Accurately forecasting temperatures in smart buildings using fewer sensors
Source of Publication
Personal and Ubiquitous Computing
© 2017, Springer-Verlag London Ltd., part of Springer Nature. Forecasts of temperature in a “smart” building, i.e. one that is outfitted with sensors, are computed from data gathered by these sensors. Model predictive controllers can use accurate temperature forecasts to save energy by optimally using heating, ventilation and air conditioners while achieving comfort. We report on experiments from such a house. We select different sets of sensors, build a temperature model from each set, and compare the accuracy of these models. While a primary goal of this research area is to reduce energy consumption, in this paper, besides the cost of energy, we consider the cost of data collection and management. Our approach informs the selection of an optimal set of sensors for any model predictive controller to reduce overall costs, using any forecasting methodology. We use lasso regression with lagged observations, which compares favourably to previous methods using the same data.
Electrical and Computer Engineering
Energy efficiency, Feature selection, Internet of things, Model predictive control, Sensor networks, Temperature forecast
Spencer, Bruce; Al-Obeidat, Feras; and Alfandi, Omar, "Accurately forecasting temperatures in smart buildings using fewer sensors" (2019). All Works. 339.
Indexed in Scopus