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Deep Reinforcement Learning for Building Climate Control Using Weather Forecast Data

Honari Latifpour, Ehsan | 2021

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  1. Type of Document: M.Sc. Thesis
  2. Language: Farsi
  3. Document No: 54027 (05)
  4. University: Sharif University of Technology
  5. Department: Electrical Engineering
  6. Advisor(s): Rezaeizadeh, Amin
  7. Abstract:
  8. Buildings account for more than 30% of the world’s total energy consumption. Among building end-uses, air conditioning and in particular cooling systems have a major share of more than 50%. Therefore, design of optimal controllers for AC systems has become increasingly important. Classical and model-free control methods typically lack the ability to optimize energy consumption. On the other hand, model-based optimal control methods rely on precise modeling, which is difficult to acquire due to the complexity of the AC system dynamics.In recent years, deep reinforcement learning has become a popular choice for optimal control of systems with complex dynamics. In this thesis, a deep reinforcement learning based optimal controller is designed for a VAV system, with the goal of temperature control and reducing energy consumption. The controller is trained using SAC, a deep reinforcement learning algorithm that has achieved state of the art performance in various benchmark tasks. The case study building is a multi-unit office building. Simulations are carried out using EnergyPlus, which is a whole building energy modeling software.Finally, the performance of the designed controller is evaluated using weather data from different regions, and compared with common control methods in terms of thermal comfort and energy consumption
  9. Keywords:
  10. Energy Efficiency ; Deep Reinforcement Learning ; Energy Consumption Optimization ; Building Thermal Modeling ; Energyplus Software ; Energy Consumption Reduction

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