Jonathan M., a reader of my Energy Management Register bulletins, sent me an interesting query about some electricity meter readings from a closed building. Here is a picture of its half-hourly consumption over a couple of days and you can understand why he thought there must be some kind of meter error “possibly involving harmonics”:

To me the pattern was reminiscent of the ‘beating’ that you get between two sounds or electrical oscillators of similar frequency. How could that be? Figuring that one of the frequencies must be the meter-reading frequency (2 per hour)  I speculated that a single load coming on and off regularly at approximately the same frequency would explain the observed waveform. Let’s put some numbers to that. Imagine a 6kW immersion heater that’s cycling on and off with no hot water being drawn. Now suppose it just happens to be on for 31 minutes and then off for 30 minutes each time. Every so often there would be a half-hour in which the heater is on for the whole time (consuming 3 kWh), followed by one in which it only registers for one-thirtieth of the interval (so  0.1 kWh). In the chart, those are the successive readings with the biggest swings. Fifteen on-off cycles later consumption would start 15 minutes into the half-hour meter interval, registering 1.5 kWh, with an equal 1.5 kWh in the following interval (roughly). This would yield no swing between successive readings. After a further fifteen on-off cycles we’d be back to the situation where 0.1 kWh is consumed in one interval and 3 kWh in the next. Overall this would give us a pattern of alternating high and low half-hourly consumptions with the magnitude of the swings varying between maxima and minima with a 15-hour periodicity, with the minim consumptions close to zero and the maxima roughly double the mean.

Amazingly (and I still cannot quite believe it) I’ve since heard that my diagnosis was correct: an immersion heater had indeed been left energised in the unoccupied building.

EFFECTIVE MANAGEMENT AND ANALYSIS of buildings’ energy consumptions for heating or cooling calls for summary data about how cold or hot the weather has been. Weekly or monthly degree-day statistics provide that information in a convenient manner. But as well as the current values (against which your weekly or monthly energy consumption can be gauged), in the United Kingdom[1] we have two kinds of long-term aggregate value that can be useful for other purposes.

Standard degree-day values

For normalising consumptions we need degree-day values for a ‘reference year’, which allow actual consumptions for buildings in different locations (and possibly measured at different times) to be adjusted back to a comparable basis. Historically, the UK government recommended a standard value of 2,463 heating degree days (to base 15.5C). The source of this number is unknown; it is, in effect, arbitrary but for the purpose of weather-adjustment it does not matter what the number is. This single point of reference was later developed into the following table providing corresponding reference values for different base temperatures, for cooling as well as heating, and disaggregated to individual months:

|     |        Heating       |       Cooling        |
|Month| 18.5'C 15.5'C 10.0'C | 15.5'C 5.0'C -20.0'C |
|     |                      |                      |
| Jan |    488    395    226 |      0    17     705 |
| Feb |    426    342    189 |      0    23     652 |
| Mar |    390    297    134 |      0    64     803 |
| Apr |    319    233     96 |      5   114     837 |
| May |    235    151     39 |     14   192     963 |
| Jun |    148     77      9 |     26   265    1015 |
| Jul |     88     42      4 |     96   380    1155 |
| Aug |    100     45      5 |     57   338    1113 |
| Sep |    162     83     10 |     14   245     995 |
| Oct |    268    177     48 |      1   158     925 |
| Nov |    359    275    124 |      0    53     719 |
| Dec |    439    346    176 |      0    29     755 |
|     |                      |                      | 
|Total|  3,422  2,463  1,060 |    213 1,878  10,637 |

Table 1: standard degree day values

It is important to appreciate that these standard values are fixed and not related to any particular geographical area.

Average degree-day values

If we are forecasting consumption we need to know what future degree-day values we can expect month by month. For this purpose we use 20-year average degree-day values. So the expected degree-day value for next February (for example) is the average of the last 20 Februaries. You can download the UK regional average figures via this link.

In contrast to ‘standard’ degree-day values, ‘average’ values differ from region to region and tend to vary with time thanks to the changing climate. I usually update the table once a year.

—

[1] Subject to the availability of suitable historical data, the same principles could be applied in other regions.

Suppose you want to analyse the relationship between consumption and its most significant driving factor, but you know that there is a secondary influence which will distort the result if you don’t take account of it. The easy way to approach this is to analyse historical data statistically using multiple regression, which will estimate the fixed consumption and give you the sensitivities to both variable driving factors.

Unfortunately, in a world where you cannot guarantee that the thing you are analysing has previously behaved in a consistent manner, this statistically-derived guess at the relationship could well be wrong. It will also be unreliable if, for example, the secondary driving factor does not vary very much.

We must bear in mind that statistical analysis has no insight into physical reality and can therefore generate implausible answers. Because of this, I always recommend using non-statistical methods of establishing how consumption responds to variation in a given driving factor. One way is to go back to first principles. Daylight-linked lighting demand provides an excellent example. If you have, say, 500 watts capacity of photocell-controlled lighting, you know for sure that it will use 0.5 kWh for each hour of darkness. Weekly and monthly hours of darkness (HD) can be obtained from standard tables and thus, for any given week or month, you can say how much electricity that lighting installation will use: it’s just 0.5 x HD. What we can do now is ‘net back’ our historical energy consumptions by deducting 0.5 x HD from the metered totals. This removes a calculated allowance for external lighting and the net consumption can then legitimately be analysed against the primary driving factor alone.

Netting-back can be used in other circumstances. On one occasion I was trying to model expected electricity consumption for cooling a computer data centre. Obviously cooling degree days are one driving factor here, but cooling demand would also be sensitive to the amount of electricity fed to the computers housed in the building (for every kWh consumed in the equipment racks, some fraction of a kWh is needed to provide the corresponding cooling). If the rack power were constant week by week, it would not be a problem, as the consequent cooling requirement would appear as part of the fixed electricity demand. On the other hand if it varied widely from week to week it would not be a problem either, because regression analysis would then have a fighting chance. The difficulty was that rack power did vary, but not very often and usually not by very much. The practical solution here was to assume a coefficient of performance for the chillers and to say that their demand would vary by 0.3 kWh per kWh of electricity delivered to equipment racks. Although this was more of an educated guess than an actual measurement, any error in the estimated coefficient was very much diluted in the overall expected-consumption model and on balance the model was more accurate than it would have been if the factor were ignored.

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A case history

I’ll conclude with a story where regression analysis was problematic and a netting-back approach had to be used.

The story concerns an all-electric building which at the outset I had not yet visited, but for which I had energy data. Since no heating fuel was involved, I started by analysing electricity consumption against heating degree days only, which yielded the result shown in Figure 1. Consumption is predominantly fixed:

The gradient of the line, at 1.4 kWh/HDD, was troublingly low. Finding that the building had reversible heat pumps I concluded that I was looking at the combined overlapping effect of seasonal heating and cooling.

As there was good information on the thermal performance of the building I was able to estimate what the gradient of the line should have been in theory. It came out at 5.8 kWh/HDD. This enabled me to net back the historical totals to get figures for non-heating consumption, which, when analysed against cooling degree days, gave me Figure 2:

The gradient of the cooling relationship came out at 3.5 kWh/CDD.

Out of interest I also subjected the data to multiple regression analysis. This yielded an estimate of fixed consumption similar to the other methods but underestimated the coefficients of the two driving factors. It gave a heating degree-day coefficient of 3.6 kWh/HDD (compared with 5.8 based on the building’s physical characteristics) and a cooling coefficient of 1.8 kWh/CDD compared with the 3.5 derived above. It is always problematic when heating and cooling degree days both apply as driving factors, because they are not the completely independent variables that statistical theory demands.

Summary

‘Netting back’ is a useful strategy when consumption has two or more driving factors and you do not want to rely entirely on regression analysis. It is particularly useful when

• You have a good method of determining a coefficient from first principles, or empirically from a deliberate test;
• You have a known driving factor which varies only slightly or changes infrequently;
• You have driving factors that are not completely independent

This article concerns a retail chain in the UK whose stores are a mix of gas-heated and all-electric buildings, any of which could also be using air conditioning to some extent. Their analysts had the task of defining expected-consumption formulae based on historical consumption and degree-day data. The question was what driving factors they should choose: heating degree days, cooling degree days, or both?

For any store with a gas supply, the answer was reasonably obvious: we could expect gas consumption to depend on heating degree days. Furthermore, electricity in those cases was likely either to be driven by cooling degree days or to be weather-insensitive.

For all-electric stores in general the picture is less clear but the likelihood had to be that heating was the main driver in these cases. This was based on the fact that they were more likely than not to behave like their gas-heated counterparts. So my advice was to treat heating degree days as the primary factor driving week-by-week variation in consumption. After that the only question to answer was whether there was any cooling influence, and that can be answered quickly by looking at the consumption profile through the year. Three scenarios are likely:

1. Higher consumption in winter only. This suggests there is no cooling influence;

2. Higher consumption in winter and summer than in spring and autumn. This clearly indicates a cooling load;

3. Broadly constant consumption all year. This also implies a cooling load.

Why does scenario 3, which shows no seasonal changes, imply the presence of cooling load? Precisely because higher winter consumption is not evident. These buildings must in fact be using heating, but they must also have a seasonal demand for cooling which overlaps the heating season, and adding the two together creates the flat profile.

Updated 30/4/21 and 4//4/22

“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 (1) 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 on an equal footing with other energy sources means that SECR differs from emissions reporting, in which fuels and pirchased electricity are considered under different ‘scopes’. 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