Evaluation of temperature-based machine learning and empirical models for predicting daily global solar radiation-Reference-Cited by-同舟云学术

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Evaluation of temperature-based machine learning and empirical models for predicting daily global solar radiation

Published:2019-10 Issue: Volume:198 Page:111780
ISSN:0196-8904
Container-title:Energy Conversion and Management
language:en
Short-container-title:Energy Conversion and Management

Author:

Feng Yu,Gong Daozhi,Zhang Qingwen,Jiang Shouzheng,Zhao Lu,Cui Ningbo

Funder

National Key Research and Development Program of China

Central University Special Fund Basic Research and Operating Expenses

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

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

Reference51 articles.

1. Classification of solar radiation zones and general models for estimating the daily global solar radiation on horizontal surfaces in China;Liu;Energy Convers Manage,2017

2. Carbon emission coefficient measurement of the coal-to-power energy chain in China;Yu;Appl Energy,2014

3. Identifying the most significant input parameters for predicting global solar radiation using an ANFIS selection procedure;Mohammad;Renew Sustain Energy Rev,2016

4. A new approach to estimate the spatial distribution of solar radiation using topographic factor and sunshine duration in South Korea;Park;Energy Convers Manage,2015

5. Assessing the potential of random forest method for estimating solar radiation using air pollution index;Sun;Energy Convers Manage,2016

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