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Burak Gunay, M.A.Sc Improving energy efficiency in office buildings through adaptive control of the indoor climate by H. Burak Gunay, M.A.Sc. A thesis submitted to the Faculty of Graduate and Postdoctoral Affairs in partial fulfillment of the requirements for the degree of Doctor of Philosophy Engineering Ottawa-Carleton Institute for Civil Engineering Department of Civil and Environmental Engineering Carleton University Ottawa, Ontario July, 2016 c Copyright H. Burak Gunay, 2016 The undersigned hereby recommends to the Faculty of Graduate and Postdoctoral Affairs acceptance of the thesis Improving energy efficiency in office buildings through adaptive control of the indoor climate submitted by H. Burak Gunay, M.A.Sc. in partial fulfillment of the requirements for the degree of Doctor of Philosophy Engineering Dr. Jos´eCandanedo Dr. William O'Brien, Thesis Supervisor Dr. Ian Beausoleil-Morrison, Thesis Supervisor Dr. Matthew Johnson Dr. Amir Hakami Dr. John Gales Professor Paul Van Geel, Chair, Department of Civil and Environmental Engineering Ottawa-Carleton Institute for Civil Engineering Department of Civil and Environmental Engineering Carleton University July, 2016 ii Abstract Zone level heating and cooling systems, lighting and blinds in offices have been either controlled by occupants or automated based on fixed setpoints and schedules. Occu- pants' control of these systems target maintaining a comfortable indoor climate with minimum number of control actions with little intention to save energy. This leads to the suboptimal utilization of daylight and passive solar heat gains. However, when they are automated to mitigate the extravagance in occupant behaviour, operators tend to choose conservative setpoints and schedules that maintain the comfort of the majority and minimize the frequency of complaints with little consideration to save energy again. Although we have been treating the building controllers as our servants main- taining fixed setpoints and schedules, they represent great potential to implement distributed artificial intelligence. To this end, within the scope of this thesis, an adap- tive indoor climate control tool was developed. The tool contains novel algorithms that recursively learn from the occupancy patterns, adaptive occupant behaviours, and develop an inverse model of the heat transfer problem in each zone. The algo- rithms were designed to be embedded inside local building controllers to undertake the learning process in real-time using a small number of low-cost building sensors. The information derived from the occupants and the building's temperature response was used to autonomously choose operating setpoints and schedules { tailored to exploit the nuances amongst subspaces in a building. The algorithms were implemented and tested for over a year in a controls labora- tory which was a shared-office space with a standalone controls network. Furthermore, iii they were implemented and tested in private offices for over a year. Energy and day- lighting simulations were conducted to analyze alternative scenarios regarding the design of these algorithms. Implementation results indicate that the use of adaptive indoor climate control algorithms developed in this thesis can substantially reduce the space heating, cooling, and lighting loads in office buildings { without adversely affecting the occupant comfort. The simulation results indicate that adaptive control of the indoor climate not only reduces the expected load intensities but also reduces the risk of poor operational decisions leading to discomfort and excessive energy use. iv Hofstadter's Law: It always takes longer than you expect, even when you take into account Hofstadter's Law. Douglas Hofstadter v Acknowledgments I would like to thank my supervisors Drs. Liam O'Brien and Ian Beausoleil-Morrison. They simply went above and beyond what one can ever expect from a supervisor. You can feel them being truly happy with your successes and sad with your setbacks. I wish to have them as my lifelong mentors and friends. I would like to acknowledge my co-workers at Carleton Building Research Groups: Brent Huchuk, Jayson Bursill, Dr. Sara Gilani, Isis Bennet, Chris Baldwin, Zach Bur- goyne, Cynthia Cruickshank, Shawn Shi, Adam Wills, S´ebastien Brideau, Aly Abde- lalim, Anthony Fuller, Austin Selvig, and Dr. Scott Bucking. Also, I would like to acknowledge Philippe Bisaillon for his friendship, critical assistance, and knowledge. I would like to acknowledge the entire team of Carleton Facilities Management and Planning. Particularly, the support from visionaries like Darryl Boyce, Lisa Paterick, Scott Macdonald, and Philip Mansfield made this research project possible. I would like to acknowledge Delta Controls and CopperTree Analytics for their financial and in-kind contributions. Particularly, the technical knowledge of Chris Kwong, Timo Kinna, and Carl Neilson in building controls was an essential anchor to ensure the practicality of the findings of this research project. I would like to thank Regulvar { particularly St´ephanRiffault, Gilles Lebrun { for training me and donating/installing automated-shades. I would like to thank my colleagues from IEA Annex 66 for their critical assistance; in particular Dr. Ardeshir Mahdavi, Dr. Darren Robinson, Farhang Tahmasebi, Simona D'Oca, Dr. Tianzhen Hong, and Dr. Da Yan. I would like to thank to my other industry collaborators: Autodesk Research (Rhys Goldstein, Azam Khan, Simon Breslav), NRCan (Phylroy Lopez, Meli Stylianou), and Green Power Labs. I would like to thank NSERC for supporting my research project through doctoral scholarships (CGS and PGS) and through a research grant (CRD). I would like to thank ASHRAE for the Grant-in Aid Award, Carleton for the internally administrated awards, and IBPSA for the travel award. I would like to acknowledge the staff at Civil and Environmental Engineering Department in Carleton University: Payal Chadha, Kay Casselman, Sara Seiler, Ben Griffin, Stan Conley, and Kristin Holtz. Finally, thank you to my family for all their support and encouragement through not just this process but everything leading up until this point. Thanks so much Yagmur Babaoglu, your presence made everything easier. vi Table of Contents Abstract iii Acknowledgments vi Table of Contents vii List of Tables ix List of Figuresx Nomenclature xvii 1 Introduction1 1.1 Background on indoor climate control .................. 2 1.1.1 Control for thermal comfort and IAQ .............. 2 1.1.2 Control for visual comfort .................... 9 1.2 Motivation and research objectives ................... 13 1.3 Research methodology .......................... 17 1.4 Document Structure ........................... 23 2 Learning from occupants' presence 25 2.1 Detecting presence ............................ 25 2.1.1 Literature review ......................... 26 2.1.2 Methodology ........................... 30 2.1.3 Results and discussion ...................... 31 2.2 Predicting presence ............................ 46 2.2.1 Literature review ......................... 46 2.2.2 Model forms ............................ 49 2.2.3 Diversity aspects ......................... 53 2.2.4 Occupancy-learning algorithm . 58 2.3 Summary ................................. 62 3 Learning from adaptive behaviours 66 3.1 Literature review: adaptive behaviour modelling . 66 3.2 Development of the thermostat learning algorithm . 71 3.2.1 Analysis of the observational data . 71 3.2.2 Thermostat learning algorithm . 79 vii 3.3 Development of the lighting and blinds learning algorithm . 87 3.3.1 Analysis of the observational data . 87 3.3.2 Lighting and blinds learning algorithm . 102 3.4 Summary ................................. 106 4 Learning from the temperature response 109 4.1 Literature review ............................. 109 4.2 Objectives and scope . 113 4.3 Methodology ............................... 114 4.3.1 Monitored offices and data . 114 4.3.2 Control-oriented models . 121 4.3.3 Recursive parameter estimation . 130 4.4 Predictive accuracy of the models . 135 4.5 Residual analysis and parameter estimates . 141 4.6 Summary ................................. 149 5 Adaptive control of the indoor temperature 152 5.1 Literature review ............................. 152 5.2 Objectives and scope . 155 5.3 Adaptive temperature control algorithm . 155 5.4 Implementation results and comfort implications . 165 5.4.1 Laboratory implementation . 167 5.4.2 Field implementation . 174 5.5 Estimating energy-savings potential . 189 5.6 Summary ................................. 202 6 Adaptive control of the indoor illuminance 205 6.1 Literature review ............................. 205 6.2 Objectives and scope . 209 6.3 Lighting and Blinds Control Algorithm . 210 6.4 Implementation results and comfort implications . 213 6.4.1 Laboratory implementation . 213 6.4.2 Field implementation . 219 6.5 Estimating energy-savings potential . 223 6.6 Summary ................................. 236 7 Conclusions and future work 238 7.1 Future work ................................ 245 List of References 250 viii List of Tables 1.1 An overview of the dataset used during the occupant model and data- driven greybox building model development process. 19 2.1 Characteristics of the sensory data used in occupancy detection study. 32 2.2 The parameters of the model shown in Eqn. 2.1. The parameters are shown in mean ± standard deviation format. 43 2.3 The occupancy parameters in the monitored offices. 57 3.1 List of rooms from which the data were collected. 73 3.2 List of rooms from which the data were collected.
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