Sürdürülebilir Kalkınma Amaçları

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• MakaleAçık Erişim2022/r/adakent/10091
  Integration of Kouprey-Inspired Optimization Algorithms with Smart Energy Nodes for Sustainable Energy Management of Agricultural Orchards

  Suanpang, Pannee; Pothipassa, Pattanaphong; Jermsittiparsert, Kittisak; Netwong, Titiya

  Energy expenditures are now the main cost for two businesses that generate huge incomes each year for Thailand, which are agribusiness and community tourism. As entrepreneurs have to share a portion of their income as energy utility bills each month. This is a factor which results in them getting a low net return. 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.

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• MakaleAçık Erişim2022/r/adakent/10089
  Autonomous Energy Management by Applying Deep Q-Learning to Enhance Sustainability in Smart Tourism Cities

  Suanpang, Pannee; Jamjuntr, Pitchaya; Jermsittiparsert, Kittisak; Kaewyong, Phuripoj

  Autonomous 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.

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