According to Zico Kolter's course website, one of the goals of the course is to "address issues regarding the prediction, modeling, and control of electricity". It is exciting to see that we are not the only ones involved with this and that there have been efforts made that we can build off of. Although the video was a bit difficult to understand conceptually, he has some earlier videos of the same course that I plan on looking into. The link to both the course website and the YouTube video are below.

http://www.cs.cmu.edu/~zkolter/course/15-884/index.html

https://www.youtube.com/watch?v=wof0eipWP1I

]]>

I looked on the Internet for a source that had lessons on regression, and I found an on-line course from Penn State University dedicated to Regression methods. This looked like a good place to start for me, and I immediately began reading the notes on the website. Hopefully learning more about statistics as I look at techniques to fit data will allow me to better understand what I am dealing with. I looked at the syllabus for the course and there are a select few chapters that I hope to look at in the future, specifically the chapters on Model Building, Regression(logistic, Poisson, nonlinear), and Data Transformations. Today I covered a few sections on the basics of linear regression and over the weekend, I hope to have the areas I mentioned above covered so that I will be able to implement these areas in a script.

]]>

The Poisson Distribution is a model that defines the probability a certain amount of events takes place in an interval given the average amount of times the event took place. There are a few restrictions that determine whether or not a Poisson Distribution can be used. The events that occur must be independent of one another, the rate which events occur must be constant, and the probability of an event occurring in a small interval is also proportionate to the length of the interval. Thinking about how this can be applied to our energy data, it seems to me that one way that Poisson Distributions can be implemented is by separating dates into groups which use similar amounts of energy (seasonal, monthly, bimonthly, etc.), and creating a Poisson Distribution for each of those groups. In this way, we can say that the rate at which events occur is a constant value.

One point of interest with these distributions is to make sure that the groupings neither have too many nor too few dates. If there are too many dates within the group, what may be a change in energy usage due to a temperature fluctuation would be perceived as a inefficient day. If there are too few dates in a group, then even the smallest of changes in energy output may also be seen as an anomaly. In the next couple of days, I hope to do a bit more research on this topic and also do some testing with code. The Poisson Distribution is a powerful tool, but there are certain guidelines that must be met in order for it to be used.

]]>

]]>

]]>

After taking a look at the refrigerator, we continued our energy audit. In total, we finished the first and second floor and will be looking towards finishing the third floor tomorrow. An important thing to note are that most rooms on the second floor have some sort of a printer, whether it be a small or large one. This is somewhat unnecessary, as more often than not these printers are hardly ever used despite being plugged in all day.

Before we left the building, we had one more important thing to take care of. After using the Kill-A-Watt meter to measure the consumption of the refrigerator, we plugged the meter into one of the large printers to see how much energy it consumes. Hopefully, these readings will help us determine how much energy these objects are wasting over a year.]]>