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Energies 2016, 9(7), 523; doi:10.3390/en9070523

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

School of Electrical and Electronic Engineering, North China Electric Power University, Baoding 071003, China
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Academic Editor: João P. S. Catalão
Received: 25 May 2016 / Revised: 2 July 2016 / Accepted: 2 July 2016 / Published: 8 July 2016
(This article belongs to the Special Issue Distributed Renewable Generation)
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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. View Full-Text
Keywords: photovoltaic power forecast; error calibration; typical climate categories; nonparametric kernel density estimation; Latin hypercube sampling photovoltaic power forecast; error calibration; typical climate categories; nonparametric kernel density estimation; Latin hypercube sampling
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MDPI and ACS Style

Gao, Y.; Zhu, J.; Cheng, H.; Xue, F.; Xie, Q.; Li, P. Study of Short-Term Photovoltaic Power Forecast Based on Error Calibration under Typical Climate Categories. Energies 2016, 9, 523.

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