Demirezen, G., & Fung, A. S. (2019). Application of artificial neural network in the prediction of ambient temperature for a cloud-based smart dual fuel switching system. Energy Procedia, 158, 3070-3075. https://doi.org/10.1016/j.egypro.2019.01.992
The cloud-based smart dual fuel switching system (SDFSS) for hybrid heating, ventilation and air conditioning (HVAC) systems being developed enables flexible and cost-optimized control between the natural gas furnace and air source heat pump (ASHP), allowing simultaneous reduction in energy costs and greenhouse gas (GHG) emissions. This study introduces a novel approach to obtaining the outdoor temperature that could potentially replace smart sensors with a data-driven model utilizing weather station data at time resolutions of 2 minutes and 1 hour. This model is applicable world-wide but more appropriate for cold North American Climate due to the nature of our Smart Dual Fuel Switching System.
hvac industry, thermostat companies
- Key Inputs
ambient temperature from various weather stations, weather related parameters such as wind speed, humidity
- Key Outputs
|Gulsun Demirezen||Ryerson University|