D. Yu, A. Abhari, A.S. Fung, K. Raahemifar, F. Mohammadi, “Predicting indoor temperature from smart thermostat and weather forecast data”, Proceedings of the Communications and Networking Symposium, Baltimore, 2018. https://dl.acm.org/doi/10.5555/3213200.3213209
- Danilo Yu
Using the ecobee thermostat data from sixteen Canadian and US houses, the prediction accuracy of the generalized regression neural network (GRNN) algorithm and the resilient back propagation neural network (ANN) algorithm were evaluated. The physical range of this model encompasses a single building.
residential building demand, load forecasting
- Key Inputs
outdoor temperature, solar radiation, thermostat temperature setpoints, humidity, heating and cooling durations, fan duration
- Key Outputs
|Danilo Yu||Ryerson University|