Improving solar energy prediction in complex topography using artificial neural networks: Case study Peninsular Malaysia-Reference-Cited by-同舟云学术

Improving solar energy prediction in complex topography using artificial neural networks: Case study Peninsular Malaysia

Published:2015-04-30 Issue:5 Volume:34 Page:1528-1535
ISSN:1944-7442
Container-title:Environmental Progress & Sustainable Energy
language:en
Short-container-title:Environ. Prog. Sustainable Energy

Author:

Wadi Abbas Al-Fatlawi Ali¹²³,Rahim Nasrudin Abdul¹,Saidur Rahman⁴,Ward Thomas Arthur²

Affiliation:

1. University of Malaya Power Energy Dedicated Advanced Centre (UMPEDAC); 59990 Kuala Lumpur Malaysia

2. Department of Mechanical Engineering; University of Malaya; 50603 Kuala Lumpur Malaysia

3. Department of Material Engineering; University of Kufa; Iraq

4. Center of Research Excellence in Renewable Energy (CoRE-RE); King Fahd University of Petroleum and Minerals (KFUPM); Dhahran 31261 Kingdom of Saudi Arabia

Funder

Ministry of Higher Education of Malaysia and the University of Malaya

Publisher

Wiley

Subject

General Environmental Science,Waste Management and Disposal,Water Science and Technology,General Chemical Engineering,Renewable Energy, Sustainability and the Environment,Environmental Chemistry,Environmental Engineering

Link

http://onlinelibrary.wiley.com/wol1/doi/10.1002/ep.12130/fullpdf

Reference37 articles.

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4. Empirical models for estimating global solar radiation: A review and case study;Besharat;Renewable and Sustainable Energy Reviews,2013

5. Solar forecasting methods for renewable energy integration;Inman;Progress in Energy and Combustion Science,2013

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