應用資料包絡分析法及集群分析探討空調系統運轉操作

Application of Data Envelopment Analysis and Cluster Analysis to Investigate the Operation of Air Conditioning Systems

10.29911/JEHVACE_NEW.202409_(147).0001

李文興(Wen-Hsing Lee)；盧昌明(Chang-Ming Lu)；王士維(Shi-Wei Wang)；高睿謙(Jui-Chien Kao)

資料包絡分析 ； 運轉最佳化 ； K-means集群分析 ； Data Envelopment Analysis ； Operational Optimization ； K-means Clustering

冷凍空調&能源科技

147期（2024 / 09 / 10）

26 - 33

繁體中文;英文

當前社會中，空調已經成為人們日常生活中的重要組成部分。然而，空調的使用也帶來了巨量的能源消耗，為了應對這一挑戰，節能節電成為了空調技術發展的重要方向。本研究應用資料包絡分析法及集群分析法，分析建築物空調系統之運轉記錄，找出影響空調系統運轉參數及其設定值，並探討不同設定值對能耗之影響。在案例分析上，以台北市某辦公大樓過去二年之歷史運轉資料進行分析，分析結果顯示，冰水泵之開啟台數對於系統性能有著顯著的影響。如能適當的分析並製定冰水泵的開啟策略，將可有效提升整體空調系統水側性能。

In contemporary society, air conditioning has become an essential part of daily life. However, the use of air conditioning also results in significant energy consumption. To address this challenge, energy conservation has become a key focus in the development of air conditioning technology. This study employs Data Envelopment Analysis (DEA) and Cluster Analysis to analyze the operational records of building air conditioning systems, identify the operational parameters and their settings that affect the system, and explore the impact of different settings on energy consumption. In a case study, we analyzed the historical operational data of an office building in Taipei City over the past two years. The analysis results indicate that the number of chillers in operation has a significant impact on system performance. By appropriately analyzing and formulating chiller operation strategies, the overall water-side performance of the air conditioning system can be effectively improved.