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MPC-based Adaptive Climate Control of Multi-unit Buildings Using Weather Forecast

Mohammadzadeh Mazar, Mohammad | 2019

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 52408 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Rezaeizadeh, Amin
  7. Abstract:
  8. Energy management in buildings play an important role in minimizing the global energy consumption. Providing an optimal temperature in buildings and workplaces is a significant step toward the energy management, regardless of the local climate. Buildings account for 20-40% of the world’s total energy consumption, and this consumption is increasing in developed countries at 0.5-5% per annum.In various studies, energy consumption potential has been investigated using model predictive control (MPC) and system identification.In this paper, the building system is modeled as a gray-box model in which the formulas are developed using the heat transfer equation and the solar radiation heat is considered as an external disturbance. In the propose model, all parameters have physical meanings and are estimated using the UKF algorithm. This nonlinear estimation strategy provides a flexible model that eventually conforms to any probable changes in the system, i.e., adapts to dynamic environment. After a pure estimation interval (star-up phase) the system parameters are converged to the real values and the controller can be applied as the model becomes reliable (steady-state phase). A binary optimization problem is solved in the steady-state phase to be used in a centralized MPC beside the weather is prediction. A binary cost function is used since the boiler has an on/off behavior which is minimized at a specific time horizon. The time delay in the system is also considered and approximated as a system parameter in order to be obtained in the estimation process. Finally, a system of two-unit building is solved as a numerical example
  9. Keywords:
  10. Smart Home ; Building Thermal Modeling ; Predictive Control ; Energy Consumption Optimization ; System Identification

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