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研究生： 許宗強
Danny - Vernatha
論文名稱： 建築信息模型於大學設施能源管理之應用
Application of Building Information Modeling for Energy Management of University Facility
指導教授： 杜功仁
Kung-Jen Tu
口試委員: 江維華
Wei-Hwa Chiang
邱韻祥
YUN-SHANG CHIOU
蔡欣君
Lucky Tsaih
學位類別： 碩士
Master
系所名稱： 設計學院 - 建築系
Department of Architecture
論文出版年： 2016
畢業學年度： 104
語文別： 英文
論文頁數： 92
中文關鍵詞： occupancysensor 、electricitysub-meters 、database 、BIMmodel 、energyanomalydetection
外文關鍵詞： occupancy sensor, electricity sub-meters, database, BIM model, energy anomaly detection
相關次數： 點閱：199  下載：4 
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    • Individual buildings within university campuses are often occupied by different departments
    in Taiwan, which has created a great challenge to the energy management task. To assist individual
    departments within universities in their energy management tasks, this study explores the
    application of Building Information Modelling in establishing the ‘BIM-based Energy
    Management Support System’ (BIM-EMSS).
    The BIM-EMSS consists of five components: (1) sensors installed for each occupant and each
    equipment, (2) electricity sub-meters (constantly logging lighting, HVAC, and socket electricity
    consumptions of each room), (3) data warehouse (for storing occupancy status and logged
    electricity consumption data), (4) BIM models of all rooms within individual departments’
    facilities, and (5) energy management console that provide energy managers with energy
    management functions such as occupancy and equipment status display, energy consumption
    analyses and energy anomaly detection.
    Through the energy management console, the energy manager is able to (a) have 3D
    visualization (BIM model) of each room, in which the occupancy and equipment status detected
    by the sensors and the electricity consumptions data logged are displayed constantly; (b) to review
    daily, weekly and monthly energy consumption profiles and compare them against historical
    energy profiles; and (c) to obtain energy consumption anomaly detection warnings on certain
    rooms so that energy management corrective actions can be further taken (fault detection and
    diagnostic logic is employed to analyze the relation between space occupancy pattern with current
    space equipment setting to indicate an anomaly, such as when appliances turn on without
    occupancy).
    The BIM-EMSS was further implemented in a research lab in the Department of Architecture
    of NTUST in Taiwan and implementation results presented to illustrate how it can be used to assist
    individual departments within universities in their energy management tasks.

    Individual buildings within university campuses are often occupied by different departments
    in Taiwan, which has created a great challenge to the energy management task. To assist individual
    departments within universities in their energy management tasks, this study explores the
    application of Building Information Modelling in establishing the ‘BIM-based Energy
    Management Support System’ (BIM-EMSS).
    The BIM-EMSS consists of five components: (1) sensors installed for each occupant and each
    equipment, (2) electricity sub-meters (constantly logging lighting, HVAC, and socket electricity
    consumptions of each room), (3) data warehouse (for storing occupancy status and logged
    electricity consumption data), (4) BIM models of all rooms within individual departments’
    facilities, and (5) energy management console that provide energy managers with energy
    management functions such as occupancy and equipment status display, energy consumption
    analyses and energy anomaly detection.
    Through the energy management console, the energy manager is able to (a) have 3D
    visualization (BIM model) of each room, in which the occupancy and equipment status detected
    by the sensors and the electricity consumptions data logged are displayed constantly; (b) to review
    daily, weekly and monthly energy consumption profiles and compare them against historical
    energy profiles; and (c) to obtain energy consumption anomaly detection warnings on certain
    rooms so that energy management corrective actions can be further taken (fault detection and
    diagnostic logic is employed to analyze the relation between space occupancy pattern with current
    space equipment setting to indicate an anomaly, such as when appliances turn on without
    occupancy).
    The BIM-EMSS was further implemented in a research lab in the Department of Architecture
    of NTUST in Taiwan and implementation results presented to illustrate how it can be used to assist
    individual departments within universities in their energy management tasks.

    Abstract ii Acknowledgement iii Table of Contents iv List of Tables viii List of Figures ix Chapter 1 Introduction 1 1.1. Research Background 1 1.1.1. Importance of Energy Saving 1 1.1.2. BIM (Building Information Modeling) Trend 1 1.1.3. Energy Management System Issues in University Facilities 2 1.2. Research Objectives 3 1.3. Research Method 3 Chapter 2 Literature Review 5 2.1. Existing Energy Management Tools and Frameworks 5 2.1.1. Existing Energy Management Tools 5 2.1.2. Existing Energy Management Frameworks 7 2.2. BIM based Energy Management Tools and Frameworks 9 2.2.1. BIM based Energy Management Tools 9 2.2.2. BIM based Energy Management Frameworks 10 2.3. Energy Management Technologies 11 2.4. Summary of Literature Review 13 2.5. Future BIM based Energy Management Tools 14 Chapter 3 Field Observation: Understanding Occupancy Pattern, Equipment Use, and Energy Consumption of a Laboratory Space in Architecture Department 15 3.1. Method 15 3.1.1. Subject of Observation 15 3.1.2. Data Collection Method 16 3.2. Data Collection Results 18 3.2.1. Energy Consumption Data 19 3.2.2. Occupancy Status Data 19 3.2.3. Equipment Status Data 19 3.3. Data Analysis Method 23 3.4. Relation between RB 906 Occupancy Level and HVAC Appliances Usage 23 3.5. Relation between RB 906 Occupancy Level and Lighting Appliances Usage 24 3.6. Relation between RB 906 Occupancy Level and Office Appliances Usage 24 3.7. Summary 28 3.7.1. Findings from Data Analysis 28 3.7.2. Suggestion of Ideal Operation Setting 29 Chapter 4 Proposing BIM based Energy Management Support System Framework 31 4.1. Problems and Needs of Energy Manager 31 4.2. BIM EMSS Framework 32 4.2.1. Sensor Devices 32 4.2.2. Sub Metering Devices 32 4.2.3. Building Energy Management System 33 4.2.4. Building Information Modeling 33 4.2.5. PC Running Java 33 4.2.6. Data Warehouse 34 4.2.7. Energy Monitoring Tool 34 4.2.8. User Interface 34 4.3. Energy Management Functions 35 4.3.1. Real Time Consumption Analysis 35 4.3.2. Anomaly Detection 36 4.3.3. HVAC – Passive Design Benefits Detection 41 Chapter 5 BIM EMSS Function and Interface Mock Up: Illustrations for a Research Laboratory Case 44 5.1. Method 44 5.2. Real Time Consumption Analysis Function Illustrations 46 5.2.1. Scenario 1: Benchmarking RB 906 May 11th Energy Consumption against 2014 Month of May Monday Average Energy Consumption 46 5.2.2. Scenario 2: Benchmarking RB 906 Energy Consumption against Power Purchase Agreement Value 49 5.3. Anomaly Detection Function Illustrations 50 5.3.1. Scenario 1: Checking Ongoing Anomaly on Architecture Department Spaces 50 5.3.2. Scenario 2: Reviewing Anomaly of RB 906 Space 52 5.3.3. Scenario 3: Reviewing Anomaly from May 11th – May 17th 54 5.4. HVAC – Passive Design Benefits Detection Function Illustrations 56 5.4.1. Scenario 1: Checking Passive Design Benefits Availability for HVAC 56 5.4.2. Scenario 2: Reviewing Passive Design Benefits from May 11th – May 13th 58 Chapter 6 Conclusion 60 6.1. Summary 60 6.2. Discussion 61 6.2.1. Research Limitation: 61 6.2.2. Future Recommendation 61 References 62 Appendices A: Table of Occupancy Level and Appliances Status 65 A-1: 2015 May 11th 65 A-2: 2015 May 12th 66 A-3: 2015 May 13th 67 A-4: 2015 May 14th 68 A-5: 2015 May 15th 69 A-6: 2015 May 17th 70 Appendices B: Energy Consumption of RB 906 Space from Metering System 71 B-1 May 11th Energy Consumption 71 B-2 May 12th Energy Consumption 71 B-3 May 13th Energy Consumption 72 B-4 May 14th Energy Consumption 72 B-5 May 15th Energy Consumption 73 B-6 May 16th Energy Consumption 73 B-7 May 17th Energy Consumption 74 Appendices C: Anomaly Detection Report from May 11th – May 17th 75 C-1 HVAC Energy Consumption Anomaly 75 C-2 Lighting Energy Consumption Anomaly 75 C-3 Office Equipment Energy Consumption Anomaly 76

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