Data for the Heat Pump System: Heating Season 2016-2017

I update the documentation of measurement data [PDF] about twice a year. This post is to provide a quick overview for the past season.

The PDF also contains the technical configuration and sizing data. Based on typical questions from an ‘international audience’ I add a summary here plus some ‘cultural’ context:

Building: The house is a renovated, nearly 100-year old building in Eastern Austria: a typical so-called ‘Streckhof’ – an elongated, former small farmhouse. Some details are mentioned here. Heating energy for space heating of two storeys (185m2) and hot water is about 17.000-20.000kWh per year. The roof / attic had been rebuilt in 2008, and the facade was thermally insulated. However, the major part of the house is without an underground level, so most energy is lost via ground. Heating only the ground floor (75m2) with the heat pump reduces heating energy only by 1/3.

Climate: This is the sunniest region of Austria – the lowlands of the Pannonian Plain bordering Hungary. We have Pannonian ‘continental’ climate with low precipitation. Normally, monthly average temperatures in winter are only slightly below 0°C in January, and weeks of ‘ice days’ in a row are very rare.

Heat energy distribution and storage (in the house): The renovated first floor has floor loops while at the ground floor mainly radiators are used. Wall heating has been installed in one room so far. A buffer tank is used for the heating water as this is a simple ‘on-off’ heat pump always operating at about its rated power. Domestic hot water is heated indirectly using a hygienic storage tank.

Heating system. An off-the-shelf, simple brine-water heat pump uses a combination of an unglazed solar-air collector and an underwater water tank as a heat source. Energy is mainly harvested from rather cold air via convection.

Addressing often asked questions: Off-the-shelf =  Same type of heat pump as used with geothermal systems. Simple: Not-smart, not trying to be the universal energy management system, as the smartness in our own control unit and logic for managing the heat source(s). Brine: A mixture of glycol and water (similar to the fluid used with flat solar thermal collectors) = antifreeze as the temperature of brine is below 0°C in winter. The tank is not a seasonal energy storage but a buffer for days or weeks. In this post hydraulics is described in detail, and typical operating conditions throughout a year. Both tank and collector are needed: The tank provides a buffer of latent energy during ‘ice periods’ and it allows to harvest more energy from air, but the collector actually provides for about 75% of the total ambient energy the heat pump needs in a season.

Tank and collector are rather generously sized in relation to the heating demands: about 25m3 volume of water (total volume +10% freezing reserve) and 24m2 collector area.

The overall history of data documented in the PDF also reflects ongoing changes and some experiments, like heating the first floor with a wood stove, toggling the effective area of the collector used between 50% and 100%, or switching off the collector to simulate a harsher winter.

Data for the past season

Finally we could create a giant ice cube naturally. 14m3 of ice had been created in the coldest January since 30 years. The monthly average temperature was -3,6°C, 3 degrees below the long-term average.

(Re the oscillations of the ice volume are see here and here.)

We heated only the ground floor in this season and needed 16.600 kWh (incl. hot water) – about the same heating energy as in the previous season. On the other hand, we also used only half of the collector – 12m2. The heating water inlet temperatures for radiators was even 37°C in January.

For the first time the monthly performance factor was well below 4. The performance factor is the ratio of output heating energy and input electrical energy for heat pump and brine pump. In middle Europe we measure both energies in kWh 😉 The overall seasonal performance factor was 4,3.

The monthly performance factor is a bit lower again in summer, when only hot water is heated (and thus the heat pump’s COP is lower because of the higher target temperature).

Per day we needed about 100kWh of heating energy in January, while the collector could not harvest that much:

In contrast to the season of the Ice Storage Challenge, also the month before the ‘challenge’ (Dec. 2016) was not too collector-friendly. But when the ice melted again, we saw the usual large energy harvests. Overall, the collector could contribute not the full ‘typical’ 75% of ambient energy this season.

(Definitions, sign conventions explained here.)

But there was one positive record, too. In a hot summer of 2017 we consumed the highest cooling energy so far – about 600kWh. The floor loops are used for passive cooling; the heating buffer tank is used to transfer heat from the floor loops to the cold underground tank. In ‘colder’ summer nights the collector is in turn used to cool the tank, and every time hot tap water is heated up the tank is cooled, too.

Of course the available cooling power is just a small fraction of what an AC system for the theoretical cooling load would provide for. However, this moderate cooling is just what – for me – makes the difference between unbearable and OK on really hot days with more than 35°C peak ambient temperature.

Mr. Bubble Was Confused. A Cliffhanger.

This year we experienced a record-breaking January in Austria – the coldest since 30 years. Our heat pump system produced 14m3 of ice in the underground tank.

The volume of ice is measured by Mr. Bubble, the winner of The Ultimate Level Sensor Casting Show run by the Chief Engineer last year:

The classic, analog level sensor was very robust and simple, but required continuous human intervention:

Level sensor: The old way

So a multitude of prototypes had been evaluated …

Level sensors: The precursors

The challenge was to measure small changes in level as 1 mm corresponds to about 0,15 m3 of ice.

Mr. Bubble uses a flow of bubbling air in a tube; the measured pressure increases linearly with the distance of the liquid level from the nozzle:

blubber-messrohr-3

Mr. Bubble is fine and sane, as long as ice is growing monotonously: Ice grows from the heat exchanger tubes into the water, and the heat exchanger does not float due to buoyancy, as it is attached to the supporting construction. The design makes sure that not-yet-frozen water can always ‘escape’ to higher levels to make room for growing ice. Finally Mr. Bubble lives inside a hollow cylinder of water inside a block of ice. As long as all the ice is covered by water, Mr. Bubble’s calculation is correct.

But when ambient temperature rises and the collector harvests more energy then needed by the heat pump, melting starts at the heat exchanger tubes. The density of ice is smaller than that of water, so the water level in Mr. Bubble’s hollow cylinder is below the surface level of ice:

Mr. Bubble is utterly confused and literally driven over the edge – having to deal with this cliff of ice:

When ice is melted, the surface level inside the hollow cylinder drops quickly as the diameter of the cylinder is much smaller than the width of the tank. So the alleged volume of ice perceived by Mr. Bubble seems to drop extremely fast and out of proportion: 1m3 of ice is equivalent to 93kWh of energy – the energy our heat pump would need on an extremely cold day. On an ice melting day, the heat pump needs much less, so a drop of more than 1m3 per day is an artefact.

As long as there are ice castles on the surface, Mr. Bubble keeps underestimating the volume of ice. When it gets colder, ice grows again, and its growth is then overestimated via the same effect. Mr. Bubble amplifies the oscillations in growing and shrinking of ice.

In the final stages of melting a slab-with-a-hole-like structure ‘mounted’ above the water surface remains. The actual level of water is lower than it was before the ice period. This is reflected in the raw data – the distance measured. The volume of ice output is calibrated not to show negative values, but the underlying measurement data do:

Only when finally all ice has been melted – slowly and via thermal contact with air – then the water level is back to normal.

In the final stages of melting parts of the suspended slab of ice may break off and then floating small icebergs can confuse Mr. Bubble, too:

So how can we picture the true evolution of ice during melting? I am simulating the volume of ice, based on our measurements of air temperature. To be detailed in a future post – this is my cliffhanger!

>> Next episode.

Where to Find What?

I have confessed on this blog that I have Mr. Monk DVDs for a reason. We like to categorize, tag, painstakingly re-organize, and re-use. This is reflected in our Innovations in Agriculture …

The Seedbank: Left-over squared timber met the chopsaw.

The Nursery: Rebirth of copper tubes and newspapers.

… as well as in my periodical Raking The Virtual Zen Garden: Updating collections of web resources, especially those related to the heat pump system.

Here is a list of lists, sorted by increasing order of compactification:

But thanks to algorithms, we get helpful advice on presentation from social media platforms: Facebook, for example, encouraged me to tag products in the following photo, so here we go:

“Hand-crafted, artisanal, mobile nursery from recycled metal and wood, for holding biodegradable nursery pots.” Produced without crowd-funding and not submitted to contests concerned with The Intersection of Science, Art, and Innovation.

Earth, Air, Water, and Ice.

In my attempts at Ice Storage Heat Source popularization I have been facing one big challenge: How can you – succinctly, using pictures – answer questions like:

How much energy does the collector harvest?

or

What’s the contribution of ground?

or

Why do you need a collector if the monthly performance factor just drops a bit when you turned it off during the Ice Storage Challenge?

The short answer is that the collector (if properly sized in relation to tank and heat pump) provides for about 75% of the ambient energy needed by the heat pump in an average year. Before the ‘Challenge’ in 2015 performance did not drop because the energy in the tank had been filled up to the brim by the collector before. So the collector is not a nice add-on but an essential part of the heat source. The tank is needed to buffer energy for colder periods; otherwise the system would operate like an air heat pump without any storage.

I am calling Data Kraken for help to give me more diagrams.

There are two kinds of energy balances:

1) From the volume of ice and tank temperature the energy still stored in the tank can be calculated. Our tank ‘contains’ about 2.300 kWh of energy when ‘full’. Stored energy changes …

  • … because energy is extracted from the tank or released to it via the heat exchanger pipes traversing it.
  • … and because heat is exchanged with the surrounding ground through the walls and the floor of the tank.

Thus the contribution of ground can be determined by:

Change of stored energy(Ice, Water) =
Energy over ribbed pipe heat exchanger + Energy exchanged with ground

2) On the other hand, three heat exchangers are serially connected in the brine circuit: The heat pump’s evaporator, the solar air collector, and the heat exchanger in the tank. .

Both of these energy balances are shown in this diagram (The direction of arrows indicates energy > 0):

Energy sources, transfer, storage - sign conventions

The heat pump is using a combined heat source, made up of tank and collector, so …

Ambient Energy for Heat Pump = -(Collector Energy) + Tank Energy

The following diagrams show data for the season containing the Ice Storage Challenge:

Season 2014 - 2015: Monthly Energy Balances: Energy Sources, Transfer, Storage

From September to January more and more ambient energy is needed – but also the contribution of the collector increases! The longer the collector is on in parallel with the heat pump, the more energy can be harvested from air (as the temperature difference between air and brine is increased).

As long as there is no ice the temperature of the tank and the brine inlet temperature follow air temperature approximately. But if air temperature drops quickly (e.g. at the end of November 2014), the tank is still rather warm in relation to air and the collector cannot harvest much. Then the energy stored in the tank drops and energy starts to flow from ground to the tank.

2014-09-01 - 2015-05-15: Temperatures and ice formation

2014-09-01 - 2015-05-15: Daily Energy Balances: Energy Sources, Transfer, Storage

On Jan 10 an anomalous peak in collector energy is visible: Warm winter storm Felix gave us a record harvest exceeding the energy needed by the heat pump! In addition to high ambient temperatures and convection (wind) the tank temperature remained low while energy was used for melting ice.

On February 1, we turned off the collector – and now the stored energy started to decline. Since the collector energy in February is zero, the energy transferred via the heat exchanger is equal to the ambient energy used by the heat pump. Ground provided for about 1/3 of the ambient energy. Near the end of the Ice Storage Challenge (mid of March) the contribution of ground was increasing while the contribution of latent energy became smaller and smaller: Ice hardly grew anymore, allegedly after the ice cube has ‘touched ground’.

Mid of March the collector was turned on again: Again (as during the Felix episode) harvest is high because the tank remains at 0°C. The energy stored in the tank is replenished quickly. Heat transfer with ground is rather small, and thus the heat exchanger energy is about equal to the change in energy stored.

At the beginning of May, we switched to summer mode: The collector is turned off (by the control system) to keep tank temperature at 8°C as long as possible. This temperature is a trade-off between optimizing heat pump performance and keeping some energy for passive cooling. The energy available for cooling is reduced by the slow flow of heat from ground to the tank.

My Data Kraken – a Shapeshifter

I wonder if Data Kraken is only used by German speakers who translate our hackneyed Datenkrake – is it a word like eigenvector?

Anyway, I need this animal metaphor, despite this post is not about facebook or Google. It’s about my personal Data Kraken – which is a true shapeshifter like all octopuses are:

(… because they are spineless, but I don’t want to over-interpret the metaphor…)

Data Kraken’s shapeability is a blessing, given ongoing challenges:

When the Chief Engineer is fighting with other intimidating life-forms in our habitat, he focuses on survival first and foremost … and sometimes he forgets to inform the Chief Science Officer about fundamental changes to our landscape of sensors. Then Data Kraken has to be trained again to learn how to detect if the heat pump is on or off in a specific timeslot. Use the signal sent from control to the heat pump? Or to the brine pump? Or better use brine flow and temperature difference?

It might seem like a dull and tedious exercise to calculate ‘averages’ and other performance indicators that require only very simple arithmetics. But with the exception of room or ambient temperature most of the ‘averages’ just make sense if some condition is met, like: The heating water inlet temperature should only be calculated when the heating circuit pump is on. But the temperature of the cold water, when the same floor loops are used for cooling in summer, should not be included in this average of ‘heating water temperature’. Above all, false sensor readings, like 0, NULL or any value (like 999) a vendor chooses to indicate as an error, have to be excluded. And sometimes I rediscover eternal truths like the ratio of averages not being equal to the average of ratios.

The Chief Engineer is tinkering with new sensors all the time: In parallel to using the old & robust analog sensor for measuring the water level in the tank…

Level sensor: The old way

… a multitude of level sensors was evaluated …

Level sensors: The precursors

… until finally Mr. Bubble won the casting …

blubber-messrohr-3

… and the surface level is now measured via the pressure increasing linearly with depth. For the Big Data Department this means to add some new fields to the Kraken database, calculate new averages … and to smoothly transition from the volume of ice calculated from ruler readings to the new values.

Change is the only constant in the universe, paraphrasing Heraclitus [*]. Sensors morph in purpose: The heating circuit, formerly known (to the control unit) as the radiator circuit became a new wall heating circuit, and the radiator circuit was virtually reborn as a new circuit.

I am also guilty of adding new tentacles all the time, too, herding a zoo of meters added in 2015, each of them adding a new log file, containing data taken at different points of time in different intervals. This year I let Kraken put tentacles into the heat pump:

Data Kraken: Tentacles in the heat pump!

But the most challenging data source to integrate is the most unassuming source of logging data: The small list of the data that The Chief Engineer had recorded manually until recently (until the advent of Miss Pi CAN Sniffer and Mr Bubble). Reason: He had refused to take data at exactly 00:00:00 every single day, so learned things I never wanted to know about SQL programming languages to deal with the odd time intervals.

To be fair, the Chief Engineer has been dedicated at data recording! He never shunned true challenges, like a legendary white-out in our garden, at the time when measuring ground temperatures was not automated yet:

The challenge

White Out

Long-term readers of this blog know that ‘elkement’ stands for a combination of nerd and luddite, so I try to merge a dinosaur scripting approach with real-world global AI Data Krakens’ wildest dream: I wrote scripts that create scripts that create scripts [[[…]]] that were based on a small proto-Kraken – a nice-to-use documentation database containing the history of sensors and calculations.

The mutated Kraken is able to eat all kinds of log files, including clients’ ones, and above all, it can be cloned easily.

I’ve added all the images and anecdotes to justify why an unpretentious user interface like the following is my true Christmas present to myself – ‘easily clickable’ calculated performance data for days, months, years, and heating seasons.

Data Kraken: UI

… and diagrams that can be changed automatically, by selecting interesting parameters and time frames:

Excel for visualization of measurement data

The major overhaul of Data Kraken turned out to be prescient as a seemingly innocuous firmware upgrade just changed not only log file naming conventions and publication scheduled but also shuffled all the fields in log files. My Data Kraken has to be capable to rebuild the SQL database from scratch, based on a documentation of those ever changing fields and the raw log files.

_________________________________

[*] It was hard to find the true original quote for that, as the internet is cluttered with change management coaches using that quote, and Heraclitus speaks to us only through secondary sources. But anyway, what this philosophy website says about Heraclitus applies very well to my Data Kraken:

The exact interpretation of these doctrines is controversial, as is the inference often drawn from this theory that in the world as Heraclitus conceives it contradictory propositions must be true.

In my world, I also need to deal with intriguing ambiguity!

Same Procedure as Every Autumn: New Data for the Heat Pump System

October – time for updating documentation of the heat pump system again! Consolidated data are available in this PDF document.

In the last season there were no special experiments – like last year’s Ice Storage Challenge or using the wood stove. Winter was rather mild, so we needed only ~16.700kWh for space heating plus hot water heating. In the coldest season so far – 2012/13 – the equivalent energy value was ~19.700kWh. The house is located in Eastern Austria, has been built in the 1920s, and has 185m2 floor space since the last major renovation.

(More cross-cultural info:  I use thousands dots and decimal commas).

The seasonal performance factor was about 4,6 [kWh/kWh] – thus the electrical input energy was about 16.700kWh / 4,6 ~ 3.600kWh.

Note: Hot water heating is included and we use flat radiators requiring a higher water supply temperature than the floor heating loops in the new part of the house.

Heating season 2015/2016: Performance data for the 'ice-storage-/solar-powered' heat pump system

Red: Heating energy ‘produced’ by the heat pump – for space heating and hot water heating. Yellow: Electrical input energy. Green: Performance Factor = Ratio of these energies.

The difference of 16.700kWh – 3.600kWh = 13.100kWh was provided by ambient energy, extracted from our heat source – a combination of underground water/ice tank and an unglazed ribbed pipe solar/air collector.

The solar/air collector has delivered the greater part of the ambient energy, about 10.500kWh:

Heating season 2015/2016: Energy harvested from air by the collector versus heating-energy

Energy needed for heating per day (heat pump output) versus energy from the solar/air collector – the main part of the heat pump’s input energy. Negative collector energies indicate passive cooling periods in summer.

Peak Ice was 7 cubic meters, after one cold spell of weather in January:

Heating season 2015/2016: Temperature of ambient air, water tank (heat source) and volume of water frozen in the tank.

Ice is formed in the water tank when the energy from the collector is not sufficient to power the heat pump alone, when ambient air temperatures are close to 0°C.

Last autumn’s analysis on economics is still valid: Natural gas is three times as cheap as electricity but with a performance factor well above three heating costs with this system are lower than they would be with a gas boiler.

Is there anything that changed gradually during all these years and which does not primarily depend on climate? We reduced energy for hot tap water heating – having tweaked water heating schedule gradually: Water is heated up once per day and as late as possible, to avoid cooling off the hot storage tank during the night.

We have now started the fifth heating season. This marks also the fifth anniversary of the day we switched on the first ‘test’ version 1.0 of the system, one year before version 2.0.

It’s been about seven years since first numerical simulations, four years since I have been asked if I was serious in trading in IT security for heat pumps, and one year since I tweeted:

Hacking My Heat Pump – Part 2: Logging Energy Values

In the last post, I showed how to use Raspberry Pi as CAN bus logger – using a test bus connected to control unit UVR1611. Now I have connected it to my heat pump’s bus.

Credits for software and instructions:

Special thanks to SK Pang Electronics who provided me with CAN boards for Raspberry Pi after having read my previous post!!

CAN boards for Raspberry Pi, by SK Pang

CAN extension boards for Raspberry Pi, by SK Pang. Left: PiCAN 2 board (40 GPIO pins), right: smaller, retired PiCAN board with 26 GPIO pins – the latter fits my older Pi. In contrast to the board I used in the first tests, these have also a serial (DB9) interface.

Wiring CAN bus

We use a Stiebel-Eltron WPF 7 basic heat pump installed in 2012. The English website now refers to model WPF 7 basic s.

The CAN bus connections described in the German manual (Section 12.2.3) and the English manual (Wiring diagram, p.25) are similar:

Stiebel-Eltron WPF 7 basic - CAN bus connections shown in German manual

CAN bus connections inside WPF 7 basic heat pump. For reference, see the description of the Physical Layer of the CAN protocol. Usage of the power supply (BUS +) is optional.

H, L and GROUND wires from the Pi’s CAN board are connected to the respective terminals inside the heat pump. I don’t use the optional power supply as the CAN board is powered by Raspberry Pi, and I don’t terminate the bus correctly with 120 Ω. As with the test bus, wires are rather short and thus have low resistance.

Stiebel-Eltron WPF 7 basic - CAN bus connections inside the heat pump, cable from Raspberry Pi connected.

Heat pump with cover removed – CAN High (H – red), Low (L – blue), and Ground (yellow) are connected. The CAN cable is a few meters long and connects to the Raspberry Pi CAN board.

In the first tests Raspberry Pi had the privilege to overlook the heat pump room as the top of the buffer tank was the only spot the WLAN signal was strong enough …

Raspberry Pi, on top of the buffer tank

Typical, temporary nerd’s test setup.

… or I used a cross-over ethernet cable and a special office desk:

Working on the heat pump - Raspberry Pi adventures

Typical, temporary nerd’s workplace.

Now Raspberry Pi has its final position on the ‘organic controller board’, next to control unit UVR16x2 – and after a major upgrade to both LAN and WLAN all connections are reliable.

Raspberry Pi with PiCAN board from SK Pang and UVR16x2

Raspberry Pi with PiCAN board from SK Pang and UVR16x2 control unit from Technische Alternative (each connected to a different CAN bus).

Bringing up the interface

According to messpunkt.org the bit rate of Stiebel-Eltron’s bus is 20000 bit/s; so the interface is activated with:

sudo ip link set can0 type can bitrate 20000
sudo ifconfig can0 up

Watching the idle bus

First I was simply watching with sniffer Wireshark if the heat pump says anything without being triggered. It does not – only once every few minutes there are two packets. So I need to learn to talk to it.

Learning about CAN communications

SK Pang provides an example of requesting data using open source tool cansend: The so-called CAN ID is followed by # and the actual data. This CAN ID refers to an ‘object’ – a set of properties of the device, like the set of inputs or outputs – and it can contain also the node ID of the device on the bus. There are many CAN tutorials on the net, I found this (German) introduction and this English tutorial very useful.

I was able to follow the communications of the two nodes in my test bus as I knew their node numbers and what to expect – the data logger would ask the controller for a set of configured sensor outputs every minute. Most packets sent by either bus member are related to object 480, indicating the transmission of a set of values (Process Data Exchange Objects, PDOs. More details on UVR’s CAN communication, in German)

Network trace on test CAN bus: UVR1611 and BL-NET

Sniffing test CAN bus – communication of UVR1611 (node no 1) and logger BL-NET (node number 62 = be). Both devices use an ID related to object ID 480 plus their respective node number, as described here.

So I need to know object ID(s) and properly formed data values to ask the heat pump for energy readings – without breaking something by changing values.

Collecting interesting heat pump parameters for monitoring

I am very grateful for Jürg’s CAN tool can_scan that allow for querying a Stiebel-Eltron heat pump for specific values and also for learning about all possible parameters (listed in so-called Elster tables).

In order to check the list of allowed CAN IDs used by the heat pump I run:

./can_scan can0 680

can0 is the (default) name of the interface created earlier and 680 is my (the sender’s) CAN ID, one of the IDs allowed by can_scan.

Start of output:

elster-kromschroeder can-bus address scanner and test utility
copyright (c) 2014 Jürg Müller, CH-5524

scan on CAN-id: 680
list of valid can id's:

  000 (8000 = 325-07)
  180 (8000 = 325-07)
  301 (8000 = 325-07)
  480 (8000 = 325-07)
  601 (8000 = 325-07)

In order to investigate available values and their meaning I run can_scan for each of these IDs:

./can_scan can0 680 180

Embedded below is part of the output, containing some of the values (and /* Comments */). This list of parameters is much longer than the list of values available via the display on the heat pump!

I am mainly interested in metered energies and current temperatures of the heat source (brine) and the ‘environment’ – to compare these values to other sensors’ output:

elster-kromschroeder can-bus address scanner and test utility
copyright (c) 2014 Jürg Müller, CH-5524

0001:  0000  (FEHLERMELDUNG  0)
0003:  019a  (SPEICHERSOLLTEMP  41.0)
0005:  00f0  (RAUMSOLLTEMP_I  24.0)
0006:  00c8  (RAUMSOLLTEMP_II  20.0)
0007:  00c8  (RAUMSOLLTEMP_III  20.0)
0008:  00a0  (RAUMSOLLTEMP_NACHT  16.0)
0009:  3a0e  (UHRZEIT  14:58)
000a:  1208  (DATUM  18.08.)
000c:  00e9  (AUSSENTEMP  23.3) /* Ambient temperature */
000d:  ffe6  (SAMMLERISTTEMP  -2.6)
000e:  fe70  (SPEICHERISTTEMP  -40.0)
0010:  0050  (GERAETEKONFIGURATION  80)
0013:  01e0  (EINSTELL_SPEICHERSOLLTEMP  48.0)
0016:  0140  (RUECKLAUFISTTEMP  32.0) /* Heating water return temperature */
...
01d4:  00e2  (QUELLE_IST  22.6) /* Source (brine) temperature */
...
/* Hot tap water heating energy MWh + kWh */
/* Daily totaly */   
092a:  030d  (WAERMEERTRAG_WW_TAG_WH  781)
092b:  0000  (WAERMEERTRAG_WW_TAG_KWH  0)
/* Total energy since system startup */
092c:  0155  (WAERMEERTRAG_WW_SUM_KWH  341)
092d:  001a  (WAERMEERTRAG_WW_SUM_MWH  26)
/* Space heating energy, MWh + kWh */
/* Daily totals */
092e:  02db  (WAERMEERTRAG_HEIZ_TAG_WH  731)
092f:  0006  (WAERMEERTRAG_HEIZ_TAG_KWH  6)
/* Total energy since system startup */
0930:  0073  (WAERMEERTRAG_HEIZ_SUM_KWH  115)
0931:  0027  (WAERMEERTRAG_HEIZ_SUM_MWH  39)

Querying for one value

The the heating energy to date in MWh corresponds to index 0931:

./can_scan can0 680 180.0931

The output of can_scan already contains the sum of the MWh (0931) and kWh (0930) values:

elster-kromschroeder can-bus address scanner and test utility
copyright (c) 2014 Jürg Müller, CH-5524

value: 0027  (WAERMEERTRAG_HEIZ_SUM_MWH  39.115)

The network trace shows that the logger (using ID 680) queries for two values related to ID 180 – the kWh and the MWh part:

Network trace on heat pump's CAN bus: Querying for space heating energy to date.

Network trace of Raspberry Pi CAN logger (ID 680) querying CAN ID 180. Since the returned MWh value is the sum of MWh and kWh value, two queries are needed. Detailed interpretation of packets in the text below.

Interpretation of these four packets – as explained on Jürg’s website here and here in German:

00 00 06 80 05 00 00 00 31 00 fa 09 31  
00 00 01 80 07 00 00 00 d2 00 fa 09 31 00 27
00 00 06 80 05 00 00 00 31 00 fa 09 30 
00 00 01 80 07 00 00 00 d2 00 fa 09 30 00 73
|---------| ||          |---| || |---| |---|
1)          2)          3)    4) 5)    6)

1) CAN-ID used by the sender: 180 or 680 
2) No of bytes of data - 5 for queries, 8 for replies
3) CAN ID of the communications partner and type of message. 
For queries the second digit is 1. 
Pattern: n1 0m with n = 180 / 80 = 3 (hex) and m = 180 mod 7 = 0 
(hex) Partner ID = 30 * 8 (hex) + 00 = 180 
Responses follow a similar pattern using second digit 2: 
Partner ID is: d0 * 8 + 00 = 680 
4) fa indicates that the Elster index no is greater equal ff. 
5) Index (parameter) queried for: 0930 for kWh and 0931 for MWh
6) Value returned 27h=39,73h=115

I am not sure which node IDs my logger and the heat pump use as the IDs. 180 seems to be an object ID without node ID added while 301 would refer to object ID + node ID 1. But I suppose with two devices on the bus only, and one being only a listener, there is no ambiguity.

Logging script

I found all interesting indices listed under CAN ID 180; so am now looping through this set once every three minutes with can_scan, cut out the number, and add it to a new line in a text log file. The CAN interfaces is (re-)started every time in case something happens, and the file is sent to my local server via FTP.

Every month a new log file is started, and log files – to be imported into my SQL Server  and processed as log files from UVR1611 / UVR16x2, the PV generator’s inverter, or the smart meter.

(Not the most elegant script – consider it a ‘proof of concept’! Another option is to trigger the sending of data with can_scan and collect output via can_logger.)

Interesting to-be-logged parameters are added to a ‘table’ – a file called indices:

0016 RUECKLAUFISTTEMP
01d4 QUELLE_IST
01d6 WPVORLAUFIST
091b EL_AUFNAHMELEISTUNG_WW_TAG_KWH
091d EL_AUFNAHMELEISTUNG_WW_SUM_MWH
091f EL_AUFNAHMELEISTUNG_HEIZ_TAG_KWH
0921 EL_AUFNAHMELEISTUNG_HEIZ_SUM_MWH
092b WAERMEERTRAG_WW_TAG_KWH
092f WAERMEERTRAG_HEIZ_TAG_KWH
092d WAERMEERTRAG_WW_SUM_MWH
0931 WAERMEERTRAG_HEIZ_SUM_MWH
000c AUSSENTEMP
0923 WAERMEERTRAG_2WE_WW_TAG_KWH
0925 WAERMEERTRAG_2WE_WW_SUM_MWH
0927 WAERMEERTRAG_2WE_HEIZ_TAG_KWH
0929 WAERMEERTRAG_2WE_HEIZ_SUM_MWH

Script:

# Define folders
logdir="/CAN_LOGS"
scriptsdir="/CAN_SCRIPTS"
indexfile="$scriptsdir/indices"

# FTP parameters
ftphost="FTP_SERVER"
ftpuser="FTP_USER"
ftppw="***********"

# Exit if scripts not found
if ! [ -d $scriptsdir ] 
then
    echo Directory $scriptsdir does not exist!
    exit 1
fi

# Create log dir if it does not exist yet
if ! [ -d $logdir ] 
then
    mkdir $logdir
fi

sleep 5

echo ======================================================================

# Start logging
while [ 0 -le 1 ]
do

# Get current date and start new logging line
now=$(date +'%Y-%m-%d;%H:%M:%S')
line=$now
year=$(date +'%Y')
month=$(date +'%m')
logfile=$year-$month-can-log-wpf7.csv
logfilepath=$logdir/$logfile

# Create a new file for every month, write header line
# Create a new file for every month
if ! [ -f $logfilepath ] 
then
    headers="Datum Uhrzeit"
    while read indexline
    do 
        header=$(echo $indexline | cut -d" " -f2) 
        headers+=";"$header
    done < $indexfile ; echo "$headers" > $logfilepath 
fi

# (Re-)start CAN interface
    sudo ip link set can0 type can bitrate 20000
    sudo ip link set can0 up

# Loop through interesting Elster indices
while read indexline
do 
    # Get output of can_scan for this index, search for line with output values
    index=$(echo $indexline | cut -d" " -f1)
    value=$($scriptsdir/./can_scan can0 680 180.$index | grep "value" | replace ")" "" | grep -o "\<[0-9]*\.\?[0-9]*$" | replace "." ",")     
    echo "$index $value"     

    # Append value to line of CSV file     
    line="$line;$value" 
done < $indexfile ; echo $line >> $logfilepath

# echo FTP log file to server
ftp -n -v $ftphost << END_SCRIPT
ascii
user $ftpuser $ftppw
binary
cd RPi
ls
lcd $logdir
put $logfile
ls
bye
END_SCRIPT

echo "------------------------------------------------------------------"

# Wait - next logging data point
sleep 180

# Runs forever, use Ctrl+C to stop
done

In order to autostart the script I added a line to the rc.local file:

su pi -c '/CAN_SCRIPTS/pkt_can_monitor'

Using the logged values

In contrast to brine or water temperature heating energies are not available on the heat pump’s CAN bus in real-time: The main MWh counter is only incremented once per day at midnight. Then the daily kWh counter is added to the previous value.

Daily or monthly energy increments are calculated from the logged values in the SQL database and for example used to determine performance factors (heating energy over electrical energy) shown in our documentation of measurement data for the heat pump system.