Lab Energy Sleuths: Better Visualization and Fault Diagnostic Methods for Improving Lab Energy Performance

Mark Mullins, Massachusetts Institute of Technology
Nick Gayeski, KGS Buildings

Today's laboratories are controlled with increasingly sophisticated energy management systems. Over time, as components fail and adjustments are made to these systems, energy performance tends to deteriorate. Consequently, maintaining and improving building energy performance can often be a complex and time consuming task.

Automated Fault detection and diagnostic (AFDD) software is designed to help overcome this problem by automatically diagnosing problems. However, understanding the link between these problems and overall building energy performance can be challenging. Further, techniques for evaluating energy performance are limited.

Hourly or sub-hourly energy and energy-related data (aka interval data) provides a wealth of information on building energy use. This data can be mined to help facility staff more readily evaluate energy performance, identify energy related problems, prioritize energy related work, and track energy use trends over time. Further, this data can be used to verify the impact of problems identified by FDD software.

We will present methods for visualizing, analyzing, and diagnosing energy data to better understand and evaluate building energy performance. We will describe how this information can augment FDD software to evaluate building energy use, prioritize efforts, and improve performance, as well as how FDD software has enabled virtual metering to help investigate key performance metrics at a systems level beyond energy metering.

The presentation will include case studies describing how these techniques were used to evaluate building energy performance at MIT. These case studies will discuss how these techniques helped prioritize energy conservation work, as well as how these tools helped guide fault detection and diagnostic efforts. We will discuss limitations of this approach and describe areas for future development.

Learning Objectives

  • Understand basics of automated Fault detection and diagnostics.
  • Understand methods for visualizing energy performance.
  • Understand techniques for evaluating building energy performance.
  • Understand how to use building energy performance data to save energy and improve performance.


Mark Mullins, PE is Senior Energy Efficiency Engineering in MIT's Department of Facilities. Mr. Mullins has 25 years of experience in the energy efficiency industry. Prior to joining MIT, Mr. Mullins worked in the energy services industry developing energy savings projects in the institutional, higher education, municipal, industrial, and correctional markets.

Nick Gayeski is the CEO and Co-Founder of KGS Buildings. KGS provides automated intelligence software, Clockworks, for facility managers, engineers, and service providers to help them save time, energy and money while improving comfort and performance through smarter facility management. Nick has a Masters and PhD in Building Technology from the Massachusetts Institute of Technology.


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