This semester I'm teaching Energy Analytics for the fifth time. The course has earned its reputation on the UNC Charlotte campus and even around the utility industry, for its toughness, high withdraw rate, and challenging nature. Here are some comments from the students in 2015 and 2017. Nowadays, not many students even dare to register the course.
After the first midterm exam last week, I have five students left in the class. These five "survivors" (out of more than a dozen students at the beginning of the semester) have completed two assignments and one exam. I am impressed by their submissions every time. I must confess that this is by far the most academically strong class I've ever had for this course, even stronger than the group that won several award plaques in GEFCom2014.
Previously, I sent students of this course to the competitions, such as GEFCom2014 and NPower Forecasting Challenge, where they can solve some conventional energy forecasting problems while competing with others around the globe.
This year, thanks to the outstanding performance of these students, I was spending a lot of time trying to figure out a challenge for them. Finally, I decided to give them a new load forecasting problem to solve.
I'll keep the problem secret for now, but I can tell that a practical solution to this problem can save power companies a lot of money. To those who are interested in writing academic papers, a winning solution to this problem should greatly increase the likelihood of having the manuscript accepted by the top venues for energy forecasting papers, such as International Journal of Forecasting (IJF) and IEEE Transactions on Smart Grid (TSG).
The competition is by invitation only. The ones who are interested in joining this competition should first pass the qualifying match. I will use the first homework problem of Energy Analytics for the qualifying match. A contestant has to beat the last-ranked student of my class to receive the invitation to BFCom2018. If nobody beats any of my students, I'll just run the competition with the in-class students.
For the qualifying match, I'll provide three years of hourly load and temperature, and one year of hourly temperature for the fourth year. The contestants should submit the ex post load forecast for the fourth year. The temperature data is from 28 weather stations. To excel in the qualifying match, the contestants may want to read two of my IJF papers on weather station selection and recency effect.
Oct 8, 2018 - Registration open.
Oct 21, 2018 - Registration close.
Oct 22, 2018 - Qualifying match data release.
Nov 4, 2018 - Qualifying match submission due.
Nov 5, 2018 - Leaderboard published; BFCom2018 invitation sent.
Dec 3, 2018 - BFCom2018 winners announced.
Note: There is no monetary prize for this competition. The leaderboard will be published on this blog. I will consider providing research assistantships to the top three contestants if they are interested in joining my lab as PhD students.
If you are interested, please register HERE. See you in the game!