Browsing by Author "Pitchaya Jamjuntr"
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Item Adaptive Multi-Agent Reinforcement Learning for Optimizing Dynamic Electric Vehicle Charging Networks in Thailand(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Pitchaya Jamjuntr; Chanchai Techawatcharapaikul; Pannee Suanpang; P. Suanpang; Department of Information Technology, Faculty of Science & Technology, Suan Dusit University, Bangkok, 10300, Thailand; email: pannee_sua@dusit.ac.thThe rapid growth of electric vehicles (EVs) necessitates efficient management of dynamic EV charging networks to optimize resource utilization and enhance service reliability. This paper explores the application of adaptive multi-agent reinforcement learning (MARL) to address the complexities of EV charging infrastructure in Thailand. By employing MARL, multiple autonomous agents learn to optimize charging strategies based on real-time data by adapting to fluctuating demand and varying electricity prices. Building upon previous research that applied MARL to static network configurations, this study extends the application to dynamic and real-world scenarios, integrating real-time data to refine agent learning processes and also evaluating the effectiveness of adaptive MARL in maximizing rewards and improving operational efficiency compared to traditional methods. Experimental results indicate that MARL-based strategies increased efficiency by 20% and reduced energy costs by 15% relative to conventional algorithms. Key findings demonstrate the potential of extending MARL in transforming EV charging network management, highlighting its benefits for stakeholders, including EV owners, operators, and utility providers. This research contributes insights into advancing electric mobility and energy management in Thailand through innovative AI-driven approaches. The implications of this study include significant improvements in the reliability and cost-effectiveness of EV charging networks, fostering greater adoption of electric vehicles and supporting sustainable energy initiatives. Future research directions include enhancing MARL adaptability and scalability as well as integrating predictive analytics for proactive network optimization and sustainability. These advancements promise to further refine the efficacy of EV charging networks, ensuring that they meet the growing demands of ThailandÕs evolving electric mobility landscape. © 2024 by the authors.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 Can Optimized Genetic Algorithms Improve the Effectiveness of Homestay Recommendation Systems in Smart Villages? A Case of Thailand(John Wiley and Sons Ltd, 2024) Pannee Suanpang; Pitchaya Jamjuntr; Arunee Lertkornkitja; Chompunuch Jittithavorn; C. Jittithavorn; College of Management, University of Phayao, Bangkok, Thailand; email: chompunuchj@gmail.comThis paper introduces a novel approach to optimize genetic algorithms (GAs) for homestay recommendation systems, specifically designed for smart village tourism destinations. Researchers developed an advanced GA focused on maximizing user satisfaction, the main quality metric. The algorithm was tailored to address the dynamic nature of homestay offerings and the varied preferences of travelers, using users' reviews, listing attributes, and historical booking data. The GA framework included a custom encoding scheme, fitness function, and parameters. Validation occurred through a case study in a smart village, with the algorithm's effectiveness tested via user surveys and ratings. Results showed that GA-driven recommendations surpassed traditional methods, enhancing user satisfaction, trust, and booking rates while benefiting hosts with positive reviews. The optimized GA improved recommendation accuracy and efficiency, boosting economic benefits for local communities and contributing significantly to recommendation system research. © 2024 John Wiley & Sons Ltd.Item Enhanced Decision Making in Smart Grid Management by Optimizing Adaptive Multi-Agent Reinforcement Learning with Vehicle-to-Grid Systems(Regional Association for Security and crisis management, 2024) Pannee Suanpang; Pitchaya Jamjuntr; P. Suanpang; Department of Information Technology, Faculty of Science & Technology, Suan Dusit University, Bangkok, Thailand; email: pannee_sua@dusit.ac.thThis research proposes a decision-making framework in which the Adaptive Multi-Agent Reinforcement Learning (MARL) model and the concept of Vehicle-to-Grid (V2G) interactivity are employed to improve the effective management of smart grids. The research hypothesis introduces innovations for improving the efficiency and security of power systems in the global south, primarily by controlling the net energy transmission between the defined electric vehicles (EVs) and the grid. Other issues that require attention to ensure the proper functioning of smart grids include demand response, load management, and energy storage optimization. In this instance, these gaps are filled by the systemÕs proposed framework. With the help of MARL, the system dynamics' autonomous learning aspects allow the system to adapt to the capacity of renewable energy sources and electricity demand, which is also time-dependent. Because of the MARL, the autonomous coordination of decision-making has resulted in very positive changes in the system's effectiveness. In particular, this framework permitted an increase of 13.6% in the total energy exchange between EVs and the grid, and the grid stability index improved from 0.84 to 0.87 compared to what would have been achieved with the conventional methods. Enhanced energy management and pricing rehabs added another 22% to net savings. Further, it is stated that deploying MARL-based V2G systems in developing areas has many benefits, including more robust grid reliability and energy security and better integration of renewable energy resources. Such changes aid in reducing fossil fuel use and greenhouse gas emissions. © 2024 Regional Association for Security and crisis management. All rights reserved.Item Machine Learning Models for Solar Power Generation Forecasting in Microgrid Application Implications for Smart Cities(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Pannee Suanpang; Pitchaya Jamjuntr; P. Suanpang; Department of Information Technology, Faculty of Science & Technology, Suan Dusit University, Bangkok, 10300, Thailand; email: pannee_sua@dusit.ac.thIn the context of escalating concerns about environmental sustainability in smart cities, solar power and other renewable energy sources have emerged as pivotal players in the global effort to curtail greenhouse gas emissions and combat climate change. The precise prediction of solar power generation holds a critical role in the seamless integration and effective management of renewable energy systems within microgrids. This research delves into a comparative analysis of two machine learning models, specifically the Light Gradient Boosting Machine (LGBM) and K Nearest Neighbors (KNN), with the objective of forecasting solar power generation in microgrid applications. The study meticulously evaluates these modelsÕ accuracy, reliability, training times, and memory usage, providing detailed experimental insights into optimizing solar energy utilization and driving environmental sustainability forward. The comparison between the LGBM and KNN models reveals significant performance differences. The LGBM model demonstrates superior accuracy with an R-squared of 0.84 compared to KNNÕs 0.77, along with lower Root Mean Squared Error (RMSE: 5.77 vs. 6.93) and Mean Absolute Error (MAE: 3.93 vs. 4.34). However, the LGBM model requires longer training times (120 s vs. 90 s) and higher memory usage (500 MB vs. 300 MB). Despite these computational differences, the LGBM model exhibits stability across diverse time frames and seasons, showing robustness in handling outliers. These findings underscore its suitability for microgrid applications, offering enhanced energy management strategies crucial for advancing environmental sustainability. This research provides essential insights into sustainable practices and lays the foundation for a cleaner energy future, emphasizing the importance of accurate solar power forecasting in microgrid planning and operation. © 2024 by the authors.Item Optimal Electric Vehicle Battery Management Using Q-learning for Sustainability(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Pannee Suanpang; Pitchaya Jamjuntr; P. Suanpang; Department of Information Technology, Faculty of Science & Technology, Suan Dusit University, Bangkok, 10300, Thailand; email: pannee_sua@dusit.ac.thThis paper presents a comprehensive study on the optimization of electric vehicle (EV) battery management using Q-learning, a powerful reinforcement learning technique. As the demand for electric vehicles continues to grow, there is an increasing need for efficient battery-management strategies to extend battery life, enhance performance, and minimize operating costs. The primary objective of this research is to develop and assess a Q-learning-based approach to address the intricate challenges associated with EV battery management. This paper starts by elucidating the key challenges inherent in EV battery management and discusses the potential advantages of incorporating Q-learning into the optimization process. Leveraging Q-learningÕs capacity to make dynamic decisions based on past experiences, we introduce a framework that considers state-of-charge, state-of-health, charging infrastructure, and driving patterns as critical state variables. The methodology is detailed, encompassing the selection of state, action, reward, and policy, with the training process informed by real-world data. Our experimental results underscore the efficacy of the Q-learning approach in optimizing battery management. Through the utilization of Q-learning, we achieve substantial enhancements in battery performance, energy efficiency, and overall EV sustainability. A comparative analysis with traditional battery-management strategies is presented to highlight the superior performance of our approach. A comparative analysis with traditional battery-management strategies is presented to highlight the superior performance of our approach, demonstrating compelling results. Our Q-learning-based method achieves a significant 15% improvement in energy efficiency compared to conventional methods, translating into substantial savings in operational costs and reduced environmental impact. Moreover, we observe a remarkable 20% increase in battery lifespan, showcasing the effectiveness of our approach in enhancing long-term sustainability and user satisfaction. This paper significantly enriches the body of knowledge on EV battery management by introducing an innovative, data-driven approach. It provides a comprehensive comparative analysis and applies novel methodologies for practical implementation. The implications of this research extend beyond the academic sphere to practical applications, fostering the broader adoption of electric vehicles and contributing to a reduction in environmental impact while enhancing user satisfaction. © 2024 by the authors.Item Optimizing Autonomous UAV Navigation with D* Algorithm for Sustainable Development(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Pannee Suanpang; Pitchaya Jamjuntr; P. Suanpang; Department of Information Technology, Faculty of Science & Technology, Suan Dusit University, Bangkok, 10300, Thailand; email: pannee_sua@dusit.ac.thAutonomous navigation for Unmanned Aerial Vehicles (UAVs) has emerged as a critical enabler in various industries, from agriculture, delivery services, and surveillance to search and rescue operations. However, navigating UAVs in dynamic and unknown environments remains a formidable challenge. This paper explores the application of the D* algorithm, a prominent path-planning method rooted in artificial intelligence and widely used in robotics, alongside comparisons with other algorithms, such as A* and RRT*, to augment autonomous navigation capabilities in UAVsÕ implication for sustainability development. The core problem addressed herein revolves around enhancing UAV navigation efficiency, safety, and adaptability in dynamic environments. The research methodology involves the integration of the D* algorithm into the UAV navigation system, enabling real-time adjustments and path planning that account for dynamic obstacles and evolving terrain conditions. The experimentation phase unfolds in simulated environments designed to mimic real-world scenarios and challenges. Comprehensive data collection, rigorous analysis, and performance evaluations paint a vivid picture of the D* algorithmÕs efficacy in comparison to other navigation methods, such as A* and RRT*. Key findings indicate that the D* algorithm offers a compelling solution, providing UAVs with efficient, safe, and adaptable navigation capabilities. The results demonstrate a path planning efficiency improvement of 92%, a 5% reduction in collision rates, and an increase in safety margins by 2.3 m. This article addresses certain challenges and contributes by demonstrating the practical effectiveness of the D* algorithm, alongside comparisons with A* and RRT*, in enhancing autonomous UAV navigation and advancing aerial systems. Specifically, this study provides insights into the strengths and limitations of each algorithm, offering valuable guidance for researchers and practitioners in selecting the most suitable path-planning approach for their UAV applications. The implications of this research extend far and wide, with potential applications in industries such as agriculture, surveillance, disaster response, and more for sustainability. © 2024 by the authors.Item Optimizing Electric Vehicle Charging Recommendation in Smart Cities: A Multi-Agent Reinforcement Learning Approach(Multidisciplinary Digital Publishing Institute (MDPI), 2024) Pannee Suanpang; Pitchaya Jamjuntr; P. Suanpang; Department of Information Technology, Suan Dusit University, Bangkok, 10300, Thailand; email: pannee_sua@dusit.ac.thAs global awareness for preserving natural energy sustainability rises, electric vehicles (EVs) are increasingly becoming a preferred choice for transportation because of their ability to emit zero emissions, conserve energy, and reduce pollution, especially in smart cities with sustainable development. Nonetheless, the lack of adequate EV charging infrastructure remains a significant problem that has resulted in varying charging demands at different locations and times, particularly in developing countries. As a consequence, this inadequacy has posed a challenge for EV drivers, particularly those in smart cities, as they face difficulty in locating suitable charging stations. Nevertheless, the recent development of deep reinforcement learning is a promising technology that has the potential to improve the charging experience in several ways over the long term. This paper proposes a novel approach for recommending EV charging stations using multi-agent reinforcement learning (MARL) algorithms by comparing several popular algorithms, including the deep deterministic policy gradient, deep Q-network, multi-agent DDPG (MADDPG), Real, and Random, in optimizing the placement and allocation of the EV charging stations. The results demonstrated that MADDPG outperformed other algorithms in terms of the Mean Charge Waiting Time, CFT, and Total Saving Fee, thus indicating its superiority in addressing the EV charging station problem in a multi-agent setting. The collaborative and communicative nature of the MADDPG algorithm played a key role in achieving these results. Hence, this approach could provide a better user experience, increase the adoption of EVs, and be extended to other transportation-related problems. Overall, this study highlighted the potential of MARL as a powerful approach for solving complex optimization problems in transportation and beyond. This would also contribute to the development of more efficient and sustainable transportation systems in smart cities for sustainable development. © 2024 by the authors.Item OPTIMIZING LAST-MILE DELIVERY BY DEEP Q-LEARNING APPROACH FOR AUTONOMOUS DRONE ROUTING IN SMART LOGISTICS(Regional Association for Security and crisis management, 2024) Pannee Suanpang; Pitchaya Jamjuntr; P. Suanpang; Department of Information Technology, Faculty of Science and Technology, Suan Dusit University, Bangkok, 10300, Thailand; email: pannee_sua@dusit.ac.thThe advancement technology of artificial intelligence and e-commerce has increased and this has called for new ways to improve last-mile transportation, which is regarded as an essential part of the logistics value chain, especially in smart logistics. This paper addresses the problem of developing effective routes for autonomous drones in last-mile logistics using deep Q-learning. This paper aims to improve the process of delivery by utilizing the flexibility and intelligence of self-driven autonomous drones in smart logistics transportation. The key challenge for the effective provision of last-mile delivery services remains the decision on the routing of many aerial drones in an indoor urban environment, concerning the restrictions of a time window for delivery, energy consumption and traffic. This paper implements a deep Q-learning paradigm that allows drones to relearn their flight paths and delivery strategy during the lifecycle, thereby reducing the cost in the long run while using the costing strategies as part of the reengineering process. The approach has been validated through extensive experimentation and simulations. Results obtained indicate that the delivery drones modified for the study attained the designed requirements of deep Q-learning, including optimal navigation and performance that attained 12.8% shorter delivery time, an increase in energy efficiency by 8.4%, and a route quality improvement of 20.1%. Furthermore, highlights the performance of the system in various situations where deep Q-learning and standard routing approaches are compared. This paper not only aids in the minimization of the last-mile delivery constraint by the use of shipping drones but also emphasizes the capacities of reinforcement learning strategies such as deep Q-learning in tackling the routing problems in smart logistics systems. At last, it advocates carrying on deeper into the application of reinforcement learning in the solving of complex optimization problems in various other fields. © 2024 Regional Association for Security and crisis management. All rights reserved.Item Optimizing Service Scheduling by Genetic Algorithm Support Decision-Making in Smart Tourism Destinations(Regional Association for Security and crisis management, 2024) Pannee Suanpang; Pitchaya Jamjuntr; P. Suanpang; Department of Information Technology, Faculty of Science & Technology, Suan Dusit University, Bangkok, Thailand; email: pannee_sua@dusit.ac.thSmart tourism destinations are characterised by the integration of advanced technologies and devices to ensure visitors enjoy a seamless and environmentally responsible experience. A key challenge for such destinations lies in efficiently managing and delivering services to meet tourists' expectations while upholding sustainability principles and resource management practices. This study aimed to explore the application of genetic algorithms (GAs) in optimising service scheduling, thereby supporting decision-making processes and enhancing tourism destination services. The research employed a service scheduling methodology that directed the algorithm towards maximising efficiency and customer satisfaction, in contrast to traditional organisational scheduling methods. The methodology centred on the implementation of an algorithmic approach in service delivery management, prioritising operational efficiency and improved customer experience over conventional scheduling techniques. Data collected were systematically analysed, resulting in the development of a theoretical framework based on the findings. The results demonstrated that genetic algorithms significantly enhance service scheduling efficiency when used alongside other methods. The findings underscore the pivotal role of GAs in enabling businesses to achieve time and cost savings while improving customer satisfaction. Furthermore, the study highlights GAs' capacity for adaptability, allowing schedules to be adjusted rapidly in response to changing circumstances, thus providing flexibility and responsiveness to variations in demand. Finally, the research identifies opportunities for innovation and diversification in applying GAs for time scheduling within the tourism sector. It also emphasises the importance of integrating real-time information into scheduling systems to improve service provision at tourist sites. This approach not only enhances the competitiveness of tourism destinations but also adds substantial value to the industry by enriching tourists' experiences and fostering sustainable practices © 2024 Regional Association for Security and crisis management. 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.