https://ph01.tci-thaijo.org/index.php/RAST <p>The purposes of the journal are to disseminate articles relating to renewable energy and smart grid technology, written by the professors and scholars of educational institutes, research institutes and other organizations. It encourages and supports the exchange of renewable energy and smart grid technology academic information, in order to develop renewable energy and smart grid technology for concrete use. The quality research papers are solicited in. The Journal also carries reviews on important development areas and these may either be submitted in the normal way or invited by the editors.</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/254636 <p>The increase in electric vehicles (EVs) has resulted in a substantial escalation in electricity consumption. This increased demand puts more stress on the overall power system. The current study offers a method to predict energy usage patterns by looking closely at when electric vehicles typically need to charge during the day. After that, the collected data were used to create a predictive model using three different deep learning methods: Recurrent Neural Networks (RNNs), Long Short-Term Memory networks (LSTMs), and Gated Recurrent Units (GRUs). This study employs data pertaining to electric power consumption for EVs charging derived from Kamphaeng Phet Rajabhat University. The practical results show that the proposed model significantly outperforms in predicting power needs at the mentioned charging spots. This is evident in its precise prediction of the total power demands using the algorithm. Among the three types of deep learning structures studied, it's clear that the LSTMs type stands out as the best, achieving the most accurate results. This is supported by a Root Mean Square Error (RMSE) of 0.372 and a Mean Absolute Percentage Error (MAPE) of 11.508%. Additionally, the inquiry facilitates a comprehensive comparison between the dynamics of demand and the parameters of supply. This process yields data that offers valuable insights crucial for the strategic identification of potential electric vehicle charging stations. It also enables the prudent utilization of remaining electrical capacity derived from production processes. These combined efforts converge to ensure the utmost extraction of utility.</p> Nivadee Klungsida Pakin Maneechot Narut Butploy Kanokwan Khiewwan Copyright (c) 2024 School of Renewable Energy and Smart Grid Technology (SGtech) https://creativecommons.org/licenses/by-nc-nd/4.0 2024-04-19 2024-04-19 19 1 1 6