Smart Building with Predictive Air Conditioning Control: A Knapsack Approach

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This thesis proposes a policy to control the heating, ventilation and air conditioning (HVAC) systems in an industrial building. The policy designed in this thesis aims to minimize the electricity

This thesis proposes a policy to control the heating, ventilation and air conditioning (HVAC) systems in an industrial building. The policy designed in this thesis aims to minimize the electricity cost of a building while maintaining human comfort. Occupancy prediction and building thermal dynamics are utilized in the policy. Because every building has a power constraint, the policy balances different rooms' electricity needs and electricity price to allocate AC unit power for each room. In particular, energy costs are saved by reducing the system's power for times when the occupancy is low. Human comfort is preserved by restricting the temperature to a given range when the room occupancy is above a preset threshold. This thesis proposes a greedy policy, with provably good performance bound, to reduce costs for a building while maintaining overall comfort levels. The approximation ratio of the policy is developed and analyzed, demonstrating the effectiveness of this approach as compared to an ideal optimal policy.