https://ph01.tci-thaijo.org/index.php/RAST <p>Welcome to the official website of the <strong>Journal of Renewable Energy and Smart Grid Technology (RAST).</strong></p> <p>The journal aims to publish articles about renewable energy and smart grid technology written by professors, scholars, and business practitioners. It encourages the exchange of information and discussions to promote widespread deployment and investment in these technologies. The journal seeks high-quality research articles for regular submission and also publishes reviews on important development areas, which may be submitted or invited by the editors.</p> <p>All papers in RAST undergo double-blind peer review by at least three reviewers from various outside institutions to ensure scientific quality.</p> <p><strong>Journal of Renewable Energy and Smart Grid Technology (RAST)<br /></strong><strong>Journal Abbreviation:</strong> J. Renew. Energy Smart Grid Technol.<br /><strong>Start Year: </strong>2018</p> <p><strong>ISSN: </strong><strong>2630-0036 (Electronic version)</strong><strong><br />Frequency: 2 Issues/Year (1) January-June (2) July-December</strong></p> <p><strong>Language: </strong>English</p> <p><strong>Editor-in-Chief</strong> : Nipon Ketjoy<br /><strong>ORCID</strong> : <a href="https://orcid.org/0000-0002-9324-0905" target="_blank" rel="noopener">https://orcid.org/0000-0002-9324-0905</a></p> <p><strong>Crossref Membership &amp; DOI assignment</strong><br />RAST is a member of Crossref, with a DOI prefix of 10.69650. The direct DOI link is https://doi.org/10.69650/rast. Starting from Vol. 19 (2024), all of our published articles are assigned a DOI and registered in Crossref. Additionally, RAST implements reference linking, ensuring that each article's references are verified and DOIs are added.<br /><img src="https://ph01.tci-thaijo.org/public/site/images/niponk/crossref-logo.png" alt="" width="184" height="100" /></p> School of Renewable Energy and Smart grid Technology (SGtech), Naresuan University en-US Journal of Renewable Energy and Smart Grid Technology 2586-8764 <p>All copyrights of the above manuscript, including rights to publish in any media, are transferred to the SGtech.</p> <p>The authors retain the following rights;</p> <p> 1. All proprietary rights other than copyright.</p> <p> 2. Re-use of all or part of the above manuscript in their work.</p> <p> 3. Reproduction of the above manuscript for author’s personal use or for company/institution use provided that</p> <p> (a) prior permission of SGtech is obtained,</p> <p> (b) the source and SGtech copyright notice are indicated, and</p> <p> (c) the copies are not offered for sale.</p> https://ph01.tci-thaijo.org/index.php/RAST/article/view/259017 <p>Net radiation is the difference between downward and upward radiation, considering both shortwave and longwave radiation. The net radiation controls the water cycle, plant photosynthesis, the earth’s climate changes, and the energy balance. In this paper, the Artificial Neural Network (ANN) model is developed for estimating daily net radiation from meteorological data that are based on maximum air temperature, minimum air temperature, daily relative humidity, and daily solar radiation. Net radiation and meteorological data collected for 7 years (2017-2023) from Chiang Mai meteorological station (CM: 18.77°N, 98.96°E), Ubon Ratchathani meteorological station (UB: 15.24°N, 105.02°E), Nakhon Pathom meteorological station (NP: 14.01°N, 99.96°E), and Songkhla meteorological station (SK: 7.41°N, 100.62°E) were used to train and test the model. The discrepancy between the net radiation estimated by the ANN and the measured net radiation was presented in terms of determination coefficient (R2), relative root mean square error (RMSE), and relative mean bias error (MBE). The model showed 0.98, 14.48%, and -2.17%, respectively. The result shows that the artificial neural network model is an accurate and easy option for estimating surface net radiation.</p> Chutimon Phoemwong Rungrat Wattan Serm Janjai Copyright (c) 2025 School of Renewable Energy and Smart Grid Technology (SGtech) https://creativecommons.org/licenses/by-nc-nd/4.0 2025-01-24 2025-01-24 20 1 1 7 10.69650/rast.2025.259017