Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia-Reference-Cited by-同舟云学术

Short-term electricity demand forecasting using machine learning methods enriched with ground-based climate and ECMWF Reanalysis atmospheric predictors in southeast Queensland, Australia

Published:2019-10 Issue: Volume:113 Page:109293
ISSN:1364-0321
Container-title:Renewable and Sustainable Energy Reviews
language:en
Short-container-title:Renewable and Sustainable Energy Reviews

Author:

AL-Musaylh Mohanad S.,Deo Ravinesh C.,Adamowski Jan F.,Li Yan

Funder

United Nations

Ministry of Higher Education and Scientific Research

Publisher

Elsevier BV

Subject

Renewable Energy, Sustainability and the Environment

Reference94 articles.

1. Resolution adopted by the general assembly on 25 september 2015;Assembly,2015

2. Energy transformed: sustainable energy solutions for climate change mitigation;Smith,2007

3. Australian electricity market analysis report to 2020 and 2030;Brinsmead,2014

4. Forecasting regional electricity load based on recurrent support vector machines with genetic algorithms;Pai;Electr Power Syst Res,2005

5. The Australian Energy Market Operator is responsible for operating Australia's largest gas and electricity markets and power systems];AEMO. Aggregated Price and Demand Data - Historical,2018

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