Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems

Hossain, Monowar and Mekhilef, Saad and Danesh, Malihe and Olatomiwa, Lanre and Shamshirband, Shahaboddin (2017) Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems. Journal of Cleaner Production, 167. pp. 395-405. ISSN 0959-6526, DOI

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The power output (PO) of a photovoltaic (PV) system is highly variable because of its dependence on solar irradiance and other meteorological factors. Hence, accurate PO forecasting of a grid-connected PV system is essential for grid stability, optimal unit commitment, economic dispatch, market participation and regulations. In this paper, a day ahead and 1 h ahead mean PV output power forecasting model has been developed based on extreme learning machine (ELM) approach. For this purpose, the proposed forecasting model is trained and tested using PO of PV system and other meteorological parameters recorded in three grid-connected PV system installed on a roof-top of PEARL laboratory in University of Malaya, Malaysia. The results obtained from the proposed model are compared with other popular models such as support vector regression (SVR) and artificial neural network (ANN). The performance in terms of accuracy and precision of the prediction models is conducted with standard statistical error indicators including: relative root mean square error (RMSE), mean absolute percentage error (MAPE), mean absolute bias error (MABE) and coefficient of determination (R2). The comparison of results obtained from the proposed ELM model to other models showed that ELM model enjoys higher accuracy and less computational time in forecasting the daily and hourly PV output power.

Item Type: Article
Funders: University of Malaya , Malaysia, through the Postgraduate Research Grant (PPP) PG156-2016A, Ministry of Higher Education of Malaysia through the Fundamental Research Grant Scheme (FRGS) FP014-2014A
Uncontrolled Keywords: PV system; Forecasting; ELM; Statistical indicators
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Engineering
Depositing User: Ms. Juhaida Abd Rahim
Date Deposited: 07 Aug 2019 08:17
Last Modified: 07 Aug 2019 08:17

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