Roo and I presented at Open Tech 2008 this weekend on the Current Cost hacking that has been going on in these parts. It was my first time presenting to quite such a large audience and all things considered, it was a lot of fun and went very well.
Although I took some notes through the day of the other talks I went to, Roo has done a much better write-up than I would. So I’ll point you his way.
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:
This was the easy part; AndySC had already put together a perl script for reading the serial port and doing the necessary parsing.
Again, not much for me to do here as Andy’s script already publishes the data to a set of topics over MQTT.
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.
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.
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.
