臺灣博碩士論文加值系統

At early stage of designing a building, a fast and trustable prediction is superbly needed. The designer needs to propose the design of the building to owner within a very limited amount of time when bidding is held. However, simulation is genuinely needed if the designed building is expected to achieve some points in any green building rating system. Handling simulation which has dozens, hundreds building surrounding require huge additional time for modeling and running the model, but implementing and predicting the output of the simulation is really advantageous for any researcher. Case-based Reasoning (CBR) approach really gives this problem a great solution since in the CBR approach there is no any complicated algorithm that needs very long time to learn if there is any update in the dataset and give the solution almost instantly. Instead of learning the experience, CBR approach retrieve the most similar case then adapt to give the solution which only requires a very short period of time. Based on the fact of these reasons, this research makes a CBR approach to predict the outdoor daylight that influence by outdoor condition. There is some software available to evaluate buildings’ lighting during design stage, but these tools tend to require extended calculation times when it comes to making model or daylight analysis. This research is done using outdoor daylight simulation data which collected from the output of Autodesk Ecotect Analysis. It is used to determine the effect of the outdoor condition of a building which will be represented as building skins. Lastly, the performance of the prediction will be evaluated using MAPE with Leave-one-out validation.

At early stage of designing a building, a fast and trustable prediction is superbly needed. The designer needs to propose the design of the building to owner within a very limited amount of time when bidding is held. However, simulation is genuinely needed if the designed building is expected to achieve some points in any green building rating system. Handling simulation which has dozens, hundreds building surrounding require huge additional time for modeling and running the model, but implementing and predicting the output of the simulation is really advantageous for any researcher. Case-based Reasoning (CBR) approach really gives this problem a great solution since in the CBR approach there is no any complicated algorithm that needs very long time to learn if there is any update in the dataset and give the solution almost instantly. Instead of learning the experience, CBR approach retrieve the most similar case then adapt to give the solution which only requires a very short period of time. Based on the fact of these reasons, this research makes a CBR approach to predict the outdoor daylight that influence by outdoor condition. There is some software available to evaluate buildings’ lighting during design stage, but these tools tend to require extended calculation times when it comes to making model or daylight analysis. This research is done using outdoor daylight simulation data which collected from the output of Autodesk Ecotect Analysis. It is used to determine the effect of the outdoor condition of a building which will be represented as building skins. Lastly, the performance of the prediction will be evaluated using MAPE with Leave-one-out validation.

TABLE OF CONTENT
ACKNOWLEDGEMENTS i
ABSTRACT ii
TABLE OF CONTENT iii
LIST OF FIGURES v
LIST OF TABLES vii
CHAPTER 1 INTRODUCTION 1
1.1 Research Background 1
1.2 Research Scope, Motivations and Objectives, and Assumptions 4
1.2.1 Research Scope 4
1.2.2 Research Motivation and Objectives 5
1.2.3 Research Outline 5
CHAPTER 2 LITERATURE REVIEW 8
2.1 Daylight Simulation 8
2.2 K-means 10
2.2.1 K-means Parameter 11
2.3 Leave-one-out Validation 12
CHAPTER 3 RESEARCH METHODOLOGY 14
3.1 Case-based Reasoning 15
3.1.1 Retrieval Techniques in Case-based Reasoning 18
3.1.2 Adaptation Process 21
3.2 Assembling Outdoor Daylight Data Collection 24
3.2.1 Design of Experiment 24
3.2.2 Autodesk Ecotect Analysis 26
CHAPTER 4 MODELING AND PARAMETER 28
4.1 Pre-Processing Outdoor Daylight Dataset 28
4.1.1 K-means Zoning 30
4.1.2 Solar Altitude and Solar Azimuth 32
4.1.3 Building Area Variation 33
4.1.4 Building Surrounding Reflectance Value 34
4.1.5 Design of Experiment using Taguchi Orthogonal Array 37
4.2 Modeling Outdoor Daylight Illuminance 39
CHAPTER 5 ANALYSIS AND RESULT 45
5.1 Analyzing Ecotect Output Data 45
5.2 Case-based Reasoning Implementation 45
5.2.1 Retrieving Daylight Dataset 46
5.2.2 Adaptation from Retrieval Result 46
5.2.3 Result and Evaluation on CBR Algorithm 48
CHAPTER 6 CONCLUSION AND FUTURE RESEARCH 58
6.1 Conclusion 58
6.2 Future Research 58
REFERENCES 59

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