UNC Charlotte's M.S. Program in Engineering Management with Energy Analytics Concentration

Update 8/12/2019: revision of the forecasting course (EMGT6910) and computational intelligence course (EMGT6912), and addition of the clustering course (EMGT6113).

Five years ago, I realized the big gap between academic offerings and industry needs in the energy analytics field. I took a brave step and a big salary cut to join UNC Charlotte, with the mission of "producing the next generation of finest analysts for the industry."

Since then, I have trained hundreds of students and working professionals, through the various courses I taught within and outside UNC Charlotte, and through the research activities at the BigDEAL lab that I founded to incubate the elites. I am pleased to see the growing interest in this emerging field of energy analytics and the enthusiasm from the my audience.

However, the gap did not really shrink despite my effort. In fact, the demand increase for energy analysts, which is now called energy data scientists, is more than the amount of graduates I can produce from my small shop!

To further enrich the teaching materials and serve a greater audience in the industry with diverse academic and professional backgrounds, I designed a 60-hour energy analytics curriculum. Many people who took those courses asked me the following question:

Is there a degree program that I can enroll to further my education in this area (energy analytics/forecasting)?

Previously, I directed them to BigDEAL and showed them the path to PhD. For those who were not ready for a PhD program, I didn't have much to offer, unfortunately.

Today, if you asked me the same question, I have a different and better answer:

We are launching a master program specifically designed for those who want to pursue a data science career in the energy industry. 

Upon graduation, the student receives a M.S. degree in Engineering Management with a concentration in Energy Analytics. All courses are offered both online and on campus. If you are an on campus student, you can come to the classroom, and enjoy the face-to-face interactions with the professors and students. If you are a remote student, you can take the courses at home or in your office, and enjoy the flexibility brought by the modern communication technologies.

The program requires a minimum of 31 credits to graduate. These credits can be split into three segments:

1. Required core courses (10 credits)

Students will take the following four core courses at the beginning of the program:

• EMGT 6980 - Industrial and Technology Management Seminars (1)
• EMGT 5201 - Fundamentals of Deterministic System Analysis (3)
• EMGT 5202 - Fundamentals of Stochastic System Analysis (3)
• EMGT 5203 - Fundamentals of Engineering Management (3)

2. Elective and concentration courses (15 credits for thesis option, or 18 credits for project option)

To claim the energy analytics concentration, students should complete two of the following three courses:

• EMGT 5961 - Introduction to Energy Systems (3)
• EMGT 5962 - Energy Markets (3)
• EMGT 5963 - Energy Systems Planning (3)

plus two of the following three courses: 

• EMGT 5964 - Case Studies in the Energy Industry (3)
• EMGT 6965 - Energy Analytics (3)
• EMGT 6910 - Forecasting Techniques, Methodologies, and Practice (3)

In addition, I recommend the following elective courses to the energy analytics students:

• EMGT 5154 - Bayesian Analysis for Human Decision (3)
• EMGT 6113 - Cluster Analysis and Applications (3)
• EMGT 6905 - Designed Experimentation (3)
• EMGT 6912 - Computational Intelligent (3)
• EMGT 6952 - Engineering Systems Optimization (3)
• EMGT 6955 - Systems Reliability Engineering (3)

The on campus students also have the opportunity to take courses from other departments. Up to two of them can be recognized as electives. The remote students may also transfer up to two courses from other universities upon the approval of the graduate director.

3. Capstone (6 credits for thesis option, or 3 credits for project option)

Students interested in conducting research at BigDEAL should choose the 6-credit thesis option. Many real-world problems are good candidate topics for master thesis research. Here are two examples of master thesis research from former BigDEAL graduates (TSG2015; TSG2018). For those who are not interested in research, a 3-credit project option is available too.

We plan to launch the program this fall semester. The university has not updated the catalog yet, so this is a preview of the program. If you are interested, you may start the application HERE. Note that we offer a GRE waiver to the applicants with a bachelor degree in engineering from a U.S. ABET accredited school and two years of relevant industry experience.

Last but not least, Happy Valentine's Day! 

Mark Your 2017 Calendar: Tao's Recommended Conferences for Energy Forecasters

I didn't realize the overdue of this post until I just hit the road for my first trip of 2017. Here is the 2017 list of my recommended conferences for energy forecasters:

1. International Symposium on Energy Analytics (ISEA2017, Cairns, Australia, June 22-23, 2017)

Even if you missed all the other events down this list, you can still find the year rewarding by attending ISEA2017, the first-ever gathering of world-wide energy forecasters. Our generous sponsors, the International Institute of Forecasters (Super Sponsor), Tangent Works (Gigawatt Sponsor) and the State Grid Electric Power Research Institute (Kilowatt Sponsor), have helped bring the registration fees down. There are many reasons to join the party. You will meet the winners of GEFCom2017. You will hear the presentations from world-class energy forecasting researchers and practitioners. You will network with energy forecasting colleagues from more than a dozen countries. And of course, you will enjoy two World Heritage sites side-by-side.

2. Tao's courses

The next two SAS courses on load forecasting have been scheduled in Charlotte, March 27-29.

• Electric Load Forecasting: Fundamentals and Best Practices
• Electric Load Forecasting: Advanced Topics and Case Studies

In addition, I'm going to teach these three courses through EUCI:

• Fundamentals of Utility Analytics: Techniques, Applications and Case Studies
• Introduction to Energy Forecasting
• Long Term Load Forecasting

Stay tuned with the training page of Hong Analytics for the recent updates of all training courses.

3. Conferences from other professional organizations

I will attend the following three, as always:

• International Symposium on Forecasting (ISF2017, Cairns, Australia, June 25-28, 2017)
• IEEE PES General Meeting (PESGM2017, Chicago, IL, USA, July 16-20, 2017)
• AEIC/WLRA Annual Conference (Time & Location TBD)

Look forward to seeing you in these fantastic events!

7 Reasons to Send Your Best Papers to IJF

Last week, I was surfing the Web of Science to gather some papers to read during the holidays. Yes, some poor professors like myself work 24x7, including holidays. Suddenly I found that FIVE of my papers are listed by the Essential Science Index (ESI) as Highly Cited Papers. (Check them out HERE!) What a good surprise for Thanksgiving :)

What's even more surprising is that all of these five papers were published by the International Journal of Forecasting! As an editorial board member of two very prestigious and highly ranked journals, IEEE Transactions on Smart Grid (TSG) and International Joirnal of Forecasting (IJF), I send my best papers to these two journals every year, with an even split. So far, I've had six papers in TSG (not counting two editorials) and six in IJF. How come only my IJF papers were recognized by ESI?

The curiosity ate most of my Thanksgiving time. I was doing some research to answer this question, which eventually led to this blog post. In short,

you should send your best energy forecasting papers to IJF first!

Here is why:

1. No page limit. IJF does not charge authors for extra pages. You can take as many pages as you like to elaborate your idea. The longest IJF paper I've read is Rafal Weron's 52-page review paper on price forecasting. My IJF review on probabilistic load forecasting is 25 pages long. Both reviews are now ESI Highly Cited Papers. 
2. Short review time. A manuscript first reaches EIC, editor and then Associate Editor. It may be rejected by any of these three people. In other words, if it is a clear rejection, the decision would be coming to you rather quickly. If the manuscript is assigned to the reviewers, the first decision typically comes back within three to four months. 
3. Very professional comments. I have seen many IJF review reports by far, as an author, reviewer and editor. Most of them are very professional. Eventually these review comments help the authors improve their work. I haven't seen any nonsense reviewer in the IJF peer-review system, which is quite remarkable! I guess the editors have done their job well by filtering out the nonsense reviewers before passing the comments to the authors. 
4. High quality copy-editing service free of charge. Once the manuscript is accepted, it will be forwarded to a professional copy editor to polish the English for free, so you don't need to spend too much time with wordsmith. You don't need to worry about formatting either, because there is another copy editor doing that before the publisher sends you the proof. 
5. Bi-annual awards. Every other year, IJF awards a prize for the best paper published in a two-year period. The prize is $1000 plus an engraved plaque. Details of the most recent one can be found HERE. Making some money and getting recognized for your paper, isn't it nice? 
6. Publicity. Six years ago when I was pursuing my PhD, I was frustrated about the many useless papers in the literature. I brought my frustration to David Dickey. He made a comment that shocked me for a while. Instead of encouraging me to publish, he said that he had lost interest in publishing papers, because "the excellent papers are often buried by so many bad ones". Having been a professor for about three years, I have to agree with him. I believe in the era of "publish or perish", we have to "publish and publicize" to make our papers highly cited. Publishing your energy forecasting papers with IJF means that you get the opportunity of leveraging various channels, such as Hyndsight, Energy Forecasting, and the social media accounts of Elsevier and those renowned IJF editors. 
7. "Business and economics" category in ESI. This is probably the most important distinction between IEEE Transactions and IJF. Many IEEE Transactions papers (including the ones in TSG) are grouped into engineering, while IJF papers are in the category of business and economics. The business and economics papers get much fewer citations on average than the engineering ones, which makes the ESI thresholds of business and economics lower than those of engineering. For instance, my TSG2014 paper is not an ESI paper, but it would have been if it were published by IJF. 

Unfortunately, IJF's acceptance rate is very low. To increase the chance to have the paper accepted, you should understand how reviewers evaluate the manuscript.

Look forward to your next submission!

Statistical Learning and Machine Learning

Machine learning is a field that studies how to make computers (machines) learn from the data to make predictions or help with data-driven decisions. The term "machine learning" started to become popular in 1990s, and then was somewhat surpassed, if not replaced, by "analytics". In the recent few years, following the buzzword wave of big data and deep learning,  the field of machine learning is gaining some momentum again. Since 1980s, machine learning techniques have been on most papers in the load forecasting literature.

Statistical learning, on the other hand, is not a familiar term to many people. I first got to know this term during my graduate school days, when I was reading the book The Elements of Statistical Learning. Since many techniques and methodologies introduced in this book can be applied to forecasting, I'm using this book as a reference book for my forecasting course this semester.

As of today, I still don't quite understand the difference between the two terms. It seems to me that statisticians like to use the term "statistical learning", while computer scientists and engineers are used to the term "machine learning". Maybe statistical learning has more statistical rigor, while machine learning emphasizes more on the algorithmic aspect. Another discussion about the difference between the two can be found on Wikipedia.

Early this year, I registered two online courses from Stanford University:

• Introduction to statistical learning by Trevor Hastie and Rob Tibshirani
• Machine learning by Andrew Ng

I thought it would be a good investment of my time for three reasons:

• As a researcher in energy forecasting, I want to refresh my memory on machine/statistical learning;
• As an instructor, I want to leverage the materials from other relevant courses when preparing for my forecasting course;
• As a Graduate Program Director for a top-ranked online MS program, I want to see how Stanford University sets up its online courses.

I started both courses almost at the same time, though I was quickly addicted to the statistical learning one and gave up the other course. While both courses are taught by world-class professors and equipped with state-of-the-art online teaching technologies, the one by Trevor and Rob fits my taste much better:

• The instruction style is very much grounded. These two statistics professors are very good at explaining the theory intuitively without involving much math. 
• The subtitle is awesome.
• Most of the quiz questions are testing the understanding of the concept, so I don't have to pull my computers to run R code. 
• The guest speakers bring valuable perspectives and insights about the subject. 
• The design of the progress bar is very simple and effective. Due to my busy schedule for other commitments, I almost quit the course several times. That progress bar helped me stay focus and on track. 

There are other nice features this course offers but I never had time to try, such as free textbook and an online discussion forum. I'm sure they are useful to the ones who can devote more time than me. In January and early February, I was watching video and answering the quiz questions late night before sleep every day. After getting about 35% overall progress, I was distracted by other commitments. Then in early April, I went through a few more lectures to pass the 50% bar. 

I will most likely take this course again, or at least watch the 15-hour video lectures. I would like to recommend this course to the energy forecasting community as well :)

Back to Load Forecasting Terminology.

Mark Your 2016 Calendar: Tao's Recommended Conferences for Energy Forecasters

Last year I made a list of recommended conferences for energy forecasters. I think it is time to refresh the list for my 2016 calendar. Hopefully you will find the list useful as well.

1. Tao's courses

The next two SAS courses on load forecasting have been scheduled in Charlotte, March 9-11.

• Electric Load Forecasting: Fundamentals and Best Practices
• Electric Load Forecasting: Advanced Topics and Case Studies

I'm also going to teach three courses through EUCI:

• Fundamentals of Utility Analytics: Techniques, Applications and Case Studies
• Introduction to Energy Forecasting
• Long Term Load Forecasting

Stay tuned with the training page of Hong Analytics for the recent updates of all training courses.

2. 2016 PES T&D Conference & Exposition (T&D2016, Dallas, TX, May 2-5, 2016)

This is a bi-annual conference gathering "more than 700 companies and thousands of professionals from more than 80 countries around the world". Although the conference does not have much to do with forecasting, the exposition is indeed eye-opening. It is important for an energy forecaster to understand how the power grid is operated. This conference is that one-stop shop to see the new products and service offerings from the major industry players. I've been attending this conference since 2010. This year I'm going to present a paper "from high-resolution data to high-resolution probabilistic load forecasts".

3. 13th International Conference on the European Energy Market (EEM16, Porto, Portugal, June 6-9, 2016)

The conference organizing committee is launching an electricity price forecasting competition. It will be interesting to hear the methodologies used by the winners.

4. 36th International Symposium on Forecasting (ISF2016, Santander, Spain, June 19-22, 2016)

I'm organizing a session "load forecasting: research progress and challenges" at ISF2016. The session includes four talks:
What’s new in load forecasting since 2010? 
    Tao Hong | University of North Carolina at Charlotte, USA
Load forecasting using Lasso based time series methods
    Florian Ziel | European University Viadrina, Germany
Analysis of ex-ante probabilistic load forecasts at the low voltage substation level
    Stephen Haben, Siddharth Arora, Georgios Giasemidis, Tamsin Lee | University of Oxford, United Kingdom
Aggregate consistent forecasting algorithms for hierarchical electricity demand data
    Souhaib Ben Taieb | Monash University, Australia.
    James W. Taylor | University of Oxford, United Kingdom.
    Rob J. Hyndman | Monash University, Australia.

5. IEEE Power and Energy Society General Meeting 2016 (PESGM2016, Boston, MA, July 17-21, 2016)

We will have the 4th offering of our tutorial "energy forecasting in the smart grid era".

6. AEIC/WLRA annual conference (Chicago, IL, September 18-21, 2016)

AEIC Load Research Committee and Western Load Research Association will again have their joint meeting in Chicago.

7. 2016 International Conference on Probabilistic Methods Applied to Power Systems (PMAPS2016, Beijing, China, October 16-20, 2016)

This is also a bi-annual conference. The conference plays a big emphasis on dealing with uncertainties in power systems,  which is highly related to probabilistic forecasting. The full paper is due on March 31.

Happy Valentine's Day, and happy traveling!

JREF: Journal Rankings in Energy Forecasting (2015)

Publication is a very important activity for researchers. It is often tied to graduation, tenure, promotion, funding, and so forth. As far as I know, there is not yet a journal for energy forecasting.

As an author, I often asked myself,

Which journal shall I send my paper to?

As a reader, I had a similar question,

Which journals shall I read papers from?

Since I want my papers to show up with the other best papers, the interaction between the two questions above becomes

Where are the best energy forecasting papers?

Reading for Writing

Writing skills are badly needed in the professional world, no matter in the industry or academia. I have seen many people (mostly international students, including myself) struggling with their writing skills. I don't believe there is any shortcut but constant and regular writing practice to improve writing skills. In addition, I think reading is a good compliment to writing practice. Here I'm putting together a list of recommended books.

The first two are on general writing (not necessarily scientific writing):

• On Writing Well by William Zinsser
• The Elements of Style by William Strunk and E. B. White

Then here are two books for scientific writing:

• Scientific Writing and Communication: Papers, Proposals, and Presentations by  Angelika Hofmann
• Writing Science: How to Write Papers That Get Cited and Proposals That Get Funded by Joshua Schimel 

I also got a list of classic novels from my colleague Jason Wilson (co-author of my TSG2014 paper):

• Slaughterhouse Five by Kurt Vonnegut, Jr.
• Gulliver’s Travels by Jonathan Swift
• Ulysses by James Joyce
• Hamlet by Shakespeare 
• 1984 by George Orwell
• Brave New World by Aldous Huxley
• Animal Farm by George Orwell
• The Grapes of Wrath by John Steinbeck
• A Farewell to Arms by Ernest Hemingway
• The Old Man and The Sea by Ernest Hemingway
• Charlotte’s Web by E.B. White
• War and Peace by Leo Tolstoy
• Jurassic Park by Michael Crichton

Here I'm trying not to recommend a list of scientific papers in energy forecasting. I would rather suggest that we focus on the analytical and technical aspects of those papers.

Happy reading!

Keep Reading Every Day: Tao's Recommended Websites for Energy Forecasters

Last month, I moved all of my students to the newly renovated BigDEAL. At the same time, I started the BigDEAL seminar series. Part of the seminar is an information sharing session - each participant takes a 5-minute slot to share the most interesting readings of the week with the audience. The purposes are to:

1. practice the story-telling skills; 
2. broaden their views of the field; and 
3. establish the habit of reading every day.

Mark Your 2015 Calendar: Tao's Recommended Conferences for Energy Forecasters

Update (3/1/2015): Due to high demand of my load forecasting courses, we added an offering at the New York City in May. 

Update (2/18/2015): I just confirmed two more speakers for ISF2015. Now we have six presentations in two sessions. 

Recently I received many inquiries about recommended energy forecasting conferences in 2015. First of all, I have never attended a conference that is perfectly designed and organized for energy forecasters, which motivates me to organize the ultimate energy forecasting conference. If you have to wait for this ultimate one, close this page and stay tuned for another two years. Otherwise, keep reading. I will provide a list of 6 venues for you to consider, in the chronological order.

1. Tao's load forecasting courses (May 27-29, 2015, New York, NY)

I have taught the fundamental course 15 times. More than 150 energy forecasters have attended the course. (See some statistics based on the first 10 offerings) Recently I have developed a one-day advanced level course for those who want some in depth coverage of the subject and hands on experience of SAS. The links to the courses are listed below:

• Electric Load Forecasting: Fundamentals and Best Practices
• Electric Load Forecasting: Advanced Topics and Case Studies

2. 35th International Symposium on Forecasting (ISF2015, June 21-24, 2015, Riverside, CA)

ISF2015 is a great conference if you want to learn the frontiers of forecasting. I'm organizing an energy forecasting session at ISF2015. The two sessions includes six talks with a balanced mix of state-of-the-art research and practice.

Session Title: Frontiers in Electricity Demand Forecasting I: The State of The Practice
Chair: Tao Hong (University of North Carolina at Charlotte, USA)

• SAS Energy Forecasting: Hourly load forecasting for all horizons
• Bradley Lawson (SAS, USA)
• Combining sister load forecasts
• Tao Hong (University of North Carolina at Charlotte, USA)
• Bidong Liu (University of North Carolina at Charlotte, USA)
• MEFM: An R package for long-term probabilistic forecasting of electricity demand
• Rob J. Hyndman (Monash University, Australia)

Session Title: Frontiers in Electricity Demand Forecasting II: Probabilistic Electric Load Forecasting
Chair: Tao Hong (University of North Carolina at Charlotte, USA)

• Quantile regression algorithms for forecasting uncertainty in electricity smart meters data
• Souhaib Ben Taieb, King Abdullah University of Science and Technology, Saudi Arabia.
• Rob J. Hyndman, Monash University, Australia
• Marc G. Genton, King Abdullah University of Science and Technology, Saudi Arabia.
• Electricity demand interval forecasting with Quantile Regression Averaging
• Jakub Notowarski (Wrocław University of Technology, Poland)
• The myths of residual simulation for probabilistic load forecasting 
• Jingrui Xie (University of North Carolina at Charlotte, USA)

3. 3rd International Conference Energy & Meteorology (ICEM2015, June 22-26, 2015, Denver, CO)

Weather drives electricity demand and wind/solar power generation. The conference has a unique focus on the interdisciplinary field of energy and meteorology. For more information, please read Make a Difference in the Energy & Meteorology World written by the guest blogger and conference chair Alberto Troccoli.

4. Modern Electric Power Systems Conference 2015 (MEPS2015, July 6-9, 2015, Wroclaw, Poland)

If you are in the area of load and price forecasting, you must be familiar with Rafal Weron and his book "Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach" and the recent IJF open access paper "Electricity Price Forecasting: A Review of the State-of-the-art with a Look into the Future". Rafal is based in Wroclaw, Poland. He is also a key player on the organizing committee of MEPS2015. I'm sure he will present some nice forecasting work at the conference.

5. IEEE Power and Energy Society General Meeting 2015 (PESGM2015, July 26-30, 2015, Denver, CO)

We have organized two days of agenda on energy forecasting. A full-day tutorial on "Energy Forecasting in the Smart Grid Era", and a full-day session of GEFCom2014 finalist presentations. I will write another blog post when the schedule is finalized.

6. AEIC/WLRA annual conference (time & location TBD)

This is a joint conference organized by AEIC Load Research Committee and Western Load Research Association. The two groups used to organize conferences separately. Last year was the first time they held a joint conference. I hope they will continue the joint conference this fall.

Mark your calendar and enjoy the trips!

10 Recommended Papers for GEFCom2014 Contestants

Update 9/20/2014:

Rafal Weron's review on price forecasting is available on Science Direct with open access. The probabilistic load forecasting review paper written by Shu Fan and myself is currently under review by IJF. The link to the working paper is listed below.

Update 8/14/2014:

Thanks to Rob Hyndman, who generously put Rafal Weron's forthcoming IJF paper on the web. Now we have a super well-written review paper on price forecasting on the list as Ref [11]. This paper will be published in the coming issue of International Journal of Forecasting.

The Global Energy Forecasting Competition 2014 (GEFCom2014) is the first probabilistic forecasting competition in the power and energy industry. As of today, over 200 people from more than 40 countries have signed up the interest list. To help the folks quickly get use to the theme of this competition, we organizers collaboratively picked up 10 recommended papers for GEFCom2014 contestants.

Ref [1-3] are general readings on probabilistic forecasting, energy forecasting and the previous competition respectively. Ref [4-6], Ref [7-8] and Ref [9-10] are for load, wind and solar forecasting respectively. We did not recommend any specific paper on probabilistic price forecasting. The contestants on the price forecasting track may refer to the recommended papers on load and wind forecasting. The literature on probabilistic wind power forecasting is dominantly more extensive and mature than the other three categories. Therefore, we would highly recommend the contestants to read Ref [7-8] regardless which track to work on. Shu and I are preparing the tutorial review on probabilistic load forecasting (Ref [4]), which will serve as a general guideline as well.

Three Free Online Non-Forecasting Courses

As mentioned in my recent Foresight article (Energy Forecasting: Past, Present and Future), virtually all types of energy forecasts are connected. I believe that the best way to further advance our knowledge in such an interdisciplinary field is to take an interdisciplinary approach. Statistics is obviously a core subject in energy forecasting. On the other hand, many other important subjects are essential for developing useful forecasts, such as mathematics, electrical engineering and computer science. In this post, I'm going to introduce three courses from MIT OpenCourseWare that are very helpful to energy forecasters. All of these three courses have video lectures available.

Tao's Recommended SAS Courses for Energy Forecasters

Mastering advanced forecasting tools is one of the Three Skills of the Ideal Energy Forecaster. Most, if not all of my clients are using or going to use SAS for load analysis, load research and load forecasting. In my previous consulting jobs, I sometimes recommended learning path customized for a client based on his/her background. Below is my recommended list of SAS courses in chronological order for an entry level energy/utility analyst, assuming that the person has no experience with SAS or statistical forecasting. The numbers in the brackets indicate the amount of time (in hours) to learn the subjects.

Tao's Recommended Reading List for Energy Forecasters

At the past IEEE PES General Meeting in Vancouver, there was an interesting discussion on the noisiness of the energy forecasting community during our tutorial and panel sessions. First my colleague and friend Prof. Pierre Pinson mentioned that the current wind forecasting research community is too "noisy"; there are too many wind forecasting papers being published every year; he (or maybe a student of his) had to read 450 papers to write the literature review for the PhD dissertation.

450 papers, FOUR HUNDRED AND FIFTY!

"You were too lucky," I made a comment, "I had to read 1200 papers on load forecasting to get my PhD!"