A framework for monitoring-based commissioning: Identifying variables that act as barriers and enablers to the process

Abstract

The practice of monitoring-based commissioning (MBCx) using energy management and information systems (EMIS) has been shown to enable and help sustain up to 20% energy savings in buildings. Despite research that has quantified the costs, benefits, and energy savings of MBCx, the process remains underutilized. To understand why MBCx is not more frequently adopted and how to encourage its use, this research synthesizes qualitative data from over 40 organizations, currently engaging in MBCx. The outcome of this research is a framework containing variables that emerged from the qualitative data, marked as barriers or enablers, organized by phases of the MBCx process. The framework is comprised of 51 emergent variables that fall within 13 different categories. The variables that most frequently act as barriers are data configuration, measurement & verification (M&V), developing specifications for EMIS, and data architecture. Although some variables that act as barriers for one organization were identified as enablers for another. For example, payback/ROI was considered a barrier 7 times and an enabler 3 times. One organization had difficulty making the business case for the initial investment for MBCx due to lack of cost information, while another was able to justify large investments with documented savings of previously implemented measures identified through MBCx. The framework formally validates barriers found in previous research, and can be used by practitioners to better understand common experiences with MBCx. This research highlights the need for a similar collective data set to validate common enablers to MBCx and also the need for empirical research to determine relationships between variables. 2.3. Examples of variables impacting MBCx Previous case studies describe some variables [3,6,16-18] that act as barriers, impeding the process, or enablers, supporting the institutionalization of EMIS and/or MBCx, and energy saving goals. These variables can be inherent to the MBCx process, but cause unexpected challenges or barriers. For example, the University of California, Merced (UC Merced), adopted an MBCx process and reported that one of the biggest issues was data quality [3]. Although the performance of data quality checks is a step within the MBCx process, organizations might not know how often this actually impedes the process or that this can lead to issues like false positive alarms that cause cascading alert notifications during the implementation phase.

Cognitive barriers during monitoring-based commissioning of buildings

“…There was evidence of cognitive bias in 30% of the elements. For a summary of the barriers without cognitive biases, see Harris et al (2018). In the next section, examples of practical solutions to overcome these biases are suggested.…”

“…Previous research describes some commonly experienced technical barriers faced by organizations during the monitoring-based commissioning (MBCx) process such as data configuration and quality. In a study of over 40 organizations using MBCx, Harris et al (2018) found data configuration to be the top barrier. Organizations frequently experienced issues integrating data from hardware, such as submeters, into energy management and information systems (EMIS) due to things like legacy building automation systems (BAS) or information technology (IT) issues such as data security.…”

“…The approach to classify concepts into elements is similar to other prior approaches in qualitative data analysis (e.g. Blizzard & Klotz, 2012;Harris et al, 2018)…”

“…To help address the energy efficiency gap, this research uses a behavioral decision science perspective to better understand organizational decision making that often slows or halts the implementation of MBCx. Prior research investigates MBCx from a technological point of view Harris, Shealy, Kramer, Granderson, & Reichard, 2018;Smith et al, 2011) and more broadly energy reduction from a normative perspective (e.g. optimization, utility theory) (Henderson & Waltner, 2013).…”

A data-driven workflow to improve energy efficient operation of commercial buildings: A review with real-world examples

“…43 Although many efforts to improve metadata by developing semantic models 44,45 are ongoing, the engineering costs for improving metadata for data analytics are still high; this is recognized as the main barrier for implementing smart building solutions in existing buildings. 31,46 Methods to improve quality of metadata. Improving metadata requires, first and foremost, identifying the sources of data.…”