Since getting my meter online, it has been sending its readings to a server in the sky so charts like these can be produced. To really play with the data, I needed to start logging it locally and producing my own graphs. Here is a rambling rundown of how I got from XML being spat out by the device every 6 seconds to something like this:

Step One - Parsing the data

This was the easy part; AndySC had already put together a perl script for reading the serial port and doing the necessary parsing.

Step Two - Publishing the data

Again, not much for me to do here as Andy’s script already publishes the data to a set of topics over MQTT.

Step Three - Logging the data

Finally, something for me to do. A couple years ago, I would have joined Andy’s perl script with one of my own, but python is more my thing these days. I already had a piece of python that subscribes to the appropriate topics and posts the temperature values to twitter. It didn’t take much to get the same script to subscribe to the power data and dump it into a MySQL database on the local machine.

+----------------+---------+
| when           | power |
+----------------+---------+
| 20080502231747 |    0.34 |
| 20080502231753 |    0.33 |
| 20080502231804 |    0.34 |
+----------------+---------+

The table in the database is a very simple one at the moment; logging the power along with a timestamp. An entry is added to the table for every reading from the meter and having been running for 3 days there are just over 11,000 of them. I’ll have to keep an eye on this to make sure it doesn’t run away with my free disk space.

Step Four - Graphing the data

Roo showed me some stuff he’s been playing with using the Google Chart API. Whilst I generally prefer to roll my own (aka, reinvent the wheel), I couldn’t ignore just how easy it is to produce pretty graphs this way.

Before delving into the API, I needed to decide just what I wanted to produce. As there is such a range of chart types available, there are plenty of interesting things that could be done. Initially, however, I decided to stick with the traditional “power-usage-in-the-last-24-hours” chart.

The API has a limit on the amount of data that can be passed to it. So I needed to find a meaningful way to reduce the 4200 data points generated in 24 hours to around 100 at most. I soon settled on using the average value for each 10 minute period. This loses some resolution in the data, but it still shows the trends.

Generating the averages is a simple question of the right query on the database. With some trial and error, I eventually got to:

select concat(substring(substring(`when`,1,11),9,4),'0'), truncate(avg(`power`),3) from currentcost where `when` > SUBDATE(NOW(), INTERVAL 1 DAY) group by substring(`when`,1,11) order by `when`;

Simple huh?

This produces results like this:

+------+-------+
| 2240 | 0.714 |
| 2250 | 0.637 |
| 2300 | 0.406 |
+------+-------+

With that in place, all it took was to throw it together into an appropriate URL for google to generate the image:

http://chart.apis.google.com/chart?chg=100,20,1,7&chxt=y,x&
chxl=0:|0|1|2|3|4|5| 1:|2100|2300|0100|0300|0500|0700|0900|1100|1300|1500|1700|1900& chxp=1,2,11,19,27,36,44,53,61,69,78,86,95&chs=400×200 &cht=lc&chds=0,5&chm=B,f3f3f3,0,0,0&chco=aaaaff& chd=t:0.348,0.348,0.319,0.311,1.285,0.683,0.338,0.349, 0.398,0.39,0.356,0.477,0.274,0.264,0.572,0.637,0.406, 0.382,0.368,0.324,0.325,0.917,0.322,0.358,0.342,0.318, 0.191,0.124,0.209,0.204,0.192,0.213,0.205,0.196,0.374, 1.753,1.537,0.976,0.552,0.556,0.513,0.433,0.363,0.341, 0.575,0.46,0.188,0.123,0.248,0.204,0.195,0.116,0.215, 0.207,0.226,1.574,0.636,0.214,0.206,0.209,0.204,0.648, 0.694,0.628,0.599,0.689,0.743,1.222,0.278,0.394,0.326, 0.394,0.225,0.184,0.13,0.145,0.23,0.226,0.222,0.133, 0.133,0.237,0.231,0.223,0.13,0.234,0.229,0.222,0.203, 0.197,0.193,0.17,0.198,0.176,0.275,0.328,0.287,0.212, 0.159,0.192,0.19,0.212,0.292,0.298,0.418,0.332,0.789,0.894,0.719

Again, simple huh? Well, maybe not so much. Here’s a break down of what that does (and just to confuse matters, this is in a different order to where things appear in the url above…)

http://chart.apis.google.com/chart?

This is base url of the Google Charts api - all the magic comes from here.

chs=400×200

Set the size of the image.

&cht=lc

Set the type of chart - a line chart.

&chg=100,20,1,7

Gives the chart a grid in the background.

&chxt=y,x

&chxl=0:|0|1|2|3|4|5| 1:|2100|2300|0100|0300|0500|0700|0900|1100|1300|1500|1700|1900

&chxp=1,2,11,19,27,36,44,53,61,69,78,86,95

Describes the axis labels. 0 to 5 on the y-axis, and the relevant times along the x-axis. The script figures out approximate positions along the axis for the labels.

&chds=0,5

Specifies the minimum and maximum values for the data - although I occasionally go over 5Kw, I decided it wasn’t worth squeezing the data for 99% of the time when it is below that.

&chm=B,f3f3f3,0,0,0

Fills the area under the line with a light gray.

&chco=aaaaff

Draws the line with a light blue.

&chd=t:0.348,0.348,0.319,0.311,1.285,0.683,0.338,0.349, 0.398,0.39,0.356,0.477,0.274,0.264,0.572,0.637,0.406, 0.382,0.368,0.324,0.325,0.917,0.322,0.358,0.342,0.318, 0.191,0.124,0.209,0.204,0.192,0.213,0.205,0.196,0.374, 1.753,1.537,0.976,0.552,0.556,0.513,0.433,0.363,0.341, 0.575,0.46,0.188,0.123,0.248,0.204,0.195,0.116,0.215, 0.207,0.226,1.574,0.636,0.214,0.206,0.209,0.204,0.648, 0.694,0.628,0.599,0.689,0.743,1.222,0.278,0.394,0.326, 0.394,0.225,0.184,0.13,0.145,0.23,0.226,0.222,0.133, 0.133,0.237,0.231,0.223,0.13,0.234,0.229,0.222,0.203, 0.197,0.193,0.17,0.198,0.176,0.275,0.328,0.287,0.212, 0.159,0.192,0.19,0.212,0.292,0.298,0.418,0.332,0.789,0.894,0.719

Specifies the data points.

What next?

• Chris has started doing some interesting data analysis to see if he can automatically spot ‘events’ on the graph. Will be interesting to see what can be achieved here.
• Andy’s twittering house got some linkage last week from both Earth2Tech and Wired Science. They mention the power orb that was written about last year. I really like the idea of an ambient device for displaying this information - another project for the arduino list.

Following Andy’s example, I have setup a twitter account for my own house to share its thoughts with the world on.

So far, it is busy twittering the changing temperature of my living room, as measured by the CurrentCost meter. At the moment it only tweets when the temperature changes at least 10 minutes since the previous change. This reduces some of the noise, but I think there is more to do. I don’t necessarily care if the living room is 16°C rather than 17°C but do care if it is too cold or hot. This is a subject I will come back to another time once I have done some more experiementing.

More immediately, I set myself a challenge this weekend; to get the doorbell twittering.

To begin with I bought a cheap wireless doorbell set from Wickes to play with. This came with two receivers; a portable battery-powered one and a mains-powered one. Wasting no time I took one of them apart to find this:

The circuit has a very conveniently packaged daughterboard containing the wireless receiver. The clue was the aerial connecting to it (the white wire to the right) and even better, in the top left corner are the four pins connecting to the rest of the circuit. With my trusty multimeter, and the labels printed on the board, it didn’t take long to work out the leftmost pin is ground, the rightmost pin is +3v and the other two are the magic data pins.

With my newly acquired arduino, itself a topic for another day, I hooked-up the four pins and fairly instantly had the arduino writing over its serial link to my laptop whenever the doorbell button is pushed. Of the two data pins, I found the one labeled “DAT” is the best trigger to use; the other one, “IDEL”, seems to be more noisy and needs investigating.

The doorbell lets you pick from one of 15 channels to ensure you don’t get interference from the neighbours. It also lets you pick one of three chimes to use, from the traditional ding-dong to the full Big Ben. As that setting is on the button itself, it must be sending it over the wireless signal. Currently my circuit triggers regardless of the channel setting of the button. I assume I need to do some more signal analysis on both the DAT and IDEL pins to figure this part out. For now, it works enough to prove the idea.

Here is the sketch I used. It does some simple debouncing by not triggering twice within 3 seconds.

int potPin = 5; // Connected to 'DAT'
int val = 0;

long time = 0;
long debounce = 3000;

void setup() {
  Serial.begin(9600);
}

void loop() {
  val = analogRead(potPin);

  if (val > 0) {
    if (millis()-time > debounce) {
      Serial.print("ON:");
      time = millis();
    }
  }
}

From this point, a bit of python and mqtt magic and the doorbell would be twittering. I say “would” as I haven’t done this final piece of plumbing yet. I only have one arduino at the moment and I am not yet ready to dedicate it to any one task. Clearly I need to order a second arduino - its always good to have separate development and production systems.

As a part of the “CurrentCost craze” that is rife at Hursley these days, I’ve had mine up and running for a few days and am very excited by the possibilities.

The CurrentCost meter gives you a real-time display of how much electricity your house is using. On its own, this would be interesting, but hard to relate to anything. One of its cool features is its awareness of how much electricity actually costs - being told your usage costs £17 per month is much more effective than saying you’re using 1.23kw.

I’m not the only one who has got this meter setup and then obsessively gone around the house to reduce how much is being wasted.

What is great about this particular device, and what appeals to the Hursley crowd, is the ability to plug it into a PC and capture the information it produces. It doesn’t take much plumbing to get the data being graphed online - something which does raise an interesting issue.

I can now see when Jo has got home from work as I see the spike of the kettle being used, but equally, anyone can get a pretty good idea if we’re in the house. Is that a genuine concern? There are certainly easier ways of working out if we’re in or not.

That aside, the graph it produces definitely does give food for thought. Here’s a snapshot of the graph from today (click through for a fully annotated version on flickr):

This graph has left me intrigued to find out what the 100watt-ish blip is that happens every 2 hours; there is a PC running all the time, which has the meter connected - I wonder what cron tasks are configured.

The meter has definitely left me feeling slightly guilty for the 600watts of halogen spot-lights we put in the new kitchen.