On recent advances in PV output power forecast

Raza, Muhammad Qamar, Nadarajah, Mithulananthan and Ekanayake, Chandima (2016) On recent advances in PV output power forecast. Solar Energy, 136 125-144. doi:10.1016/j.solener.2016.06.073

Author Raza, Muhammad Qamar
Nadarajah, Mithulananthan
Ekanayake, Chandima
Title On recent advances in PV output power forecast
Journal name Solar Energy   Check publisher's open access policy
ISSN 0038-092X
Publication date 2016-10-15
Year available 2016
Sub-type Critical review of research, literature review, critical commentary
DOI 10.1016/j.solener.2016.06.073
Open Access Status Not Open Access
Volume 136
Start page 125
End page 144
Total pages 20
Place of publication Oxford, United Kingdom
Publisher Elsevier
Collection year 2017
Formatted abstract
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.
Keyword Artificial intelligence (AI)
Artificial neural network (ANN)
Auto-regressive moving average
Fuzzy logic
Hybrid forecast models
Persistence method
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Critical review of research, literature review, critical commentary
Collections: HERDC Pre-Audit
School of Information Technology and Electrical Engineering Publications
Version Filter Type
Citation counts: Scopus Citation Count Cited 0 times in Scopus Article
Google Scholar Search Google Scholar
Created: Fri, 29 Jul 2016, 10:45:50 EST by Muhammad Qamar Raza on behalf of Learning and Research Services (UQ Library)