Predicting Indoor Temperature using Machine Learning

Modelling project

References

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

Developer(s)
Danilo Yu

Description

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.

Applications

residential building demand, load forecasting

Key Inputs

outdoor temperature, solar radiation, thermostat temperature setpoints, humidity, heating and cooling durations, fan duration

Key Outputs

indoor temperature

Registered developers

NameOrganization
Danilo YuRyerson University