Fault detection and diagnosis (FDD) represents one of the most active areas of research and commercial product development in the buildings industry. This paper addresses two questions concerning FDD implementation and advancement 1) What are today's users of FDD saving and spending on the technology? 2) What methods and datasets can be used to evaluate and benchmark FDD algorithm performance? Relevant to the first question, 26 organizations that use FDD across a total 550 buildings and 97M sf achieved median savings of 8%. Twenty-seven FDD users reported that the median base cost for FDD software, annual recurring software cost, and annual labor cost were $8, $2.7 and $8 per monitoring point, with a median implementation size of approximately 1300 points. To address the second question, this paper describes a systematic methodology for evaluating the performance of FDD algorithms, curates an initial test dataset of air handling unit (AHU) system faults, and completes a trial to demonstrate the evaluation process on three sample FDD algorithms. The work provided a first step toward a standard evaluation of different FDD technologies. It showed the test methodology is indeed scalable and repeatable, provided an understanding of the types of insights that can be gained from algorithm performance testing, and highlighted the priorities for further expanding the test dataset.

As building energy and system-level monitoring becomes more common, facilities teams are faced with an overwhelming amount of data. This data does not typically lead to insights, corrective actions, and energy savings unless it is stored, organized, analyzed, and prioritized in automated ways. The Smart Energy Analytics Campaign is a public-private sector partnership program focused on supporting commercially available energy management and information systems (EMIS) technology use and monitoring-based commissioning (MBCx) practices. MBCx is an ongoing commissioning process with focus on analyzing large amounts of data on a continuous basis. EMIS tools are used in the MBCx process to organize, present, visualize, and analyze the data. With Campaign data from over 400 million square feet (sq ft) of installed space, this paper presents the results achieved by owners that are implementing EMIS, along with associated technology costs. The study's EMIS users that reported savings achieved median cost savings of $0.19/sq ft and 7 percent annually, with savings shown to increase over time. For 35 portfolio owners, median base cost to install an EMIS was $0.03/sq ft, with an annual recurring software cost of $0.02/sq ft and estimated annual labor cost of $0.03/sq ft. Two types of EMIS systemsenergy information systems and fault detection and diagnostic systems-are defined in the body of the paper. Of the two, we find that fault detection and diagnostic systems have both higher savings and higher costs. The paper offers a characterization of EMIS products, MBCx services, and trends in the industry.

Influence of EV charging on distribution gridsEV owner want to charge their car immediately after the purchase Distribution system officer should be informed when charging infrastructure is installed Grid expansion (3 -6 months duration) will be necessary in several distribution grids A stopgap measure has to be found to enable EV charging until grid expansion is completed Battery storage is one possibility Dimensioning of battery storage as temporary equipment during grid reinforcement caused by electric vehicles -Lukas Held 26.03.2020

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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.