Electricity for heating, ventilation, and air condition (HVAC) machines takes up a large percentage of energy consumption in the buildings and thus in turn, a large portion of the energy monetary cost. Optimization of air conditioners use throughout the day will reduce energy consumption and expenditure. This study introduces a second-order differential equation model to capture the indoor temperature dynamics of a building. An experimental test bed is developed to collect a set of indoor/outdoor temperature and sunlight data. Using a least-squares-based system identification process, the model parameters are identified and checked through simulation. Optimization of the room temperature is then determined by solving a mixed-integer quadratic programming problem in relation to the hourly-updated energy prices. Mixed-integer quadratic programming solution is compared to a two-point thermostatic control system. A hybrid solution compromising the quadratic programming algorithm and the conventional thermostatic control scheme is proposed as a tractable approach for the near-optimal energy management of the system.
- Dynamic Systems and Control Division
Modeling and Energy Cost Optimization of Air Conditioning Loads in Smart Grid Environments
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Chan, K, & Bashash, S. "Modeling and Energy Cost Optimization of Air Conditioning Loads in Smart Grid Environments." Proceedings of the ASME 2017 Dynamic Systems and Control Conference. Volume 3: Vibration in Mechanical Systems; Modeling and Validation; Dynamic Systems and Control Education; Vibrations and Control of Systems; Modeling and Estimation for Vehicle Safety and Integrity; Modeling and Control of IC Engines and Aftertreatment Systems; Unmanned Aerial Vehicles (UAVs) and Their Applications; Dynamics and Control of Renewable Energy Systems; Energy Harvesting; Control of Smart Buildings and Microgrids; Energy Systems. Tysons, Virginia, USA. October 11–13, 2017. V003T27A013. ASME. https://doi.org/10.1115/DSCC2017-5284
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