臺灣博碩士論文加值系統

據台灣經濟部能源局在2016年非生產性質行業能源查核年報統計，空調設備在能源大用戶申報資料各類建築物總用電比例約為40%以上，為建築物電力消耗主因。再者，現行空調壓縮機性能評估使用設備性能係數(Coefficient of Performance，簡稱COP)或能源效率比值(Energy Efficiency Ratio，簡稱EER)兩項指標屬於靜態性能指標，僅反映空調設備在單一特定環境條件下的運轉效率，未能將空調設備實際使用情況之負載變化對壓縮機起閉的影響呈現出來。為分析在不同環境條件下對空調壓縮機的使用效率及設備異常診斷，增進電能使用效率，達節能減碳成效，本文應用溫濕度感測器及智慧電表，構築即時監測及歷史資料庫系統，以五種便捷資料探勘技術分析設備過往數據建立預測模型，使用統計方法評估設備異常狀態。結果顯示，誤差率最小之數值分析方法為支援向量機(Support Vector Machine，SVM)，相關係數(Correlation Coefficient, R)達0.898，平均絕對值誤差率(Mean Absolute Percentage Error, MAPE)值為1.643%，絕對誤差(Mean Absolute Error, MAE)為0.004，均方根誤差(Root Mean Square Error, RMSE)為0.006。另將環境因素對COP值之影響轉化成計量模型，比對不同年度之同期數據，並以統計方法有效判別設備異常狀態之界定。本研究以即時監測及歷史資料庫系統結合便捷方法人工智慧模型，評估壓縮機效能，使用者得以掌握設備性能趨勢，達撙節成本效益。本研究成果亦能將系統化建構與資料分析流程技術移轉至節能科技產業。

According to Bureau of Energy, Ministry of Economic Affairs in Taiwan, air-conditioning is more than 40% of total electricity for the non-productive industries building power consumption in 2016. In addition, currently using Coefficient of Performance (COP) or Energy Efficiency Ratio (EER) to evaluate compressor performance only reflects air conditioning in specific environmental conditions. It does not demonstrate the actual load change impact on compressor performance. In order to analyze the efficiency of air conditioning compressor and the abnormal diagnosis of equipment in different environmental conditions, this paper uses temperature sensors, humidity sensors, smart meter to build real-time monitoring and historical database system. The prediction models were created based on previous data of five data mining techniques. The abnormal status of the compressor was evaluated by statistical method. The results show that the numerical analysis method with minimum error rates is Support Vector Machine (SVM), in which Correlation Coefficient (R) is 0.898, Mean Absolute Percentage Error (MAPE) is 1.643%, Mean Absolute Error (MAE) is 0.004, and Root Mean Square Error (RMSE) is 0.006. The influence of environmental factors on COP value is transformed into the measurement model, compared with the same period in different years. The statistical method was then used to determine the abnormal status of the compressor. This study contributes to the domain knowledge by proposing an effective artificial intelligence approach for predicting the compressor performance in different environmental conditions.

摘要
Abstract
誌謝
目錄
第一章　緒論
1.1. 研究背景
1.2. 研究動機與目的
1.3. 研究流程
第二章 文獻回顧
2.1. 資料探勘於製冷設備性能預測
2.2. 耗能設備異常檢測與診斷
2.3. 即時動態監測及資料庫系統於建築物能源管理應用
第三章 研究方法
3.1. 空調主機耗能監測系統
3.2. 資料探勘技術
3.3. 交叉驗證法
3.4. 模型預測衡量方法
3.4.1. 模型誤差衡量方法
3.4.2. 模型診斷設備異常方法
第四章　節能設施性能系統開發與設計
4.1. 空調主機循環原理
4.2. 實驗設計
4.3. 實驗設備與量測裝置
4.3.1. 空調設備系統
4.3.2. 智慧電表監測系統
4.3.3. 即時監測系統及歷史數據資料庫
第五章 資料蒐集與模型建立
5.1. 資料蒐集及預處理
5.1.1. 資料蒐集
5.1.2. 資料前置處理
5.2. 模型建立與交叉驗證
5.3. 分析結果與討論
第六章 結論與建議
參考文獻 42
附錄一 人工類神經網路預測模型於R語言流程
附錄二 支援向量機預測模型於R語言流程
附錄三 分類回歸樹預測模型於R語言流程
附錄四 廣義線性迴歸預測模型於R語言流程
附錄五 多重迴歸預測模型於R語言流程

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