This is a solution I tried out for measuring the power consumption of a single device, and was a little surprised that it actually worked (although it's not automatic, so this doesn't really help the OP):
Last year I finally bought a new computer, but decided to keep the old one as a server. Feeling a bit guilty about having it constantly on only for those rare occasions when I actually needed it, I decided to start running Folding@home on it. But then I wondered if I wouldn't actually be using up even more energy by virtue of actually having something running, rather than just having the PC idle. So I wanted to compare the power usage of (a) having the computer off, (b) on but idle, and (c) running Folding@home.
So this is what I did: during a week when I was home alone (everyone else was on vacation), every morning when I left the house I wrote down the electric meter reading, and then wrote it down again when I got home. During the first few days I kept the PC off, to get a base reading (the fridge, all those appliances which may be on standby, etc.), and then for the next couple of days took readings with the computer on and running Folding@home.
A little surprisingly, the distinction between the power consumption when the computer was on or off actually showed up very clearly in the data, and although I had only a few data points (two of the computer on, three off), the difference between the two cases was much greater than the variance within each case.
To sum this up, using a statistical approach can actually help you measure the power consumption of a single device using your electric meter, without unplugging everything in the house.
[Just for those of you who are interested, the power consumption of this PC whicle running folding@home was 0.035 kWh. Unfortunately, the rest of the family came back from vacation before I had a chance to make measurements comparing the idle Pc to the running one, so I haven't yet resolved my original question...
This is what the raw data looked like (the readings are in kWh), straight from my palm:
72409.7 2/6/05 7:07 (Thu comp off)
72412.4 2/6/05 21:30
72416.0 3/6/05 9:39 (weekend)
72444.9 5/6/05 7:56 (Sun comp off)
72446.6 5/6/05 17:13
72451.2 6/6/05 9:08 (Mon Comp on, FAH running)
72453.5 6/6/05 19:19
72458.3 7/6/05 7:21 (Tue Comp on, FAH running)
72461.4 7/6/05 21:18
72465.0 8/6/05 7:11 (Wed Comp off)
72466.1 8/6/05 12:51
]
"The environmental impacts of getting a newspaper dropped on your doorstep each morning vastly outweigh those of receiving the same information via a handheld electronic device such as a personal digital assistant (PDA)", according to this article.
I am certain that many people here would gladly "take the time to code all that", for free even, if they only could. But this is not an open source app, so that is not possible.
Come to think about it, this could make a great project. GPL'ed clone of Google Earth, anyone?
So how about joining the effort for porting NASA's World Wind, instead? (Also check out this thread, with comments from the World Wind developer)
I don't really do much statistical work, but I've been looking into the various Matlab clones for my physics lab reports, and have come up with a few different options --- all free/opensource --- which as a suite provide a very good, free, alternative to Matlab:
Octave
Octave is closest to Matlab in terms of source compatibility: you can (almost) take the m-files you wrote for Matlab and run them through Octave, and vice-versa. Octave has no GUI (it uses gnuplot for plotting); the programming language is very similar to Matlab's.
Scilab
For some reason, Scilab doesn't seem to be as well-known as many of the other projects, but in my opinion it is one of the best Matlab clones. The latest version provides tools for translating m-files to scilab's native format. Scilab uses a syntax which is slightly different than matlab's, but the same kind of style, and pretty easy to learn. It also has many toolboxes which are provided for various uses (check the contributions section on the site). Scilab does have a GUI, and some of the toolboxes provide further GUI enhancements.
Grace
Grace is a graphing tool for 2D graphs, so it's not a general-purpose Matlab clone --- but for graphing, it's the best (I prefer it to Matlab's graphing capabilities!). As an important bonus, it provides many data-set transformations, such as interactive curve-fitting capabilities. It has a full GUI, but also provides a scripting language for non-interactive use as a backend for producing graphs.
Maxima
This is a great tool for symbolic computations. It has no GUI, and the syntax is a little strange (it may be similar to LISP, in which it is written; I don't know LISP;) ).
Other tools which I have come across, but haven't really worked with:
Axiom (symbolic computations, CAS); Scigraphica (graphing); opendx (data explorer + visualization).
I've actually never really used R (by the time I came across it, I was done with my physics labs), so I can't really compare any of the others to it. But it definitely looks like one of the tools that I should add to my suite.
Slashdot Mirror
This is a solution I tried out for measuring the power consumption of a single device, and was a little surprised that it actually worked (although it's not automatic, so this doesn't really help the OP):
Last year I finally bought a new computer, but decided to keep the old one as a server. Feeling a bit guilty about having it constantly on only for those rare occasions when I actually needed it, I decided to start running Folding@home on it. But then I wondered if I wouldn't actually be using up even more energy by virtue of actually having something running, rather than just having the PC idle. So I wanted to compare the power usage of (a) having the computer off, (b) on but idle, and (c) running Folding@home.
So this is what I did: during a week when I was home alone (everyone else was on vacation), every morning when I left the house I wrote down the electric meter reading, and then wrote it down again when I got home. During the first few days I kept the PC off, to get a base reading (the fridge, all those appliances which may be on standby, etc.), and then for the next couple of days took readings with the computer on and running Folding@home.
A little surprisingly, the distinction between the power consumption when the computer was on or off actually showed up very clearly in the data, and although I had only a few data points (two of the computer on, three off), the difference between the two cases was much greater than the variance within each case.
To sum this up, using a statistical approach can actually help you measure the power consumption of a single device using your electric meter, without unplugging everything in the house.
[Just for those of you who are interested, the power consumption of this PC whicle running folding@home was 0.035 kWh. Unfortunately, the rest of the family came back from vacation before I had a chance to make measurements comparing the idle Pc to the running one, so I haven't yet resolved my original question...
This is what the raw data looked like (the readings are in kWh), straight from my palm:
72409.7 2/6/05 7:07 (Thu comp off)
72412.4 2/6/05 21:30
72416.0 3/6/05 9:39 (weekend)
72444.9 5/6/05 7:56 (Sun comp off)
72446.6 5/6/05 17:13
72451.2 6/6/05 9:08 (Mon Comp on, FAH running)
72453.5 6/6/05 19:19
72458.3 7/6/05 7:21 (Tue Comp on, FAH running)
72461.4 7/6/05 21:18
72465.0 8/6/05 7:11 (Wed Comp off)
72466.1 8/6/05 12:51
]
"The environmental impacts of getting a newspaper dropped on your doorstep each morning vastly outweigh those of receiving the same information via a handheld electronic device such as a personal digital assistant (PDA)", according to this article.
Other tools which I have come across, but haven't really worked with: Axiom (symbolic computations, CAS); Scigraphica (graphing); opendx (data explorer + visualization).
I've actually never really used R (by the time I came across it, I was done with my physics labs), so I can't really compare any of the others to it. But it definitely looks like one of the tools that I should add to my suite.