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

Monowar Hossain, Saad Mekhilef, Malihe Danesh, Lanre Olatomiwa, Shahaboddin Shamshirband
  • Journal of Cleaner Production, November 2017, Elsevier
  • DOI: 10.1016/j.jclepro.2017.08.081

Short term output power forecasting of three grid-connected PV systems

What is it about?

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.

Why is it important?

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.

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The following have contributed to this page: Dr Lanre Olatomiwa