A Hybrid Online Forecasting Model for Ultrashort-Term Photovoltaic Power Generation

Fei Mei, Yi Pan, Kedong Zhu and Jianyong Zheng
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Fei Mei: College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
Yi Pan: Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Southeast University, Nanjing 210096, China
Kedong Zhu: Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Southeast University, Nanjing 210096, China
Jianyong Zheng: Jiangsu Key Laboratory of Smart Grid Technology and Equipment, Southeast University, Nanjing 210096, China

Sustainability, 2018, vol. 10, issue 3, 1-17

Abstract: A hybrid photovoltaic (PV) forecasting model is proposed for the ultrashort-term prediction of PV output. The model contains two parts: offline modeling and online forecasting. The offline module uses historical monitoring data to establish a weather type classification model and PV output regression submodels. The online module uses real-time monitoring data for weather type identification on target days and the forecasting of irradiation intensity and temperature time series. The appropriate regression submodel can be selected based on the subsequent results, and the ultrashort-term real-time forecasting of PV output can be performed over a short time scale. The model incorporates power generation and historical meteorological data from the PV station and is suitable for practical engineering applications. In addition to the irradiation intensity and temperature, other factors related to photovoltaic output are evaluated; however, they are excluded from the model for simplicity and efficiency. The performance of the model is verified by practical modeling analysis.

Keywords: hybrid forecasting; photovoltaic; KFCM; SVR (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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