November 2021 - M&V Focus Issue # 9

EVO's IPMVP Non-Routine Events and Adjustments application guide (NRE/A Guide) was used by Amir Kamandlooie, Scott Rouse, and Lucas Oliveira of Energy@Work to verify energy savings during COVID. Non-Routine Adjustment (NRA) Method #2, #6, and #10 were used to help Toronto Hydro verify 16,717,742 kWh across 21 commercial offices from 2017 to December 31, 2020. This article reports results for one of these buildings.

Vilnis Verma explains how clamp-on ultrasonic flow meters were fitted to measure cooling energy as part of a measurement and verification project. Preliminary analysis in the early weeks of the project showed that all was not well: there were big apparent performance swings unrelated to what M&V experts knew was going on in the plant—an interesting case of the importance of the human factor in M&V.

The energy transition calls for rational use of all forms of energy, and resulting energy savings must be credibly validated with recognized Measurement and Verification (M&V) techniques. This is particularly important for projects performed under the Energy Performance Contracting (EPC) business model. Pierre Langlois and Denis Tanguay explain how M&V activities are at the core of energy efficiency projects and discuss the role of M&V in EPC.

Bill Koran presents an interesting application of M&V for a residential building located in Boise, Idaho. He reports on an HVAC system upgrade and explains how he used the ECAM spreadsheet add-in to analyze energy use in the building.

Finally, Denis Tanguay illustrates a sad case where energy savings did not translate into financial savings. The case study presents a ground-source heat pump system installed many years ago. He shows the importance of gathering the appropriate information before installing an energy efficiency measure, during its installation, and during the performance period.

 By Vilnis Verma *gb

Clamp-on ultrasonic flow meters are tricky things to deploy and I always get a sinking feeling when somebody says they’re going to use them. In this case they were fitted to measure cooling energy as part of a measurement and verification project. Provisional analysis in the early weeks of the project showed that all was not well: there were big apparent swings in performance, which were unrelated to what we knew was going on on the plant.

Data from the meters, which were downloaded at one-minute intervals, contained computed kWh values which I was consolidating into hourly totals. The person sending me the data was extracting the kWh figures into a spreadsheet for me but some instinct prompted me to request the raw data, which I noticed contained the flow and temperature readings as well as the kWh results. My colleague Daniel wrote a fast conversion routine which saved our friend the trouble and we discovered that there were occasional huge spikes in the one-minute kWh records which were caused by errors in the volumetric flow rates. The following crude diagram of the minute-by minute flows over several weeks shows that as well as plausible results (under 500 cubic metres per hour) there were families of high readings spaced at multiples of about 750 above that:


Figure 1

Vilnis Figure 1


The discrepancies were sporadic, rare, and clearly delineated so Dan was able to modify his software to skip the anomalous readings and average over the gaps. We were lucky that flow rates and temperatures were relatively constant, meaning that the loss of an occasional minute per hour was not fatal. He also discovered that the heat meter was zeroing out low readings below a certain threshold, and he plugged those holes by using the flow and differential-temperature data to compute the values which the meter had declined to output.

The next diagram shows the relationship between the meter’s kWh output, aggregated to eight-hourly intervals (on the vertical axis) with what we believe to be the true readings (on the horizontal axis). The straight line represents a 1:1 relationship and shows that, quite apart from the gross discrepancies, readings were anomalously high in almost every eight-hour interval.

Figure 2

Vilnis Figure 2

The effect on our analysis was dramatic. Instead of erratic changes in performance not synchronised with the energy-saving measure being turned on and off, we were able to see clear confirmation that it was having the required effect.

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(*) Vilnis Vesma is a professional engineer and former energy manager who specialises in the analysis of energy consumption data.