Tim's blah blah blah

Nibe Heatpump Home Automation - WIP

I have an F1255-6 R Nibe heatpump + Triplesolar cooling module with SMO 20 or 40 controller in my home which I’d like to read out and possibly automate/optimize. Here I document my approach.

Intro

I have a Nibe F1255-6 heatpump with SMO 40(?) control box.

Update firmware from here: https://www.nibe.eu/nl-nl/producten/smart-home/nibe-uplink-to-myuplink?HPModel=nibe-e10 (nibe.eu)

Goal

  1. Show live performance of the heatpump (e.g. power/COP) during both heating and cooling
  2. Optimize consumption to either low electricity price (via epexspot (epexspot.com)) or low carbon intensity (via co2signal (co2signal.com)?)
  3. Optimize electricity consumption / COP by tuning parameters

Todo

  1. Why is the Triple Solar cooling module running almost always?

  2. What is the difference between HW load and HW top? –> seems to be the location of the temperature sensor, one more at the bottom (load), one more at the top (top).

  3. What is the optimal amount of degree minutes to trigger heating on? –> Now: -60DM and +100DM.

  4. How can I measure the COP? (existing methods scale the theoretical COP based on specification & ambient temperatures)

  5. What is a reasonable 12h idle temperature reduction for the 180L boiler? (How) can I reduce this? –> probably not easy to improve

  6. How can I set passive cooling to start at lower temp than active cooling? –> don’t use, not useful

  7. How can I reduce the internal heating element power to improve self-consumption or battery consumption (which have capped power)?

  8. How/what energy use can I adjust to optimize low-cost energy? E.g. 60 degree cycle on specific day?

  9. Get heatpump logic from Keje/Github

  10. Get ‘Heat medium flow’ temp –> OK done

  11. Get dump of nibe settings –> OK not possible easily

  12. Explain filtertime –> OK understood

  13. Check which PM2.5 source used –> https://aqicn.org/station/@367963/de// (aqicn.org) + https://aqicn.org/station/@373501/de/ (aqicn.org) https://community.home-assistant.io/t/openaq-air-quality-data/597059/2 (home-assistant.io) // https://explore.openaq.org/#12/52.10131/5.12819 (openaq.org) https://community.home-assistant.io/t/air-quality-meters/147655 (home-assistant.io)

Setting up hardware

Hardware setup

There’s a few (hardware) options for reading out the heatpump. I chose a pre-built LilyGO T-CAN485 ESP board (opencircuit.nl) with esphome-nibe (github.com). Other options are listed below

Raspberri Pi with Modbus

Hardware (total: 52 EUR):

Software: nibepi (github.com) OR using nibe (github.com) and nibe-mqtt (github.com) libraries

Arduino/ESP32 with Modbus (preferred)

Hardware (total: 17-36 EUR):

OR a combined board:

Software: esphome-nibe (github.com)

Pre-built PRODINo

See here (kmpelectronics.eu) or here (web-engineering.info)

Installation

For details, read the installer manual (nibe.eu). First remove the front panel of the heat pump with the two TX25 Torx screws at the bottom, then tilt the panel and lift up (see page 8 of the installer manual (nibe.eu)). See page 12 for the location of the distribution boxes, and page 20 on how to open it (specifically: box AA3). Below a few pictures of my setup

Removing front panel from NIBE F1255 heatpump

Removing front panel from NIBE F1255 heatpump

NIBE heatpump with front cover removed

NIBE heatpump with front cover removed

Distribution box AA3 opening latch

Distribution box AA3 opening latch

An overview of electrical diagrams is available here (nibe.eu) (Dutch). here, you can see you need to connect 9-10-11-12 (GND-B-A-12V) on print AA3 rail X4. Alternatively, see the Nibe Modbus moduledocumentation (nibe.eu), or this topic (energiesparhaus.at). In my case there was already a wire connected to the pins (blue/yellow/green/orange wires to 9/10/11/12 = GND/B/A/12V), but it was not connected on the other side (?). I used this to connect my LilyGo.

Distribution box AA3 opened, showing the connector rails X6 (left) and X4 (top)

Distribution box AA3 opened, showing the connector rails X6 (left) and X4 (top)

Connection scheme for the MODBUS40 accessory, using pins 9-10-11-12 to GND-B-A-12V on MODBUS. I used the blue/yellow/green/orange wires that were already available.

Connection scheme for the MODBUS40 accessory, using pins 9-10-11-12 to GND-B-A-12V on MODBUS. I used the blue/yellow/green/orange wires that were already available.

Connection to LilyGO-T CAN485 ESP board.

Connection to LilyGO-T CAN485 ESP board.

Software setup

For the LilyGO T-CAN485 ESP board with esphome-nibe combination I chose, I combined the default esphome config template with the LilyGO example (github.com) config of esphome-nibe. This connects to the heatpump via modbus and sets up a UDP daemon for interfacing, which can be read out via nibe_heatpump (home-assistant.io) integration on Home Assistant. You can add this addon and then connect to the LilyGO board by choosing ‘NibeGW’ and fill in the remote address of your esphome

Reading out diagnostics

Determining diagnostics

Once you have a hardware setup for modbus, you can read out certain ‘coils’ which are diagnostics parameters, see e.g. this list of parameters (github.com) (F1155/F1255 (github.com) specifics). In my case many of the >100 parameters were not active. You can plug in a USB stick as well which prints a log file and allows you to

Translation tables - WIP

Translation table for eb100_ep14_pca_base_relays_43514:

Sensor values

Data about the glycol/water mix to your roof (PVT) or ground loop

  1. Brine in: sensor.eb100_ep14_bt10_brine_in_temp_40015
  2. Brine out: sensor.eb100_ep14_bt11_brine_out_temp_40016
  3. Brine pump speed: sensor.ep14_gp2_brine_pump_status_ep14_43439

Data about the flow to your (underfloor) heating and hot water line

  1. Heat medium flow: sensor.bt2_supply_temp_s1_40008 ??
  2. Return temp.: sensor.eb100_ep14_bt3_return_temp_40012 ??
  3. Pump speed heating medium: sensor.supply_pump_speed_ep14_43437
  4. Calculated Flow Temp: sensor.calc_supply_s1_43009
  5. Actual Supply Temp: sensor.bt25_ext_supply_40071
  6. Actual Return Temp: sensor.bt25_ext_supply_40071

Compressor related data

  1. Hot gas temp: sensor.eb100_ep14_bt14_hot_gas_temp_40018
  2. Suction gas: sensor.eb100_ep14_bt17_suction_40022
  3. Liquid Line: sensor.eb100_ep14_bt15_liquid_line_40019
  4. Condensor out: sensor.eb100_ep14_bt12_condensor_out_40017
  5. Current compr. frequency: sensor.compressor_frequency_actual_43136
  6. Compressor power: sensor.compr_in_power_43141
  7. Internal el. add. power: sensor.int_el_add_power_43084

Thermostat & hot water data

  1. Room temperature: sensor.bt50_room_temp_s1_40033
  2. Outdoor temp.: sensor.bt1_outdoor_temperature_40004
  3. Hot water temp.: sensor.bt6_hw_load_40014
  4. Hot water temp. (@ top): sensor.bt7_hw_top_40013
  5. climate system: climate.climate_system_s1

Settings (to control hot water charging by changing from economy to luxury)

  1. hot water mode: select.hot_water_comfort_mode_47041
  2. start temp luxury: number.start_temperature_hw_luxury_47043
  3. stop temp luxury: number.stop_temperature_hw_luxury_47047

Calculate COP

From https://gathering.tweakers.net/forum/list_message/77230924#77230924 (tweakers.net) Based on syntax of this post (tweakers.net) and content of this post (tweakers.net) (see also here (tweakers.net) and here (home-assistant.io)). I added a x100 such that the unit percentage is correct (actual COP is a coefficient, but not sure how to show that unitless in HA):

  - sensor:
      - name: "Nibe F1255 COP Heating"
        unique_id: "nibe_f1255_cop_energy_heating"
        unit_of_measurement: "%"
        state: >
          {% set cop0035 = 4.72 | float %}
          {% set cop0045 = 3.61 | float %}
          {% set cop1035 = 6.49 | float %}
          {% set cop1045 = 4.79 | float %}
          {% set bt2 = float(states('sensor.bt2_supply_temp_s1_40008')) %}
          {% set bt3 = float(states('sensor.eb100_ep14_bt3_return_temp_40012')) %}
          {% set bt10 = float(states('sensor.eb100_ep14_bt10_brine_in_temp_40015')) %}
          {% set bt14 = float(states('sensor.eb100_ep14_bt14_hot_gas_temp_40018')) %}
          {% set bt17 = float(states('sensor.eb100_ep14_bt17_suction_40022')) %}
          {% set t_cop_helper = (45-35)*(bt2-35)/100 %}
          {% set tb_cop_helper = bt10/10 %}
          {% set copm = (1-t_cop_helper) * cop0035 + t_cop_helper * 3.61 + (1-t_cop_helper) * (cop1035 - cop0035) * tb_cop_helper + tb_cop_helper * (cop1045-cop0045) * t_cop_helper %}
          {% set bt14minbt2abs = (bt14 - bt2 ) | float | abs  %}
          {% set bt17minbt10abs = (bt17 - bt10) | float | abs %}
          {% set coptboven = (5 - (bt2 - bt3) ) / 2 %}
          {% set copt = 100 * copm * ( 1 - ( coptboven / (bt2 - (5.0/2) + bt14minbt2abs - ( bt10 - bt17minbt10abs  ) ) ) ) | round(0) %}
          {% if states('sensor.prio_43086') != 'OFF' %} {{ copt }} {% else %} 0.0 {% endif %}

Understanding automation - WIP

Cooling starts late

  1. 1.1.2 koeling temp 22 deg
  2. 1.2.2 program: 17:00-9:00 @ off
  3. 1.9.1.2 koeling curve 50 - 25 deg @ 17 deg, then ramp to 20 deg @ 20 deg outside
  4. 1.9.2 ext instelling verwarming: 20.0 deg / koeling 20.0 deg –> ??
  5. 1.9.3.2 koeling min aanvoer temp 17 deg
  6. 1.9.4 factor verwarming: 2.0 / koeling: 1.0
  7. 1.9.5 koeling koel/verw sensor: BT50 / inst pt-wrd koel/verw-sensor: 21 deg / verw. bij ondertemp. kamer 1.0 deg / koeling bij overtemp. kamer 1.0 deg / passieve koeling @ 10 GM / actieve koeling @ 60 GM / stapverschil compressor 30 GM / tijd t. scahekelen warm/koel @ 0uur
  8. 4.9.2 start koeling 21 deg / stop verwarming 15 deg stop bijverwarmen 0 deg / filtertijd 1uur
  9. 5.1.9 dT-koelen 5deg –> ??

20240908 11:00

Options (from Nibe (nibe.eu) (pdf (nibe.eu))) 20240908 12:00

  1. 4.9.2 Reduce ‘start koeling’ temp, 15 deg/18 deg, starts earlier, filter tijd 24h (to ensure we’re really in summer/winter before we start)
  2. 1.9.4 factor verwarming: 2.0 / koeling: 0.0
  3. 1.9.5 koeling koel/verw sensor: BT50 / inst pt-wrd koel/verw-sensor: 22 deg / verw. bij ondertemp. kamer 1.0 deg / koeling bij overtemp. kamer 1.0 deg / passieve koeling @ 10 GM / actieve koeling @ 60 GM / stapverschil compressor 30 GM / tijd t. scahekelen warm/koel @ 0uur

Options from GoT 20240909 14:30

  1. 1.9.5 Disable room sensor

Options 202409xx xx:xx

  1. Re-enable room sensor

Options 202410xx xx:xx

  1. Disable room sensor to optimize heating curve It seems

Tweaks & improvements

  1. Shield outdoor temp sensor
  2. Optimize curve
  1. Understanding control scheme

20241011:

20241105 22:30:

20241106 12:00:

20241107 16:00:

20241112 23:50

20241214 23:00

Tijdsvak | Koelcurve | Programma | Buitentemp | Binnentemp data | Binnentemp gevoel

20241112 23:50 | C1-2 | 11-17: -1 | 5-10deg | 21.1deg | Koel

Tweaks & improvements - waterzijdig inregelen

20241112 23:50

20241113 12:20

20241114 19:40

Separating hot water & heating energy - WIP

Not possible in InfluxDB (stackoverflow.com), as data are spread across two measurements.

SELECT state,energy FROM
  (select first(value) as energy from energyv3 where source='nibe' and uniqueid='compr_in_energy_43141' group by time(30m)),
  (select value as state from systemv3 where quantity='eb100_ep14_pca_base_relays_43514')
  WHERE time>now()-1d GROUP BY TIME(30m)

Post-processing in Python

# Approach one: grouped
energy = select first(energy) group by time(5m)

mask_hw = (pca == 15)
mask_heat = (pca == 7)

energy

Calibration of power usage

There are two sensors giving power:

  1. compr_in_power_43141: heat pump compressor power
  2. int_el_add_power_43084: resistive heater

Using a Riemann integration sensor in Home Assistant gives me energy use (ensure to use left integration, although trapezoidal should work better it doesn’t).

Automating / optimizing

There’s a few ways to optimize a heatpump

  1. Heating curves - determining heating circuit temperature depending on outside temperature
  2. Timing - optimize consumption by price / CO2 intensity / PV production
  3. Calibrating heating circuit (‘Waterzijdig inregelen’) - ensuring optimal heat transfer of the heating circuit

Optimizing curves

TODO

By electricity price / CO2 intensity

Rationale: consume depending on electricity price

  1. Use the built-in smart price adaptation (nibe.eu)
  2. Use hard-coded schedule based on average electricity prices over the day
  3. Use dynamic control via e.g. Home Assistant

By PV production

Rationale: consume depending on local electricity production. This is close to the above, except less at night and more during the day.

Sources

  1. NIBE L/W warmtepomp optimalisatie (tweakers.net)
  2. Haal meer uit je NIBE warmtepomp met nibepi (tweakers.net)
  3. COP berekening voor Nibe warmtepompen (tweakers.net) – NB this is not really COP, but COP scaled from specs based on working point.

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