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On recent advances in PV output power forecast

期刊

SOLAR ENERGY
卷 136, 期 -, 页码 125-144

出版社

PERGAMON-ELSEVIER SCIENCE LTD
DOI: 10.1016/j.solener.2016.06.073

关键词

Photovoltaic; Artificial intelligence (Al); Regressive techniques; PV output forecasting; Hybrid forecast models; Artificial neural network (ANN); Auto-regressive moving average; Fuzzy Logic; Persistence method; Statistical methods

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In last decade, the higher penetration of renewable energy resources (RES) in energy market was encouraged by implementing the energy polices in several developed and developing countries due to increasing environmental concerns. Among wide range of RES, Photovoltaic (PV) electricity generation get higher attention by researcher, energy policy makers and power production companies due to its economic and environmental benefits. Therefore, a large PV penetration was observed in energy market with rapid growth in the last decade. The PV output power is highly uncertain due to several meteorological factors such as temperature, wind speed, cloud cover, atmospheric aerosol levels and humidity level. The inherent variability of PV output power creates different issues directly or indirectly for power grid such as power system control and reliability, reserves cost, dispatchable and ancillary generation, grid integration and power planning. Therefore, there is need to accurately forecast the PV output over the spectrum of forecast horizon at different chronological scales. In this paper, a comprehensive and systematic review of PV output power forecast models were provided. This review covers the different factors affecting PV forecast, PV output power profile and performance matrices to evaluate the forecast model. The critical analysis regressive and artificial intelligence based forecast models are also presented. In addition, the potential benefits of hybrid techniques for PV forecast models are also thoroughly discussed. (C) 2016 Elsevier Ltd. All rights reserved.

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