Monitoring & Targeting plays a key part in the management of an energy estate. Conceptually, M&T is an ongoing improvement methodology which starts with measuring energy consumption, then understanding where and why it is being consumed and then making changes to optimise performance. Ultimately, this supports:

1) Detection of avoidable energy waste or excessive energy use – e.g. caused by poor control, equipment issues
2) Quantification of savings achieved – by any and all energy projects and campaigns that have been implemented
3) Identification of fruitful lines of investigation – finding specific issues to investigate further
4) Stakeholder engagement – providing feedback and insight for staff awareness
5) Development of performance targets.

Regression and Cumulative Sum (CuSUM) analysis are fundamental techniques that can be used to assist this process. Here, energy consumption data is related to factors that influence the usage (for example, weather, building occupancy, production output) to draw meaningful conclusions about the use of energy across a system and how it should perform, helping to highlight periods of suspect usage. The analysis can then be re-performed as changes are implemented to show that the desired effects have been achieved.

Regression Analysis

This gives insight into the baseline relationship between two sets of data and shows how they interact with each other to determine the expected or ‘normal’ performance (e.g. as one goes up, so does the other one). If the energy consumption is less than the norm then this indicates good energy performance, if the energy consumption is greater than the norm then this indicates poor energy performance. For example, when plotting temperature against gas consumption, it might be expected that the consumption increases as the outside temperature cools (as central heating will be in operation). If the data fit is poor, but we know there should be a relationship, it indicates a poor level of control and hence a potential for energy savings and further analysis.

CuSUM Analysis

Once the regression (i.e. expected performance) has been determined, it can then be extended to show a CuSUM representation of the data. This is effectively a cumulative sum of the difference between the expected performance and the actual performance (e.g. 4000 kWh was used, but based on the performance regression line 3500 was expected. This leads to a difference of 500 kWh). This will highlight the step change in performance over time and highlight periods where there is significant performance degradation (i.e. line on the graph goes up), or improvement (i.e. line on the graph goes down). This trend over time may otherwise be hidden in a large scatter of the data. The CuSUM technique is a simple but remarkably powerful statistical method, which highlights small differences in energy efficiency performances over time. A typical CuSUM graph follows a trend and shows the random fluctuation of energy consumption and should oscillate around zero (standard or expected consumption). This trend will continue until something happens to alter the pattern of consumption such as the effect of an energy saving measure or, conversely, a worsening in energy efficiency (poor control, housekeeping or maintenance). When looking at CuSUM chart, the changes in direction of the line indicate events that have relevance to the energy consumption pattern.

Sigma has a dynamic, visual and interactive Regression and CuSUM tool which supports the ability to model the relationship between driving factors over configurable periods of time: plotting these independent variable datasets against the consumption.

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