Employing genetic programming to find the best correlation to predict temperature of solar photovoltaic panels-Reference-Cited by-同舟云学术

Employing genetic programming to find the best correlation to predict temperature of solar photovoltaic panels

Published:2020-11 Issue: Volume:224 Page:113291
ISSN:0196-8904
Container-title:Energy Conversion and Management
language:en
Short-container-title:Energy Conversion and Management

Author:

Sohani Ali^ORCID,Sayyaadi Hoseyn^ORCID

Publisher

Elsevier BV

Subject

Energy Engineering and Power Technology,Fuel Technology,Nuclear Energy and Engineering,Renewable Energy, Sustainability and the Environment

Reference88 articles.

1. The influence of the solar radiation database and the photovoltaic simulator on the sizing and economics of photovoltaic-diesel generators;de Freitas Moscardini;Energy Convers Manage,2020

2. Research frontiers in sustainable development of energy, water and environment systems in a time of climate crisis;Kılkış;Energy Convers Manage,2019

3. Floating photovoltaic plants: Ecological impacts versus hydropower operation flexibility;Haas;Energy Convers Manage,2020

4. Comprehensive study and optimization of concentrated photovoltaic-thermoelectric considering all contact resistances;Shittu;Energy Convers Manage,2020

5. An economic analysis of the integration between air-conditioning and solar photovoltaic systems;Novaes Pires Leite;Energy Convers Manage,2019

Cited by 61 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Experimentally validated thermal modeling for temperature prediction of photovoltaic modules under variable environmental conditions;Renewable Energy;2024-09

2. Investigation into the thermodynamic properties of three coupling schemes involving CAES, MSHS, and CFPP systems;Journal of Energy Storage;2024-09

3. A systematic review of a photoelectrical innovation: dye-sensitized solar cells;International Journal of Ambient Energy;2024-07-02

4. Experimental and numerical modeling of photovoltaic modules temperature under varying ambient conditions;Energy Conversion and Management;2024-07

5. Photovoltaic module temperature prediction using various machine learning algorithms: Performance evaluation;Applied Energy;2024-06