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Data-Driven Operation of Building Systems: Present Challenges and Future Prospects

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Advanced Computing Strategies for Engineering (EG-ICE 2018)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 10864))

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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.

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Notes

  1. 1.

    An animation can be seen at https://www.autodeskresearch.com/publications/samestats.

  2. 2.

    The activation function \(\sigma \) is normally in the form of sigmoid, tanh or ReLU.

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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.

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Authors and Affiliations

  1. Civil and Environmental Engineering Department, Carnegie Mellon University, 5000 Forbes Avenue, Pittsburgh, PA, USA

    Mario Bergés & Henning Lange

  2. Alibaba Group, Hangzhou, China

    Jingkun Gao

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  1. Mario Bergés
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  2. Henning Lange
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Correspondence to Mario Bergés .

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  1. Applied Computing and Mechanics Laboratory (IMAC), School of Architecture, Civil and Environmental Engineering (ENAC), Swiss Federal Institute of Technology, Lausanne (EPFL), Lausanne, Switzerland

    Ian F. C. Smith

  2. Institute for Landscape, Architecture, Construction and Territory (inPact) Construction and Environment Department (CED), University of Applied Sciences, Geneva (HEPIA), Geneva, Switzerland

    Bernd Domer

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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

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  • DOI: https://doi.org/10.1007/978-3-319-91638-5_2

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