Category Archives: Wet goods project

The ‘Appiest Days of My Life

One of the consequences of integrating a smart home is the large number of different apps, web portals and potentially sources of APIs involved. The ones I use include:

TitleAppPortalAPIPurposeComment
BrightYNYReads and stores consumption from smart meter.No price data for my tariff due to smart meter limitations.
EveY /3NNEve’s alternative to Home for all HomeKit accessories with additional functionality for Eve’s own devices.I prefer this to Home for editing rules.
I use Eve products mostly for central heating control.
HomeY /3NNApple’s own app for the HomeKit smart home ecosystem.Need to refer to device manufacturers own apps (such as Eve or WIFIPLUG) for some configuration and data.
HEMSNYN My own web portal to view HEMS schedule and status via Apache web-server on Raspberry Pi.
MyImmersunYYY /1Control of ImmerSUN power diverter.Available API provides some measurement and status data as per main screen of the app.
PowervaultNYY /2Control of Powervault storage system.Available APIs provide some user scheduling and status capability.
OctoWatchdogY /3YYFuture cost, and historic costs and consumption (30 prior days) from Octopus (electricity supplier).APIs provided by Octopus.
App developed by an enthusiast using Octopus APIs.
Octopus’s own web portal provides historic consumption but does not pair this with cost. Monthly statements show graph of consumption and cost for each day.
WIFIPLUGYNYControl and measurements from own brand smart plugs.Plugs also appear in Home and Eve apps.
I use for dishwasher and washing machine.

Notes to table:

  1. APIs not officially released. Reverse-engineered by an enthusiast and available on line.
  2. APIs not officially released. Used as part of a sponsored trial when I first got the battery and re-used by myself with some manufacturer support.
  3. iOS only. Not available for Android.

Some of these apps have similarities:

  • Both Bright and OctoWatchdog show whole of house energy consumption (and potentially cost) derived from the smart meter. However they have differences too. A smart meter sits on two networks: (i) the Wide Area Network (WAN) via which the meter communicates with the energy supplier and (ii) the Home Area Network (HAN) which links the devices in the home (electricity meter, gas meter, CADs/IHD and gateway). Bright connects to the HAN via small piece of hardware called a Glow Stick Wi-Fi CAD and collects its own data in real time and stores its own records of energy consumption in the cloud; while OctoWatchdog involves no extra in-home hardware, and takes data a day in arrears from Octopus not storing anything in the cloud itself. Bright’s USP is the real time consumption and current day’s data (neither of which OctoWatchdog supports), while OctoWatchdog’s USP is the availability of electricity price which isn’t available from the meter.
  • Both Eve and Home interact with all devices in the whole HomeKit ecosystem. Eve is best for creating rules and has more ability to configure Eve’s own devices, while Home is best for sharing access with family members. WIFIPLUG’s app is more limited only interacting with their own devices, and thus cannot see Eve or other HomeKit devices.
  • Both MyImmersun and WIFIPLUG apps, and the Powervault portal, allow configuration of their own manufacturer devices. They all have, for example, timer capability and data logging. MyImmersun is better for giving a whole-of-home view showing solar panel output and net input to house (so provides a more comprehensive energy monitor), Powervault shows no solar panel output but does give a view of whole-of-home, while WIFIPLUG provides only a view of the energy consumption of devices plugged in to the WIFIPLUGs.

Testing Times

So, over the last few days I’ve been adding control of my wet goods (dishwasher and washing machine) to my HEMS so that it can start washing loads at the optimum time – that is the times with the lowest projected electricity cost for a typical wash cycle.

Electricity prices for 18th and 19th December.

The above image shows the prices for December 18th and 19th as downloaded by the HEMS at 16:45 on the 18th after publication of prices for the 19th. The HEMS then reviews this pricing against the need for electricity to determine when to use electricity and, in the case of the battery when to discharge the battery. I currently have the HEMS configured to provide:

  • 3 hours of car charging
  • Not more than 2 hours of water heating (but only when electricity is cheaper than gas which can also heat water)
  • 6 hours of fixed battery charging.
  • Best start time for dishwasher and washing machine.
HEMS plan for 18/12 17:00 to 19/12 17:00.

I’ve described the operation of car charger, water heating, and battery previously; the new content here is the two final columns on each side for dishwasher and washing machine. The numbers in the columns are the estimated cost of a washing cycle if started at the beginning of the corresponding half-hour. The yellow colouring reflects the selected half-hour to start the appliance i.e. the one with the lowest estimated cycle cost. There’s also the option to set a threshold above which one is not prepared to pay to wash today which has resulted in the red box. If the whole day was red then the washing cycle would be deferred for consideration the next day.

History from Dishwasher WIFIPLUG

The extract from the WIFIPLUG history for the dishwasher shows the typical operating sequence:

  1. @22:19 I turn the WIFIPLUG on to enable the dishwasher to be programmed and the cycle started. The plug for the dishwasher is inconveniently at the back of a low kitchen cupboard so this was achieved via the WIFIPLUG app.
  2. @20:20 I turn the WIFIPLUG off via the app suspending the cycle in its first moments.
  3. @02:00 the HEMS turns the plug on remotely allowing the dishwasher cycle to continue.

The equivalents for the first two actions for the washing machine can more conveniently be achieved via the push button on the WIFIPLUG itself as the plug for the washing machine is above the counter.

Electricity consumption 19/12.

The above image shows the measured power consumption from my smart meter. Almost 9 kW is being drawn at times under the action of the HEMS when the electricity price is cheapest, but also zero at times when the price is highest. The washing machine contributes to the peak spike around 02:00 when both it and the car charger are enabled. The later spiking during the peak period is the electric oven cycling on and off under control of its thermostat as the battery isn’t powerful enough to run the oven, so the excess power is drawn from the mains.

Electricity consumption and price 19/12.

The screenshot above shows the half-hourly electricity consumption and costs from Octopus. It should be noted that this is half-hourly consumption in kWh whereas the prior chart was power so, for example, an average 6.6 kW of power consumption results in 3.3 kWh of energy consumption in a half-hour. That the blue line of price is almost the inverse of the red cost columns indicates the HEMS is doing its job to shift most energy use to when the energy price is lowest, and use the battery to offset demand when the price is highest.

Overall on the 19th I paid £1.07 for 21.776 kWh of electricity including standing charge which is independent of use. That’s 86 p for 21.776 kWh without the standing charge, or a weighted average of 3.95 p/kWh. That weighted average of 3.95 p/kWh compares to a range of 1.10 to 26.24 p/kWh during the day. According to UK power the average cost of electricity in the UK is 14.37 p/kWh so I paid 27.5% of the average UK price on December 19th.

The 5 Step Program

This post describes the evolution of my HEMS code to control my dumb wet goods (dishwasher and washing machine) using smart plugs.

The program for my HEMS works as described below. For clarity I’ve emboldened the new steps associated with the control of the wet goods:

  1. Download the cost data for Agile from Octopus. The API provides 48 hours of data, but I use only 24 hours at a time. I download at 16:45 to create a schedule from 17:00 today to 17:00 tomorrow. I use two fields only: the date-time stamp and the energy price inc-VAT.
  2. Calculate cycle cost. Reverse sort the unit cost data in descending time, and combine the energy price with the load profile for each non-interruptible device (dishwasher and washing machine) to estimate the cost of running a washing cycle starting on each half hour. Add as third and fourth fields to the data file.
  3. Establish start time for each non-interruptible load. For each appliance in turn, sort the cost data in ascending cycle cost. Enable the appliance for interval with the lowest cycle cost within an acceptable time window (typically the first row), and overwrite remaining instructions from the prior day. Repeat for other appliances.
  4. Establish on times for each interruptible load. For all interruptible loads (battery, car charger and immersion heater) sort the data in ascending unit cost. Replicate the unit cost column for each interruptible load. For each load enable for the required number of half-hourly intervals within the time window set by the user, and disable for higher cost half-hourly intervals.
  5. Prepare user screens. Sort data file by ascending time, split into first and second 12-hour periods, and present as two HTML files.
Shell script to create daily schedules and user screens.

The top level scheduling script which runs automatically at 16:45 each day is a shell script which calls a series of awk scripts to: (i) calculate cycle costs for wet goods, (ii) determine start time(s) for each wet-goods appliance, (iii) determine on/off times for interruptible loads.

Awk is a pattern-matching program for processing text files. Such text files may be thought of a series of records and fields in a textual database. Awk may seem an odd choice of scripting languages, but essentially the processing of a text file of 96 time and unit pairs to create HTML files of 48 times and cost combinations is a text file processing task. Along the way as each on or off decision is made a system call to the OS is made by awk to copy an on or off script to a time-stamped script (e.g. either washingmachineon.sh or washingmachineoff.sh is copied to washingmachine_0930.sh). An internal timer called cron runs each half-hour so, for example, at 09:30 it runs all the scripts with 0930 in their names which updates the status of each controlled device.

At the time of writing we have 5 HEMS-controlled devices:

  1. Battery storage – interruptible – the only device with 3 states (normal, charge-only, force charge).
  2. Car charger (EVSE) – interruptible – on/off.
  3. Dishwasher – non-interruptible- on only.
  4. Immersion heater – interruptible – on/off.
  5. Washing machine – non-interruptible – on only.

Characterising the loads

The loads that I already control via the HEMS (that is loads which can be interrupted like battery charging, car charging and water heating) are essentially constant, that is that they draw the same power regardless of progress. The battery charging does start to tail off at particularly high states of charge, but that effect is neglected here. However the non-interruptible loads like the dishwasher and washing machine are expected to have very variable loads as the cycle continues – largely because periods of water heating demand much more power than spraying water around, spinning the drum, or pumping water out.

I thus decided that I would measure the pattern of these variable loads and use that information as part of the decision when to run the load for lowest cost. Rather than tabulate the half-hourly energy price as I do for interruptible loads, I would instead estimate the energy price to complete a load for each half hour in which I might start the cycle. This process of measuring the load pattern during a cycle I have referred to as characterisation.

I characterised the loads using a plug-in energy meter reporting kWh which I read manually every half hour during a typical wash cycle to determine the blue lines above. I then calculated the energy usage in each half hour period as per the orange line. I did all of this half-hourly as that’s the interval after which the energy price changes and correspondingly the interval at which the HEMS updates its output controls.

The measured loads confirmed my suspicions regarding variation with heat inputs providing the largest loads – twice for the dishwasher as it both heats the water at the start and heats to dry the dishes at the end, while the washing machine also heats the water at the start but spins to dry the clothes at the end (which is more energy efficient). The effect of this is that the washing machine would generally be expected to start in the cheapest half hour as that’s when it uses most energy, while the optimum time for the dishwasher is more complex to determine.

Of course the actual loads for the wash cycle will vary from the prediction for various reasons including not only the ability to select different wash cycles and options but also the smartness of the device in assessing how full it is or how dirty the utensils are.

I also decided that I would not explicitly turn these loads off from the HEMS – only turn them on. Not turning the loads off allows for some uncertainty as to the length of a cycle depending on the actual cycle selected, options selected, loading of machine etc. Not turning the outlet off also provides for the user being able to do additional loads under manual control if at home later in the day.

Internally my HEMS creates a schedule of 48 half-hourly actions each day for each load. Typically each action is either ‘on’ or ‘off’ (the fixed battery is more complex). The actions for these devices will follow a similar pattern of 48 half-hourly actions, but typically only one of those is ‘on’ and the 47 actions that would normally be ‘off’ do nothing. There existence is largely for equivalence and re-use of code, but they also ensure that the previous day’s ‘on’ command is over-written as required if the start time for the new day is different to that for the prior day.

Choosing the smart plug

One of the enhancements to my HEMS that I’ve had in mind for some time is to control wet goods – that is dishwasher and washing machine. I had previously thought that this might have to wait until I replaced my current machines with smart equivalents, but recently discovered that both existing machines recover after a power outage and continue their cycles. This creates the opportunity to put the machines on smart plugs, to manually start washing cycles, but then immediately turn the smart plugs off, and then use the smart plug to resume the cycle at the optimum time as instructed by the HEMS.

I already have two Eve Energy smart plugs as part of my smart heating system using Apple HomeKit, but these aren’t ideal for this application as I really need an exposed API to allow the HEMS to have control. However I then came across WIFIPLUG which, not only has an exposed API, but also Apple HomeKit compatibility (and indeed compatibility with other smart home ecosystems).

The WIFIPLUG (unlike the Eve Energy) also features an integrated on/off button allowing the washing cycle to be paused using this button, or via the HomeKit app, before later resuming under HEMS control when the energy cost is lowest.

The WIFIPLUG also has its own app which allows timers to be set and energy consumption viewed (although not in great detail) giving the ability to control via Apple’s own Home app or via the WIFIPLUG app. The one thing that I hadn’t spotted first time around is that it’s preferred to add the unit to the WIFIPLUG app (which then automatically adds to Home) whereas if you add to Home directly then you lose the ability to connect to the WIFIPLUG app later.

I was sufficiently impressed to buy a second unit even before I’d integrated the first, and indeed I now have another two on order.