A little over a year ago we bought a whole house energy monitor (The Energy Detective (T.E.D)). It’s got a pair of clamping ammeters that measure the current going through the main power leads inside the breaker panel in our house. This information is transmitted to a small console that we’ve got in the living room, as well as a unit that is connected to our wireless router and displays a web page with the data. I wrote a script that reads this data every minute and stores it in a database. It can also send the data to Google Power if you’re not interested in storing the data yourself.
The console that comes with it runs on rechargeable batteries charged by the cradle it sits in. That means that you can take the console with you to see how much electricity various devices in the house consume. You just watch the display, turn on whatever it is you want to measure, and within a second or two, you can see the wattage increase on the display. We’ve measured the power that the major devices around our house consume, shown in the table on the right.
The lights aren’t in the table. Much of the downstairs is lit with R40 bulbs in ceiling cans, in which we’ve placed 23 Watt CFLs. The most common lighting pattern in the winter is to have the four lights in the kitchen and the single can above the couch on, which is just under 100 Watts. We also have six motion sensing lights, four in the dog yard and one on the shed. Four of these are CFL bulbs (around 20 Watts), and two are outdoor floods (70 Watts). When the dogs are outside in the dark, we’re typically using around 180 Watts to light the dog yard, and the motion sensors also consume some energy even when the bulbs aren’t lit.
Of the devices in the table, we’re always running the sewage treatment plant, the time capsule and the Linux server (around 380 Watts). In winter the circulating pump is running all the time keeping the water and septic lines thawed and we use lights and various other heaters quite a bit more.
Here’s a series of boxplots showing the distribution of our power consumption, by month, for the last year:
Box and whisker plots show the distribution of a dataset without making any assumptions about the statistical distribution of the data. The box shows the range over which half the points fall (the inter-quartile distance, or IQD), and the horizontal line through the box is the median of the data. For example, in November, over half our power usage falls between about 480 and 750 Watts, with the median consumption just over 500 Watts. If that were the average, we’d use 12 KWHours / day (500 Watts * 24 hours / 1000 Watts/KWatt). The blue vertical lines extending from the boxes (the whiskers) indicate the spread of the majority of the rest of the points. The actual length of these is a somewhat arbitrary 1.5 times the IQD and provides a view of how variable the data is above and below the median. In the case of power consumption, you can tell that the distribution of the points is heavily skewed toward lower consumption values, but that there is a long tail toward higher consumption values. This makes sense when you realize that we’re almost never going to be using less than 380 Watts, but if it’s the dead of winter, our cars are plugged in and the water pump and refrigerator are on, we’ll get a very high spike in our usage. The orange points that extend beyond the whiskers are the actual data values that were outside of the box and whiskers. Again, for this data, we’ve got a lot of these “outliers” because those exceptionally high draw events happen all the time, just not for very long.
Another way to look at the data is to divide it into summer and winter, and examine the kernel density and cumulative frequency distribution of the data. In this case, the dependent variable (power consumption) is on the x-axis, and we’re looking at how often we’re using that amount of electricity. Looking at the winter density data (the red, solid curve on the left), you can see that there’s a peak just under 500 Watts where most of our usage is, but there’s also smaller peaks around 1,200 Watts and 1,800 Watts. These are probably spikes due to plugging in the vehicle heaters in the morning before we go to work (480 baseline + 640 + 650 = 1,770). The 1,200 Watt peak may be due to just having one vehicle plugged in, or due to the combination of other heaters and the lights being turned on. In summer, there’s a big spike around 350 and a secondary spike around 400. I’m not sure what that cause of that pair of peaks is, but if you look at the monthly density plots, it’s a common pattern for all the summer months. My guess is it’s the baseline sewage treatment plant, plus turning on the lights in the evening. But there aren’t really any other peaks after the distribution drops to close to zero after 1,000 Watts.
The blue line is the cumulative frequency distribution, and it can tell you what percentage of the data points occur on either side of a particular usage value. For example, if you read horizontally across the winter plot from the 0.5 (50%) mark, this intersects the blue line at just over 500 Watts. That means that half of the time, we’re using more than 500 Watts, and half the time we’re using less. The difference between summer and winter is really clear when you look at the cumulative frequency: in summer 95% of our electricity usage is below 750 Watts, but in winter 20% of our usage is above that value.
There’s still a lot more that could be done with all this data. At some point I’d like to relate our usage to other data such as the outside temperature, and I know there are statistical techniques that could help pull apart the total consumption data into it’s individual pieces. For example, as I look at the console right now it’s showing 697 Watts. I know that the sewage treatment plant, stereo, time machine, Linux server, and my laptop are all on. When the ventilation fan goes on, the signal will jump by that amount, and remain at that level until it goes off. Given enough data, these individual, incremental changes in the total consumption should reveal themselves (assuming I actually knew what the technique was, and how to perform the analysis…).
What does all this data really mean? Well, I’m not entirely sure. The idea behind these devices is that they will cause people to use less electricity because they’re getting instant feedback on how what they’re doing affects their usage. The problem is that we’ve already done almost everything what we can do to reduce our usage. Even so, it’s nice to see what’s happening, and a sudden, unexplained spike on the console can let us know when something is on that shouldn’t be.