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Development of a Grey Box Thermal Dynamic Model Without Building Construction Knowledge Development of a Grey Box Thermal Dynamic Model without Building Construction Knowledge by Elizabeth LeRiche B.Sc. in Mechanical Engineering, Queen’s University, 2016 A thesis presented to Ryerson University in partial fulfillment of the requirements for the degree of Master of Applied Science in the program of Building Science Toronto, Ontario, Canada, 2019 © Elizabeth LeRiche, 2019 i AUTHOR'S DECLARATION FOR ELECTRONIC SUBMISSION OF A THESIS I hereby declare that I am the sole author of this thesis. This is a true copy of the thesis, including any required final revisions, as accepted by my examiners. I authorize Ryerson University to lend this thesis to other institutions or individuals for the purpose of scholarly research. I further authorize Ryerson University to reproduce this thesis by photocopying or by other means, in total or in part, at the request of other institutions or individuals for the purpose of scholarly research. I understand that my thesis may be made electronically available to the public. ii Development of a Grey Box Thermal Dynamic Model without Building Construction Knowledge Master of Applied Science 2019, Elizabeth LeRiche Building Science, Department of Architectural Science, Ryerson University ABSTRACT Model Predictive Controllers (MPC) in building Heating Ventilation and Air Conditioning (HVAC) systems have demonstrated significant energy savings when compared to typical on/off controllers. MPCs require information about the building’s thermal dynamics which is challenging to model, especially for older structures without buildings specifications. This research investigates the ability to develop a grey box thermal dynamic model that can determine the net thermal dynamics, without any building construction information. Sensors were installed within a test cell to monitor the building automation system (BAS) points, and collect building element surface temperature data. The simulation program Simulink was used to develop and test iterations of grey box models. The final model, that relies solely on BAS points, is able to predict the ambient temperature for a 3-hour Prediction Window to within 1.7% accuracy. This model demonstrates the potential for more buildings to implement HVAC MPC systems with grey box thermal dynamic modeling. iii ACKNOWLEDGMENTS I would like to send a special thank you to my supervisor, Jenn McArthur, who has been incredibly supportive throughout this research. I learnt a lot from working with Jenn over the past year and I am grateful for her guidance throughout this project. I thank Zaiyi Liao for sharing his knowledge of Simulink and providing feedback throughout this year. I thank Alan Fung for accepting to be third reader, and the entire defense committee for providing their knowledge and feedback on this work. I would also like to thank Greg Labbe, who was instrumental in acquiring the calibration equipment and rooftop weather data for this research, as well as for being a confidant throughout the past year. Finally, I thank my parents for their love and support, along with the wonderfully supportive network of friends and family who encouraged me during the completion of my masters. iv Table of Contents 1 INTRODUCTION ...................................................................................................................... 18 1.1 Research Objective ..................................................................................................................................... 20 1.2 Research Questions .................................................................................................................................... 20 2 BACKGROUND ........................................................................................................................ 21 2.1 Model Predictive Control ............................................................................................................................ 21 2.1.1 Economic Model Predictive Control ........................................................................................................ 22 2.1.2 Thermal Dynamic Modeling .................................................................................................................... 23 2.2 Use of Test Cells in Predictive Control Development .................................................................................. 23 2.2.1 Use of Temperature Sensors ................................................................................................................... 24 2.2.2 Internal Load ............................................................................................................................................ 24 2.2.3 HVAC Systems .......................................................................................................................................... 25 2.2.4 Occupancy Modeling ............................................................................................................................... 25 2.2.5 Modeling of External Conditions ............................................................................................................. 26 2.3 White Box Model ........................................................................................................................................ 26 2.4 Black Box Model ......................................................................................................................................... 27 2.4.1 Linear Models .......................................................................................................................................... 28 2.4.2 Neural Networks ...................................................................................................................................... 28 2.4.3 Black Box Models vs Grey Box Model ...................................................................................................... 31 2.4.4 Challenges of Black box Models .............................................................................................................. 32 2.5 Grey Box Models ........................................................................................................................................ 32 2.5.1 RC-Network Structure ............................................................................................................................. 34 2.5.2 RC Network Structure Optimisation ........................................................................................................ 37 2.6 Building Parameter Estimation ................................................................................................................... 40 2.7 Subtractive Linear Regression ..................................................................................................................... 41 2.8 Validation and Error Analysis ...................................................................................................................... 42 v 2.9 Lessons from Literature Review used within this Thesis ............................................................................. 43 3 METHODOLOGY ..................................................................................................................... 46 3.1 Test Cell Apparatus ..................................................................................................................................... 47 3.2 Sensor Description ...................................................................................................................................... 47 3.3 Sensor Installation ...................................................................................................................................... 51 3.4 Sensor Modifications .................................................................................................................................. 54 3.5 Data Analytics to Overcome Data Gaps ...................................................................................................... 55 3.6 Data Collection ........................................................................................................................................... 57 4 WHITE BOX MODEL DEVELOPMENT ...................................................................................... 58 4.1 Calculation of Internal Heat Loads .............................................................................................................. 61 4.1.1 Occupancy ............................................................................................................................................... 62 4.1.2 Interior Lighting ....................................................................................................................................... 62 4.1.3 Ventilation ............................................................................................................................................... 62 4.1.4 Heater ...................................................................................................................................................... 63 4.1.5 Thermal Storage ...................................................................................................................................... 63 4.1.6 Calculation of External Heat Loads .......................................................................................................... 64 4.2 White Box Model Implementation in Matlab ............................................................................................
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