A survey of artificial intelligence methods for renewable energy forecasting: Methodologies and insights
Authors:
- Blessing Olatunde AbisoyeBABlessing Olatunde Abisoye
- Yanxia SunYSYanxia Sun
- Wang ZenghuiWZWang Zenghui
Open Access
- 20 December 2023
- journal article
- research article
- Published by Elsevier in Renewable Energy Focus
- Vol. 48
Abstract
No abstract availableKeywords
Funding Information
- National Research Foundation (132797)
- National Research Foundation (137951)
- National Research Foundation (141951)
- University of Johannesburg
This publication has 105 references indexed in Scilit:
- Support vector regression based prediction of global solar radiation on a horizontal surfaceEnergy Conversion and Management, 2015
- Prediction of the solar radiation on the Earth using support vector regression techniqueInfrared Physics & Technology, 2015
- Prediction of hourly solar radiation with multi-model frameworkEnergy Conversion and Management, 2013
- Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS databaseSolar Energy, 2011
- Reservoir computing approaches to recurrent neural network trainingComputer Science Review, 2009
- Renewable energy today and tomorrowProceedings of the IEEE, 2001
- Support vector machinesACM SIGKDD Explorations Newsletter, 2000
- Long Short-Term MemoryNeural Computation, 1997
- A Decision-Theoretic Generalization of On-Line Learning and an Application to BoostingJournal of Computer and System Sciences, 1997
- Stacked generalizationNeural Networks, 1992