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.
Development and Optimization of Artificial Neural Network Algorithms in the Prediction of Ambient Temperature
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
hvac industry, thermostat companies
ambient temperature from various weather stations, weather related parameters such as wind speed, humidity