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研究生(外文):Ch'ng Yoong Pin
論文名稱:以多參數統計分析作為建築能源系統效率診斷之研究
論文名稱(外文):A Research of Building Energy Diagnosis via Multiple Parameters Statistical Analysis Using the Concept of System Efficiency
指導教授:蔡尤溪蔡尤溪引用關係
學位類別:碩士
校院名稱:國立臺北科技大學
系所名稱:能源與冷凍空調工程系碩士班
學門:工程學門
學類:其他工程學類
論文種類:學術論文
論文出版年:2012
畢業學年度:100
語文別:英文
論文頁數:109
中文關鍵詞:建築能源管理建築能源Bin 方法系統性能
外文關鍵詞:BEMSbuilding energyBin methodsystem performance
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In the era of global energy crisis many researchers focused on increasing the efficiency of the HVAC equipment. However the results of the relevant research have reached the limits and hence many start to explore the methods of energy management to improve the efficiency of the whole HVAC system.
This research applied the idea of Building Energy Managing System (BEMS) using an innovative method to diagnose building HVAC systems and suggest control method to the important parameters. This method should be simple, precise, and user-friendly, taking the idea of Bin method and statistical analysis. This research collected and analyzed the output data from a complex simulation. The data library about the particular building HVAC system can be constructed and controlled through diagnosing the system by analyzing the corresponding data from the library. Through all these diagnosing and controlling method, system optimization can be achieved thus much unnecessary energy consumption can be avoided.
This research used DOE – eQUEST as a tool to simulate a building model in order to obtain output data as prerequisite to diagnose a building. By inserting appropriate inputs, the simulations may provide correct data to be diagnosed. After the processing and analysis of the simulated results, a library was created by taking outdoor dry-bulb, wet-bulb temperatures and enthalpy differences as base parameters. The simulated results provide outputs which are System Performance Factor (SPF) of the whole HVAC system the HVAC Energy Consumption. The raw data is complex and abundant, hence the concept of Bin method was use to create Bin parameters such as Bin temperatures and Bin enthalpy diffenrences. Using t-distribution as base statistical method, the confidence intervals of desired output data are created, to be used for diagnosing purpose.
The data collected was a scattering data with some unwanted desired information. Therefore, the data were statistically filtered and collected since not all data is appropriate. However, the results show that the method is fit for predicting or estimating the desired result data such as energy consumption and system performance, although only a small portion of the simulated data could be collected from simulation data. This database provided standards or bases to be compared or verified on real-time measurement.
In short, this research showed a potential of diagnosing buildings almost accurate as the simulations while only few steps have to be followed with the researched method, considering the building is similar as the modeled building. Control system may be attached onto the diagnosing tool in order to provide complete system of BEMS.

ABSTRACT i
ACKNOWLEDGEMENT iii
CONTENTS iv
LIST OF TABLES vii
LIST OF FIGURES viii
Chapter 1 INTRODUCTION 1
1.1 Introduction to Energy in overall and building energy 1
1.1.1 Global and National Energy Consumption – An Introduction 1
1.1.2 National Energy Consumption – An Introduction 1
1.1.3 National Energy Demand – An Introduction 3
1.1.4 Energy consumption by Heating, Ventilating and Air-Conditioning System (HVAC) in Buildings – An Introduction 4
1.1.5 Energy consumption in the areas mentioned and its solutions – An Introduction 5
1.2 Objective of the project 6
Chapter 2 LITERATURE REVIEW 7
2.1 Classical control systems in buildings 7
2.2 Optimal control systems in buildings 8
2.3 Predictive control systems in buildings 9
2.4 Building Energy Management Systems (BEMS) 10
2.5 Simulation Assisted Control 15
2.5.1 Emulators 15
2.5.2 Evaluators 15
2.6 Building energy performance simulation programs 16
2.7 DOE-2.2 As Simulation Program 19
2.8 The need to develop a simplified simulation tool 21
2.9 Degree day as modeling strategies 22
2.10 Bin method as modeling strategies 23
2.11 Concept of correlation method as simplified calculation model 24
2.12 Validation of significant variables in correlation method 26
2.13 Shortage to correlation or mathematical calculation model 28
Chapter 3 METHODOLOGY 30
3.1 Model of the weather data 31
3.2 Base case modeling 34
3.2.1 Window settings 36
3.2.2 Exterior wall settings 36
3.2.3 Roof’s construction and settings 38
3.2.4 Floor construction 40
3.2.5 HVAC settings 42
3.2.6 Other settings of the modeled building 44
3.3 Categorizing and statistical analysis using concept bin methods 48
3.4 Limitations for the results output of the simulation 51
Chapter 4 RESULTS AND DISCUSSION 54
4.1 Hourly data output analysis 54
4.2 Analysis of sampling hourly data: full occupancy 58
4.2.1 Bin method on the required parameters 59
4.2.2 Analysis according to enthalpy differences (∆H) 59
4.2.3 Importance of enthalpy difference in affecting the results 85
4.3 Statistical processing of the data using t-distribution 87
4.4 Hourly data for other values of occupancy 90
4.5 Validation of the analysed data 90
4.6 Applications of the Bin-statistical analysis method 92
Chapter 5 CONCLUSIONS AND RECOMMENDATIONS 94
5.1 Conclusions for the study 94
5.1.1 Potential of statistical method for data analysis compared to mathematical regression 94
5.1.2 Applications of this combination method (Bin simplicantions-statistical analysis) are possible 94
5.1.3 Feasibility in diagnosing the building energy 95
5.2 Recommendations for improving the research results 96
5.2.1 Variety of sources for results data 96
5.2.2 Adding or reconsidering the required paramaters or conditions 96
5.3 Recommendations for future work 96
5.3.1 Comparison with real cases 96
5.3.2 Variety of the building cases 97
REFERENCES 98
APPENDIX 101
Appendix A 101
NOMENCLATURE 107

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