Energy Analytics (Fall 2017)

This semester is the fourth time I'm teaching Energy Analytics at UNC Charlotte. I have been offering this course every Fall since 2014. Previously I blogged about the offering in Fall 2015 (see THIS POST).

Student Profile

The class started with 11 master students and 1 PhD student. The master students were from three programs: engineering management (8), applied energy (2), and economics (1). The PhD student was from the PhD program in infrastructure and environmental systems.

After the first mid-term exam, 4 master students from engineering management withdrew the class. The other 8 students completed the course at the end. Here is the group picture including the 8 students, graduate teaching assistant Masoud Sobhani, and myself.

Energy Analytics group picture (Fall 2017)

Topics

The following 12 topics were covered throughout the semester. Due to Thanksgiving holiday, the last two topics were covered in one week. Each of the first 10 topics was covered in one week.

1. Greetings; Introduction to analytics
2. Forecasting principles and practices
3. Electric load forecasting
4. Weather station selection
5. Retail energy forecasting
6. Model selection
7. Probabilistic load forecasting 
8. Robustness of load forecasting models
9. Electricity price forecasting 
10. Wind and solar power forecasting
11. Demand response analytics
12. Outage analytics

Comparing with the offering in Fall 2015, I merged short and long term load forecasting, and added two topics based on my recent papers: model selection (Wang, Liu and Hong, IJF 2016) and robustness of load forecasting models (see Luo, Hong and Fang, IJF 2018). 

Homework, Group Presentations, Project and Exams

The course grading is based on 4 homework assignments (5' x 4), 2 group presentations (5' x 2), 1 project (30') and 2 exams (20' x 2). The students were ranked for their forecast accuracy in the homework assignments and exams. The rankings were tied to the credits they were receiving.

In 2015, I opened the in-class competitions to the external participants. This year, I didn't do that. Instead, I sent the students to the npower forecasting challenge, where a top 5 position would be counted as some bonus credits in the class. At the end, the No. 1 team of the npower forecasting challenge was from this class. (See the announcement HERE.)

If a student can pass the SAS programmer certification(s), s/he would also receive some bonus credits.

Training Objectives

The training objectives are the same as those in 2015 (See THIS POST). Most importantly, I kept reminding them about my promise: "the more time you spend, the more you learn."

To-do List

Again, the objectives have been met. Nevertheless, I have not yet completed the to-do list from the 2015 offering (See THIS POST).

The withdraw rate is 4 out of 12, which is higher than the previous three offerings. I noticed that all of the 4 students who withdrew the class were distance learning students. Only one distance learning student was able to complete the class. In the 2015 offering, all of the survivors were on campus students. I guess this is due to the heavy workload of the course. Most, if not all of the distance learning students are working professionals, who may not have the flexibility of spending 20-30 hours for a course.

I don't think I can complete my book by the next offering of this course. I have to hurry up on that!