Category Archives: Energy analysis and reporting

SECR in a nutshell

Updated 30/4/21 

“Streamlined energy and carbon reporting” (SECR) is the term commonly used to describe the regime introduced with the Companies (Directors’ Report) and Limited Liability Partnerships (Energy and Carbon Report) Regulations 2018, Statutory Instrument 1155. This is not a self-contained set of regulations like ESOS; instead it consists of nothing but dozens of amendments to existing company reporting law. In short, undertakings covered by SECR simply need to collate annual total energy and emissions data and give them to their company secretary or accountant for inclusion in the annual report that they already have to prepare.

As this is an extension of financial reporting, compliance will be policed by the Financial Reporting Council, and not, as one might have thought, by the Environment Agency. The good news is that in terms of accuracy and completeness, your SECR reports need only be free of material misstatements, and according to the Government’s published guidance it is fine for a company to omit 2-5% of its energy or emissions if it considers them not to be material in the grand scheme of things.

Who is affected?

SECR applies to all quoted companies, and to unquoted companies and limited liability partnerships (LLP) which meet two of the following three criteria:

  1. At least 250 employees;
  2. £36 million annual turnover or more
  3. Balance sheet of £18 million or more

This is not quite the same as the ESOS regulations, in which an undertaking would be obliged to participate if it met criterion (a) alone.

Undertakings which consumed less than 40,000 kWh in the year being reported do not have to report their actual figures but must still state that they fell below that threshold.

It is fine for a company to omit 2-5% of its energy or emissions if it considers them not to be material

Group reports should include the figures for all subsidiaries apart from those that would be exempt. Under these circumstances a subsidiary need not report its own figures although, of course, it will still need to collate the data for group use.

What must be reported?

The requirement covers energy use and greenhouse gas emissions arising from all use of electricity, gas, and transport fuels. Incidentally the definition of “gas” is not limited to natural gas, but refers to any gaseous fuel so it even includes hydrogen. The inclusion of electricity means that SECR differs from emissions reporting. Somewhat bizarrely liquid and solid fuels do not have to be accounted for, unlike in CRC (which SECR supposedly replaces) ESOS and EUETS. Bought-in heat, steam and cooling are included but not compressed air.

Quoted companies must report global figures, but LLPs and unquoted companies only have to declare UK consumption and emissions.

In the main, therefore, any undertaking that already keeps even very basic monthly fuel and electricity consumption records for its fixed assets will have no trouble collating the necessary energy data. Transport fuel, of course, is a different issue. As many an ESOS participant has found, transport fuel data are disproportionately hard to collect relative to its importance in the mix. Luckily, if you can reasonably assert that your transport energy and emissions are not material to the overall picture, you can just leave them out.

My advice would therefore be to look first at transport fuels, decide whether they are material, and if so put resources into capturing the data or estimating the figures.

SECR requires emissions to be reported as well as energy consumptions. The necessary factors are published by the government and undertakings would be well advised to set up a methodical procedure for carrying out the calculations, because they must include details of their methodology alongside the data that they report.

Undertakings must report intensity metrics, of which an example would be kWh per unit of saleable product output. The idea is that stakeholders will be able to see, once a year, what progress the company is making in energy efficiency. This is actually a somewhat naïve and fanciful aim, given all the ways that such simple ratios can be distorted by external factors nothing to do with energy performance. Even more implausible is the idea of making ‘benchmarking’ comparisons between enterprises, but that is the government’s stated objective.

Companies are entitled not to report intensity metrics if, in their opinion, it would be prejudicial to their interests to do so. For example it might entail disclosing sensitive information about their sales volume. One option is to quote a metric based on financial turnover (which is already disclosed anyway). This may not be meaningful, but then neither is anything else they might report.

Finally, annual reports must now include descriptions of the principal measures taken to improve energy efficiency during the year in question, if there were any.

What is the compliance deadline?

Energy, emissions, intensity metrics and associated methodologies must be stated in annual reports covering accounting years starting in April 2019 or later, so by now all companies will have had full reporting years covered by the scheme (the last wave was for reporting years ending in February 2021). Actual report submission deadlines fall six months later for public companies, nine for private companies.

See links to SECR resources

Control charts in energy performance monitoring

Once you have discovered how to routinely calculate expected consumptions for comparison with actual recorded values, you can get some very useful insights into the energy behaviour of the processes, buildings and vehicles under your supervision. One thing you can do is chart the history of how actual and expected consumption compare. In this example we are looking at the daily electricity consumption of a large air-compressor installation:

Comparison of actual daily consumptions with what they should have been given the output of the compressors

The green trace represents expected kWh (computed by a formula based on the daily air output) and the individual points represent the actual metered kWh. Most of the time the two agree, but there were times in this case when they diverged.

It is illuminating to concentrate on the extent to which actual consumption has deviated from expected values, so in the following chart we focus on the difference between them:

The difference between actual and expected consumption.

There will always be some discrepancy between actual and expected consumptions. Part of the difference is purely random, and the limits of this typical background variation are signified by the red dotted lines. If the difference goes outside these bounds, it is probably because of an underlying shift in how the object is performing. In the above diagram there were three episodes (one moderate, two more severe) of abnormal performance. Significant positive deviations (above the upper control limit) are more usual than negative ones because consuming more energy than required for a given output is much more likely than using less.

For training in energy consumption analysis look for ‘monitoring and targeting’ at VESMA.COM

In a well-constructed energy monitoring and targeting scheme, every stream of consumption that has a formula for expected consumption will also have its own control limit. The limits will be narrow where data are reliable, the formula is appropriate, and the monitored object operates in a predictable way. The limits will be wider where it is harder to model expected consumption accurately, and where there is uncertainty in the measurements of consumption or driving factors. However, it is not burdensome to derive specific control limits for every individual consumption stream because there are reliable statistical methods which can largely automate the process.

Control charts are useful as part of an energy awareness-raising programme. It is easy for people to understand that the trace should normally fall between the control limits, and that will be true regardless of the complexity of the underlying calculations. If people see it deviate above the upper limit, they know some energy waste or losses have occurred; so will the person responsible, and he or she will know that everyone else could be aware of it as well. This creates some incentive to resolve the issue, and once it has been sorted out everyone will see the trace come back between the limits.

Demand visualisation with heatmap views

The principle

Widespread adoption of automatic meter reading has given many energy users a huge volume of fine-grained data about energy consumption. How best to use it? A ‘heat-map’ chart is a powerful visualisation technique that can easily show ten weeks’ half-hourly data in a single screen. This for example is the pattern of a building’s gas consumption between November and January:

Each vertical slice of the chart is one day, running midnight to midnight top to bottom, with each half-hourly cell colour-coded according to demand . This creates a contour-map effect and when you look at this specifi example, you can see numerous features:

  • Fixed ‘off’ time;
  • Optimised startup time (starts later when the building has not cooled down as much overnight);
  • Peak output during startup;
  • Off at weekends but with some heating early on Saturday mornings;
  • Shut-down over Christmas and New Year; and
  • A brief burst of consumption during the Christmas break, presumably frost protection.

Further examples

This building’s gas consumption pattern is quite similar to the previous one’s (they both belong to the same organisation), but the early-morning startup boost is much more evident and occurs even during the Christmas and New Year break:

Next we have a fairly typical profile for electricity consumption in an office building. What is slightly questionable is the higher daytime consumption near the start (April) compared with the end (June). This suggests the use of portable heaters. Note also that the peak half-hourly demands can easily be seen (Friday of the second week and Wednesday of the fiifth week). In both cases it is evident that those peaks occurred not because of any specific incident but because consumption had generally been higher than usual all day:

In this final example we are looking at short-term heatmap views of electricity feeding a set of independent batch processes in a pharmaceutical plant. The left-hand diagram is the actual measured consumption while the right-hand diagram is the expected profile based on a mathematical model of the plant into which we had put information about machine scheduling:







Energy targeting for humidity-control systems

The amount of moisture in the atmosphere varies through the year because the amount of water vapour that the air can hold is temperature-dependent. We human beings are sensitive to the relative humidity (RH), which is the ratio between the actual moisture content and the maximum that the air could hold at its prevailing temperature: generally, we don’t feel uncomfortable if the RH is between 30% and 70%. Problems will obviously occur in very hot, humid weather (when the RH will be high) but they can also occur in the depths of winter. This is because, if you take some cold outside air and heat it up for use in your building, its relative humidity falls as the temperature increases without the addition of any moisture. In an air-conditioned building, hot outside air is chilled to the comfort temperature, and the RH rises. Suppose that you want to maintain 20°C inside, with RH in the range 40% and 60%. If the ambient air contains less than 0.006 kg of water vapour per kg of dry air (regardless of its temperature), it will need humidifying; but should it exceed 0.009, moisture will need to be removed. The demand for moisture addition or removal will be proportional to the deficit or surplus in the mixing ratio. For example, ambient air at 0.013 kg/kg needs twice as much dehumdification as air at 0.011 kg/kg (0.013-0.009 is double 0.011-0.009).

Figures 1 and 2 show typical weekly histories of moisture deficit and excess for a site in Ireland. Notice how the demand is seasonal, with warmer summer air able to hold more moisture.

Figure 1
Figure 2

How does this affect energy demand? Well, to dehumidify air you need to chill it to its saturation temperature at the required moisture content. Excess moisture condenses out, and the partially dried air is then reheated to take it back to the required target temperature. This where the extra energy demand comes from.

You can see the difference between cooling-only and full air conditioning in figures 3 and 4. Figure 3 shows a case where the relationship between chiller electricity and cooling degree days is evidently a straight line: this building has no humidity control and chiller demand is effectively driven only by the outside dry-bulb temperature.

Figure 3

Figure 4, by contrast, is curved; this building has humidity control. The curve occurs because as the weather gets hotter, the amount of moisture in the air increases, and with it the demand for dehumidification.

Figure 4

Figure 5 shows the deviation from expected consumption that results when one tries to model electricity demand with the single straight-line relationship of figure 4 in which predicted consumption is

23,151 kWh per week, plus
162 kWh per cooling degree day
Figure 5

The model can be improved by accounting for the dehumidification demand: Figure 6 shows the history of deviations when a two-factor model is used, in which predicted consumption is

24,259 kWh per week, plus
107 kWh per cooling degree day, plus
16.4 kWh per unit of dehumidification demand
Figure 6

The reduced error in the calculation of expected electricity consumption makes overspend alerts more reliable and the monitoring and targeting scheme more effective.

To implement this scheme one needs local dry-bulb and relative humidity readings at frequent intervals, which are used to calculate the ambient mixing ratio. Two running totals are then kept: one of the accumulated atmospheric moisture deficit, and one of the accumulated excess. The procedure is not unlike the “accumulated temperature deficit” which is used in the calculation of heating degree days.

For details of training courses in energy management visit

Tracking performance of light vehicles

Here is a monitoring challenge: suppose you want to do a weekly check on the performance of a small fleet of hotel minibuses. Although you can record the mileage at the end of each week, you will have a lot of error in your fuel measurement because you’ll only know how much fuel was purchased but not when. How can you adjust for the inconsistent fuel tank level at the end of the week?

One method would be to use the trip computer display which will show the estimated remaining miles (see picture). The vehicle in question has a 45-litre tank: at its typical achieved average mpg, it has a range of 613 miles of which it has used 39%, so we can add 45 x 0.39 = 18 litres to our calculated fuel consumption. Note that we will need to deduct an equal amount from next week’s consumption, and this “carry forward” is likely to reduce the error in the adjustment.

This procedure also helps if drivers do not consistently fill to the top. To the extent that they underfill on the last occasion in the week, the shortfall will increase the adjustment volume to compensate. The adjustment can only ever be approximate, however, so it’s better if they consistently brim the tank.

The other advice I would give is to track not miles per gallon (or any similar performance ratio) but to plot a regression line of fuel versus distance. This will pick up, and detect changes in, idling behaviour.

Monitoring electrically heated and cooled buildings

WHEN you use metered fuel  to heat a building (or indeed if you use the building’s electricity supply, but have no air-conditioning) it is straightforward to monitor heating performance critically because you can relate energy consumption to the weather expressed as degree days.

Things get difficult if you use electricity for both heating and cooling and everything shares a meter, as would be the case if you use reversible heat pumps (air-source or otherwise). Because the seasonal variations in demand for heating and cooling complement each other (one being high when the other is low), you may encounter cases where the sum of the two appears almost constant every week. Such was the case on this 800-m2 office building:

Figure 1: apparent low sensitivity to weather


Without going into detail, this relationship implied a heating capacity of little over 1 kW, which is obvious nonsense as there was no other source of heat. The picture had to be caused by overlapping and complementary seasonal demands for heating and cooling, which is illustrated conceptually in Figure 2:

Figure 2: total consumption is the sum of heating and cooling demands


The challenge was how to discover the gradients of the hidden heating and cooling lines. The answer in this case lay in the fact that we had sufficient information to estimate the building’s heat rate, which is the net heat flow from the building in watts per unit inside-outside temperature difference (W/K). The heat rate depends on the thermal conductivity of the building envelope and the rate at which outside air enters. There is a formula for the heat rate Q:

Q = Σ(UA) + NV/3

Where U and A are the U-values and superficial areas of each building element (roof, wall, window, etc), V is the volume of the building and N is the number of air changes per hour. Figure 3 shows the spreadsheet in which Q was calculated for the building in question (an on-line tool to do this job is available at

Figure 3: calculation of heat rate

In this case the building measurements were taken from drawings, the U-values were found on the building’s Energy Performance Certificate (EPC), and the figure of 0.5 air changes per hour is just a guess.

The resulting heat rate of 955.5 W/K equates to 955.5 x 24 / 1000  = 22.9 kWh per degree day. This is heat loss from the building but it uses a heat pump and will therefore require less input electricity by a factor of, in this case, 3.77 (that being the coefficient of performance cited on its EPC).  So the input energy required for heating this building is 22.9 / 3.77 = 6.1 kWh per degree day. This is the gradient of the unknown heating characteristic, the upper dotted line in Figure 2.

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To work out the sensitivity to cooling demand we use a little trick. We take the actual consumption history and deduct an allowance for heating load which, in each week, will be 6.1 times the number of heating degree days (remember we just worked out the building needed 6.1 kWh per degree day for heating). This non-heating electricity demand can now be analysed against cooling degree days and this was the result in this case:

Figure 4: variation of non-heating electricity with cooling degree days


The gradient of this line is 3.5 kWh per (cooling) degree day. It is of similar order to the 6.1 kWh per degree day for heating, which is to be expected; the building’s heat loss and gain rates per degree difference are likely to be similar. As importantly, we now have an intercept on the vertical axis (a shade over 1,200 kWh per week) which represents the non-weather-related demand. Taking Figure 1 at face value we would have erroneously put the fixed consumption at around 1,500 kWh per week.

Also significant is the fact that Figure 4 was plotted against cooling degree days to a base of only 5°C. That was the only way to get a rational straight line and it means there is a finite amount of cooling going on at outside temperatures down to that value. I had been assured that cooling was only enabled “when the weather got hot”. But plotting demand against cooling degree days to, say, 15.5°C (a common default for summer-only use) gave the result shown in Figure 5:

Figure 5: non-heating electricity demand against cooling degree days to a base of 15.5C


This is not as good a correlation as Figure 4 and my conclusion in this case was that when the outside temperature is between 5 and 12°C, this building is likely to have some rooms heating and some cooling.

Carbon emissions – a case of rubbish data and wrong assumptions

The UK Government provides tables for greenhouse gas emissions including generic figures for road vehicles. For example a rigid diesel goods vehicle of 7.5 to 17 tonnes has an indicative figure of 0.601 kg CO2e per km. You need to apply such generic figures with caution, though. I saw a report from a local council that used that particular number to back-calculate emissions from its refuse collection trucks. Leaving aside the fact that many of their vehicles are 26 tonners, they spend much of their time accelerating, braking to a halt, idling and running hydraulic accessories, with the result that one would expect them to do no better than about 4 mpg with emissions more like 1.8 kg CO2e per km, three times the council’s assumed value.

For the council in question that is not a trivial error. Even on their optimistic analysis domestic waste collection represents 33% of their total emissions. Properly calculated (ideally from actual fuel purchases) they will turn out to be more than all their other emissions taken together.

Further reading


For sustainability professionals to make a real practical difference to carbon emissions they need a broad appreciation of technical energy-saving opportunities. To help them understand the potential more clearly I run a one-day course called ‘Energy Efficiency A to Z‘. Details of this can be found at


Justifying additional meters

Additional metering may be required for all sorts of reasons. There are three relatively clear-cut cases where the decision will be dictated by policy:

  • Departmental accountability or tenant billing: it is often held that making managers accountable for the energy consumed in their departments encourages economy. Where this philosophy prevails, departmental sub-metering must be provided unless estimates (which somewhat defeat the purpose) are acceptable. Similar considerations would apply to tenant billing (I am talking about commercial rather than domestic tenants here).
  • Environmental reporting: accurate metering is essential if, for example, consumption data is used in an emissions trading scheme: an assessor could refuse certification if measurements are held to be insufficiently accurate.
  • Budgeting and product costing: this use of meter data is important in industries where energy is a significant component of product manufacturing cost, and where different products (or different grades of the same product) are believed to have different energy intensities.

The fourth case is where metering is contemplated purely for detecting and diagnosing excessive consumption in the context of a targeting and monitoring scheme. This may well be classified as discretionary investment and will require justification. This could be based on a rule of thumb, or on the advice in the Building Regulations (for example). A more objective method is to identify candidates for submetering on the basis of the risk of undetected loss (RoUL). The RoUL method attempts to quantify the absolute amount of energy that is likely to be lost through inability to detect adverse changes in consumption characteristics. It comprises four steps for each candidate branch:

  1. Estimate the annual cost of the supply to the branch in question (see below).
  2. Decide on the level of risk (see table below) and pick the corresponding factor.
  3. Multiply the cost in step 1 by the factor in step 2, to get an estimate of the annual average loss.
  4. Use the result from step 3 to set a budget limit for installing, reading and maintaining the proposed meter.
Risk Typical characteristics Suggested
High Usually associated with highly-intermittent or very variable loads under manual control, or under automatic control at unattended installations (the risk is that equipment is left to run continually when it should only run occasionally, or is allowed to operate ‘flat out’ when its output ought to modulate in response to changes in demand). Examples of highly-intermittent loads include wash-down systems, transfer pumps, frost protection schemes, and in general any equipment which spends significant time on standby. Typical continuous but highly-variable loads would include space heating and cooling systems. It should be borne in mind that oversized plant, or any equipment which necessarily runs at low load factor, is at increased risk. 20%
Medium Typified by variable loads and intermittently-used equipment operating at high load factor under automatic control, in manned situations where failure of the automatic controls would probably become apparent quickly. 5%
Low Anything which necessarily runs at high load factor (and therefore has little capacity for excessive operation) or where loss or leakage, if able to occur at all, would be immediately detected and rectified. 1%

*Note: the risk percentages are suggested only; the reader should use his or her judgment in setting percentages appropriate to individual circumstances

The RoUL method tries to quantify the cost of not having a meter, but this relies on knowing the consumption in the as-yet-unmetered circuit. The circular argument has to be broken by estimating consumption:

  • by step testing
  • using regression analysis to determine sensitivity to driving factors such as product throughput and prevailing weather
  • using ammeter readings for electricity, condensate flow for steam, etc.
  • multiplying installed capacity by assumed (or measured) load factors
  • from temporary metering

Uncertainty in savings estimates: a worked example

To prove that energy performance has improved, we calculate the energy performance indicator (EnPI) first for a baseline period and again during the subsequent period which we wish to evaluate. Let us represent the baseline EnPI value as P1 and the subsequent period’s value as P2

Most people would then say that as long as P2 is less than Pwe have proved the case. But there is uncertainty in both P1 and P2 and this will be translated into uncertainty in the estimate of their difference. We strictly need to show not only that the difference (P1 – P2) is positive, but that the difference exceeds the uncertainty in its calculation. Here’s how we can do that.

In the example which follows I will use a particular form of EnPI called the ‘Energy Performance Coefficient’ (EnPC), although any numerical indicator could be used. The EnPC is the ratio of actual to expected consumption. By definition this has a value of 1.00 over your baseline period, falling to lower values if energy-saving measures result in consumption less than otherwise expected. To avoid a long explanation of the statistics I’ll also draw on Appendix B of the International Performance Measurement and Verification Protocol (IPMVP, 2012 edition) which can be consulted for deeper explanations.

IPMVP recommends evaluation based on the Standard Error, SE, of (in this case) the EnPC. To calculate SE you first calculate the EnPC at regular intervals and measure the Standard Deviation (SD) of the results; then divide SD by the square root of the number of EnPI observations. In my sample data I use 2016 and 2017 as the baseline period, and calculate the EnPC month by month.

In my sample data the standard deviation of the EnPC during the baseline period was 0.04423 and there being 24 observations the baseline Standard Error was thus

SE1 = 0.04423 / √24 = 0.00903

Here is the cusum analysis with the baseline observations highlighted:

The cusum analysis shows that performance continued unchanged after the baseline period but then in July 2018 it improved. We see that the final five months show apparent improvement; the mean EnPC after the change was 0.94, and these five observations had a Standard Deviation of 0.02402. Their Standard Error was therefore

SE2 = 0.02402 / √5 = 0.01074

SEdiff , the Standard Error of the difference (P1 – P2) is given by

SEdiff = √( SE12 + SE22 )

= √( 0.009032 + 0. 010742 )

= 0.01403

SE on its own does not express the true uncertainty. It must be multiplied by a safety factor t which will be smaller if we have more observations (or if we can accept lower confidence) and vice versa. This table is a subset of t values cited by IPMVP:

	     |     Confidence level     |
             |   90%  |   80%  |   50%  |
Observations |        |        |        |
      5      |  2.13  |  1.53  |  0.74  |
     10      |  1.83  |  1.38  |  0.70  |
     12      |  1.80  |  1.36  |  0.70  |
     24      |  1.71  |  1.32  |  0.69  |
     30      |  1.70  |  1.31  |  0.68  |

Let us suppose we want to be 90% confident that the true reduction in the EnPC lies within a certain range. We therefore need to pick a t-value from the “90%” column of the table above. But do we pick the value corresponding to 24 observations (the baseline case) or 5 (the post-improvement period)? To be conservative—as required by IPMVP—we take the lower number, meaning we must in this case use a t value of 2.13.

Now in the general case ∆P, the EnPC reduction, is given by

∆P = (P1 – P2) ± t.SEdiff

Which, substituting the values from our example, would yield

∆P = (1.00 – 0.94) ± (2.13 x 0.01403)

∆P = 0.06 ± 0.03

The lowest probable value of the improvement ∆P is thus (0.06 – 0.03) = 0.03 . It may in reality be less, but the chances of that are only 1 in 20 because we are 90% confident that it falls within the stated range and by implication 5% confident that it is above the upper limit.

Footnote: example data

The analysis is based on real data (preview below). These are from an anonymous source and  multiplied by a secret factor to disguise their true values. Anybody wishing to verify the analysis can download the anonymous data as a spreadsheet here.

Note: to compute the baseline EnPC

  1. do a regression of MWh against tonnes using the months labelled ‘B’
  2. create a column of ‘expected’ consumptions by substituting tonnage values in the regression formula 
  3. divide each actual MWh figure by the corresponding expected value

Bulk measurement and verification

Anyone familiar with the principles of monitoring and targeting (M&T) and measurement and verification (M&V) will recognise the overlap between the two. Both involve establishing the mathematical relationship between energy consumption and one or more independently-variable ‘driving factors’, of which one important example would be the weather expressed numerically as heating or cooling degree days.

One of my clients deals with a huge chain of retail stores with all-electric services. They are the subject of a rolling refit programme, during which the opportunity is taken to improve energy performance. Individually the savings, although a substantial percentage, are too small in absolute terms to warrant full-blown M&V. Nevertheless he wanted some kind of process to confirm that savings were being achieved and to estimate their value.

My associate Dan Curtis and I set up a pilot process dealing in the first instance with a sample of a hundred refitted stores. We used a basic M&T analysis toolkit capable of cusum analysis and regression modelling with two driving factors, plus an overspend league table (all in accordance with Carbon Trust Guide CTG008). Although historical half-hourly data are available we based our primary analysis on weekly intervals.

The process

The scheme will work like this. After picking a particular dataset for investigation, the analyst will identify a run of weeks prior to the refit and use their data establish a degree-day-related formula for expected consumption. This becomes the baseline model (note that in line with best M&V practice we talk about a ‘baseline model’ and not a baseline quantity; we are interested in the constant and coefficients of the pre-refit formula). Here is an example of a store whose electricity consumption was weakly related to heating degree days prior to its refit:

Cusum analysis using this baseline model yields a chart which starts horizontal but then turns downwards when the energy performance improves after the refit:

Thanks to the availability of half-hourly data, the M&T software can display a ‘heatmap’ chart showing half-hourly consumption before, during and after the refit. In this example it is interesting to note that savings did not kick in until two weeks after completion of the refit:

Once enough weeks have passed (as in the case under discussion) the analyst can carry out a fresh regression analysis to establish the new performance characteristic, and this becomes the target for every subsequent week. The diagram below shows the target (green) and baseline (grey) characteristics, at a future date when most of the pre-refit data points are no longer plotted:

A CTG008-compliant M&T scheme retains both the baseline and target models. This has several benefits:

  • Annual savings can be projected fairly even if the pre- or post-refit periods are less than a year;
  • The baseline model enables savings to be tracked objectively: each week’s ‘avoided energy consumption’ is the difference between actual consumption and what the baseline model yielded as an estimate (given the prevailing degree-day figures); and
  • The target model provides a dynamic yardstick for ongoing weekly consumptions. If the energy-saving measures cease to work, actual consumption will exceed what the target model predicts (again given the prevailing degree-day figures). See final section below on routine monitoring.

I am covering advanced M&T methods in a workshop on 11 September in Birmingham

A legitimate approach?

Doing measurement and verification this way is a long way off the requirements in IPMVP. In the circumstances we are talking about – a continuous pipeline of refits managed by dozens of project teams – it would never be feasible to have M&V plans for every intervention,. Among the implications of this is that no account is taken (yet) of static factors. However, the deployment of heat-map visualisations means that certain kinds of change (for example altered opening hours) can be spotted easily, and others will be evident. I would expect that with the sheer volume of projects being monitored, my client will gradually build up a repertoire of common static-factor events and their typical impact. This makes the approach essentially a pragmatic one of judging by results after the event; the antithesis of IPMVP, but much better aligned to real-world operations.

Long-term routine monitoring

The planned methodology, particularly when it comes to dealing with erosion of savings performance, relies on being able to prioritise adverse incidents. Analysts should only be investigating in depth cases where something significant has gone wrong. Fortunately the M&T environment is perfect for this,  since ranked exception reporting is one of its key features. Every week, the analyst will run the Overspend League Table report which ranks any discrepancies in descending order of apparent weekly cost:

Any important issues are therefore at the top of page 1, and a significance flag is also provided: a yellow dot indicating variation within normal uncertainty bands, and a red dot indicating unusually high deviation. Remedial effort can then be efficiently targeted, and expected-consumption formulae retuned if necessary.