Energy monitoring and targeting: Difference between revisions

Revision as of 09:52, 26 December 2022

Revision as of 20:01, 29 December 2023

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Before the M&T measures themselves are implemented, a few preparatory steps are necessary. First of all, key energy consumers on the site must be identified. Generally, most of the energy consumption is concentrated in a small number of processes, like heating, or certain [[machinery]]. This normally requires a certain survey of the [[building]] and the equipment to estimate their energy consumption level.

It is also necessary to assess what other [[measurements]] will be required to [[analysis|analyze]] the consumption appropriately. This data will be used to [[chart]] against the energy consumption: these are underlying factors which influence the consumption, often [[production, costs, and pricing|production]] (for industry processes) or exterior [[temperature]] (for heating processes), but may include many other variables.

Once all variables to be measured have been established, and the necessary meters installed, it is possible to initiate the M&T procedures.

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====Define the base-line====

The data compiled must then be plotted on a [[chart|graph]] in order to define the general consumption base-line. Consumption rates are plotted in a [[scatter plot]] against [[production, costs, and pricing|production]] or any other variable previously identified, and the [[best fit line]] is identified. This graph is the image of the business’ average energy performance, and conveys a lot of information:

* The [[y-intercept]] gives the minimal consumption in the absence of the variable (no [[production, costs, and pricing|production]], zero [[degree-day]]...). This is the base load of the system, the minimal consumption when it is not operating.

* The [[slope]] represents the relationship between the consumption and the previously identified [[Variable (mathematics)|variable]]. This represents the efficiency of the process.

* The scatter is the degree of variability of the consumption with operational factors.

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The next step is to monitor the difference between the expected consumption and the actual measured consumption. One of the tools most commonly used for this is the [[control chart|CUSUM]], which is the CUmulative SUM of differences. This consists in first calculating the difference between the expected and actual performances (the [[best fit line]] previously identified and the points themselves).

The [[CUSUM]] can then be plotted against time on a new [[chart|graph]], which then yields more information for the energy efficiency specialist. [[Variance]]s scattered around zero usually mean that the process is operating normally. Marked variations, increasing or decreasing steadily usually reflect a modification in the conditions of the process.

[[Image:CUSUM graph.jpg|right|Example of a CUSUM graph]]In the case of the [[control chart|CUSUM]] graph, the [[slope]] becomes very important, as it is the main indicator of the [[savings]] achieved. A [[slope]] going steadily down indicates steady [[savings]]. Any variation in the [[slope]] indicates a change in the process. For example, in the graph on the right, the first section indicated no [[savings]]. However, in September (beginning of the yellow line), an energy efficiency measure must have been implemented, as [[savings]] start to occur. The green line indicates an increase in the [[savings]] (as the [[slope]] is becoming steeper), whereas the red line must reflect a modification in the process having occurred in November, as [[savings]] have decreased slightly.

====Identify causes====

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Targeting consists in two main parts: the measure to which the consumption can be reduced, and the [[timeframe]] during which the compression will be achieved.

A good initial target is the [[best fit line]] identified during step 2. This line represents the [[average]] historical performance. Therefore, keeping all consumption below or equal to the historical average is an achievable target, yet remains a challenge as it involves eliminating high consumption peaks.

Some companies, as they improve their energy consumption, might even decide to bring their [[average]] performance down to their historical best. This is considered a much more challenging target.<ref>{{cite web|url=http://www.logicenergy.com/live-energy-monitoring/building-energy-use/case-studies/|title=Building Energy Generation & Usage|work=Case Studies|publisher=Logic Energy|accessdate=16 May 2013|archive-url=https://web.archive.org/web/20121209193707/http://www.logicenergy.com/live-energy-monitoring/building-energy-use/case-studies/|archive-date=9 December 2012|url-status=dead}}</ref>