Systematic literature review of photovoltaic output power forecasting
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Tarih
2020
Dergi Başlığı
Dergi ISSN
Cilt Başlığı
Yayıncı
Inst Engineering Technology-Iet
Erişim Hakkı
info:eu-repo/semantics/openAccess
Özet
Since the harmful effects of climate warming on our planet were first observed, the use of renewable energy resources has been significantly increasing. Among the potential renewable energy sources, photovoltaic (PV) system installations keep continuously increasing world-wide due to its economic and environmental contributions. Despite its significant benefits, the inherent variability of PV power generation due to meteorological parameters can cause power management/planning problems. Thus, forecasting of PV output data (directly or indirectly) in an accurate manner is a critical task to provide stability, reliability, and optimisation of the grid systems. In considering the literature reviewed, there are various research items utilizing PV output power forecasting. In this study, a systematic literature review based on the search of primary studies (published between 2010 and 2020), which forecast PV power generation using machine learning and deep learning methods, is reported. The studies are evaluated based on the PV material used, their approaches, generated outputs, data set used, and the performance evaluation methods. As a result, gaps and improvable points in the existing literature are revealed, and suggestions which include novelties are offered for future works.
Açıklama
Anahtar Kelimeler
learning (artificial intelligence), photovoltaic power systems, renewable energy sources, systematic literature review, photovoltaic output power forecasting, renewable energy resources, potential renewable energy sources, photovoltaic system installations, world-wide, economic contributions, environmental contributions, PV power generation, PV output data, grid systems, PV output power forecasting, PV material, generated outputs
