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

摘要

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.