance and the relative affective factor(RAF). At first, the historical climate data are categorized according to the definition of typical climate types and the RAF is put forward; Then the probabilistic model of predictive relative tolerance is established by using the t Location-Scale distribution and the Latin hypercube technique is used for sampling of the predictive relative tolerances; Finally, the predictive relative tolerance and the predicted value are superimposed to obtain the final prediction results. A case study has proved the feasibility and effectiveness of the model.

CLC Number:

TM715
TK519

CHEN Yaoqi. Short-term Photovoltaic Power Prediction Based on Typical Climate Types and Stochastic Prediction Error[J]. Electric Power, 2016, 49(5): 157-162.

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URL: https://www.electricpower.com.cn/EN/10.11930/j.issn.1004-9649.2016.05.157.06

https://www.electricpower.com.cn/EN/Y2016/V49/I5/157

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