BPL: Automating the Diagnosis of Building System Performance


For medium-to-large buildings that do not have Building Automation Systems, develop ways to capture the benefits of Building Re-Tuning.  Specifically, analyze the various monitoring and diagnosing methods that the CUNY Building Performance Lab has developed, and find ways to effectively “automate” the procedures and communicate essential results to building operators.


Multiple studies have shown that improvements in building operations can typically reduce overall building energy consumption by 15%. The many buildings that have not yet embarked on these improvements represent a huge untapped opportunity to increase building energy efficiency.

Building Re-Tuning (BRT), developed by Pacific Northwest National Laboratory, is a techniques for identifying opportunities for energy efficiency improvements in building operations. BRT techniques can identify ways to improve performance by better system scheduling and ventilation, use of air-side economizing (“free cooling”), appropriate set points, etc. However, BRT generally relies on the presence of a Building Automation System (BAS), i.e., a sophisticated control system that uses digital controls to monitor and manage mechanical and electrical systems within buildings. The practical problem: Most medium-sized buildings (25,000 – 50,000 square feet)—and even many larger buildings—do not use BAS, hence are not candidates for Building Re-Tuning.

Building engineers have long used sensors and loggers to assess building system performance. Could one combine this traditional engineering approach with “Re-Tuning” methods, and apply the result to the many under-served medium-size buildings? This is the general question the project will explore. In particular, the project will investigate the techniques that have been developed and piloted by the CUNY Building Performance Lab. These techniques are effective, and do not require an underlying BAS system. However, they require significant effort and expertise to carry out. As a result, building operators have been willing to rely upon the diagnostic results of the Re-Tuning-like efforts, but less willing to carry out the somewhat laborious process of implementing the techniques themselves. This project aims to explore and test potential solutions to this dilemma.

Suggested Approaches

  1. Tap into the techniques / protocols being piloted by CUNY Building Performance Laboratory: Become familiar with CUNY BPL techniques and protocols, and the relatively labor-intensive implementation they currently demand.
  2. Analyze and automate:  Examine each step in the process: sensor/logger kit design; installation and kit commissioning; data acquisition; trend visualization; applying rules-based analytics for building system diagnostics; communicating results to building operator. Identify possible ways to “automate” the steps of the process to facilitate communication of results to building operator.
  3. Create protocols for communicating results to building operators:  Automated building systems often generate alarms and alerts in quantities that fatigue and desensitize operators. Develop guidelines and techniques that determine what diagnostics should be communicated as alert/alarm, or kept in the background for later reference.

Pre-requisities/Ideal Team

Programming and/or coding skills are strongly recommended for teams pursuing projects in this area.