Model-Based Predictive Control of Window Shades

Huchuk, Brent

doi:10.22215/etd/2014-10444

Cited by 2 publications

(3 citation statements)

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“…Researchers have extensively studied and demonstrated the viability of different optimization algorithms: (1) linear programming [169], (2) dynamic programming [171], (3) quadratic programming [172], (4) particle swarm optimization [173], (5) evolutionary algorithm [174], (6) firefly algorithm [175], and (7) Newton's method of optimization [152,154]. The researchers have employed these methods inside modern computers with established statistical tools [176].…”

Section: Literature Reviewmentioning

confidence: 99%

“…Furthermore, when the parameters of a thermal zone model are derived from detailed physical descriptions, the model becomes nontransferable to other zones without manual fine-tuning. Therefore, control-oriented models should autonomously identify their parameters in recursion[151].Beyond model complexity, the choice of recursive parameter estimation methodologies plays an important role over the robustness of the control-oriented models.As it was the case for model input and complexity choices, the reviewed literature is vastly fragmented among three broad recursive parameter estimation methodologies: the recursive least squares filters[152,153], Bayesian filters such as the Extended and Unscented Kalman Filters[16,141,145,148,153], and Monte Carlo implementation of the Bayesian Filters such as the Ensemble Kalman Filters[154,155]. The recursive least squares filter merely attempts to minimize the misfit between the measurements and model predictions by assuming that the model can perfectly capture the physics of the problem.…”

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Improving energy efficiency in office buildings through adaptive control of the indoor climate

Gunay¹

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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. Occupants' 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 maintaining fixed setpoints and schedules, they represent great potential to implement distributed artificial intelligence. To this end, within the scope of this thesis, an adaptive 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 algorithms 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 laboratory 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 daylighting 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.

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Section: Literature Reviewmentioning

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Improving energy efficiency in office buildings through adaptive control of the indoor climate

Gunay¹

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“…The results from their work showed simulated energy savings of 17%, compared to the industry-standard RBC. Also, Huchuk's work on MPC of blinds using an RC model resulted in 34% energy savings, when applied to an office space in Ottawa [20]. Unlike detailed building simulation based on first principles of heat transfer, an RC model uses a simplified representation of the building.…”

Section: Model Predictive Control (Mpc)mentioning

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An Evaluation of Model Predictive Control of Automated Shading to Optimize Passive Solar Gains

Lawal¹

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ii Abstract Space heating and cooling account for 65% of the total energy consumption in Canada's residential sector. As a result, the design and construction of passive houses is becoming mainstream. However, passive houses are prone to overheating, even sometimes in the winter. Proper operation of window shading devices can provide substantial savings to the space conditioning loads in homes.Model predictive control (MPC) of automated window blinds has proved to be effective at managing solar gains to reduce the heating and cooling loads in buildings. MPC involves predicting a system's response to a control input over a finite period of time in order to determine the best current control decision. This predictive quality of MPC is beneficial in buildings due to their delayed thermal response to solar gains.This thesis details the framework for an MPC application for automated blind control of a single-family home in Ottawa. Building performance simulation (BPS) software was used to predict the interactions between the blind positions and the energy demand in the home. Optimizations were performed to minimize the total energy demand attributed to heating and cooling the home.The performance of the MPC application was simulated over representative weeks of the year, and compared to a reactive, rule-based controller (RBC), which is the standard practice in blind automation. Energy savings of up to 36% were recorded in comparison to the RBC.iii I wish to thank my office colleagues, Roxana Barcelo, Sadeem Al-Attar and Amr Daouk, for making my time in Ottawa enjoyable.Lastly, I would like to thank my friends and family for their continuous support and encouragement. Thanks to my parents and sisters for always being there for me in my time of need, and to Nafisa Jadavji for her excellent mentorship. iv R Thermal resistance [K/W] QFA Future thermal flux into air nodes [W] QFC Future thermal flux into construction nodes [W] QFS Future thermal flux into surface nodes [W] TFA Future temperature of air nodes [ • C] TFC Future temperature of construction nodes [ • C] TFS Future temperature of surface nodes [ • C] xii Chapter 1

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Model-Based Predictive Control of Window Shades

Cited by 2 publications

References 59 publications

Improving energy efficiency in office buildings through adaptive control of the indoor climate

Improving energy efficiency in office buildings through adaptive control of the indoor climate

An Evaluation of Model Predictive Control of Automated Shading to Optimize Passive Solar Gains

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