Accurately forecasting temperatures in smart buildings using fewer sensors
- 362 Downloads
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.
KeywordsEnergy efficiency Sensor networks Model predictive control Temperature forecast Feature selection Internet of things
The authors gratefully acknowledge the financial support of their organizations, and Zayed University’s RIF 17062 fund.
- 3.EIA (2017) Frequently asked questions how much energy is consumed in u.s. residential and commercial buildings? https://www.eia.gov/tools/faqs/faq.php?id=86&t=1
- 5.Friedman J, Hastie T, Simon N, Tibshirani R (2016) Package glmnet: lasso and elastic-net regularized generalized linear models ver 2.0-. https://cran.r-project.org/web/packages/glmnet/glmnet.pdf
- 6.Mevik BH, Wehrens R, Liland KH (2015) pls: Partial least squares and principal component regression. https://CRAN.R-project.org/package=pls, r package version 2.5-0
- 9.Pan D, Yuan Y D W, Xu X, Peng Y, Peng X, Wan P J (2012) Thermal inertia: towards an energy conservation room management system. In: Greenberg A, Sohraby K (eds) INFOCOM. IEEE, pp 2606–2610Google Scholar
- 11.Prívara S, Široký J, Ferkl L, Cigler J (2011) Model predictive control of a building heating system: The first experience. Energ Buildings 43(2):564–572. https://doi.org/10.1016/j.enbuild.2010.10.022 . http://www.sciencedirect.com/science/article/pii/S0378778810003749 CrossRefGoogle Scholar
- 12.Romanski P, Kotthoff L (2016) Package ‘FSelector’ selecting attributes. R package version 0:21. https://cran.r-project.org/web/packages/FSelector/FSelector.pdf Google Scholar
- 15.Spencer B, Al-Obeidat F, Alfandi O (2016) Short term forecasts of internal temperature with stable accuracy in smart homes. Int J Thermal Environ Eng 13(2):81–89Google Scholar
- 16.Spencer B, Al-Obeidat F, Alfandi O (2017) Selecting sensors when forecasting temperature in smart buildings, vol 109, pp 777–784. https://doi.org/10.1016/j.procs.2017.05.321. http://www.sciencedirect.com/science/article/pii/S1877050917309857, 8th International Conference on Ambient Systems, Networks and Technologies, ANT-2017 and the 7th International Conference on Sustainable Energy Information Technology, SEIT 2017, 16-19 May 2017, Madeira, PortugalCrossRefGoogle Scholar
- 18.UCI (2010) Sml2010 data set. https://archive.ics.uci.edu/ml/datasets/SML2010
- 19.United States Department of Energy (2012) Solar decathlon Europe competition. http://www.solardecathlon.gov
- 20.Yuan Y, Pan D, Wang D, Xu X, Peng Y, Peng X, Wan PJ (2013) A study towards applying thermal inertia for energy conservation in rooms. ACM Trans Sen Netw 10(1):7:1–7:25. https://doi.org/10.1145/2529050 . http://doi.acm.org.proxy.hil.unb.ca/10.1145/2529050 CrossRefGoogle Scholar