Development of Machine Learning Models based on Air Temperature for Estimation of Global Solar Radiation in India

2021 ◽  
Author(s):  
Shahid Husain ◽  
Uzair Ali Khan
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Air Temperature ◽  
Global Solar Radiation ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Global solar radiation modeling using different machine learning and empirical models in Northeast China

2021 ◽  
Author(s):  
Yue Jia ◽  
Yongjun Su ◽  
Fengchun Wang ◽  
Pengcheng Li ◽  
Shuyi Huo
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Solar Energy ◽  
Northeast China ◽  
Sunshine Duration ◽  
Meteorological Data ◽  
Global Solar Radiation ◽  
Empirical Models ◽  
Learning Models ◽  
Machine Learning Models

Abstract Reliable global solar radiation (Rs) information is crucial for the design and management of solar energy systems for agricultural and industrial production. However, Rs measurements are unavailable in many regions of the world, which impedes the development and application of solar energy. To accurately estimate Rs, this study developed a novel machine learning model, called a Gaussian exponential model (GEM), for daily global Rs estimation. The GEM was compared with four other machine learning models and two empirical models to assess its applicability using daily meteorological data from 1997–2016 from four stations in Northeast China. The results showed that the GEM with complete inputs had the best performance. Machine learning models provided better estimates than empirical models when trained by the same input data. Sunshine duration was the most effective factor determining the accuracy of the machine learning models. Overall, the GEM with complete inputs had the highest accuracy and is recommended for modeling daily Rs in Northeast China.

A review on global solar radiation prediction with machine learning models in a comprehensive perspective

2021 ◽  
Vol 235 ◽  
pp. 113960
Author(s):  
Yong Zhou ◽  
Yanfeng Liu ◽  
Dengjia Wang ◽  
Xiaojun Liu ◽  
Yingying Wang
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Learning Models ◽  
Machine Learning Models

Machine learning models to quantify and map daily global solar radiation and photovoltaic power

2020 ◽  
Vol 118 ◽  
pp. 109393 ◽  
Author(s):  
Yu Feng ◽  
Weiping Hao ◽  
Haoru Li ◽  
Ningbo Cui ◽  
Daozhi Gong ◽  
...  
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Global Solar Radiation ◽  
Learning Models ◽  
Photovoltaic Power ◽  
Machine Learning Models

Modeling soil temperature using air temperature features in diverse climatic conditions with complementary machine learning models

2021 ◽  
Vol 185 ◽  
pp. 106158
Author(s):  
Maryam Bayatvarkeshi ◽  
Suraj Kumar Bhagat ◽  
Kourosh Mohammadi ◽  
Ozgur Kisi ◽  
M. Farahani ◽  
...  
Keyword(s):  
Machine Learning ◽  
Soil Temperature ◽  
Air Temperature ◽  
Climatic Conditions ◽  
Learning Models ◽  
Machine Learning Models

scholarly journals Simulation of Daily Diffuse Solar Radiation Based on Three Machine Learning Models

2020 ◽  
Vol 123 (1) ◽  
pp. 49-73
Author(s):  
Jianhua Dong ◽  
Lifeng Wu ◽  
Xiaogang Liu ◽  
Cheng Fan ◽  
Menghui Leng ◽  
...  
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Learning Models ◽  
Diffuse Solar Radiation ◽  
Machine Learning Models

scholarly journals Hybrid Machine Learning for Solar Radiation Prediction in Reduced Feature Spaces

Energies ◽  
2021 ◽  
Vol 14 (23) ◽  
pp. 7970
Author(s):  
Abdel-Rahman Hedar ◽  
Majid Almaraashi ◽  
Alaa E. Abdel-Hakim ◽  
Mahmoud Abdulrahim
Keyword(s):  
Machine Learning ◽  
Solar Radiation ◽  
Root Mean Square ◽  
Prediction Models ◽  
Weather Prediction ◽  
Hybrid Models ◽  
Mean Square ◽  
Learning Models ◽  
Mean Square Errors ◽  
Machine Learning Models

Solar radiation prediction is an important process in ensuring optimal exploitation of solar energy power. Numerous models have been applied to this problem, such as numerical weather prediction models and artificial intelligence models. However, well-designed hybridization approaches that combine numerical models with artificial intelligence models to yield a more powerful model can provide a significant improvement in prediction accuracy. In this paper, novel hybrid machine learning approaches that exploit auxiliary numerical data are proposed. The proposed hybrid methods invoke different machine learning paradigms, including feature selection, classification, and regression. Additionally, numerical weather prediction (NWP) models are used in the proposed hybrid models. Feature selection is used for feature space dimension reduction to reduce the large number of recorded parameters that affect estimation and prediction processes. The rough set theory is applied for attribute reduction and the dependency degree is used as a fitness function. The effect of the attribute reduction process is investigated using thirty different classification and prediction models in addition to the proposed hybrid model. Then, different machine learning models are constructed based on classification and regression techniques to predict solar radiation. Moreover, other hybrid prediction models are formulated to use the output of the numerical model of Weather Research and Forecasting (WRF) as learning elements in order to improve the prediction accuracy. The proposed methodologies are evaluated using a data set that is collected from different regions in Saudi Arabia. The feature-reduction has achieved higher classification rates up to 8.5% for the best classifiers and up to 15% for other classifiers, for the different data collection regions. Additionally, in the regression, it achieved improvements of average root mean square error up to 5.6% and in mean absolute error values up to 8.3%. The hybrid models could reduce the root mean square errors by 70.2% and 4.3% than the numerical and machine learning models, respectively, when these models are applied to some dataset. For some reduced feature data, the hybrid models could reduce the root mean square errors by 47.3% and 14.4% than the numerical and machine learning models, respectively.

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