Turn Your Data Into A Crystal Ball: How big data analytics can target energy savings opportunities as well as predict impending system failures to achieve improved energy efficiency, comfort and sustainability.


Fault detection and root cause analysis using big data provide a strategic approach to energy savings in high-performance buildings. Insidious HVAC faults are often superseded by reactive maintenance. By analyzing building data, large scale operational issues can be mitigated and persistent alarms can be minimized. Predictive fault detection algorithms identify potential faults and equipment failures before they occur, allowing for predictive rather than reactive maintenance. The economic impact associated with these issues can be used to quantify building performance improvement potential. BMS data from representative government, pharmaceutical, and university facilities will be used to highlight the procedure for fault detection and predictive analysis. This presentation will discuss typical high-value controls, mechanical, and operational faults. Using aggregated facility data, I will explore fault identification and failure prediction, root cause analysis, and issue remediation. The presentation will then focus on quantifying the energy savings that result from the appropriate corrective actions. Lastly, I will cover the impact of data quality and reliability on our outcomes. Through the examples provided, this presentation will demonstrate how a methodical approach to BMS analysis and design can result in high-functioning, energy efficient building operation.

Presented By

Julianne Rhoads, C.E.M.
Senior Analyst
Cimetrics, Inc.

Julianne Rhoads joined Cimetrics in 2017 and is responsible for energy analysis and reporting on more than 35 buildings, including over 6.5 million square feet of facilities in the healthcare, higher education, federal, and pharmaceutical research sectors throughout the United States. She has identified and helped to implement more than $3.3 million in annual energy savings.