Home electricity – 1 year update

(see https://plord.co.uk/b/8vd)

A year or so ago I presented How to reduce your electricity bill to zero talk (initially over on u3acommunities.org computing group) – it’s now been running for a year, so time to check progress.

General approach

After getting my solar + house battery installed, I’ve tried to reduce my electricity (£ and carbon footprint) as much as possible by :

• Choosing a good tariff plan from energy supplier
• Charge house battery when electricity is cheap
• Use house battery to power house when electricity is expensive
• Use solar to top-up house battery and sell excess back to the grid
• Heat hot water tank when electricity is cheap
• Use any special deals from energy supplier
• Automate with Home Assistant

My specific

This is what I have :

• Octopus intelligent go tariff for electricity import – so overnight I’m charged 7p/kWh. I also get the 7p/kWh when I plug my car in (at which point the whole house gets the rate). Octopus determines when exactly my car is charged based on my requirements. The daytime rate is currently 27.7p/kWh but thats less important since I rarely use it.
• Octopus outgoing tariff for electricity export – this pays be 15p/kWh for anything I can export back to the grid
• myenergi zappi, for car charging, and a myenergi libbi for house storage (with just a 5.1kWh battery)
• 4.5kW peak solar facing south-east (so catches the morning sun)
• Given the above, my main home automation rules via home assistant rules are :
  1. Charge battery when electricity is cheap (overnight + car charging)
  2. Heat hot water via immersion heater so that it’s up to temperature as the cheap rate finishes
  3. If the battery has any charge left at the end of the day, export to grid just before cheap rate starts
  4. During cheap rate, once battery is charged, export & re-charge
  5. Join octopus saving sessions (if its worth it)
  6. During cheap rates enable some ancillary devices (like tool battery charging, pond pump)
  7. Make use of free electricity when available
  8. Use a calendar to disable some automations when away

1 year results

Solar was pretty good this year. Expected was 3.97MWh actual 4.06MWh.

So this is what a typical day looks like :

Over 12 months, winter time costs but summer earns :

So over this period, the totals are :

• 4,060kWh of solar
• 7,048kWh imported, costing £563.43 + £220.17 standing charge = £783.60
• 4,638kWh exported, earning £694.87
• £26.69 of saving sessions and other “gimmics”
• Giving a total cost of £62.04 / year, or £5.17 / month – so not quite zero as the talk predicted
• Since house consumption was 6,469kWh over the year, with no solar/battery/smart tariffs that would have cost about £1,790.80/year
• Estimated savings of £1,728.76 / year, or a return on investment in 4.9 years
• Estimated CO2 savings 888kg/year (carbon negative in june/july/august)

This is for all household electricity use, which includes hot water and car charging. Excluded is car charging away from home. Currently, gas is used only during the winter months for heating.

Problems, risks & concerns

Over the last 12 months, there have been some issues :

• Some automations failed – some due to me, others due to loosing connectivity with the myenergi servers. Means I saved slightly less than I could have done.
• I’ve had some issues with the libbi battery – eg trip switches flipping and CT failing. Again, means I saved slightly less than I could have done whilst those issues were fixed.
• Octopus “saving sessions” went national with much less rewards than the previous year. Mostly this meant participating in had a risk of loosing money, so most were skipped

Also, there are some concerns about this approach :

• Tariffs can change … export and intelligent octopus go are now variable rates, which mean they could change at any time
• Although the libbi battery warranty is 10 years with unlimited cycles, there is some concern that additional charge/discharge cycles could wear out the battery quicker
• Changes in home assistant, home assistant plugins and octopus/myenergi API’s
• Myenergi products are only controllable via the myenergi servers (no local API available), so should connection be lost, automations will fail

Next steps

• Continue to monitor – seek out best tariffs and tweak automations
• Consider alternatives to gas central heating – as well as further reducing carbon footprint, this would eliminate gas standing charge
  • air-to-water heat pump
  • electric boiler
  • air-to-air heat pump
• Consider more solar
  • Adding some additional panels facing south west on our garage roof would be a benefit since it would catch the evening sun. Eg 2 panels would generate about 670kWh, or £100.50 (ROI about 8 years)
• Consider more battery capacity
  • More battery capacity would reduce peak grid usage when the existing batter is depleted (mostly in the winter). However, the yearly cost of peak grid was only £90.78 so it doesn’t really seem worth it.

In search for a better filter

https://blog.narf.ssji.net/2025/05/10/in-search-for-a-better-filter/

Linear vs. Polynomial calibration to get water level from sensor reading are pretty much the same.

Low-pass filtering too slow to react to actual changes

Works well enough for immediate reading and daily consumption aggregate

Too noisy for hourly readings and other event detection

Water delivery took hours to register the full scale

Kalman?

I thought it was for sensor fusion, but it also works for single-sensor filtering.

Good videos: https://www.youtube.com/watch?v=HCd-leV8OkU https://www.youtube.com/watch?v=qCZ2UTgLM_g

Home Assistant has some integration for Kalman filters, but they seem unmaintained https://community.home-assistant.io/t/kalman-filter/58483

ESPHome: https://esphome.io/components/sensor/combination.html

Dug out and translated the sheet that came with the current loop liquid pressure sensor. It looked like an ad, but actually contained useful info on the characteristics of the sensor.

Likely useful to calibrate the Kalman filter

Also has a “debugging” / calibration method, which would allow to get a wider range.

Adding a Kalman-filtered entity in ESPHome

Error on the sensor Got the process stddev from overnight data from Home Assistant (when the water level is expected not to change), and chucked it into LibreOffice’s STDEV function: ~600L

Standard deviation on the process: change per second. For lack of a better estimate, using the baseline of 400L/day use: 400/(24*3600) = 0.00462963

This seem very low as “A low value here will place high importance on the current state and be slow to respond to changes in the measured samples.”

Also trying a larger value for likely changes, e.g., one tap running for a minute at 9L/min: 0.15

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