Abstract
In this paper we review the current landscape of data-driven decision making in the context of operating residential and commercial building systems with energy management objectives. First, we present results from a literature review focused on identifying new sources of data that have become available (e.g., smart-phone sensors, utility smart meters) and their potential to impact the decision making processes involved in operating these facilities. Existing obstacles to realizing the full potential of these novel data sources are discussed and later explored more in depth through case studies. These include limited interoperability and standardization practices, high labor and/or maintenance costs for installing and maintaining the instrumentation and computationally expensive inference procedures for extracting useful information out of the measurements. Finally, two specific research projects that address some of these challenges are presented in detail: one on disaggregating the total electricity consumption of a building into its constituent loads for informing predictive maintenance practices; and another on standardizing meta-data about sensors and actuators in existing Building Automation Systems (BAS) so that software applications targeting building systems can be deployed in different buildings without the need for manual configuration. Our case studies reveal that the rapid proliferation of sensing/control devices, alone, will not improve the building systems being monitored or significantly alter the way these systems are managed or controlled. When data about the physical world is a commodity, it is the ability to extract actionable information from this resource what generates value and, more often than not, this process requires significant domain expertise.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
An animation can be seen at https://www.autodeskresearch.com/publications/samestats.
- 2.
The activation function \(\sigma \) is normally in the form of sigmoid, tanh or ReLU.
References
U.S. Energy Information Administration: Annual Energy Review 2011. None, annual edition. Energy Information Administration, October 2012
Kiliccote, S., Piette, M.A., Hansen, D.: Advanced controls and communications for demand response and energy efficiency in commercial buildings. Technical report, Lawrence Berkeley National Laboratory (LBNL), January 2006
Froelich, J., Everitt, K., Fogarty, J., Patel, S., Landay, J.: Sensing opportunities for personalized feedback technology to reduce consumption. In: The CHI Workshop on Defining the Role of HCI in the Challenge of Sustainability (2009)
Roth, K.W., Westphalen, D., Feng, M.Y., Llana, P., Quartararo, L.: Energy impact of commercial building controls and performance diagnostics: market characterization, energy impact of building faults and energy savings potential. Technical report TIAX LLC D0180, TIAX LLC, Cambridge, August 2005
Ma, Y., Borrelli, F., Hencey, B., Coffey, B., Bengea, S., Haves, P.: Model predictive control for the operation of building cooling systems. IEEE Trans. Control Syst. Technol. 20(3), 796–803 (2012)
Agbi, C., Song, Z., Krogh, B.H.: Parameter identifiability for multi-zone building models. In: CDC, pp. 6951–6956 (2012)
Gubbi, J., Buyya, R., Marusic, S., Palaniswami, M.: Internet of things (IOT): a vision, architectural elements, and future directions. Future Gener. Comput. Syst. 29(7), 1645–1660 (2013)
Horch, A., Kubach, M., Roßnagel, H., Laufs, U.: Why should only your home be smart?-a vision for the office of tomorrow. In: 2017 IEEE International Conference on Smart Cloud (SmartCloud), pp. 52–59. IEEE (2017)
Chilipirea, C., Ursache, A., Popa, D.O., Pop, F.: Energy efficiency and robustness for IOT: building a smart home security system. In: 2016 IEEE 12th International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 43–48. IEEE (2016)
Ploennigs, J., Dibowski, H., Ryssel, U., Kabitzsch, K.: Holistic design of wireless building automation systems. In: 2011 IEEE 16th Conference on Emerging Technologies Factory Automation (ETFA), pp. 1–9 (2011)
Livingood, W., Stein, J., Considine, T., Sloup, C.: Review of current data exchange practices: providing descriptive data to assist with building operations decisions. Technical report NREL/TP-5500-50073, National Renewable Energy Laboratory, Golden, May 2011
Kastner, W., Neugschwandtner, G., Soucek, S., Newmann, H.: Communication systems for building automation and control. Proc. IEEE 93(6), 1178–1203 (2005)
Dawson-Haggerty, S., Jiang, X., Tolle, G., Ortiz, J., Culler, D.: sMAP: a simple measurement and actuation profile for physical information. In: Proceedings of the 8th ACM Conference on Embedded Networked Sensor Systems, SenSys 2010, pp. 197–210. ACM, New York (2010)
Krioukov, A., Fierro, G., Kitaev, N., Culler, D.: Building application stack (BAS). In: Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, BuildSys 2012, pp. 72–79. ACM, New York (2012)
Rowe, A., Berges, M., Bhatia, G., Goldman, E., Rajkumar, R., Garrett, J.H., Moura, J.M.F., Soibelman, L.: Sensor Andrew: large-scale campus-wide sensing and actuation. IBM J. Res. Dev. 55(1.2), 6:1–6:14 (2011)
Agarwal, Y., Gupta, R., Komaki, D., Weng, T.: BuildingDepot: an extensible and distributed architecture for building data storage, access and sharing. In: Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, BuildSys 2012, pp. 64–71. ACM, New York (2012)
Liu, X., Akinci, B., Berges, M., Garrett Jr., J.H.: An integrated performance analysis framework for HVAC systems using heterogeneous data models and building automation systems. In: Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, BuildSys 2012, pp. 145–152. ACM, New York (2012)
Rahm, E., Bernstein, P.A.: A survey of approaches to automatic schema matching. VLDB J. 10(4), 334–350 (2001)
Granderson, J., Piette, M.A., Ghatikar, G.: Building energy information systems: user case studies. Energ. Effi. 4(1), 17–30 (2011)
Jagpal, R.: Computer aided evaluation of HVAC system performance: technical synthesis report. Technical report, International Energy Agency (2006)
Katipamula, S., Brambley, M.R.: Review article: methods for fault detection, diagnostics, and prognostics for building systems—a review, part I. HVAC&R Res. 11(1), 3–25 (2005)
Liu, X., Akinci, B., Berges, M., Garrett Jr., J.H.: Extending the information delivery manual approach to identify information requirements for performance analysis of HVAC systems. Adv. Eng. Inf. 27(4), 496–505 (2013)
Botts, M., Percivall, G., Reed, C., Davidson, J.: OGC® sensor web enablement: overview and high level architecture. In: Nittel, S., Labrinidis, A., Stefanidis, A. (eds.) GSN 2006. LNCS, vol. 4540, pp. 175–190. Springer, Heidelberg (2008). https://doi.org/10.1007/978-3-540-79996-2_10
Whitehouse, K., Zhao, F., Liu, J.: Semantic streams: a framework for composable semantic interpretation of sensor data. In: Römer, K., Karl, H., Mattern, F. (eds.) EWSN 2006. LNCS, vol. 3868, pp. 5–20. Springer, Heidelberg (2006). https://doi.org/10.1007/11669463_4
Potter, D.: Smart plug and play sensors. IEEE Instrum. Meas. Mag. 5(1), 28–30 (2002)
Greveler, U., Glösekötterz, P., Justusy, B., Loehr, D.: Multimedia content identification through smart meter power usage profiles. In: Proceedings of the International Conference on Information and Knowledge Engineering (IKE), The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), p. 1 (2012)
Mohassel, R.R., Fung, A., Mohammadi, F., Raahemifar, K.: A survey on advanced metering infrastructure. Int. J. Electr. Power Energy Syst. 63, 473–484 (2014)
Zeifman, M., Roth, K.: Nonintrusive appliance load monitoring: review and outlook. IEEE Trans. Consum. Electron. 57(1), 76–84 (2011)
Zoha, A., Gluhak, A., Imran, M.A., Rajasegarar, S.: Non-intrusive load monitoring approaches for disaggregated energy sensing: a survey. Sensors 12(12), 16838–16866 (2012)
Jia, R., Gao, Y., Spanos, C.J.: A Fully Unsupervised Non-intrusive Load Monitoring Framework (2015)
Johnson, M.J., Willsky, A.S.: Bayesian nonparametric hidden semi-markov models. J. Mach. Learn. Res. 14(1), 673–701 (2013)
Kolter, J.Z., Jaakkola, T.: Approximate inference in additive factorial HMMs with application to energy disaggregation. In: International Conference on Artificial Intelligence and Statistics, pp. 1472–1482 (2012)
Lange, H., et al.: Disaggregation by State Inference a Probabilistic Framework for Non-intrusive Load Monitoring (2016)
Ghahramani, Z., Jordan, M.I.: Factorial hidden Markov models. Mach. Learn. 29(2–3), 245–273 (1997)
Lange, H., Berges, M.: Variational bolt: approximate learning in factorial hidden Markov models with application to energy disaggregation. In: AAAI (2018)
Ng, Y.C., Chilinski, P.M., Silva, R.: Scaling factorial hidden Markov models: stochastic variational inference without messages. In: Advances in Neural Information Processing Systems, pp. 4044–4052 (2016)
Hart, G.W.: Nonintrusive appliance load monitoring. Proc. IEEE 80(12), 1870–1891 (1992)
Jordan, M.I., Ghahramani, Z., Jaakkola, T.S., Saul, L.K.: An introduction to variational methods for graphical models. Mach. Learn. 37(2), 183–233 (1999)
Hoffman, M.D., Blei, D.M., Wang, C., Paisley, J.: Stochastic variational inference. J. Mach. Learn. Res. 14(1), 1303–1347 (2013)
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. arXiv preprint arXiv:1312.6114 (2013)
Geman, S., Geman, D.: Stochastic relaxation, Gibbs distributions, and the Bayesian restoration of images. IEEE Trans. Pattern Anal. Mach. Intell. 6, 721–741 (1984)
Rezende, D.J., Mohamed, S.: Variational inference with normalizing flows. arXiv preprint arXiv:1505.05770 (2015)
Gao, J., Giri, S., Kara, E.C., Bergés, M.: PLAID: a public dataset of high-resolution electrical appliance measurements for load identification research: demo abstract. In: Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings, BuildSys 2014, pp. 198–199. ACM, New York (2014)
Ellis, C., Scott, J., Constandache, I., Hazas, M.: Creating a room connectivity graph of a building from per-room sensor units. In: Proceedings of the Fourth ACM Workshop on Embedded Sensing Systems for Energy-Efficiency in Buildings, BuildSys 2012, pp. 177–183. ACM, New York (2012)
Hong, D., Ortiz, J., Whitehouse, K., Culler, D.: Towards automatic spatial verification of sensor placement in buildings. In: Proceedings of the 5th ACM Workshop on Embedded Systems for Energy-Efficient Buildings, BuildSys 2013, pp. 13:1–13:8. ACM, New York (2013)
Lu, J., Whitehouse, K.: Smart blueprints: automatically generated maps of homes and the devices within them. In: Kay, J., Lukowicz, P., Tokuda, H., Olivier, P., Krüger, A. (eds.) Pervasive 2012. LNCS, vol. 7319, pp. 125–142. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31205-2_9
Liu, X., Akinci, B., Garrett Jr, J.H., Berges, M.: Requirements and development of a computerized approach for analyzing functional relationships among HVAC components using building information models. In: CIB W078–W102, France (2011)
Koc, M., Akinci, B., Bergés, M.: Comparison of linear correlation and a statistical dependency measure for inferring spatial relation of temperature sensors in buildings. In: Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings, BuildSys 2014, pp. 152–155. ACM Press, New York, November 2014
Gao, J., Ploennigs, J., Bergés, M.: A data-driven meta-data inference framework for building automation systems. In: Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments, BuildSys 2015 (2015)
Holmegaard, E., Kjærgaard, M.B.: Mining building metadata by data stream comparison. In: Proceeding of the 2016 IEEE Conference on Technologies for Sustainability, pp. 28–33 (2016)
Hong, D., Gu, Q., Whitehouse, K.: High-dimensional time series clustering via cross-predictability. In: Singh, A., Zhu, J. (eds.) Proceedings of the 20th International Conference on Artificial Intelligence and Statistics, Fort Lauderdale, FL, USA. Proceedings of Machine Learning Research, PMLR, vol. 54, pp. 642–651 (2017)
Matejka, J., Fitzmaurice, G.: Same stats, different graphs. In: Proceedings of the 2017 CHI Conference on Human Factors in Computing Systems, CHI 2017, pp. 1290–1294. ACM Press, New York (2017)
Hong, D., Wang, H., Ortiz, J., Whitehouse, K.: The building adapter. In: Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments, BuildSys 2015, pp. 123–132. ACM Press, New York, November 2015
Gao, J., Berges, M.: A large-scale evaluation of automated metadata inference approaches on sensors from air handling units. In: Advanced Engineering Informatics (2018, to appear)
Wang, Z., Yan, W., Oates, T.: Time series classification from scratch with deep neural networks: a strong baseline. arXiv:1611.06455 [cs, stat] (2016)
LeCun, Y., Bengio, Y.: Convolutional Networks for Images, Speech, and Time Series, pp. 255–258. MIT Press, Cambridge (1998)
Masci, J., Meier, U., Cireşan, D., Schmidhuber, J.: Stacked convolutional auto-encoders for hierarchical feature extraction. In: Honkela, T., Duch, W., Girolami, M., Kaski, S. (eds.) ICANN 2011. LNCS, vol. 6791, pp. 52–59. Springer, Heidelberg (2011). https://doi.org/10.1007/978-3-642-21735-7_7
Turchenko, V., Chalmers, E., Luczak, A.: A Deep Convolutional Auto-Encoder with Pooling - Unpooling Layers in Caffe, January 2017
Noh, H., Hong, S., Han, B.: Learning Deconvolution Network for Semantic Segmentation, May 2015
Dong, J., Mao, X.J., Shen, C., Yang, Y.B.: Learning Deep Representations Using Convolutional Auto-encoders with Symmetric Skip Connections, November 2016
Bojanowski, P., Joulin, A.: Unsupervised learning by predicting noise. arXiv:1704.05310 [cs, stat] (2017)
Gao, J.: A metadata inference framework to provide operational information support for fault detection and diagnosis applications in secondary HVAC systems. Ph.D. thesis, CEE Department, Carnegie Mellon University, December 2017
Ioffe, S., Szegedy, C.: Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift, February 2015
Viterbi, A.J.: Error bounds for convolutional codes and an asymptotically optimum decoding algorithm. IEEE Trans. Inf. Theor. 13(2), 260–269 (1967)
Acknowledgments
We would like to acknowledge the Siebel Foundation for the funding that partially supported the research presented in this paper. This research was also partially funded by the Pennsylvania Infrastructure Technology Alliance (PITA), and the Department of Energy project grant DE-EE0007682. We would also like to sincerely thank Dr. Youngchong Park, Erik Paulson, and Andrew Boettcher from Johnson Controls International for providing the data used in the second case study; Dr. Michael Brambley and Dr. Andrew Stevens from the Pacific Northwest National Laboratory for their guidance and comments about the second case study; as well as Aarti Singh and Alex Davis for conversations that crystalized the general description provided in Sect. 1.1. The opinions expressed here are those of the authors and do not necessarily reflect the views of the sponsors.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer International Publishing AG, part of Springer Nature
About this paper
Cite this paper
Bergés, M., Lange, H., Gao, J. (2018). Data-Driven Operation of Building Systems: Present Challenges and Future Prospects. In: Smith, I., Domer, B. (eds) Advanced Computing Strategies for Engineering. EG-ICE 2018. Lecture Notes in Computer Science(), vol 10864. Springer, Cham. https://doi.org/10.1007/978-3-319-91638-5_2
Download citation
DOI: https://doi.org/10.1007/978-3-319-91638-5_2
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-91637-8
Online ISBN: 978-3-319-91638-5
eBook Packages: Computer ScienceComputer Science (R0)