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

Yajing Gao, Jing Zhu, Huaxin Cheng, Fushen Xue, Qing Xie and Peng Li
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Yajing Gao: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Jing Zhu: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Huaxin Cheng: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Fushen Xue: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Qing Xie: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
Peng Li: School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China

Energies, 2016, vol. 9, issue 7, 1-15

Abstract: 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 amplified the abandon rate of PV power. Consequently, the accuracy of PV power forecast urgently needs to be improved. Based on the amplitude and fluctuation 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 defined 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 fitting is performed to simulate probability density function (PDF) curves, and the RE samples are drawn from the fitted curves to obtain the sampling values of the RE. On the other hand, based on the fluctuation characteristic of RE, the recent RE data are utilized to analyze the error fluctuation 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 fitted values of the RE. On this basis, the fitted values of the RE are employed to correct the forecast values of PV power and improve the forecast accuracy.

Keywords: photovoltaic power forecast; error calibration; typical climate categories; nonparametric kernel density estimation; Latin hypercube sampling (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2016
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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