I would like to highlight a piece of work by Carlos Alonso Gabizón @cagabi which I think is an important piece of the home energy monitoring and assessment toolkit and a really useful tool, which we would be keen to see people use.

Carlos has been working for CarbonCoop over the last year and a half on MyHomeEnergyPlanner – an open source household energy assessment tool based on SAP 2012 – a monthly domestic energy model.

MyHomeEnergyPlanner started as a collaboration between OpenEnergyMonitor and CarbonCoop but this latest version and active work on it over the last year and a half is all down to Carlos and CarbonCoop.

MyHomeEnergyPlanner can be used to calculate the space and water heating requirements for a home from a detailed breakdown of the building fabric: floor, walls, roof, windows etc. It uses U-values and areas to calculate building fabric heat loss rates, combined with calculated heat loss from infiltration and ventilation and heat gains from solar radiation, lighting, household appliances, cooking and occupants.

It can be used to model a building in its current form and then create scenarios to explore the effect of undertaking measures such as adding insulation, improving air-tightness and changing heating systems.

MyHomeEnergyPlanner is free to use and open source and is now installed on (Login with your account)

I have written an initial getting started guide here that goes over the main parts of creating an initial household assessment:

When I was last working on building energy modelling I also wrote a brief guide on how a simple household energy model works which can also be found here:

MyHomeEnergyPlanner Github

For further context: MyHomeEnergyPlanner is used by CarbonCoop as part of their work on household retrofit, improving insulation and air-tightness of existing households in Manchester to achieve carbon emission reductions of 80% compared to the 1990 baseline. More information can be found about their work here:

Whole House Retrofit - A Community Green Deal

Whole House Retrofit Assessment Method:

Recent post-retrofit monitored results for Carbon Coop’s recent retrofit project:

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This is awesome, great work @cagabi , We should do a blog post about home energy planner. Would you like me to make a blog post from this forum post?

Is that supposed to be REFURBED_ASSESSMENT…?

Sorry, no, URBED are CarbonCoop’s technical partners :slight_smile:, thanks for asking

Gotcha. It was the “retrofit” part of the document title that made me wonder about that.


Sorry for lack of correspondence, i have just come back from a 10 days break so missed this.

Thank you so much for the forum and blog, and of course for the lovely lovely compliments, it looks very good.

Can you tell if people are using MyHomeEnergyPlanner in the emonCMS website?



@cagabi - Any chance that you could expand the geography spaces from the UK to also include the US? Specifically, I’m located in Connecticut (USA), and am pursuing green building standards and performance metrics for single-family and multi-family residences. I’m interested to apply the SAP/PHPP models, but the core geography data (assuming temperatures, humidity, solar irradiance, etc) will drastically skew models. Can you help me understand what datasets you need and I can possibly find them for you from here?

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Hello @michaelsuhl11 The datasets for MyHomeEnergyPlanner can be found in this file:

For example the UK average is the first line in each dataset:

regions: [
"UK average",


table_u1: [
[4.3, 4.9, 6.5, 8.9, 11.7, 14.6, 16.6, 16.4, 14.1, 10.6, 7.1, 4.2],

To add other countries and regions, more lines could be added to these datasets, in particular:

  • table_u1: temperature
  • table_u2: wind speed
  • table_u3: mean solar irradiance
  • table_u4: Representative latitude

Im not sure how portable the SAP solar gains calculations are for different geographical position, so that would be worth double checking.

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Thanks @TrystanLea. I’m a bit of a noob for programming, so I’ll give it a
try to adjust the datasets, but comment out until someone else reviews.
Also, I’m unsure what units are used for U1 and the reason for the
different values (12 values…so is it one temperature reading for every
two hours? Is it the average for the year? Average temp per month?) For
table U5, how are the 9 groups selected? It doesn’t seem to match the top
selection table, starting with UK Average, so I’m not sure how I should
associate new values from the US.

I’ll start with the data for my region, but after it’s reviewed, I’ll try
to add the rest of the US regions.

I appreciate your patience…

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Michael - as a guess…

Each row represents a region in the UK. There are 22 UK regions in the code and 22 rows/lines of data:

UK average
Northern Ireland

table_u1 looks like the average temperature per month in Celsius. Twelve numbers for twelve months. The first line in table_u1 is the UK average. Second line for Thames.

I can’t answer (guess) for U5.

Hi there,

Very nice initiative @michaelsuhl11, please let us know how you get on with it.

You need to change the following tables for sure:

  • regions
  • table_u1: as described by Jon
  • table_u2: the same than table_u1 but for wind speed (m/s)
  • table_u3: the same for solar irradiance (W/m2) on a horizontal plane
  • solar_declination: solar declination for all regions: this is a tricky one, the USA is bigger than the UK so probably the solar declination will be significantly different on the very North and the very South of the country for a specific month, while in the UK is probably minimum. It would be possible for you to make a similar table than table_u1 with the declination for each region and each month. This would require a change in the model when doing the calculations but I’d be happy to do it for you, should be easy.
  • table_u4: latitude and height above mean sea level for each region. The height is not used at all in the model so you can forget about it (or even better replace it with something like N/A"

For the following ones I think you can live with them as they are, mainly because it will probably be very difficult for you to find out the specific values for USA. Some of them are generalizations for the UK and probably introduce big errors for us, even worse for you.

  • table_u5: see my blurb below
  • table_1c: monthly factors of hot water use, the coefficients in this table are used to calculate the energy used to heat hot water in a specific month when you know how much you have used in the whole year.
  • table_1d: Temperature rise of hot water drawn off. This must be dependent on the mains water temperature
  • table_6d_solar_access_factor: there are different values for summer and winter so they must be dependent somehow on how high is the sun and therefore on the latitude

About this thing that @TrystanLea says and that I relate to table_u5:

The calculation of the solar gains uses a formula defined in the Appendix U3 of SAP2012 (p.174), that formula uses the constants defined in the table U5. What I am not sure is if those constants are only valid for the UK or not. Doing a bit of research I have found the paper that describes the process to calculate those constants.

My understanding (which might be totally wrong) is that:

  • those coefficients are used to calculate the flux on an inclined surface and for a specific orientation when you know the flux in the horizontal plane
  • those coefficients were calculated as described in the paper I mention above from empirical measurements comparing the flux in the horizontal plane with the flux in the inclined surface for different orientations
  • so, despite using flux data measured in the UK, I think the values of those coefficients can be applied everywhere: in USA the relation between the flux in the horizontal plane and and inclined one for a given orientation must be the same than in the UK (as physics are the same everywhere :wink:

Finally, I have used this opportunity to add a bit more documentation to the datasets file. You can find it in the development branch of MyHomeEnergyPlanner


I should be able to help with some of the light javascript programming. But the SAP calculations are over my head! So if you can supply the numbers then I can update the datasets.

Maybe start with a few regions like Hartford area, Chicagoland area, New York city area, Boston area, etc. and build from there. I live in a Chicago suburb.

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Thanks @Jon. Any help is appreciated. I’m setting up some hardware and basics. Will report back with any progress. And I’ll be sure to include some data for the Deep Dish capital…

@Jon @cagabi I’ve made the changes to the dataset to include all the IECC/ASHRAE regions for the US. I didn’t change the other tables, as recommended. I requested a “Pull Request” but I’m not sure that is the appropriate next step. Want to take a look and confirm?

This is great Michael, I’ll have a look soon and merge your commits. Thank you very much

Also, I have some comments with regards to the suitaibility of MHEP for USA from Marianne our expert in SAP and buildings:

Apart form the physical data you and Trystan have pointed out - climate data and solar gain - that probably needs to be changed for the US, there are some cultural assumptions that it’s also worth being aware of about number of people per sqm and the size and efficiency of household appliances that affects the internal gains and hot water demand calcs. The lighting calcs probably also need looked at - they’re based on assumptions about what an acceptable level of lighting is, which are partly cultural.

Carbon and primary energy factors for various fuels will also be different - they have a very different energy system and grid.

The heat loss model is just physics, so that doesn’t change. (Though they use R values rather than U values - the inverse I think - so could lead to some confusion).

Whilst the principles of the calcs probably still apply, it’s not likely to be that accurate an assessment without quite a few changes to these assumptions.


Michael, I have just merged with the development branch, thanks for your work

Also, I’ve done another commit adding your name :slight_smile:

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Thank you for all the follow up and putting the code to work. I’ve noted the assumptions in question. Given the range of cultural difference among US citizens, I expect the range and confidence level of the data to be within tolerances for most users here (occupancy/lighting levels). The pace of EnergySTAR and efficiency of appliances in the US is steady, so I anticipate the US may simply be out of step with UK for a short time. I will need to investigate the carbon/primary energy factors though and see if our national database can readily align with the metrics from the UK. I typically refer to OpenEI ( for these values. Does your team contact think that this dataset could be valuable, for emissions on an hourly basis (circa 2005-2008)? Open Energy Data Initiative (OEDI)

A similar project, WattTime, would be more ideal and updated. (GitHub - microsoft/Smart-Energy-Foundation-Demo-Stack: A demonstration solution that uses real-time Carbon Emissions and Weather data mining to show how data from several Web based APIs can be mined, visualized and acted upon in a Microsoft Azure solution)

This type of integration is well beyond my current learning curve/development skills, so hopefully others might be able to assist.

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It has been over a year since the last reply to this topic.

What is the current status of MyHomeEnergyPlanner and its level of integration with emonCMS?

Any recent news of note?