Application of extreme learning machine for short term output power forecasting of three grid-connected PV systems-Reference-Cited by-同舟云学术

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

Published:2017-11 Issue: Volume:167 Page:395-405
ISSN:0959-6526
Container-title:Journal of Cleaner Production
language:en
Short-container-title:Journal of Cleaner Production

Author:

Hossain Monowar,Mekhilef Saad,Danesh Malihe,Olatomiwa Lanre,Shamshirband Shahaboddin

Funder

University of Malaya

Postgraduate Research Grant

Ministry of Higher Education of Malaysia

Fundamental Research Grant Scheme

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Strategy and Management,General Environmental Science,Renewable Energy, Sustainability and the Environment

Reference51 articles.

1. Support Vector Machines for Pattern Classification;Abe,2005

2. A methodology based on dynamic artificial neural network for short-term forecasting of the power output of a PV generator;Almonacid;Energy Convers. Manag.,2014

3. Review of the methods for evaluation of renewable energy sources penetration and ramping used in the Scenario Outlook and Adequacy Forecast 2015. Case study for Poland;Andrychowicz;Renew. Sustain. Energy Rev.,2017

4. Review of photovoltaic power forecasting;Antonanzas;Sol. Energy,2016

5. Online short-term solar power forecasting;Bacher;Sol. Energy,2009

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