Development and Evaluation of Occupancy-Aware HVAC Control for Residential Building Energy Efficiency and Occupant Comfort
| Autor: | Gregor P. Henze, Christina Turley, Gregory S. Pavlak, Margarite Jacoby |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2020 |
| Předmět: |
Control and Optimization
Occupancy Computer science thermal comfort 020209 energy 0211 other engineering and technologies Energy Engineering and Power Technology 02 engineering and technology lcsh:Technology Automotive engineering law.invention Setpoint law energy consumption 021105 building & construction HVAC 0202 electrical engineering electronic engineering information engineering Electrical and Electronic Engineering Engineering (miscellaneous) occupancy prediction Renewable Energy Sustainability and the Environment business.industry lcsh:T Thermal comfort Energy consumption HVAC control Thermostat Hvac control Model predictive control Air conditioning Ventilation (architecture) business Energy (miscellaneous) |
| Zdroj: | Energies, Vol 13, Iss 5396, p 5396 (2020) Energies; Volume 13; Issue 20; Pages: 5396 |
| ISSN: | 1996-1073 |
| Popis: | Occupancy-aware heating, ventilation, and air conditioning (HVAC) control offers the opportunity to reduce energy use without sacrificing thermal comfort. Residential HVAC systems often use manually-adjusted or constant setpoint temperatures, which heat and cool the house regardless of whether it is needed. By incorporating occupancy-awareness into HVAC control, heating and cooling can be used for only those time periods it is needed. Yet, bringing this technology to fruition is dependent on accurately predicting occupancy. Non-probabilistic prediction models offer an opportunity to use collected occupancy data to predict future occupancy profiles. Smart devices, such as a connected thermostat, which already include occupancy sensors, can be used to provide a continually growing collection of data that can then be harnessed for short-term occupancy prediction by compiling and creating a binary occupancy prediction. Real occupancy data from six homes located in Colorado is analyzed and investigated using this occupancy prediction model. Results show that non-probabilistic occupancy models in combination with occupancy sensors can be combined to provide a hybrid HVAC control with savings on average of 5.0% and without degradation of thermal comfort. Model predictive control provides further opportunities, with the ability to adjust the relative importance between thermal comfort and energy savings to achieve savings between 1% and 13.3% depending on the relative weighting between thermal comfort and energy savings. In all cases, occupancy prediction allows the opportunity for a more intelligent and optimized strategy to residential HVAC control. |
| Databáze: | OpenAIRE |
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