Solar irradiance measurement instrumentation and power solar generation forecasting based on Artificial Neural Networks (ANN): A review of five years research trend

Abdul Rahim Pazikadin; Damhuji Rifai; Kharudin Ali; Muhammad Zeesan Malik; Ahmed N Abdalla; Moneer A Faraj

doi:10.1016/j.scitotenv.2020.136848

Solar irradiance measurement instrumentation and power solar generation forecasting based on Artificial Neural Networks (ANN): A review of five years research trend

Sci Total Environ. 2020 May 1:715:136848. doi: 10.1016/j.scitotenv.2020.136848. Epub 2020 Jan 22.

Authors

Abdul Rahim Pazikadin¹, Damhuji Rifai², Kharudin Ali³, Muhammad Zeesan Malik⁴, Ahmed N Abdalla⁵, Moneer A Faraj⁶

Affiliations

¹ Faculty of Electrical and Automation Engineering Technology, TATIUC, 24000, Terengganu, Malaysia.
² Faculty of Electrical and Automation Engineering Technology, TATIUC, 24000, Terengganu, Malaysia. Electronic address: damhuji@tatiuc.edu.my.
³ Institite of Sustainable Energy (ISE), UNITEN, 43000, Selangor, Malaysia.
⁴ Faculty of Automation Huai'an, Huaiyin Institute of Technology, Huai'an, Jiangsu, China.
⁵ Faculty of Electronic Information Engineering, Huaiyin Institute of Technology, Huai'an, Jiangsu, China.
⁶ Electrical and Electronics Engineering Department, Omar Al-Mukhtar University, Al Baida, Libya.

PMID: 32040994
DOI: 10.1016/j.scitotenv.2020.136848

Abstract

The increased demand for solar renewable energy sources has created recent interest in the economic and technical issues related to the integration of Photovoltaic (PV) into the grid. Solar photovoltaic power generation forecasting is a crucial aspect of ensuring optimum grid control and power solar plant design. Accurate forecasting provides significant information to grid operators and power system designers in generating an optimal solar photovoltaic plant and to manage the power of demand and supply. This paper presents an extensive review on the implementation of Artificial Neural Networks (ANN) on solar power generation forecasting. The instrument used to measure the solar irradiance is analysed and discussed, specifically on studies that were published from February 1st, 2014 to February 1st, 2019. The selected papers were obtained from five major databases, namely, Direct Science, IEEE Xplore, Google Scholar, MDPI, and Scopus. The results of the review demonstrate the increased application of ANN on solar power generation forecasting. The hybrid system of ANN produces accurate results compared to individual models. The review also revealed that improvement forecasting accuracy can be achieved through proper handling and calibration of the solar irradiance instrument. This finding indicates that improvements in solar forecasting accuracy can be increased by reducing instrument errors that measure the weather parameter.

Keywords: Artificial Neural Networks; Forecasting; Irradiance; Photovoltaic; Solar.