A5019090191- Building Energy and Comfort Optimization
- Advanced Control Systems Optimization
- Refrigeration and Air Conditioning Technologies
- Energy Efficiency and Management
- IoT-based Smart Home Systems
- Greenhouse Technology and Climate Control
- Microgrid Control and Optimization
- Real-time simulation and control systems
- Phase Change Materials Research
- Smart Grid Energy Management
Pacific Northwest National Laboratory
2020-2023
Battelle
2023
University of Florida
2019-2020
Although Model Predictive Control (MPC) has been widely investigated for energy efficient climate control of buildings, most prior works have neglected humidity in the problem formulation and performance evaluations. A algorithm that ignores cannot be used practice, especially hot-humid climates. Apart from discomfort occupants, high over long periods will lead to issues such as mold growth, adversely impacting occupant health. In this paper, we provide an MPC explicitly accounts constraints...
Even though energy efficient climate control of buildings using model predictive (MPC) has been widely investigated, most MPC formulations ignore humidity and latent heat. The inclusion moisture makes the problem considerably more challenging, primarily since a cooling dehumidifying coil which accounts for both sensible heat transfers is needed. In this work, we propose an controller in are incorporated principled manner. We construct low order data-driven models that can be used...
DC electrical distribution systems offer many potential advantages over their AC counterparts. They can facilitate easier integration with distributed energy resources, improve system efficiency by eliminating AC/DC converters at end-use devices (e.g., laptop chargers), and reduce installation material, time, cost. However, present additional design considerations, largely resulting from potentially greater magnitude variation in cable losses. Modeling simulation are rarely used to such...