Back in February I previewed a new online tool to help consumers choose an appropriate smart tariff in Making smart choices – smart tariff smart comparison. There are numerous price comparison sites that work for standard energy tariffs, but smart tariffs are generally excluded from such sites. Price comparison sites typically ask for a token meter read or guess consumption based on typical bills for similar homes, but this online tool takes actual half hourly consumption from your own electricity meter so its analysis is very sophisticated in comparison. As a research tool the tariff names are anonymised, although with a little thought they can be decoded.
The above graph shows my prior Octopus Agile tariff being very competitive for an extended period but then losing top spot in January 2021 to an Octopus Go Faster tariff. Subsequently I’ve moved to a related tariff called Octopus Go (without the ‘faster’). The Go tariff offers four hours of electricity overnight at 5 p/kWh for a fixed period while the ‘faster’ derivative offers 5 hours at a slightly higher price and with choice of the discounted hours.
The newer graph above shows a widening gap over the last three months with the Go tariff being increasingly advantageous as rising wholesale prices force Agile pricing higher and higher. Since Agile is linked to the daily wholesale markets the price can rise (and drop) very quickly. Go on the other hand is not just cheaper but fixed for a year. July’s analysis shows Go being less than a third of price of Agile for my usage.
Go is ideally suited for EV charging. You could also get a sign-on bonus of £50 by clicking this link to move to any Octopus tariff.
I just made a change to the way my smart central heating controls works.
Previously I had four modes:
(Enable for) Summer
(Disable for) Winter
(Going on) Vacation
Back from Vacation
These modes were the defaults from the Eve Thermo Electronic Thermostatic Radiator Valves (eTRVs). However I’ve thought for some time that there was some ambiguity around what mode the system went into when Back From Vacation was selected (Winter or Summer?) and that it would be more straightforward to have 3 modes as follows:
Temperature set Point
Proposed new smart heating control Modes
I think that this new arrangement is much more intuitive with the user just selecting which Mode they want to enter at the end of a vacation without the ambiguity of selecting Back from Vacation and then quickly following up by selecting Summer or (most likely) Winter.
So here’s the question – is it better to heat the home continuously or to only heat it when you need it? You may think that’s obvious as I did but a recent debate elsewhere suggests that there’s a variety of opinion out there, so let’s explore the relavent issues:
Heat loss from the building
The efficiency of the heating appliance (boiler, heat pump etc)
The degree to which the building acts as a thermal store.
Heat Loss from the Building
Whether we consider walls, floors, roofs or windows everything around the boundary of a building contributes to heat loss. However well insulated there will be a flow of heat through the structure (let’s assume brick for the moment). Heat will travel from hot places (usually inside the building) to cold places (usually outside the building). Insulation can help reduce the flow but the flow still makes place. One key way to reduce the flow is to reduce the internal temperature so, for example, if the outside temperature is 10 C and you reduce the internal temperature from 20 C to 19 C then you’d expect to reduce the loss by 10% – was driven by a 10 C temperature difference now driven by a 9 C temperature different which is 10% less than 10 C.
I’m an advocate of switching heating off when not needed – typically overnight when in bed asleep or during the day when out at work. Turning the heating off obviously immediately cuts the energy use, the temperature of the house then starts to drop as the heat flows out through the walls and other components of the boundary, the energy flow gradually reduces as the internal temperature drops towards the external temperature, and eventually the flow stops if the internal temperature reaches the external temperature.
The question is then how much heat energy does it take to get the temperature back up to a comfortable level and potentially are there issues with efficiency of the heating appliance.
Efficiency of the Heating Appliance
Any heating device will have an efficiency at which it coverts energy in the fuel to useful heat output. Typically these are less than 100% as some of the fuel energy ends up as waste energy rather than useful heat. The exception to this is a group of electrical heating devices known as heat pumps. Heat pumps as their name suggests pump heat from one place to another – typical from the outside air or under the ground – so as they heat the house they also cool the source and thus their efficiency is more than 100% since much of the heat output comes from the source being cooled and not from the electricity supply. However I’m going to consider a condensing gas boiler as one of the most common domestic heat sources in the UK.
A condensing gas boiler is more efficient than prior generations of gas boiler as it uses waste heat in the flue gas to preheat the water before it gets heated in the conventional main boiler. To get the preheating to work efficiently then the flue gas has to be hotter than the return water.
Typically the user sets the temperature if the hot water generated by the boiler (which may vary seasonally) – the flow temperature. The hot water is then pumped round through the radiators causing the radiators to heat the rooms and the water temperature to drop, the return water is preheated by the flue gasses and then heated in the boiler itself before going round again. Best efficiency is obtained by maximizing the extraction of heat from the flue gasses, which typically requires the following conditions:
The flow temperature is below 70 C
The return temperature is below 55 C
The difference between the above is around 20 C
There are arguably two typical uses of the boiler for space heating: (i) relatively low demand to maintain the current temperature and (ii) relatively high demand to get the building to the required temperature. The latter would be expected to push the return temperature lowest and thus most likely to condense, so if the size of the load makes a difference at all then you’d expect high load to be more efficient. That’s good news for those who tend to turn their heating off at times, as the resulting peak loads shouldn’t have lower efficiency than continuous use.
The thermal mass of the building is its ability to store heat. Storing heat alone over time doesn’t really add to the energy requirements to heat a home, but it can make a home slower to cool down or faster to heat. You might imagine a stone cottage for example having a relatively high thermal mass. On the other hand a modern house have a stud walls internally (and even externally if it’s a wood-framed home) which have very little thermal mass. At the other extreme I’ve seen experimental homes with so much mass (such as earth banks) that it can take over a year to warm up.
The combination of the amount of thermal mass and the amount of insulation will determine the time constant associated with heating and cooling or how long it takes for the structure to warm up.
In extreme, if your thermal mass is enormous, then you may be able to heat your thermal mass enough in summer to heat the house in winter but you’d need to actively heat the thermal mass in summer – such as by diverting the output from solar panels – just having a big thermal mass without actively heating it would leave the mass at an average annual temperature likely below what is comfortable in winter.
So where does that leave me with the question of turning off my heating when not required? Well, it would seem that (i) a reduced internal temperature would reduce heat losses while the heating is off which should mean less heat input to get the air temperature back later and (ii) running the boiler at relatively high load to get the temperature back isn’t an efficiency issue as high demand promotes a low return temperature.
What about experimentally?
The above graphic shows a very typical winters day with the home occupied. The blue upright bars are half-hourly gas consumption readings from my smart meter. The blue bars show six hours of no heating overnight, an hour and a half of relatively high demand to heat the house up to temperature, and then lower demand (generally to maintain the temperature) through the day. The orange dashed line shows the typical level of heat input required to maintain the temperature – 2 kWh/half-hour. The green box shows the period overnight with no heating. If the home had been heated during this time that would have been 6 hours of heating at 2 kWh/half-hour = 24 kWh of additional heat input. The red box shows the actual heat input required to reestablish the comfort temperature – 2 kWh + 6 kWh + 4 kWh in each half hour respectively = 12 kWh. Over all I saved around 24 kWh – 12 kWh = 12 kWh overnight and more if the heating had been off during the day too. Since the data is taken from the incoming gas supply then the above savings include the effects of any variation in boiler efficiency between low heat demand to maintain temperature and high heat demand to establish temperature.
It thus seems that turning off the heating when not required saves energy compared to running the heating at a constant temperature both theoretically and experimentally.
We will shortly have been in this house for six years. During that time I have created three smart control systems that improve my energy costs or efficiency:
heating controls to minimise gas purchase
self-consumption controls of the electricity generated by my solar panels to maximise value of self-consumption
smart tariff controls to buy grid electricity at the lowest price
Most homes have a single heating zone with one timer and potentially one thermostat controlling the whole house with perhaps some thermostatic radiator valves (TRVs) capping the temperature in specific rooms.
By contrast we have seven heating zones created by electronic temperature control valves (eTRVs). Each zone has its own timer. There is no central timer or thermostat. Each eTRV can summon the boiler on when cold rather than simply cap the maximum temperature like a TRV.
Some rooms also have links to other smart devices such as disabling room heating when the window is open or turning off heating early when there’s no movement in the room.
The intent is to save energy by only heating rooms that are in use.
These controls manage the diversion of any excess output from my solar panels rather than give that energy to the grid. The loads are prioritised as follows:
Powervault storage battery (fully proportional)
Car charger (stepped proportional driven by ImmerSUN relay output)
Hot water (driven by ImmerSUN fully proportional output)
Last year these controls helped me to use over 90% of the output of my solar panels avoiding buying £100s of electricity and gas. The priorities are set to maximise value – #1 avoid daytime electricity use at 16 p/kWh, #2 avoid car charging at 5-16 p/kWh, and #3 avoid gas consumption at 2.96 p/kWh.
Smart Tariff Controls
These controls manage my electric devices for lowest grid energy cost. The controlled devices are:
Battery storage (Powervault)
Electric car charger
Hot water heating (ImmerSUN)
The hardware that has this control is known as a Home Energy Management System (HEMS). My HEMS is based on a simple computer known as a Raspberry Pi. The HEMS uses foreknowledge of the electricity price and predicted solar panel output to determine when best to run the above devices. It was designed around a tariff called Octopus Agile which has 48 half-hourly prices that change daily, but is currently working with a simpler two-rate smart tariff.
Central Heating Boiler
Electric car charger
Hot water heating
Devices controlled by smart systems
Most of these solutions are made up of commercially-available items that I have perhaps combined in a way not anticipated by their manufacturers. In particular:
I created a relay module to enable the gas boiler to be turned on remotely and programmed a series of logical rules for the Apple TV’s that act as the controllers.
I identified a way to prioritise different self-consumption devices by configuring their current clamps.
I built my smart car charger integrating various items of hardware and writing the ladder logic program that runs it.
I built the HEMS from commercially-available parts and wrote the software that runs on it to control my devices.
For the last few years I’ve been a user of, and advocate for, Octopus Energy’s Agile tariff. This unique tariff in the UK is linked to half-hourly wholesale electricity prices and gives the user 48 half-hourly electricity prices each day. As the price of electricity varies considerably from one half hour to another, customers like myself could save quite a lot of money by shifting consumption around such as by charging the car or running the washing machine at different times.
However in recent months wholesale electricity prices have been high. My understanding is that this is from a combination of factors including aging power stations being offline for maintenance and Brexit-related issues around market access and trading. The result of this is that while there’s still variation by time of day in the wholesale markets the pricing is always relatively high. The Agile tariff also applies a multiplier to the market prices to cover Octopus Energy’s costs which drives the retail price even higher. Thus there really doesn’t seem to be a financial benefit to such extreme agility compared to some more conventional tariffs. (I’m not criticising Octopus Energy here – they are completely transparent about how this tariff works)
As a result of this a few days ago I switched to another Octopus tariff – Go. Go is more like a traditional time-of-use tariff – like Economy 7 in the UK – except that Go provides a shorter cheap window of 4 hours not 7 hours and a deeper discount. Go’s headline pricing is a cheap 5p/kWh from 00:30 to 04:30 with a higher standard rate that varies by region and gets adjusted from time to time. My standard rate is 15.96 p/kWh fixed for 12 months.
My electricity consumption is managed by the Home Energy Management System (HEMS) which has been optimising my energy costs for some two and a half years. Having decided to move away from Agile then I need a quick change for my HEMS to work with the new Go tariff. My quick solution (a whole two lines of code) is to edit the Agile costs on the fly each day to replace Agile costs with Go costs in the 4 cheap hours – a softy of hybrid of Agile and Go. Additionally I’ve used an existing configuration file to apply a price cap at which the battery can be charged from the grid preventing grid charging above the 5 p/kWh price as it makes no economic sense to charge the battery at the higher Go price to avoid buying electricity at the same higher grid price. A side-effect of this (which I quite like) is that daytime consumption is still managed in a grid-friendly manner via the Agile price even though I’m physically paying the Go price. (I may have to think about this further if the Agile price starts to drop below 5 p/kWh)
The immediate effect of the standardised Go price at night is that the behaviour of the HEMS for EV charging, dishwasher, washing machine and water heating has all standardised too. All these now start at 00:30 except for water heating which now never happens from the grid since mains electricity is now always more expensive than gas. Dishwasher and washing machine may also be scheduled for during the day if anticipated solar production is high enough, while EV charging and water heating may also happen from solar in a closed loop manner.
Battery charging is a little less standardised at night. Battery charging now varies in a range of 0 to 4 hours overnight varying with anticipated solar production the next day. In the last few days I’ve seen both bookends – no hours of battery charging when the day ahead will be sunny and 4 hours of charging when the day ahead will be more mixed. During the cheap window, if the battery is not charging, then the battery is not permitted to discharge. Yesterday was one of the mixed days.
As can be seen from my smart meter data above, almost all the electricity consumed was at night in the cheap window, so my average electricity cost for the day will be very close to 5p/kWh.
The battery charging and discharging is shown by the blue and gold lines above. Initially the battery discharges to avoid paying 16 p/kWh to the grid, then it charges for 4 hours at 5 p/kWh, then there’s some further discharge until the sun rises, in the morning there’s some sun which charges the battery in a somewhat variable manner, then in the afternoon it’s sunnier and the battery reports some ‘Grid Power Out’ which is actually power available for lower priority self-consumption devices, and finally the battery discharges through the evening. There would have been some opportunity to charge the battery more during the afternoon but the battery was already nearly full.
The ImmerSUN gives probably the most complete overview of the home, albeit at only hourly resolution as follows:
Purple is the electricity consumption (home, battery, dishwasher, washing machine etc)
Red is bought electricity (58% of total)
Green is generated electricity (42% of total)
Blue is electricity used for water heating (which is all from generation and part of the 97% self-consumption)
Teal is electricity export (which is minimal at 3%)
In conclusion the change to Go seems to be saving me a significant amount of money compared to current Agile prices, and the hybridisation of the tariffs seems to be working well from a control perspective giving me the financial benefits of Go and should deliver the grid-friendly behaviour of Agile (although I’d need poorer weather to demonstrate that).
If you are a GB resident and would like to switch to Octopus Energy (who have incidentally been Which-recommended for years) then we can each earn £50 credit from this referral link.
While generally the network at our smart home works well, I have in the past had some issues with inability to connect between devices. Many of the smaller smart devices use Bluetooth (and in particular Bluetooth Low Energy – BLE) because battery devices lack enough energy capacity to run WiFi with adequate battery life, but we have had some issues with WiFi-connected devices. Intermittent WiFi issues included:
connection between all HomeKit hubs (2 x Apple TV + iPad)
connection to an external HomeKit WiFi bulb or WiFi smart plug
connection from iPad to Raspberry Pi HEMS
After some head-scratching I concluded that the issue relates to my WiFi network or indeed networks. Like many people I have dual band WiFi – 2.4 and 5 GHz. The 2.4 GHz is supported by more devices, carries for a longer distance, but can carry less data; while the 5 GHz can carry more data, but is supported by fewer devices and has less range. Devices with 5 GHz capability can generally choose either frequency, but many cheaper devices are 2.4 GHz only.
It seems to me that devices on the 2.4G WiFi network can reach each other, hardwired ethernet devices in the home, and the external internet. Similarly devices on the 5G WiFi network seem to be able to reach each other, hardwired ethernet devices in the home, and the external Internet. However devices on the 2.4 GHz and 5 GHz WiFi networks don’t seem to be able to reach each other. I couldn’t find any setting in my router that might join or separate these WiFi networks.
The solution that I came to was Powerline adaptors. Powerline adaptors extended a wired ethernet connection over the existing mains electrical wiring of the home rather than require new dedicated cables. Typically these Powerline adaptors are sold in pairs – one to be wired to the router and one to a remote device – but it’s possible to pair additional units. Indeed I currently have three units from two different manufacturers all interlinked:
In my study connected to the router.
In the lounge connected to the Apple TV.
in the airing cupboard connected to the HEMS (as illustrated above).
The effect of this is to put the Apple TV on the wired ethernet with the result that the iPad (however connected to the internet) can reach it as can the external HomeKit WiFi bulb on 2.4 GHz. Similarly, with the Raspberry Pi hardwired, then the iPads can reach it regardless of their internet connection, rather than only when the iPad was also on 2.4 GHz.
The result seems to be a significant improvement in robustness and it didn’t even cost me anything as I had two pairs of Powerline adaptors already from prior projects.
From back to front:
Mains socket incorporating USB power supply for Raspberry Pi HEMS
Black USB power lead to HEMS
Powerline adaptor connecting HEMS to Powerline network with mains socket
Yellow Cat 5 ethernet cable to HEMS
Mains plug with ‘Do not unplug’ label supplying power to HEMS-driven relays and RF Solutions Mainslink radio transmitter to car charger.
From top to bottom:
Raspberry Pi HEMS (black box) incorporating relay HAT
10-way junction box (white box) typically employed for wiring central heating controls
It been over a year now since I last reviewed what return I was getting on my investment in energy smart technology – solar panels, battery storage etc – so I think an update is due. This time I’m going to take the input data from my immersun system – one year of data from start of June 2020 to end of May 2021.
Diverted – this is where the immersun sends any surplus solar electricity to my immersion heater to make hot water. In 2020/1 we diverted 1056.6 kWh to hot water saving gas at 2.82 p/kWh. However the gas boiler isn’t 100% efficient losing heat both via the flue to the outside world and also via the hot water pipes to the home rather than hot water. If we assume 80% efficiency at the tank then 2.82 p/kWh as gas at the boiler is 4 p/kWh as heat in the tank. 1056.6 kWh at 4 p/kWh saved £37.25.
Exported – this is where I’m unable to use the solar power that we generate and it overflows into the grid. I’m not paid for Export so this is worth nothing to me.
Generation – this is the energy that we generate in the solar panels. I’m on the UK’s legacy Feed-in Tariff (FiT) scheme which pays me to generate electricity. In 2020/1 I was paid 14.65 explicitly for every kWh that I generated. I also received deemed (rather than metered) Export which paid 5.5 p/kWh on 50% of the kWh that I generated (which is where the ‘deemed’ part comes from). 5.5 p/kWh on 50% is equivalent to 2.75 p/kWh on 100% of the Generation making my revenue 17.4 p/kWh per kWh generated or £693.51 on the 3985.7 kWh that I actually generated.
Imported and House – these are respectively the electricity that I buy from the grid and that which I used within the home including appliances and car charging, some of which will comes from my own solar panels. The difference between House and Imported is the electricity that I used from my solar panels which would otherwise have been bought from the grid. If I assume that each kWh that I use from my solar panels avoids buying a kWh of electricity from the grid at 16.36 p/kWh (current Energy Saving Trust value for the average UK electricity price) then I avoided buying £423.81 of electricity by using the output of my solar panels.
Return on smart energy investment @ 16.36 p/kWh grid price
With an investment of £8,670, £1,154 represents 7.5 years to pay back the capital invested.
I’m actually on a smart tariff so my electricity cost in this period at 8.05 p/kWh was significantly less than the UK’s average 16.36 p/kWh. This lower price will arguably reduce the value of the energy generated by the solar panels for self-consumption, but equally the ability to maximize the value of a smart tariff is itself a saving.
Return on smart energy investment @ 8.05 p/kWh grid price (excluding the tariff benefit itself)
Using my actual average energy price rather than the higher UK average grid price pushes down the return by over £200 (£929.29 versus £1,154.56). However the costs of buying the imported 4,748.9 kWh falls by £394.63 through the tariff benefit, increasing the annual return to £1,333.93, and reducing the payback period from 7.5 to 6.5 years.
Thus, had I invested in this technology at one time back five and a half years ago and shortly after we moved to this house, then we’d have been in sight of payback with 1 or 2 years left. In practice of course I’ve made the investments at different times (solar first five and half years ago, battery around a year later, smart tariff later still), so my payback will be achieved a little later.
Some other statistics:
Of solar panel output:
91.5% replaced bought energy (self-consumption)
65.0% replaced bought electricity
26.5% replaced bought gas for water heating
8.5% was exported to the grid
Of incoming electricity:
54.4% was from the grid
45.6% was from the solar panels (“green contribution” in ImmerSUN’s terminology)
It’s now around four and half years since I bought my Powervault G200 storage battery and two years since I started managing it from my Home Energy Management System (HEMS).
Originally the Powervault set up simply to store surplus electricity from my solar panels for later use but, following my change to Octopus’ Agile tariff, I started to use the Powervault to optimize electricity costs when there wasn’t going to be enough solar, charging when electricity was relatively cheap and discharging when electricity was relatively expensive. Initially I configured the Powervault manually to achieve this but later moved to the HEMS doing it automatically.
The fundamental principle has remained the same through both manual and automated periods, with the battery being set into one of three modes depending on price:
Force charge – where the battery charges at full power (usually from the grid) for the required number of hours – when grid electricity price is cheapest.
Only charge – when the battery charges proportionally to the solar surplus (but will not discharge) – when grid electricity is mid-price (i.e. too close to the price at which the battery was being force charged to be economically advantageous to discharge)
Normal – when the battery charges or discharges proportionately to the solar surplus/shortfall – when grid electricity is comparatively expensive compared to the force charge price.
The current logic now reflects electricity price, time of day and state of charge. The fundamental relationship is with grid electricity price as previously described, but with two further refinements:
During the day, even when the electricity price is relatively high, the battery is held in charge only until 80% state of charge is obtained. This helps ensure that a high state of charge is obtained before the early evening peak in Agile prices from 4 to 7 PM by not allowing the battery to discharge while the expectation is that it is being charged from solar. In the depths of winter, when the battery is more likely to be charged overnight and thus start the day with a high state of charge, the same logic allows the battery to discharge to 80% if electricity is costly during the day but still preserves charge for the early evening peak.
Similarly if the battery is full, but the grid price is medium, then the battery is also allowed to discharge to 95% during the day. This allows the battery to discharge slightly to cover small peaks of demand on the assumption that a small amount of solar recharge may well be possible even if the solar forecast wasn’t high enough for recharging at full power.
To consider the two bookends of behaviour:
On a really sunny day like tomorrow, the HEMS doesn’t anticipate any need to buy electricity from the grid for battery charging so all electricity is relatively expensive. The HEMS will allow the battery to discharge overnight (completely if necessary) . From 8:00 AM the battery will only be allowed to charge until 80% state of charge or 4:00 PM is reached after which the battery will be put back in Normal operation when it has the freedom to either charge or discharge.
On a winters day, the HEMS potentially fully charges the battery overnight. Between 8:00 AM and 4:00 PM the battery is allowed to discharge to 95% if grid electricity is mid-price (with some hope that the 5% may be recovered from solar), or to 80% if grid electricity is high price. After 4:00 PM the battery reverts to Normal operation for the evening peak period.
I’m very pleased with how robustly my battery integration operates. It is however reliant on the continuing availability of the Powervault G200 cloud which may not be around forever as the G200 model has now been superseded. My own example is currently four and a half years old.
We have a lot of batteries. The kids’ toys seem to use endless quantities of AA and AAA batteries plus many of my HomeKit smart devices including sensors and radiator valves are battery powered (typically AA or 1/2 AA). Over the last few years I’ve been replacing disposable batteries with rechargeable batteries to reduce waste. So far every device has worked successfully on rechargeable batteries (even when the manufacturer didn’t recommend them) although in some cases low battery warnings are triggered almost continuously since the Nickel Metal Hybrid (Ni-MH) rechargeable batteries are slightly lower voltage than regular disposable alkaline batteries (1.2 versus 1.5 Volts).
Last year I came across a Lithium AA battery that had potential to avoid such issues. Normally Lithium cells have voltages in the 3-4 Volts range, but these batteries have internal voltage regulation to reduce this down to 1.5 Volts. They need a special charger, but have the potential to eliminate the almost continuous low voltage messages.
I’ve now been using the first of these for six months. They have indeed eliminated the low battery messages. I still recharge the batteries at the end of every quarter regardless of whether I have a low battery warning or not. For the Ni-MH batteries they get replaced because the low battery warning is on most of the time anyway, while for the Lithiums I’m anticipating that the voltage may dramatically collapse not leaving time to change them after the low voltage warning is triggered. I now have three sets of eight which is enough for all my Eve Thermo smart radiator valves (eTRVs).
They are currently available via both Amazon and eBay, although Amazon seems to have the better prices whenever I’ve looked.
My sole criticism of these batteries is that they only seem to be available in sets with a charger, and not as just cells, so I now have three chargers.
I now have rechargeable Lithium cells for all my Eve Thermos (2 x 1.5V AA each) and Eve Door and Window sensors (1 x 3.7V 1/2 AA each). The Eve Room and Eve Motion sensors don’t seem to mind the lower voltage Ni-MH cells.
My home unusually uses HomeKit smart automation for central heating control among other things. One feature that I’ve not seen documented elsewhere is use of a watchdog to improve robustness of the automations. Many people of course will use HomeKit as a fancy remote control, but in my case HomeKit automations have an important role in heating control linking heat demand from rooms to enabling the boiler to provide heat. It’s thus important to me that this link works reliably. However in my experience sometimes changes in state can be missed leaving the boiler not running when it should be, or running when it shouldn’t be, an error which could last for hours.
Some two-and-a-half years ago I created a means to improve the robustness of such automations. My watchdog is a HomeKit smart plug which cycles on and off periodically. Two timers alternately turn the plug or or off every few minutes. The change of state of the watchdog is used as a second trigger for the rules in the automations causing the rules to be reevaluated every few minutes.
To illustrate what this achieves let’s imagine that the HomeKit ecosystem misses one trigger in ten or 10% of triggers. That would mean that one night in ten the boiler would fail to turn off when the last radiator valve closed, and would instead run all night. With the watchdog concept the rules are re-evaluated every few minutes, not just at the moment a valve closes. Thus, within a few minutes the rules are evaluated again and then ninety percent of the missed ten percent of events corrected – the error rate is now down to one percent from ten percent. A few minutes later the rules are evaluated a third time and ninety percent of the remaining one percent of errors corrected – the error rate is now a tenth of one percent or once in every thousand days. The risk of a continuing error state thus becomes vanishing small in minutes.
Previously the period of the cycle was five minutes i.e. the timer repeating an on/off cycle every five minutes. Five minutes was chosen as that’s the minimum cycle time available in the Eve app that I use to write rules. Today I realised that I could improve this significantly.
The illustration above shows the new solution. Here I created 3 on and 3 off rules which each repeat every six minutes, which causes the state of the watchdog to change every minute..
watchdog off (off rule #1)
watchdog on (on rule #1)
watchdog off (off rule #2)
watchdog on (on rule #2)
watchdog off (off rule #3)
watchdog on (on rule #3)
watchdog off (off rule #1).. and repeat indefinitely.
The illustration below shows a typical rule which turns off the watchdog and repeats every 6 minutes.
The net result is that my watchdog smart plug now turns on every even minute and off every odd minute which I think provides the minimum possible delay before the system responds after any missed change of state.