Forecasting of photovoltaic power generation and model optimization: A review. (January 2018)

• Record Type: Journal Article
• Authors: Das, Utpal Kumar; Tey, Kok Soon; Seyedmahmoudian, Mehdi; Mekhilef, Saad; Idris, Moh Yamani Idna; Van Deventer, Willem; Horan, Bend; Stojcevski, Alex
• Is Part Of: Renewable & sustainable energy reviews. Volume 81:Part 1(2018)
• Page Start: 912
• Page End: 928
• Publication Date: 2018-01
• Subjects: PV photovoltaic -- NWP numerical weather prediction -- AI artificial intelligence -- AR auto regressive -- MA moving average -- ARMA auto regressive moving average -- ARIMA AR integrated MA -- ARMAX ARMA exogenous -- ANN artificial neural network -- SVM support vector machine -- SVR support vector regression -- HS hybrid system -- FS fuzzy system -- ANFIS adaptive neuro fuzzy inference system -- GA genetic algorithm -- GHG greenhouse gas -- IEA international energy agency -- MSE mean square error -- RMSE root mean square error -- nRMSE normalized root mean square error -- MAE mean absolute error -- MAPE mean absolute percentage error -- MRE mean relative error -- MBE mean bias error; PV power forecasting -- Artificial intelligence -- Machine-learning -- Hybrid model -- Optimization; Renewable energy sources -- Periodicals; Power resources -- Periodicals; Énergies renouvelables -- Périodiques; Ressources énergétiques -- Périodiques; 333.794
• DOI: 10.1016/j.rser.2017.08.017
• Languages: English
• ISSNs: 1364-0321

Abstract

Abstract: 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.