基於機器學習之冰機設備運轉參數優化系統

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姓名

陳易(Yi Chen) 查詢紙本館藏

畢業系所

電機工程學系

論文名稱

基於機器學習之冰機設備運轉參數優化系統

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摘要(中)

本研究主要針對冰水主機設備(冰機)，對其進行能耗分析與設備運轉參數調整之建議。由於能耗分析涉及許多參數(如冰水入水溫、冷卻水入水溫等)，需要先建立能耗預測模型以方便日常效率監控及未來運轉參數優化之依據。本研究首先透過感測器接收冰機運轉參數，利用資料探勘分析技術，針對每台冰機的資料進行如離群值濾除與分層隨機抽樣等資料預處理流程，再透過貝葉斯最佳化方法以較快速度搜尋最佳建模參數，當中透過交叉驗證增加訓練模型的資料比例，同時更準確地挑選出誤差較小之冰機能耗預測模型。本研究比較了幾種機器學習與深度學習模型在預測冰機效率時的準確度，計算其SHAP值(Shapley Additive exPlanations)值得到不同輸入特徵對輸出的影響程度，依此影響程度對特徵進行重要性排序。在冰機運轉參數優化階段，以Z分數和平均值差異比較來檢測冰機運轉效率之優劣。透過KNN(K-Nearest Neighbors)找出可能與過去相似之運轉參數，再透過X-means分群，建立資料效率優化之調整基線。後續設計一貪婪演算法(greedy algorithm)調整冰機參數來擬合冰機實際效率，找出影響運轉參數與設備能耗之關聯。加上雲端大數據建立決策樹分析，找出冰機運轉參數區間與效率之關係，以此建立冰機診斷系統，提供使用者改善冰機能耗之參數調整方向。在實驗結果中，本系統在經過簡單的初期參數設置後，即可自動對不同場域冰機進行效率優化調整建議。本研究所設計之使用者介面，可以方便使用者修改建模參數與連接資料庫等設定，並視覺化查看冰機效率分析結果、運轉特徵對輸出之影響力、系統所分析之參數改善建議和預期改善曲線等。此外也可以使用不同時段之資料，及使用者所希望之分析參數，重新更改建模方式，來調整使用者希望之不同分析方法。

摘要(英)

This paper presents a chiller diagnosis system to analyze the energy consumption of the chiller and proposes the parameter adjustment strategy for chiller operation to improve its efficiency.Since energy consumption analysis involves many parameters (such as chilled water return temperature, cooling tower water temperature, etc.), the chiller model will be built for daily monitoring, fault detection, and diagnosis. In the beginning, the outliers in the raw data derived from sensors are removed, and then training and testing sets are split by stratified random sampling during data preprocessing step. This study compares the accuracy of several machine learning models and neural network. We apply Bayesian optimization and cross-validation techniques while training the model to find the best model hyperparameters quickly and impartially. The cross-validation can improve the prediction accuracy of the model since it can utilize more data to train the model. The input features of a chiller can be ranked by SHAP values according to their contribution to the model prediction after the model has been trained.In order to know whether the chiller system is operating at an efficient condition, the z-score and mean value difference between real-time efficiency and model prediction are employed as the criteria to judge the efficiency difference. In the diagnosis module, a baseline of operation parameters adjustment will be built first by KNN and X-means. After that, a greedy algorithm is applied to find the relationship between parameters and operation faults. The diagnosis module will further consider the analysis result of big data decision tree to find the efficiency difference among different chillers. The result shows that the system can automatically recommend parameter adjustment strategies for different chillers after a simple initial setting. The system allows users to modify many options, such as modeling options and SQL connection settings. Also, users can use the GUI to check the efficiency analysis results, features influence, recommend parameters adjustment strategy, and expected efficiency curve before and after optimization. Furthermore, users can retrain the model based on the analysis methods they want by using different period data and desired parameter settings.

關鍵字(中)

★ 冰水主機
★ 機器學習
★ 節能
★ 資料探勘
★ 故障檢測與診斷

關鍵字(英)

★ Chiller
★ Machine learning
★ Energy saving
★ Data mining
★ Fault detection and diagnosis (FDD)

論文目次

摘要 i
Abstract ii
致謝 iv
目錄 v
圖目錄 vii
表目錄 ix
第一章緒論 1
1.1. 研究背景與動機 1
1.2. 文獻回顧 1
1.3. 研究目標 5
1.4. 論文架構 5
第二章設備軟硬體介紹 6
2.1. 冰機簡介 6
2.2. 系統硬體介紹 8
2.3. 系統軟體介紹 9
第三章研究方法與演算法 13
3.1. 整合學習 13
3.2. 資料前處理 14
3.2.1. 填補遺失值 14
3.2.2. 分層隨機抽樣 14
3.2.3. 資料正規化 15
3.2.4. 四分位距離群值檢測 16
3.2.5. Loda 17
3.2.6. CBLOF 18
3.2.7. COPOD 19
3.3. 冰機建模 20
3.3.1. SVR 20
3.3.2. KNR 22
3.3.3. Decision tree 22
3.3.4. XGBoost 24
3.3.5. LGBM 26
3.3.6. 倒傳遞神經網路 27
3.3.7. K折交叉驗證 29
3.3.8. 網格搜尋 30
3.3.9. 貝葉斯最佳化 30
3.4. 冰機運轉分析 33
3.4.1. SHAP 33
3.4.2. 相依樣本t檢定 34
3.4.3. K-means 36
3.4.4. X-means 36
3.4.5. 貪婪演算法 38
第四章系統架構與功能 41
4.1. 本地場域系統 42
4.2. 雲端系統 47
第五章實驗結果 49
5.1. 濾除離群值 49
5.2. 分層隨機抽樣 53
5.3. 冰機模型 54
5.4. 交叉驗證 55
5.5. 建模超參數最佳化 56
5.6. 建模特徵選擇 57
5.7. 冰機效率優化 58
第六章結論與未來展望 63
6.1. 結論 63
6.2. 未來展望 64
參考文獻 65
研究成果 70

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指導教授

王文俊(Wen-June Wang)

審核日期

2021-7-7

推文