Too often we see frost-protection thermostats set at too high a temperature, meaning that an unoccupied building will be heated for longer, and maintained at a higher temperature, than is necessary for the purpose of preventing water services from freezing.
The diagram on the right shows how a common type of mechanical frost thermostat can be prevented from having too high a switching temperature set. Or from being set dangerously low, for that matter. There is a protrusion on the dial and a series of bendable tabs is provided on a backplate which stop it at either end of the desired travel (in this case +2°C to +6°C). Your electrician will know how to do it.
“See Why Power Companies Are Scared Over This Breakthrough Device That Cuts Your Power Bill By Up To 90%“. Yeah, right. Open up a ‘Voltex’ unit and this what you’ll find: a capacitor. You can ignore the printed-circuit board, whose purpose seems to be solely to power the LED indicator.
Several of my newsletter readers have reported this device to me. Apart from the product being obviously bogus and its claims ludicrous, Voltex’s online marketing is so poor that it’s worth a visit just for a laugh.
It includes video clips that actually show different products: (a) the Power Perfect Box (probably also a capacitor, but bigger) which is shown needing holes drilled in a wall and a connection into a distribution board; and (b) “Greenwave”, which at least is a plug-in device like Voltex but is promoted on the basis of removing dirty electricity and improving your sleep. I kid you not.
Look at the photos of Voltex units and you’ll see that they have retouched the wall sockets to look like UK 3-pin ones but of a pattern nobody has ever seen.
The testimonials from UK customers quote suspiciously-precise savings, but they have forgotten that we don’t use dollars here.
The technology is also supposedly patented – always a warning sign. I asked them for the patent number and their reply was: “the patent is a very complex thing, so in order to be able to sell in multiple places, it’s been decided to risk for the expansion“. What? When I pointed out that this made no sense they elaborated as follows: “For security reasons, we cannot disclose some information related to the product. One of which is the patent number”. So, particularly bearing in mind that they cannot have patented the capacitor, I conclude that there is no patent. They are liars and cheats and it gives me great pleasure to award Voltex the coveted Pants on Fire Award.
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.
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:
Figure 1: consumption relative to heating degree days
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:
Figure 2: non-heating consumption relative to cooling degree days
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.
The chart format on the left is a reconstruction of something I saw in an energy reporting system based on a generic platform who shall remain nameless (you know who you are). It is being used here to represent the relative total energy consumptions of a number of establishments. Although admittedly it is better than a pie chart, it is still one of the least user-friendly designs I have ever seen. The person who devised it should be ashamed of themselves.
Why have remote labels with a colour-coded key, when the labels could just be put alongside the bars they relate to as shown on the right-hand example? Especially as with so many entries the colours are hard to discriminate even for a user with perfect colour vision.
The right-hand version of the chart gives the identical information perfectly clearly with bars of the same colour, the additional advantage being that, if required, one specific item can be highlighted in a contrasting shade as shown. Oh, and it won’t matter if your computer monitor’s colour rendering is a bit off.
My client here was an energy management bureau allied to a facilities maintenance company. They’re working for a retail chain with several hundred UK sites and they needed to develop not just useful energy reports for their customer’s regional managers, but also an effective method of detecting and prioritising exceptional adverse performance, so that avoidable energy waste can be spotted and remedied in a cost-effective manner.
Working on software code with the developer
We did the training in two parts, both via Zoom video link. On the Tuesday we went through the key generic principles using a pared-back version of my one-day training course on monitoring and targeting. Then on the Friday, after they’d had a chance to experiment with some data on an Excel-based toolkit, we got to grips with the software platform that they use. The screenshot shows me working with their managing director and software developer to build some of the key functionality that they will require.
I have prepared some product category briefings are intended to assist readers who want to reject suspicious product offerings in circumstances where other people in their organisation need to be convinced that the products are worthless.
Participating in a remote meeting for one hour generates the same emissions as driving just 580 metres. That was my conclusion when someone asked me what were the relative environmental impacts of remote and in-person meetings. Here’s how I approached the question…
We’ll start by estimating the energy intensity of data communications. We know from an Ofcom study that in 2018 the average UK fixed broadband connection was using 240 GB per month, and if we assume £30 per month was the typical tariff, that works out at £0.125 per GB. Now let’s assume that this price covers the operator’s costs and that, pessimistically, 50% of that cost is for electricity which they were buying at (say) £0.15 per kWh. This implies an energy intensity of £0.125 x 50% / £0.15 = 0.42 kWh per GB.
But how much data is there in a remote meeting? Fortunately we can get a good direct estimate from the sizes of session recordings. My two-hour on-line events have typically resulted in recordings of around 500 MB, which must be the equivalent of all the data broadcast to each participant (as a sense check, that’s 250 megabytes per hour, or about 0.55 megabits per second bandwidth). To be conservative let’s add as much again for return traffic from each participant, giving a total of 500 MB (0.5 GB) per hour per participant.
At 0.42 kWh per GB that implies 0.5 x 0.42 = 0.21 kWh per participant-hour.
This only accounts for the communications element. To be fair we need to add the cost of central data processing and to do that I’m firstly going to guess that the server consumes 100 watts for the purposes of processing the meeting. Secondly I’ll assume that the meeting has four participants. That would imply 0.025 kWh per participant-hour, bringing the total to 0.235. The fact that it’s a small correction means the conclusions aren’t very sensitive to the number of participants. If we assume a grid carbon intensity of 0.3 kgCO2/kWh we arrive at emissions of 0.235 x 0.3 = 0.07 kgCO2 per participant-hour.
How does that final figure compare with car travel to the meeting? The average car in the UK emits about 0.12 kgCO2 per km, so attending an hour-long remote meeting equates, in emissions terms, to 0.07/0.12 = 0.58 km of car travel. Case closed.
–o–
This article first appeared in the Energy Management Register bulletin on 12 July, 2021. Subscriptions are free of charge: please follow this link. You can unsubscribe again from any issue.
Submitted by the Association of Midlands Energy Professionals
The Association of Midlands Energy Professionals (MEP) invites you to join us for our FOURTH annual event for energy assessors and other energy professionals, under the title: “Gearing up for Change”.
With BREXIT behind us and the TRUSTMARK now up and running, MEES is having a significant impact on the type of work we do. WHOLE HOUSE RETROFIT, PAS2038, and THE FUTURE HOMES STANDARD are becoming even bigger drivers as the gathering momentum surrounding climate change is set to make 2021 a year of real involvement and opportunity for us in changing the behaviours of our customers.
We will be on the front line, giving advice and promoting change to UK consumers.
We will hear from a selection of keynote speakers who will be bringing us up to date on all the above and more. The speakers will include the leading lights from the Retrofit Academy, TrustMark, and the Accreditation Bodies.
There will be a selection of workshops to participate in. These workshops will give you tasters of the ‘state of art’ tools and techniques.
There will be a “BBC Question Time” style session where you can put questions to a panel of experts including the Accredited Bodies
This will be a full and informative day which will provide 5 hours CPD plus valuable networking with fellow assessors and other professionals.
Date: Wednesday 29 September 2021 (10.00 to 16.30)
The charge for the event is £50 for MEP members, and £60 for non-members. Subscribers to the Energy Management Register newsletter can join at MEP members’ rates using their customary discount code. There will be a £10 early bird discount for those booking before the end of August 2021. Booking forms are available from
This story concerns a commercial data centre, and specifically its cooling system. The players are: (a) clients whose servers are housed in the centre; (b) a facilities operations team responsible for maintaining conditions in the server hall; and (c) a sustainability manager whose duty is to ensure that energy consumption is minimised. There is a service level agreement in place and the facilities team are contractually obliged to report regularly on the server-room temperature.
The sustainability manager regularly reviews consumption against weather-related targets, in order to detect excessive consumption. Specifically he uses the relationship between chiller electricity consumption and cooling degree days, as illustrated in Figure 1:
Figure 1: normal relationship between weekly kWh and weekly cooling degree days
The story
At the end of September 2020, weekly consumption began to deviate from expected values. The first few weeks of abnormal performance are highlighted in Figure 2:
Figure 2: abnormally high weekly consumption is observed
Figure 3 is a control chart which shows that the deviation is not only statistically significant compared with anything previously observed, but it’s also persistent:
Figure 3: the control chart shows the difference between actual and expected consumption
At this point the sustainability manager challenged the operations team for an explanation. The problem turned out to be the location of the temperature sensor that was used for their routine service-level reports. It was not registering the actual air temperature at equipment level, but a higher value. To get around this problem the ops team had started overcooling the building to ensure that their temperature reports were within the specification.
The problem was ultimately rectified by relocating the sensor used for reporting, and reverting to the correct space temperature set point. Figure 4 shows how consumption then came back within its normal control limits:
Figure 4: once the situation was fully resolved, the difference between actual and expected consumption drops back
