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Personal and Ubiquitous Computing

, Volume 23, Issue 5–6, pp 921–929 | Cite as

Accurately forecasting temperatures in smart buildings using fewer sensors

  • Bruce SpencerEmail author
  • Feras Al-Obeidat
  • Omar Alfandi
Original Article

Abstract

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.

Keywords

Energy efficiency Sensor networks Model predictive control Temperature forecast Feature selection Internet of things 

Notes

Funding information

The authors gratefully acknowledge the financial support of their organizations, and Zayed University’s RIF 17062 fund.

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Copyright information

© Springer-Verlag London Ltd., part of Springer Nature 2017

Authors and Affiliations

  1. 1.University of New BrunswickFrederictonCanada
  2. 2.Zayed UnivesityAbu DhabiUnited Arab Emirates

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