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空調節能系統之AI建模與HNA製作

AI Modeling and HNA Implementation for Air Conditioning Energy-Saving System

摘要


近年來,政府積極推動能源轉型,預計到2025年,再生能源的發電比例將達到20%。然而,再生能源的不確定性帶來了挑戰。提高能源使用效率成為當前的首要任務;為了提高能源的使用效率,智慧大樓、智慧城市、智慧電網等建立不再是未來式。要推動一個智慧化的環境,所有的感測器或是受控體都必須要具備通訊的功能,使其能夠納入中央管理系統進行統一管理,於是本研究將以家用冷氣為例,將其改造成具有通訊的功能。目前大多數中央監控系統的控制邏輯仍停留在基本的條件控制層面,這種方法雖然能夠實現一定程度的節能效果,但容易忽略舒適度。因此,本研究的核心在於結合人工智慧(Artificial Intelligence)建模技術,以實現更高效的溫度控制之節能策略。透過AI的分析和預測能力,時時的對環境進行監控,實現更精準的能源管理,進一步提升能源使用效率,並為綠建築、智慧建築、智慧城市等注入新的活力。

並列摘要


The government is actively driving energy transformation, aiming for 20% renewable energy in power generation by 2025. However, uncertainties in renewable energy bring challenges. Enhancing energy efficiency is now a top priority. Smart buildings, cities, and grids are becoming a reality for greater efficiency. This study focuses on retrofitting household air conditioning for communication capabilities. Most central control systems use basic conditional logic, achieving some energy savings but sacrificing comfort. This research integrates AI for smarter temperature control. Through AI analysis, continuous environmental monitoring, and precise energy management, efficiency is enhanced. This injects fresh vigor into green and smart buildings, cities, revitalizing them.

參考文獻


M. Schonlau, R. Z.-T. S. Journal, and undefined 2020, “The random forest algorithm for statistical learning,” journals.sagepub.comM Schonlau, RY ZouThe Stata Journal, 2020•journals.sagepub.com, vol. 20, no. 1, pp. 3–29, Mar. 2020, doi: 10.1177/1536867X20909688
J. N. Kok, E. J. Boers, W. A. Kosters, P. Van der Putten, and M. Poel, “Artificial intelligence: definition, trends, techniques, and cases,” Artif Intell, vol. 1, pp. 270–299, 2009.
L. Breiman, “Random forests,” Mach Learn, vol. 45, pp. 5–32, 2001.
Modbus Organization, “Modbus,” What is Modbus ® protocol? https://modbus.org/faq.php
“RS-485,” 維基百科. https://zh.wikipedia.org/wiki/EIA-485

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