Wednesday, December 2, 2015

Energy Analytics (Fall 2015)

I am teaching Energy Analytics, a graduate level course at UNC Charlotte, the second time this semester.

Student Profile

The class started with 8 master and 3 PhD students from various programs including master program in Engineering Management (3), master program in Electrical Engineering (4), PhD program in Mechanical Engineering (1), PhD program in Infrastructure and Environmental Systems PhD program (1), PhD program in Computer and Information Systems (1), and master program in Computer Science (1). Two PhD students (one from ME, and one from CIS) withdrew the course after the first mid-term exam. Since all students were on campus, the course was taught on campus.


The following topics were discussed throughout the semester with each topic covered in a week.
  1. Greetings; Introduction; Naïve Models
  2. Forecasting principles and practices 
  3. Short term electric load forecasting
  4. Long term electric load forecasting
  5. Weather station selection
  6. Probabilistic load forecasting
  7. Electricity price forecasting
  8. Wind and solar power forecasting
  9. Forecasting and trading in electricity retail market
  10. Demand response analytics
  11. Outage analytics
Assignments and Exams

There were two in-class competitions, one on short term load forecasting, and the other on long term probabilistic load forecasting. Both competitions were also open to external teams. The short term load forecasting competition was counted as 20 credits, all based on forecast accuracy. The long term probabilistic load forecasting competition as counted as 60 credits, 40 credits on forecast accuracy and 20 credits on final reports and presentation. 

After each in-class lecture, I assigned a mini project, which is relevant to the topic of the week. The projects included forecasting stock price, implementing the methodology of a journal paper, presenting the contents of a notable journal paper, and so forth. These mini projects were counted as 10 credits. 

I also had the students to take some SAS courses, which were counted as 10 credits. If a student passes the SAS Advanced Programmer Certification Exam, I would offer 10 bonus credits. 

Training Objectives

The first offering of this course was organized around GEFCom2014, so this is in fact the first regular offering. When designing this course, I would like to train the students from the following aspects:
  1. Reaching their potential. I made a commitment to the students "the more time you spend, the more you will learn". The in-class competition setup is to have them keep learning and trying new methodologies. 
  2. Training their self-learning skills. I made them learn the software tools all by themselves without giving a single in-class demo. I also asked the students to read journal papers and reproduce the models. 
  3. Practicing their presentation skills. In most of the in-class lectures, I crafted about half of the lecture time for the students to present their methodologies for the previous assignment. The presentation format includes both power-point and white-board. 
  4. Mastering the documentation skills. In addition to the reports for the mini projects, the students were asked to prepare a substantial report for the probabilistic load forecasting competition. 
  5. Understanding their competency in the field. I opened the in-class competitions to external teams, so that our students can see their rankings among the broader community. 
To-do List

Based on my observations so far, the objectives have been met. Nevertheless, there is still room for improvement:
  1. Two of 11 students withdrew the class after the mid-term. Although the drop-out rate was better than the first offering (3 of 13 students withdrew the class), I hope to find a better way to have the students understand whether this is the right course for them.
  2. Although the reading materials for this course were fairly comprehensive, a textbook would help further smooth the students' learning experience. I should complete my load forecasting book before the next offering of this course.
  3. While the in-class competitions were open to external teams, I did not spend any effort validating their methodologies. Next time I should better reinforce the rules and let the teams publish their methodologies. 
It was a great pleasure for me to teach this course. I welcome any comments and suggestions.

Look forward to teaching the next class!


  1. Energy Analytics:

    This is the most toughest course which i ever took in my entire student life but still the takeaways from this course are plenty. The most important thing which i learn from this course is self learning and self motivation to complete the tasks.

    "The more time you spend in this course the more you learn."

    The course was designed in such a way that from every lecture we learn new things like how to use different forecasting model, how to get most benefit from a journal paper and how to use advanced data analytics tools like SAS and R to implement different methods. I believe that if a demo implementation on Tao's Vanilla Benchmark model and how to generate probabilistic load forecast in either SAS or R has been provided during the class than it would be great.

    I particularly like the in-class competition which help me on improving my data analytics and programming skills. The other important takeaway form the course was how to present your work. In almost every lecture we were presenting our previous week's methodology and learning different methods from our classmates. Our GTA did a great job in providing grading before the next class so that we can change our method if its needed.

    I would highly recommend this course for anybody who wants to build his career as an analyst.



    - Devan.

  2. Thank you Dr. Hong for giving me this opportunity to write a feedback for this course.

    I must say energy analytics is a course which will demand your 100% to even survive throughout the course. It is a course which will not only teach you the techniques but will add depth to your patience and perseverance which will definitely add a positive impact in your future. I am very happy and proud to have completed this course successfully. This course has given me the courage and confidence to go out of my comfort zone and survive out there. If I ever get a chance to work in the energy/utilities industry I can assure the company hiring me that I have a solid background in this field and could be a strong asset to them because of this course.

    When it comes to the instructor and GTA, I would like to give Dr. Hong my heartily thanks for giving me chance to put in my 100%. Your methods of teaching were definitely eccentric but were effective and got things done, which mattered the most at the end.

    Bidong Liu, our GTA did his job perfectly without giving us a chance to complain at all. I thank him for his assistance.


  3. This course is a difficult and most worthy course I studied here, and also Dr. Hong has an effect way push the students to study in their off-class time. For the homework and competition, we can find the solution from the papers provided in the class. The process of studying is quite efficient with the schedule.
    The midterm is a meaningful excellent competition which is to do industry load forecast for the upcoming days, which practice the knowledge in the real industry application. At first, I am afraid it is too hard as a beginner, however, when I focus on the tasks and follow the analysis step by step, the forecast result was fairly well, and I learned a lot from it.
    Our TA, Bidong, also gave us a lot of support to study and improve our model, always response in time for help.
    Over all, from this course, a student can build up a solid foundation in data analysis and SAS, which is very helpful for the career development.

    It's a pity I should take this course at the first semester but not last, so I would be benefit more from it when I study other courses.



  4. This course has been one of the toughest course of my academic career. I learnt a lot from this course. This course has taught me to be self -motivated and dedicated to learn more. More importantly, it helped me understand, learn and grow from my mistakes. The coursework is difficult and a silly mistake in your submission or your work it can cost you a lot of marks. But still, there is a lot you can take away from this course. This course requires you to give your 100%. Along with your hard work, it requires you to continuously evolve from your previous week. You can see your improvement not only in your methodology or your analytical skills, but also in your learning curve throughout the course. The more time you spend on it, the more you will learn.
    The impromptu presentations, the in-class competitions, the short competitions etc. was a one of a kind different experience. The course helped me improve my statistical and analytical skills. I learned SAS and little bit of R during the course which i think might be helpful for me in the future.
    Some of the analytical techniques i learnt in the course will definitely add to my resume and help me perform better in a job.
    Thank you to Dr. Hong for his mentor-ship throughout the course. The teaching methodology used by him was rather different and refreshing than all other courses in the Master's Program, which makes this course a huge success.
    Our teaching assistant, Bidong Liu was of great help during the course. Thanks for all his efforts and prompt replies during the course.

    Thank you,

    Mohit Arora

  5. This is one of the greatest courses I have ever attended in my PhD program.It offers abundant contents and pretty much the whole outline of energy forecasting system. Forecasting skills were taught in depth gradually. Helpful papers were assigned to read along with the learning process, which facilitated my deep understanding of what I learned in class.
    Moreover, I was totally motivated by the way Dr. Hong taught this course: rankings of the homework and competitions in the exams. I definitely learned as the most as I could. And this course is worthy the efforts.
    Last but not the least, besides the technical forecasting skills, the most important reason that I would love to recommend this course is because this course also inspired me with an effective way to learn and do researches as a graduate student. Additionally, the abundant industrial background of Dr. Hong's also made the course interesting and special. A set of skills which is important in working place also got to be practiced in class.

    I feel lucky that I attended this course at the early stage of PhD study.

    Thanks a lot for this course.

    Xiazhi Fang


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