Forecasting of photovoltaic power generation and model optimization: A review

• Published in: Renewable & sustainable energy reviews Vol. 81; pp. 912 - 928
• Main Authors: Das, Utpal Kumar, Tey, Kok Soon, Seyedmahmoudian, Mehdi, Mekhilef, Saad, Idris, Moh Yamani Idna, Van Deventer, Willem, Horan, Bend, Stojcevski, Alex
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.01.2018
• Subjects: Artificial intelligence; Hybrid model; Machine-learning; Optimization; PV power forecasting; ANN; MRE; PV; GHG; ARMAX; SVM; HS; FS; SVR; Optimization; nRMSE; MBE; MA; PV power forecasting; GA; Hybrid model; ARMA; ANFIS; MSE; IEA; NWP; AI; MAPE; RMSE; MAE; AR; ARIMA; Artificial intelligence; Machine-learning
• ISSN: 1364-0321; 1879-0690
• DOI: 10.1016/j.rser.2017.08.017

To mitigate the impact of climate change and global warming, the use of renewable energies is increasing day by day significantly. A considerable amount of electricity is generated from renewable energy sources since the last decade. Among the potential renewable energies, photovoltaic (PV) has experienced enormous growth in electricity generation. A large number of PV systems have been installed in on-grid and off-grid systems in the last few years. The number of PV systems will increase rapidly in the future due to the policies of the government and international organizations, and the advantages of PV technology. However, the variability of PV power generation creates different negative impacts on the electric grid system, such as the stability, reliability, and planning of the operation, aside from the economic benefits. Therefore, accurate forecasting of PV power generation is significantly important to stabilize and secure grid operation and promote large-scale PV power integration. A good number of research has been conducted to forecast PV power generation in different perspectives. This paper made a comprehensive and systematic review of the direct forecasting of PV power generation. The importance of the correlation of the input-output data and the preprocessing of model input data are discussed. This review covers the performance analysis of several PV power forecasting models based on different classifications. The critical analysis of recent works, including statistical and machine-learning models based on historical data, is also presented. Moreover, the strengths and weaknesses of the different forecasting models, including hybrid models, and performance matrices in evaluating the forecasting model, are considered in this research. In addition, the potential benefits of model optimization are also discussed.