Forecasting of photovoltaic power generation and model optimization: A review

被引:620
|
作者
Das, Utpal Kumar [1 ]
Tey, Kok Soon [1 ]
Seyedmahmoudian, Mehdi [1 ]
Mekhilef, Saad [2 ]
Idris, Moh Yamani Idna [3 ]
Van Deventer, Willem [3 ]
Horan, Bend [3 ]
Stojcevski, Alex [4 ]
机构
[1] Univ Malaya, Fac Comp Sci & Informat Technol, Dept Comp Syst & Technol, Kuala Lumpur 50603, Malaysia
[2] Univ Malaya, Fac Engn, Dept Elect Engn, Power Elect & Renewable Energy Res Lab PEARL, Kuala Lumpur 50603, Malaysia
[3] Deakin Univ, Sch Engn, Geelong, Vic 3216, Australia
[4] Swinburne Univ Technol, Sch Software & Elect Engn, Hawthorn, Vic 3122, Australia
来源
关键词
PV power forecasting; Artificial intelligence; Machine-learning; Hybrid model; Optimization; ARTIFICIAL NEURAL-NETWORK; SUPPORT VECTOR REGRESSION; SOLAR-RADIATION; TIME-SERIES; HYBRID METHOD; OUTPUT; TERM; IRRADIANCE; SYSTEM; MACHINE;
D O I
10.1016/j.rser.2017.08.017
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
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.
引用
收藏
页码:912 / 928
页数:17
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