Browsing by Author "Kittisak Jermsittiparsert"
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Item An Intelligent Recommendation for Intelligently Accessible Charging Stations: Electronic Vehicle Charging to Support a Sustainable Smart Tourism City(MDPI, 2023) Pannee Suanpang; Pitchaya Jamjuntr; Phuripoj Kaewyong; Chawalin Niamsorn; Kittisak Jermsittiparsert; P. Suanpang; Faculty of Science & Technology, Suan Dusit University, Bangkok, 10300, Thailand; email: pannee_sua@dusit.ac.th; K. Jermsittiparsert; Faculty of Education, University of City Island, Famagusta, 9945, Cyprus; email: kittisak.jermsittiparsert@adakent.edu.trThe world is entering an era of awareness of the preservation of natural energy sustainability. Therefore, electric vehicles (EVs) have become a popular alternative in todayÕs transportation system as they have zero emissions, save energy, and reduce pollution. One of the most significant problems with EVs is an inadequate charging infrastructure and spatially and temporally uneven charging demands. As such, EV drivers in many large cities frequently struggle to find suitable charging locations. Furthermore, the recent emergence of deep reinforcement learning has shown great promise for improving the charging experience in a variety of ways over the long term. In this paper, a Spatio-Temporal Multi-Agent Reinforcement Learning (STMARL) (Master) framework is proposed for intelligently public-accessible charging stations, taking into account several long-term spatio-temporal parameters. When compared to a random selection recommendation system, the experimental results demonstrate that an STMARL (master) framework has a long-term goal of lowering the overall charging wait time (CWT), average charging price (CP), and charging failure rate (CFR) of EVs. © 2022 by the authors.Item Autonomous Energy Management by Applying Deep Q-Learning to Enhance Sustainability in Smart Tourism Cities(MDPI, 2022) Pannee Suanpang; Pitchaya Jamjuntr; Kittisak Jermsittiparsert; Phuripoj Kaewyong; P. Suanpang; Faculty of Science and Technology, Suan Dusit University, Bangkok, 10300, Thailand; email: pannee_sua@dusit.ac.thAutonomous energy management is becoming a significant mechanism for attaining sustainability in energy management. This resulted in this research paper, which aimed to apply deep reinforcement learning algorithms for an autonomous energy management system of a microgrid. This paper proposed a novel microgrid model that consisted of a combustion set of a household load, renewable energy, an energy storage system, and a generator, which were connected to the main grid. The proposed autonomous energy management system was designed to cooperate with the various flexible sources and loads by defining the priority resources, loads, and electricity prices. The system was implemented by using deep reinforcement learning algorithms that worked effectively in order to control the power storage, solar panels, generator, and main grid. The system model could achieve the optimal performance with near-optimal policies. As a result, this method could save 13.19% in the cost compared to conducting manual control of energy management. In this study, there was a focus on applying Q-learning for the microgrid in the tourism industry in remote areas which can produce and store energy. Therefore, we proposed an autonomous energy management system for effective energy management. In future work, the system could be improved by applying deep learning to use energy price data to predict the future energy price, when the system could produce more energy than the demand and store it for selling at the most appropriate price; this would make the autonomous energy management system smarter and provide better benefits for the tourism industry. This proposed autonomous energy management could be applied to other industries, for example businesses or factories which need effective energy management to maintain microgrid stability and also save energy. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Item Efficient use of energy resources on Malaysian farms(Econjournals, 2020) Thitinan Chankoson; Kittisak Jermsittiparsert; Thitinant Wareewanich; K. Jermsittiparsert; Contemporary Peasant Society Research Unit, Social Research Institute, Chulalongkorn University, Bangkok, Thailand; email: kittisak.j@chula.ac.thThe purpose of this study is to discover an approach where the outputs of the farms are maximum at the minimal input. Malaysia is well known for its crop (such as rubber, rice, palm oil, tea). Prior studies show that due to the climate change, there are likely chances that the farms of Malaysia will go extinct. In this study, the main focus is to efficiently use the energy resources to save it for the future in a prolonged manner. The data was collected from the website of Department of Statistics Malaysia, Official Portal. The data was taken for rubber and crops category. In order to run the analysis, the non-parametric approach was used, which is also knows as data envelopment analysis. It is used to explore the efficient use of energy resources. The findings suggest that rubber farms are the most technical efficient as compared to other farms. Further, the results show that there are many factors that counts and sums up the efficiency of the farm. Whilst studying the technical efficiency, this study finds that the soil and climate conditions contributes to the efficiency and productivity of the farms. © 2020, Econjournals. All rights reserved.Item Extensible Metaverse Implication for a Smart Tourism City(MDPI, 2022) Pannee Suanpang; Chawalin Niamsorn; Pattanaphong Pothipassa; Thinnagorn Chunhapataragul; Titiya Netwong; Kittisak Jermsittiparsert; P. Suanpang; Faculty of Science & Technology, Suan Dusit University, Bangkok, 10300, Thailand; email: pannee_sua@dusit.ac.thThe metaverse is an innovation that has created the recent phenomenon of new tourism experiences from a virtual reality of a smart tourism destination. However, the existing metaverse platform demonstrated that the technology is still difficult to develop, as the service provider did not disclose the internal mechanisms to developers, and it was a closed system, which could not use or share the userÕs data across platforms. The aim of this paper was to design and develop an open metaverse platform called the Òextensible metaverseÓ, which would allow new developers to independently develop the capabilities of the metaverse system. The acquisition of this new technology was conducted through requirements analysis, then the analysis and design of the new system architecture, followed by the implementation, and the evaluation of the system by the users. The results found that the extended metaverse was divided into three tiers that created labels, characters, and virtual objects. Furthermore, the linking tier combined the 3D elements, and the deployment tier compiled the results of the link to use all three parts by using the Blender program, Godot Engine, and PHP + WebGL as their respective key mechanisms. This system was tested in Suphan Buri province, Thailand, which was evaluated by 428 users. The results of the metaverse satisfaction, created tourism experience, and overall satisfaction of the variation of the satisfaction of using the metaverse were 86.0%, 79.7%, and 92.9%, respectively. The relative Chi-square (_2/df) of 1.253 indicated that the model was suitable. The comparative fit index (CFI) was 0.984, the goodness-of-fit index (GFI) was 0.998, and the model based on the research hypothesis was consistent with the empirical data. The root mean square error of approximation (RMSEA) was 0.024. In conclusion, the extended metaverse is more flexible than other platforms and also creates the userÕs satisfaction and tourism experience in the smart destination to support sustainable tourism. © 2022 by the authors.Item Integration of Kouprey-Inspired Optimization Algorithms with Smart Energy Nodes for Sustainable Energy Management of Agricultural Orchards(MDPI, 2022) Pannee Suanpang; Pattanaphong Pothipassa; Kittisak Jermsittiparsert; Titiya Netwrong; P. Suanpang; Faculty of Science and Technology, Suan Dusit University, Bangkok, 10300, Thailand; email: pannee_sua@dusit.ac.thEnergy expenditure is now a major cost for agribusiness and community-based tourism in Thailand. Both types of businesses can also generate huge amounts of income for the country each year. This is a result of operators having to share part of their income with utility bills each month. As a result, the net return is lower than the operator should receive. Recognizing the need for energy management for sustainable use in agriculture focusing on durian cultivation in Kantharalak district and community tourism in Sisaket province, this research used a newly developed optimization algorithm called Kouprey-inspired optimization (KIO) to assist energy management in smart agriculture to support community-based tourism. This was initiated with a smart energy node to reduce the energy and labor costs for volcanic durian planting and accommodation in community-based tourist attractions in Sisaket province. The results showed that the combination of the KIO algorithm and smart energy node allowed for efficient management of the volcanic durian orchards and the use of clean energy in combination with traditional electric power for volcanic durian cultivation and community-based tourism. As the research area in Sisaket province had eight hours of solar power per day, this was sufficient for smart agriculture and community-based tourism in the daytime and in the evening. Furthermore, this allowed operators in both the agricultural and tourism sectors to reduce the labor costs of the durian orchard business and community-based tourism by about 30%, and in the energy sector, the costs could be reduced by 50%. As a consequence, this prototype would lead to the expansion and trial in durian orchards in the Eastern Economic Corridor area, which is an important economic area producing durian for export of the country. © 2022 by the authors. Licensee MDPI, Basel, Switzerland.Item Sustainable development, humanities, and social sciences for society 5.0(IGI Global, 2024) Kittisak Jermsittiparsert; Ismail Wekke; Pannee SuanpangIn a world where business is increasingly influenced by rapid technological advancements and shifting societal norms, the definition of success has undergone a fundamental transformation. Profitability, once the sole measure of achievement, now shares the stage with sustainability as a critical component of business performance. As we transition into Society 5.0-a hyper-connected era where technology and human values converge-redefining success to include sustainability becomes not just desirable but essential. Sustainable Development, Humanities, and Social Sciences for Society 5.0 explores this paradigm shift, presenting a forward-thinking perspective on the intersection of technological innovation and sustainable business practices. This comprehensive volume bridges the gap between emerging technologies and the imperative for businesses to thrive in a way that benefits society and the environment. Through a curated collection of insights, strategies, and case studies, the book offers a valuable resource for understanding how to achieve continuous good performance while making a positive impact in the age of Society 5.0. © 2025 by IGI Global Scientific Publishing. All rights reserved.Item Technological and managerial approaches to fostering sustainable travel(IGI Global, 2024) Kittisak Jermsittiparsert; Pannee SuanpangThe rapid changes in many dimensions that have occurred in the past decade have resulted in significant transformations in people's conditions and lifestyles. The role of technology in facilitating the fastest pace of development in human history remains a major theme of our times, especially as humanity faced the most severe pandemic of the century, COVID-19. One of the major human activities particularly affected by the pandemic is tourism, which came to a near complete halt due to travel restrictions. The impact of at least two of these factors-technological developments and the emergence of epidemics-makes it imperative for tourism to adapt. However, sustainability, a trend that has spanned more than half a century, remains a path that tourism, like any other activity, needs to be aware of, guided by, and headed towards. Technological and Managerial Approaches to Fostering Sustainable Travel compiles research on sustainable tourism from experts, including academics, researchers, and practitioners in the field of tourism and related disciplines. It covers topics such as artificial intelligence, hotel management, and quality management, offering valuable resources for academics, researchers, and students in their teaching and research. Similarly, entrepreneurs and professionals can use it as data for research and development, operations, and related activities. © 2024 by IGI Global. All rights reserved.Item Tourism Service Scheduling in Smart City Based on Hybrid Genetic Algorithm Simulated Annealing Algorithm(MDPI, 2022) Pannee Suanpang; Pitchaya Jamjuntr; Kittisak Jermsittiparsert; Phuripoj Kaewyong; P. Suanpang; Faculty of Science & Technology, Suan Dusit University, Bangkok, 10300, Thailand; email: pannee_sua@dusit.ac.thThe disruptions in this era have caused a leap forward in information technology being applied in organizations to create a competitive advantage. In particular, we see this in tourism services, as they provide the best solution and prompt responses to create value in experiences and enhance the sustainability of tourism. Since scheduling is required in tourism service applications, it is regarded as a crucial topic in production management and combinatorial optimization. Since workshop scheduling difficulties are regarded as extremely difficult and complex, efforts to discover optimal or near-ideal solutions are vital. The aim of this study was to develop a hybrid genetic algorithm by combining a genetic algorithm and a simulated annealing algorithm with a gradient search method to the optimize complex processes involved in solving tourism service problems, as well as to compare the traditional genetic algorithms employed in smart city case studies in Thailand. A hybrid genetic algorithm was developed, and the results could assist in solving scheduling issues related to the sustainability of the tourism industry with the goal of lowering production requirements. An operation-based representation was employed to create workable schedules that can more effectively handle the given challenge. Additionally, a new knowledge-based operator was created within the context of function evaluation, which focuses on the features of the problem to utilize machine downtime to enhance the quality of the solution. To produce the offspring, a machine-based crossover with order-based precedence preservation was suggested. Additionally, a neighborhood search strategy based on simulated annealing was utilized to enhance the algorithmÕs capacity for local exploitation, and to broaden its usability. Numerous examples were gathered from the Thailand Tourism Department to demonstrate the effectiveness and efficiency of the proposed approach. The proposed hybrid genetic algorithmÕs computational results show good performance. We found that the hybrid genetic algorithm can effectively generate a satisfactory tourism service, and its performance is better than that of the genetic algorithm. © 2022 by the authors.