Archive for the ‘My House’ Category

First Winter with a Heat Pump

April 27, 2022

Friends, our first winter with a heat pump is over.

Last week:

  • I switched off the space heating, and…
  • I changed the heating cycle for domestic hot water (DHW) from night-time (using cheap-rate electricity) to day-time (using free solar electricity).

From now until the end of July, I am hopeful that we will be substantially off-grid.

Let me explain…

No Space Heating 

The figure below shows the temperatures relevant to our heating system for the week commencing Saturday 9th April.

The week started cold, with overnight temperatures close to 0 °C and daytime temperatures peaking at 12 °C.

But the week ended with much warmer temperatures, and even in the absence of any heating flow, the household temperature rose above 21 °C. At this point I decided to switch off the space heating. You can see this on the monitoring data below.

Up to the 15th April, the heat pump would operate each evening – you can see this because radiator temperatures oscillated overnight as the heating circuit struggled to deliver a very low heating power.

From the 16th April – with the space-heating off – you can see the radiator temperatures simply fell after the DHW water heating cycle.

Click image for a larger version. Graph showing four temperatures during the week beginning 9th April 2022. The upper graph shows the temperature of radiator flow and the domestic hot water (DHW). The lower graph shows the internal and external temperatures. In the colder weather at the start of the week, the radiator flow temperatures cycled on and off. In the warmer temperatures at the end of the week, heating stopped automatically. On 16th April I switched the space heating circuit off.

Heating DHW during the day 

The next graph shows the same data for the following week. Now there is no space-heating in the house, but the insulation is good enough that household temperature does not fall very much overnight.

On the 20th April I switched from heating the domestic hot water at night (using cheap rate electricity) to heating during the afternoon (using electricity generated using solar PV).

My plan was that by 2:00 p.m., the battery would be substantially re-charged, and heating the hot water at that time would:

  1. Minimise exports to the grid and maximise self-use of solar-generated electricity.
  2. Heat the domestic hot water using air that was ~ 10 °C hotter than it would be at night – improving the efficiency of the heat pump.

Click image for a larger version. Graph showing four temperatures during the week beginning 16th April 2022. The upper graph shows the temperature of radiator flow and the domestic hot water (DHW). The lower graph shows the internal and external temperatures. The radiator flow was switched off. On 20th April I switched from heating the domestic hot water at night to heating during the day.

One can see that household temperature has fallen a little during the week, but only to around 19 °C, which feels quite ‘spring-like’ in the sunshine.

The big picture 

The graph below shows:

  1. The amount of electricity used by the household
  2. The amount of electricity drawn from the grid

It covers the whole of 2021 and the start of 2022 up to today (almost) the end of April. The graphs show running averages over ± 2 weeks.

Click image for a larger version. Graph showing the amount of electricity used by the household each day (kWh/day) and the amount of electricity drawn from the grid each day (kWh/day). Over the 8 months of the winter heating season, 27% was supplied by solar generated electricity.

The 4 kWp solar PV system was installed in November 2020 and was just beginning to make a noticeable difference to our electricity consumption in the spring of 2021.

In March 2021 we installed the Powerwall and immediately dropped off the grid for just over 2 months! In mid-summer we had a run of very poor solar days and we began to draw from the grid again.

In July 2021 we installed a heat pump and this extra load (for DHW) coupled with the decline in solar generation caused us to need to draw a few kWh from the grid each day.

Over the 8 month heating season from the start of August to the end of April, the household used 4,226 kWh of electricity for all the normal activities (~ 2,200 kWh) plus heating using the heat pump (~2,000 kWh). Over this period the heat pump delivered just over 7,000 kWh of heat for a seasonally averaged COP of around 3.5.

However, even in this winter season, only 3,067 kWh were drawn from the grid – mostly at low cost. The balance (27%) was solar generated.

Summer and Winter Settings

The optimal strategy for the Powerwall is now becoming clear.

In the Winter season, daily consumption can reach 25 kWh/day and solar generation is only ~ 2 kWh day. So in this season:

  • We operate the household from the grid during the off-peak hours.
  • We time heavy loads (dishwashing, tumble drying and DHW heating) to take place in the off peak hours.
  • We buy electricity from the grid to fill the battery (13.5 kWh) with cheap rate electricity – and then run the household from the battery for as long as possible. Typically we would need to draw full price electricity from the grid only late in the day.

Click image for a larger version. Images showing the time of day that we have drawn power from the grid (kW) in half-hour periods through the day. Each image shows the average for one month. The graph was assembled using data from the fabulous Powershaper software (link).

In the ‘summer’ season, daily household consumption is ~11 kWh and average solar generation is typically 15 kWh/day. So given that the battery has 13.5 kWh of storage, we can still stay ‘off-grid’ even during a periods of two or three dull days.

So during this period

  • We switch the battery from ‘time-based’ mode to ‘self-powered’ mode.
  • We time heavy loads (dishwashing, tumble drying and DHW heating) to take place in the afternoon.

This year and last year 

Last year (2021), as soon as we installed the Tesla Powerwall battery, we dropped off-grid within days.

But this year (2022) we have an additional daily electrical load. Now we are heating DHW electrically with a heat pump which requires ~ 1.5 kWh/day.

Nonetheless, I hope it will be possible to remain substantially ‘off-grid’ for the next few months. Time will tell.

Could you heat your house with a hairdryer?

April 12, 2022

Click the image for a larger version. The graph shows the average electrical power (in kW) used by our heat pump to keep the 164 square metres of Podesta Towers at approximately 20.5 °C throughout the winter.  Also shown is the typical power used by a hairdryer on typical high, medium and low powers.

Friends, a chance remark on the internet intrigued me.

Someone commented that their heat pump was heating their house using less power than a hairdryer. Could that really be true?

Looking it up (link), I found that a hair dryer actually uses rather more power than I had supposed: somewhere between 850 watts (0.85 kW) and 1850 watts (1.85 kW) depending on its power setting.

I then looked up week-by-week data for our heat pump at Podesta Towers.

And slightly to my surprise I found that even in the coldest weeks, the average electrical power used by the heat pump was less than 800 watts (0.8 kW) i.e. we were heating our house with less electricity than it takes to run a hairdryer – on its lowest setting! And that includes re-heating the hot water tank each day!

So why didn’t I just buy a hairdryer?

Why? Because a hairdryer – even on full power – could not heat my house.

The wonder and fascination of heat pumps is that they don’t just squander the electricity they consume: they use it to scavenge heat from the outside air and even on the coldest days they can deliver many times more heat energy into the house than the electrical energy they consume.

The ratio of the heat energy they deliver to the electrical energy they consume is called the Coefficient of Performance (COP) and for my heat pump the average COP since installation is 3.6.

In other words the heat pump has delivered more energy than two hairdryers on full power while consuming less energy a single hair dryer on low power.

COP

The graph below shows the COP evaluated week-by-week and the average value since August 2021.

Click the image for a larger version. The graph shows the average coefficient of performance (COP) week-by-week since installation in August 2021. Also shown in the average coefficient of performance (COP) since installation, also known as the Seasonal Coefficient of Performance (SCOP).

The low average values in the autumn are because the heat pump is only delivering domestic hot water at 55 °C and is not heating the house at all.

During this time, the 20 watts of electrical power that the heat pump’s computer consumes (0.5 kWh/day) represents a significant fraction of the energy delivered.

In contrast, in winter the heat pump is delivering more than 20 kWh of heat energy per day and the consumption by the heat pumps’ control circuitry is less than 3% of the heat energy delivered.

Summary

I found this juxtaposition intriguing. 

A hair dryer is a simple device – a hand-held fan heater – and a heat pump is a much more complex machine.

But comparing them just by their electrical consumption highlights the awesome power of heat pumps.

March 2022

April 8, 2022

Friends, Spring is springing, and our first winter with a heat pump is ending.

Overall, it has been phenomenally successful. All the parts of our refurbishment have played their part.

  • The triple-glazing and external wall insulation have reduced the heating power required to heat the house.
  • The solar panels continued to deliver ~5% of our electricity requirements even in December.
  • The battery (a 13.5 kWh Tesla Powerwall) allowed us to download cheap electricity at night and use it to heat the house during the day.
  • The heat pump kept the house warm and delivered hot water, with an average Coefficient of Performance of around 3.5.

In this article I will be looking at figures for the month of March 2022.

When the heating season is a little more over than it is at present, I will write about the winter as a whole.

Solar PV and Battery

Click image for a larger version. This is on the notice board outside my house.

I have put a notice on the front of the house to advertise how little we are spending on heating and running the house. Excluding the standing charges, we spent just £14.34 heating the house and running all the electrical items in the house.

In honesty, I am embarrassed to disclose how little I am spending on fuel bills. I am embarrassed because of the suffering and anxiety that so many people will be feeling now as prices rise.

Nonetheless, when it comes to communicating the wonder of a well-insulated home powered by solar, talking about money is one way to communicate more viscerally than using kilowatt-hours and kilograms of carbon dioxide.

Energy Flows

Click image for a larger version. This graphic shows my best estimate of the energy flows around my house. There are two sources of electricity: the grid and the solar PV system. During the hours when grid electricity is cheap, the grid supplies the house directly and charges the battery. Solar PV supplies the house directly, then if household demand is met, it charges the battery, and if the battery is full, it exports electricity. My analysis suggests that the battery is only 87% efficient i.e. 13% of the energy is lost in the process of charging and discharging the battery.

The graphic above describes the energy flows in the house.

On a typical day:

  • Between 00:30 and 04:30 the house runs on cheap grid electricity, and we time the dishwasher and hot water heating to run over this period. The grid also charges the battery.
  • After 04:30 the battery runs the household and is then re-charged during daylight hours by whatever solar PV is available.
    • If the battery charge reaches 100%, then solar PV is exported.
    • If the battery discharges to 0%, then we run off full price grid electricity.

Analysing the data from the Tesla App, it looks like the battery returns 87% of the charge delivered to it. The system is specified to have a charge/discharge efficiency of 90%. I suspect that extra losses arise from the energy the battery uses to maintain its own condition.

The figure below shows the average pattern of grid use during the month. The majority of electricity is used during the cheap rate period and only a small fraction of full-price electricity is required on days when solar PV generation is insufficient to keep the battery topped up.

Click image for a larger version. This graphic shows the time of day at which the house drew electricity from the grid in March 2022. The vast majority of the electricity was consumed at night to (a) charge the battery and (b) directly operate timed loads such as the dishwasher, washing machine, and heat pump domestic hot water cycle.

Heat Pump

Click image for a larger version. Graph showing internal and external temperatures, and the temperature of water flowing in the radiators during the month of March. Data were collected every 2 minutes. The radiator flow temperature data has been smoothed. It is clear the system operates well to keep the internal temperature constant even as the external temperature varies

The average external temperature was 9.3 °C, but the month started very cold, and then later there were some exceptionally warm days (with cold nights).

The weather compensation adjusted the flow temperature in the radiators to keep the internal temperature at a comfortable average of 21.1 °C

The monthly averaged Coefficient of Performance was 3.75 which is rather more than I had hoped for.

Conclusion

When we installed the battery in March 2021, we immediately dropped of the grid for 90 days: this felt astonishing. But back then then our heating was with gas.

Now our heating and hot water systems are electrical and this adds to the daily load.

As the year progresses, Solar PV generation is growing and heating demand is falling. At some point I hope we will again be able to reduce grid use to zero for an extended period – but it will definitely not be as long as last year.

It was interesting to arrive at a figure for the battery storage efficiency. The figure of 87% was lower than I had hoped for, but since the battery is saving us so much money, it seems churlish to complain!

 

 

What Size Heat Pump Do I Need? A Rule of Thumb

April 5, 2022

Friends, a few weeks ago I wrote four articles about using the idea of Heating Degree Days to make simple calculations about heat losses from one’s home.

  • Article 1 was an introduction to the idea of Heating Degree Days as a general measure of the heating demand from a dwelling.
  • Article 2 explained how and why the idea of Heating Degree Days works.
  • Article 3 looked at the variability of Heating Degree Days across the UK, at locations around London, and from year to year.
  • Article 4 introduced some rules of thumb for estimating the Heat Transfer Coefficient for a dwelling and the size of heat pump it requires.

The Rule of Thumb for Heat Pump Sizing is dramatically simple:

The video above is about using these ‘Rules of Thumb’.

I feel these rules could be helpful to both heat pump installers and their clients.

The Powerpoint slides (.pptx) I use in the presentation can be found here.

 

 

 

 

 

Analysis of 16 years of Solar PV data.

March 16, 2022

A friend from North London kindly allowed me to analyse the data they had collected on the performance of their solar PV installation over the last 16 years.

What an opportunity to discover how solar PV panels behave over the long term!

Let me tell you what I found:

The System

Installed in July 2006, the system consisted of 16 Sanyo PV panels, each 0.88 m x 1.32 m with a nominal peak output of 210 W. This implies the panels output was initially ~180 watts per square metre.

They were installed on two adjacent roofs with a tilt of about 30° and facing 25° East of South and with no nearby trees or shading structures on the horizon other than their neighbour’s house.

The data set consisted of roughly 700 readings of the solar generation meter, most of them taken weekly but with a couple of gaps for a few months, and few points that were clearly in error. Rather than try to be sophisticated, I simply omitted points that were obviously in error.

Click image for a larger version. The ‘cleaned up’ data set.

Annual Analysis

One of things I was most anxious to search for was evidence of a year-on-year decline. The annual results are shown below:

Click image for a larger version. Graph of the Annual Output (kWh) of a North London PV system from 2006 to 2021. The dotted line is a linear fit to the data showing a systematic year-on-year decline in output.

It’s clear that there is a systematic year-on-year decline. If we re-plot the data to express this as a percentage we can compare it with what we might expect.

Click image for a larger version. The same data as in the previous graph but expressed as a fraction of the average output over the years 2007 and 2008. The dotted line is a linear fit to the data showing a systematic year-on-year decline in output.

This decline is – sadly – inevitable, arising as I understand it from atomic defects created in the silicon cells by exposure to the UV radiation in sunlight. These defects trap electrons which would otherwise reach an external contact if the crystal had been undamaged.

A decline of 6.1% per decade (0.61% per year) is quite competitive. Older panels showed higher declines (link) and more modern cells claim better performance, but not much better.

For example a 2020 Q-Cells Duo panel (link) specifies 0.54%/year decline for up to 10 years,  i.e. 5.4% per decade.

Click image for a larger version. Extract from a Q-cells data sheet showing expected decline in panel output over 25 years.

Variability

In addition to a linear decline in output the data also shows significant year-to-year variability. I wondered whether this variability arose from the natural variability of available sunshine, or some other factor.

To check this I exploited the EU Photovoltaic Geographical Information System (a.k.a. a ‘Sunshine Database’) which allows the calculation of the output of PV cells at any point in Europe or Africa over the period 2005 to 2016.

I had previously used this database to model the year-to-year variability of sunshine in West London when I was planning a battery installation.

To see if this was the cause of the year-to-year variability I plotted two quantities on the same graph:

  • The so-called ‘residuals’ of the fit to the data in the second graph above.
  • The variability of EU-database data.

The results are shown below.

Click image for a larger version. The variability of the North London PV data and the natural variability of sunshine as retro-dicted by the EU sunshine database

It is clear that in the years for which the two datasets overlap they agree well, suggesting that the variability observed is not due to some other poorly understood factor.

Upgrade?

My North London friend had one final question. Would they avoid more carbon dioxide emissions if they upgraded to modern panels?

To answer this I made two models:

  • The first model assumed that they did not upgrade and the existing panels were used to out to 2050.
  • The second model assumed they were replaced in 2022 with panels which operated with an efficiency of around 200 W/m^2 at peak illumination. This is about 20% more than the panels currently generate.

I assumed that the new panels would embody around 2 tonnes of CO2 emissions because Q-cells suggest their latest panels embody 400 kgCO2 per kWp.

I then assumed that 50% of the generated electricity was exported and 50% used domestically. As the grid currently functions:

  • Exported electricity reduces gas-fired generation which emits 450 gCO2/kWhe.
  • Domestic use avoids consumption of grid electricity with a carbon intensity of around 220 gCO2/kWhe in 2022.

Based on these assumptions, there is small advantage to replacing the panels, but this would not be realised until 2035.

Click image for a larger version. Does it make carbon-sense to replace existing PV cells with new more efficient cells?

One can model variations of these parameters, but the basic result is not affected: the carbon advantage is marginal.

My friend would help the climate more effectively by allocating his capital expenditure to something which might have more impact on CO2 emissions, perhaps buying shares in a wind farm?

But the result that really struck me from this modelling was how great the solar panels were in the first place!

Installed in 2006 and given minimal maintenance, it looks like the existing cells will avoid almost 30 tonnes of CO2 emissions by 2050. Not many technologies can achieve results like that as easily as that.

Heating Degree Days:3: How do they vary?

March 15, 2022

Friends, having read the previous two posts (1, 2) about Heating Degree Days, you may be wondering.

  • How carefully do I need to be in choosing the baseline temperature?
  • How do heating degree days vary around the UK?
  • How do heating degree days vary from year-to-year?

If you were wondering things, then the text below should provide the answers you seek.

Seek on!

Choice of Base Temperature

Click on Image for a larger version. The graph shows annual running average of the number of heating degree days for the London St. James Park Weather station. Each curve corresponds to the number of heating degree days with a difference base-temperature. For each degree Celsius increase in the internal temperature, the heating demand increase by approximately 260 °C-days.

The choice of the base temperature is important when estimating heating demand.

The evidence in the previous article is that a ‘rule of thumb’ for choosing a base temperature is to pick a value 3.5 °C below the internal thermostat setting is probably OK.

  • 19 °C thermostat setting: use a base temperature of 15.5
  • 20 °C thermostat setting: use a base temperature of 16.5
  • 21 °C thermostat setting: use a base temperature of 17.5

Using data from St James Park in London, each 1 °C change in base temperature changes the annual degree-day estimate by roughly 260 °C-days/year. So if we estimate an average value of HDD(17.5 °C) is ~ 2,100, then turning down the thermostat by 1 °C would reduce heating demand (and hence gas consumption) by ~260/2,100 = 12.4%.

Variability over Time

Click on Image for a larger version. The graph shows annual running average of the number of heating degree days based on a 16.5 °C base temperature for London Heathrow Airport (in black). The dotted lines show the 20-year average and ± 1 standard deviation. Also shown are the monthly degree day totals (in purple) from which the annual averages are derived.

Looking at the data from Heathrow – which has a longer HDD record than most stations

  • The average number of HDD(16.5)s is 2053 °C-days/year and the standard deviation is roughly 8%.
  • The average number of HDDs(15.5)s is 1778 °C-days/year and the standard deviation is roughly 9%.

First we notice that the difference between HDD(15.5) and HDD(16.5) is 275 °C-days/year, similar to the 260 °C-days/year that we deduced from looking at the St James’s Park data.

Considering the variability, a standard deviation of 8% or 9% suggests that once in 20 years or so one might expect winters which have 16% or 18% more heating demand.

Variability with Location

The number of HDDs varies from place to place. The figure and table below show the number of HDDs based on a 16.5 °C base temperature averaged over the last 3 years.

  • A wide swathe of southern England, from Manchester southward, has heating demand within approximately 16% of the heating demand at Heathrow.
    • i.e. in the range 2,150 ± 150 °C-days/year
  • In Yorkshire, the North East, and Central Scotland, heating demand is about 25% greater than Heathrow.
    • i.e. ~ 2,500 °C-days/year

Click on Image for a larger version. The annual number of heating degree days based on a 16.5 °C base temperature for various UK locations averaged over the 3-year period from 1/3/2019 – 28/2/2022. The data are also shown as deviations from the number of HDDs(16.5 °C) at Heathrow Airport.

Click on Image for a larger version. Summary of the results in the previous figure.

In addition to large scale variations across the UK, there are smaller variations due to local factors, notably elevation and the city heating effect.

Based on the typical decline in temperature with height (typically 6.5 °C/km) then each 100 m of additional elevation would be approximately 0.65 °C colder. This will result in additional HDDs roughly equivalent to 275 x 0.65 °C or 165 °C-days/year.

To look at the urban heat island effect, I downloaded data from 4 locations around London.

Click on Image for a larger version. The annual number of heating degree days based on a 15.5 °C base temperature 4 locations around London.

Compared to data at Heathrow, there are significant changes in heating demand, with the centre of London being significantly warmer, and Gatwick Airport – just 38 km from the centre of London – being significantly colder.

Variability Summary

The heating demand at Heathrow Airport with base temperature of 16.5 °C (i.e. a likely thermostat temperature of 20 °C) is very roughly 2,000 °C-days per year.

This 2000 °C-day/year varies by typically:

  • 10% in nearby locations depending on more or less urban heating.
  • 12% to 15% per °C change in base temperature
  • -3% to + 15% over England and Wales south of the latitude of Manchester.
  • Up to 30% as far north as Aberdeen
  • Year to year variability of ±9% with occasional excursions to ±18%

So if one could not look up the number of degree days for a particular location (which one can easily at Degree Days!) one could characterise heating demand against a base temperature of 16.5 °C as likely to be within 15% of 2,300 °C-days per year almost anywhere in the  UK.

Heating Degree Days:2: Do they work?

March 15, 2022

Friends, in the last article I explained how the concept of Heating Degree Days (HDDs) allowed one to estimate the Heat Transfer Coefficient (HTC) for a house (a.k.a. its ‘thermal leakiness’) in a simple way.

  • Find out how many kWh per year it takes to keep a dwelling warm.
    • For gas users, use the number of kWh of gas consumed each year
    • For oil users, multiply the volume of oil used annually (in litres) by 10.
  • Find the number of HDDs for your locale,
    • or use 2,150±150 °C-days per year as a guess for most of the southern UK
    • or use 2,350 ± 150 °C-days per year as a guess for most of the northern UK.
  • And then divide, the number of kWh/year by the number of HDDs per year to yield the overall HTC for your dwelling.

In this article I want to explain how I checked this calculation using a much more complicated process. Read on if you want to know the gory details!

Basic Observations

The reason I love the idea of HDDs so much is because I spent such a long time – several years! – trying to work out the heat transfer coefficient (HTC) for my home the long way.

Click on Image for a larger version. The graph shows weekly measurements over the last three years. In light blue, the graph shows weekly gas consumption in kWh. In green, the graph shows the difference between the internal temperature and the external temperature. In dark blue, the graph shows weekly heat output from the heat pump in kWh. It’s clear that gas consumption and heat pump output follow the heating demand quite closely.

For me it all started back in late 2018 when I bought a weather station. Fired by ‘new toy’ enthusiasm, I recorded the average daily and weekly temperatures, and wondered whether the gas consumption increased as the outside temperature fell. I started to read the gas meter, at first daily, but then settled down to reading it weekly.

Although it is completely obvious, I felt surprised to ‘discover’ that gas consumption did indeed increase as the outside temperature fell.

On the graph above I have plotted temperature ‘demand‘ (the difference between the inside and outside temperatures) and gas consumption (kWh/day) on the same graph. The data on this has been smoothed, plotting the average of ±2 weeks around each data point.

You can see quite clearly that gas consumption follows temperature demand. The Heat Transfer Coefficient (HTC) is the constant of proportionality between these two quantities. But you can see that (as a result of the new glazing and insulation) the HTC changes through the years.

For example, the graph below shows the same data as in the graph above but highlights the effect of the new glazing and insulation. The heating demand in Jan/Feb 2021 was greater than in Jan/Feb 2019 but the gas consumption was only about half that in Jan/Feb 2019. In other words. In other words, I had reduced the HTC by about half.

Click on Image for a larger version. This is the same data as in the graph above but highlighting the effect of the new glazing and insulation. The heating demand in Jan/Feb 2021 was greater than in
Jan/Feb 2019 but the gas consumption was only about half that in Jan/Feb 2019.

The four phases

The graphs above cover 4 distinct phases of the work on the house.

Click on Image for a larger version. This same data as in the graph above but highlighting the four phases of the refurbishment.

  • Phase#1 is the period before works began.
  • Phase#2 is the period after the main Triple-Glazing work was done
  • Phase#3 is the period after the final Triple-Glazing was done and the External Wall Insulation was applied.

In each of these phases, we should expect a distinctly different proportionality between heating demand and gas consumption – i.e. they each have a distinct HTC.

In Phase 3 we have data for both gas consumption (Phase#3A) and for heat pump use (Phase#3B). These should both have the same HTC – the insulation was the same – but the data is acquired in quite different ways.

I took the data in each of the phases and plotted average daily gas consumption versus temperature demand. The graphs for phases 1, 2 and 3A are plotted below.

The graphs all have the same vertical and horizontal scales and you can see that as the works progressed, the slope of the data has decreased. In other words, as the re-furbishment progressed, it took fewer kWh of gas per day to keep the house warm.

Click on Image for a larger version. Graph of average daily gas consumption versus heating demand during Phase#1 i.e. before I made any changes.

Click on Image for a larger version. Graph of average daily gas consumption versus heating demand during Phase#2 of the refurbishment i.e. after the house was triple-glazed.

Click on Image for a larger version. Graph of average daily gas consumption versus heating demand during Phase#3A of the refurbishment i.e. after the external wall insulation.

These graphs are fascinating. Firstly we note that graphs consist of two regions:

  • At low heating demand, there is no temperature-dependence of the heating demand – the graph is flat at about 5 kWh/day. This is because during the summer, the heating is used for domestic hot water and cooking only.
  • At high heating demand the data fit plausibly to a straight line, but not one that goes through the origin. The slope intercepts the 5 kWh line at roughly 3.5±0.5 °C of demand. This slope is just the HTC that we are looking for. It tells us how many extra kWh/day it takes to warm the house for each extra °C of temperature demand

The fact that the gas consumption doesn’t start to increase immediately the outside temperature falls below the thermostat set-temperature is because there are other sources of heat in the house.

  • All the electrical items in our house typically consume around 200 W continuously – or 4.8 kWh/day.
  • And each person in the house contributes around 100 W continuously, so my wife and I contribute another 4.8 kWh/day.

I investigated this phenomenon in quite some detail in these articles (1, 2), but the upshot is that the heating in my home doesn’t switch itself on until the external temperature falls about 3.5±0.5 °C below the thermostat temperature.

Looking at the slopes of the graphs, I can plot them to show how the Heat Transfer Coefficient has been reduced as a result of my refurbishments.

The Red Circles on the graph below show estimates assuming that the gas boiler is 100% efficient i.e. all the energy of burning the gas is retained within my home. A more realistic estimate is that only 90% of the heat is retained within the house. The estimate of the HTC using this assumption is showing blue.

Click on Image for a larger version. Graph showing the Heat Transfer Coefficient for my home deduced from the  slopes of the previous three graphs. The Red Circles show estimates assuming that the gas boiler was 100% efficient. The Blue Circles show more realistic estimates assuming that the gas boiler was only 90% efficient. The left hand axis shows the HTC in kWh/day/°C and the right-hand axis shows the HTC in W/°C.

I have gone through this calculation of the HTC in some detail to show just how difficult it is. Now let’s look and see how much easier it is using degree days.

Calculation Using Degree Days

To calculate the same estimates for HTC I need to do the following:

  • Look up my records to find out how many kWh of gas I used in each of the three phases. All it requires is a single reading of the gas meter at the start and end of each phase. To gain extra accuracy one can assume that at best 90% of these kWh of gas consumed resulting in heating kWh.
  • Look up the Degree Days Website to find out the number of heating degree days in each of the three phases.
  • Divide the gas consumption by the number of HDDs.

During Phases #1, #2 and #3A, our thermostat was set to 19.0 °C, and so I used HDDs with a base temperature of 15.5 °C i.e. 3.5 °C lower than 19.0 °C.

The Calculational Steps outlined in the bullet points are summarised in the table below.

Phase Gas Consumption (kWh) Heating (kWh) at 90% Efficiency HDD15.5s

(°C-days)

HTC
(kWh/day/°C)
HTC
(W/°C)
1 13,323 11,991 1,430 8.4 349
2 13,756 12,380 1,787 6.9 288
3A 6,902 6,212 1,773 3.5 146

The resulting HTC estimates are compared with those previously calculated using the long-winded method in the Graph below.

Click on Image for a larger version. Graph showing estimates for Heat Transfer Coefficient for my home during the three phases of refurbishment. The blue circles are the same data plotted in teh previous graph assuming that the gas boiler was 90% efficient. The Blue Circles. The Green Squares show the result of the same calculation using HDD15.5s. The agreement is striking. The left-hand axis shows the HTC in kWh/day/°C and the right-hand axis shows the HTC in W/°C.

The agreement between the two methods of calculating the HTC is striking.

What this means is that instead of having to record external temperatures and gas consumption week-by-week as I did for three years, one can get equivalent results by using HDDs and just one or two gas meter records.

A final test: Phase#3B

I can check the calculational method and some of my assumptions by comparing the HTC in Phases 3A and 3B. There were no changes to the insulation in these phases: what changed was that I switched from heating using a gas boiler to heating with a heat pump.

So HTC should be the same in Phases 3A and 3B.

However there was one change that arose from etc heat pump switch. As I learned to use the controls of the heat pump, I eventually stuck with settings that resulted in the house being warmer (~20.5 °C) than it had been previously (~19 °C).

For this reason I calculated the number of degree-days in Phase#3B using a base temperature of 17.0 °C rather than 15.5 °C. The result is plotted in purple on the graph below.

Click on Image for a larger version. The same graph as shown previously, but now with the calculation for Phase 3B shown as a filled purple circle. The left-hand axis shows the HTC in kWh/day/°C and the right-hand axis shows the HTC in W/°C.

All three calculations of the HTC in Phase#3 agree within a range of 10%, which is pretty much as good as any calculation or measurement of HTC can hope for.

This gives me confidence that the HDD method does indeed work, and that the likely boiler efficiency in Phases #1, #2 and #3A was probably not very different from 90%.

Summary

Apologies for this very long article. You may be asking, as I am, “Why did I write this?”

The answer is that being able to calculate the HTC for a dwelling is important. And if using HDDs makes the calculation simpler, then maybe more people will do the calculation.

And this should enable more people to rationally plan their home refurbishment and estimate the size of the heat pump they require.

And it’s all thanks to the kind people over at Heating Degree Days.

In the next article I’ll look at how HDD’s vary with:

  • choice of base temperature,
  • location in the UK, and
  • from year-to-year.

 

Heating Degree Days:1: A Brilliant Idea

March 15, 2022

Friends, on learning recently about the wonderful idea behind Heating Degree Days (HDDs) I found myself torn between two conflicting emotions.

  • On the one hand, I feel delighted at the cleverness of the concept and I rejoice in my new-found ability to save so much time on calculations about heating houses.
  • But on the other hand, I feel like an idiot for not having known about the idea previously!

Being the positive person that I am, I am writing this gripped by the more positive sentiment and have written four articles on the subject. This is first article in which I try to keep things simple-ish. I deal with complicated questions is this next article and the one that follows that.  In the final article I summarise the calculations that HDDs make easy.

HDDs and HTCs?

Heating Degree Days (HDDs) make it easy way to calculate the ‘thermal leakiness’ of a dwelling – a quantity technically called its overall Heat Transfer Coefficient (HTC).

The HTC is the most important number to know if you are considering any type of retrofit – insulation, draught-proofing or installing a heat pump. It allows you to answer the question:

When it’s (say) 8 °C outside, how much heating power (in watts or kWh/day) do I need to keep my house at (say) 20 °C“.

The answer is just the temperature difference (12 °C in this example) multiplied by the HTC.

So if the HTC of a dwelling is 300 W/°C then it would require 12 °C x 300 W/°C = 3,600 W or 3.6 kW to keep that dwelling warm.

But how do you find the HTC? This is normally quite hard work. It usually requires either extensive surveys and calculations or or prolonged measurements. But the idea of heating degree days HDDs makes it really easy. There is just one sum to do. Let me explain.

Degree Days in General

The idea of degree-days  is commonplace in agriculture.

For example, in viticulture, the number of Growing Degree Days (GDD) is calculated to allow farmers to estimate when the grapes will flower, or ripen, and when certain pests will emerge.

Click on Image for a larger version. Illustration of the concept of Growing Degree Days (GDDs). See text for more details.

GDDs are calculated as follows:

  • If the average daily temperature is below some Base Temperature – usually 10 °C – then one adds 0 to the number of GDDs
  • If the average daily temperature on a day is above the Base Temperature, then one subtracts the base temperature from the average temperature, and adds the result to the number of GDDs.
    • So if the average temperature on a particular day is 15 °C, and the base temperature is 10 °C, then one adds 5 °C to the GDD total.

Each geographic region has a characteristic number of GDDs available per year, and each grape-type requires a certain number of GDDs for a successful harvest. So using GDDs is a simple way to match vines to regions,

Alternatively, in any particular year, one can use the number of GDDs to discuss whether the grapes are likely to mature earlier or later.

Heating Degree Days

Heating Degree Days (HDDs) work in a similar way to GDDs, but count days when the temperature falls below a base temperature.

Over a winter season, the number of HDDs provides an estimate for the overall ‘heating demand’ that you want your heating system to meet.

Click on Image for a larger version. Illustration of the concept of Heating Degree Days (HDDs). See text for more details.

To keep things specific in this article I will mainly work with a base temperature of 16.5 °C, and the heating degree days are then known as HDD(16.5)s.

I’ll explain the choice of base temperature in the next article, but the choice corresponds to a thermostat setting of approximately 20 °C which is typical of many UK dwellings.

  • For much of the south of the UK – basically anywhere south of Manchester – the number of HDD(16.5)s per year typically lies in the range 2,150 ± 150 °C-days/year.
  • For regions north of Manchester up to Edinburgh in Scotland, the number of HDD(16.5)s per year typically lies in the range 2,350 ± 150 °C-days/year.
  • You can look up the exact number of HDD16.5s for your location for the last three years using the outstanding Heating Degree Days web site. At my home in Teddington, the annual number HDD(16.5) is typically 2,000 °C-days/year

What now?

In order to estimate the heat leak from a dwelling – its Heat Transfer Coefficient (HTC) – you also need to know one more number: how many kWh of heating the dwelling requires in a year.

  • If it’s heated with gas, you can use the annual number of kWh of gas used.
  • If it’s heated with oil, multiply the number of litres of oil used annually by 10.
    • e.g. 2,500 litres of heating oil per year is ~25,000 kWh.

So for example, before I did any work on our home, we used 15,000 kWh of gas each year. I looked up the annual number

So to calculate the HTC for my home I divide 15,000 kWh/year by 2,000 °C days/year to give 7.5 kWh/day/°C. This tells me that:

  • To heat my home 1 °C above the outside temperature required an additional 7.5 kWh of gas per day.
  • Or if I reduced the temperature in my home by 1 °C, I would save 7.5 kWh of gas per day.

Equivalently, if we divide by 24 and multiply by 1000, we can convert 7.5 kWh/day/°C into the more common units of watts i.e. 313 watts/°C.

  • So to heat my home 1 °C above the outside temperature required an additional continuous 313 W of heating.

Thinking about a heat pump?

Knowing the HTC, one can change a qualitative sense that “it’s a really cold house” into a quantitative measurement “It has a HTC of 400 W/°C“. that can help one to choose which refurbishments are likely to be effective.

Suppose, for example, we want to work out the size of heat pump required to heat our dwelling in the depths of winter.

Typically the coldest temperatures encountered routinely in the UK are around -3.5 °C i.e. around 20 °C colder than the base temperature.

So to estimate the heat pump power required for my house before insulation, I would simply multiply the heating demand (20 °C) by the HTC (313 W/°C) to yield 6.26 kW.

Additionally, if we make changes to the dwelling, such as adding triple-glazing, we can estimate the change in HTC by dividing fuel use (in kWh) by the number of HDD16.5s – a number which can be found for any location at the Heating Degree Days web site.

Summary

This article introduced the idea of using Heating Degree Days as an estimate of overall demand.

When combined with a measure of heating energy supplied over the same period, dividing one by the other magically yields the Heat Transfer Coefficient (HTC) for a dwelling.

Knowing the HTC one can measure the effect of any improvements one makes – such as triple-glazing or installation. And additionally, one can calculate the amount of heating required on a cold winter day.

But you may have some questions. For example:

  • I set my thermostat to 20 °C: Why did I recommend using 16.5 °C as a base temperature?
  • Does it really work?
  • How do HDD(16.5)s vary from one location to another and from year-to-year?

Heat Pump Explainer

February 24, 2022

Friends, Everyone is talking about heat pumps!

But many people are still unfamiliar with the principles behind their ‘engineering magic’.

This ‘explainer’ video was shot on location in my kitchen and back garden, and uses actual experiments together with state-of-the-art Powerpoint animations (available here) to sort-of explain how they work.

I hope it helps!

 

Reducing Carbon Dioxide Emissions from my home: Video and Slides

February 4, 2022

Friends, Good Evening.

As I mentioned in my previous post I gave a talk to Richmond & Twickenham Friends of the Earth on Wednesday, 2nd February 2022.

The video above is the dullest of the dull repetition of that presentation.

It lasts 45 minutes, so make yourself a cup of tea before you start!

You can also download the PowerPoint slides from this presentation using this link.


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