Computational Intelligence

Last semester at UNC Charlotte, I taught a new graduate-level course Computational Intelligence.

Student Profile

The course started with 15 students on 8/21/2019, and ended with 10 students:

• INES PhD students: 6 => 6;
• ECE PhD student: 1 => 1;
• Applied Energy student: 1 => 1;
• MSEM on campus student: 6 => 1; 
• MSEM remote student: 1 => 1.

The picture was taken at the last lecture with the on campus students, the Teaching Assistant, and myself.

Computational Intelligence Class 2019
From left to right: Tao Hong; Richard Alaimo; Vinayak Sharma; Sepehr Sabeti; Shreyashi Shukla; Deeksha Dharmapal; Bhav Sardana; Zehan Xu; Masoud Sobhani (TA); Yike Li. Students not on the picture: Allison Campbell and Nima Nader. 

Topics

I developed this course to help the students better grasp the fundamentals in this hype of AI/ML. The following topics were covered in Fall 2019.

• Mathematical programming and statistical forecasting
• Fuzzy set, fuzzy logic, fuzzy regression, and fuzzy clustering
• Support vector machine and support vector regression
• Neural networks, neural fuzzy systems, recurrent neural networks, and deep learning
• Metaheuristic search algorithms, A*, simulated annealing, and tabu search
• Artificial immune systems
• Genetic algorithms
• Swarm intelligence, ant colony optimization, and particle swarm optimization
• Bayesian network
• Designing your tools

Assignments and Exams

The course has 4 homework assignments, a 3-phase course project, a mid-term exam and a final exam.

Traditionally, when this course is offered by Industrial & Systems Engineering faculty, the applications are various optimization problems. When I took Soft Computing during my PhD days at NC State University, we were mostly solving nonlinear optimization problems as homework, project and exam problems.

However, there are not many situations in daily life for us to find a global optimal solution of a sophisticated function made of several trigonometric functions. Instead of penetrating the homework and exam problems with unrealistic mathematical equations, I had the students work on realistic problems for most part of the semester.

For instance, the first homework was to predict my son Leo's jump rope performance. Leo is a very competitive jumper. In the national jump rope competition last year in Florida, he ranked top 5 among 10 and under kids for speed jump. But before the competition, I had to decide whether to have him participate or not. Students were asked to make that decision given his training records. I also taught some Texas Hold'em strategies when teaching Bayesian network after a light coverage of the traditional rain/sprinkler example. Some students used Texas Hold'em as their final project topic.

The final exam was jointly held with my collaborator Robertas Gabrys. The exam problem was on variable selection for forecasting, which I believe is a much more commonly seen problem than those traditional non-linear optimization problems.

Teaching Methods

For my other PhD level courses, I have minimized traditional lectures and maximized the homework. The classroom becomes a discussion forum, where the students learn from each other and myself by sharing their homework experience. The more efforts students put into the homework, the more they learn on their own and from each other. It was super effective, as many students improved their forecasting skills rapidly in a semester.

This course is different. There is a lot of theoretical contents to cover. My goal is not to have them be an operator of a black box. I want them to understand the details and fundamentals of those algorithms. Therefore, I was teaching them to hand-calculate parameters for a neural network, hand-calculate parameters for support vector regression, and so forth. I wanted to break down those fancy concepts, so that they can eventually build their own CI tools from scratch.

I greatly appreciate the students for their time being the first batch of this class and all the efforts they devoted to the course. This course is currently scheduled for every other year, so the next offering is Fall 2021. If you have any ideas or comments that can help me improve this course, please let me know!

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! 

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

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!

A Five-minute Introduction to Electric Load Forecasting

I was recently interviewed by Prof. Galit Shmueli for her recently launched free online course Business Analytics Using Forecasting. In this interview, I gave a 5 minutes introduction to electric load forecasting, discussing the special characteristics of load forecasting and what is needed for successful solutions.

Hong Analytics One Year Anniversary: A 60-hour Energy Analytics Curriculum

One year ago, I incorporated Hong Analytics LLC to house my consulting practices. At this anniversary, I would love to review a major milestone that was recently accomplished:

A 60-hour energy analytics curriculum. 

One of the frequently asked questions I have been getting from my clients is

Tao, can you recommend some training courses I should take?

If a SAS user asked me this question, my answer would be easy:

Check out the list of my recommended SAS courses.

While the list was put together two years ago, it can no longer address all the needs from my clients. For instance, some clients want to know more about the applications of analytics in the utility industry; some do not have access to advanced analytics software; some need to develop wind and solar forecasts rather than load forecasts; some are interested in the state-of-the-art load forecasting methodologies.

To bridge the gap, I have developed a 60-hour (or 7.5 days) energy analytics curriculum. The curriculum is made of 5 courses as illustrated below:

A 60-hour Energy Analytics Curriculum

1. T101/ Fundamentals of Utility Analytics: Techniques, Applications and Case Studies
2. T201/ Introduction to Energy Forecasting
3. T301/ Electric Load Forecasting I: Fundamentals and Best Practices
4. T302/ Long Term Load Forecasting
5. T401/ Electric Load Forecasting II: Advanced Topics and Case Studies

If you are new to the industry, analytics, or both, you can start with T101. If you are new to energy forecasting, T201 would be a good start. T301 is the flagship course that has accommodated a wide range of audience. If you are a long term load forecaster using MS Excel, you may take T302. If you are looking for the secret sauce, T401 is the level you should reach.

Did I forget to develop a master level course? No. Nobody can become a master in 60 hours. One may be able to talk like an expert after completing this 60-hour curriculum. To reach the master level, one has to spend 10,000 hours on the subject. Of course, the BigDEAL would be the #1 choice for energy forecasters!

The next offering of Fundamentals of Utility Analytics has been scheduled in Chicago, IL, August 10-11, 2016. Look forward to seeing some of you over there!

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.

Hong Analytics New Course: Fundamentals of Utility Analytics

Five or six years ago, I had the idea of developing an analytics course for the professionals in the utility industry. I ran the idea through Jim Burke. The first response he gave me was

What's analytics?

After I explained the meaning of analytics, Jim recommended me preparing something more conventional, such as statistics and/or operations research for the utility professionals. Since then, the course outline for that utility analytics course has been quietly sitting on my hard drive.

Nowadays, despite the fact that analytics is being well-known to the utility industry (see my recent post on Analytics, Smart Grid and Big Data: Are They Like Teenage Sex?), there is still that gap between analytics education and business needs. In other words, it is really difficulty to find instructors with the knowledge base in both analytics and power systems. Although there are utility analytics conferences and meetings where speakers talk about case studies or visions at conceptual level, few people are offering courses in utility analytics to teach people how to make the techniques work in the real-world applications.

In 2013, I went back to the university to try to fill this gap in the academic environment (see Why I Left a Great Place to Work - from Industry to Academia). Understanding that many industry professionals do not have the bandwidth to go through out graduate program, I decided to develop and offer a series of short courses through Hong Analytics.

So here comes another new Hong Analytics course, to be offered for the first time through EUCI in Boston, MA, March 14-15, 2016.

Fundamentals of Utility Analytics: Techniques, Applications and Case Studies

Analytics — the scientific process of transforming data into insight for making better decisions — is now a must-have skill for almost all utility professionals: from planning, to operations, to trading, to mid-level management, to the C-suite, and everywhere in between. During the recent decade this knowledge requirement has emerged and accelerated in the utility industry. The deployment of various sensors and meters has brought a large amount of data to the industry.  Increased computing power has made quantitative analytics plausible, timely and economically feasible.  Meanwhile, the advancement of information technologies has enabled utilities to make real-time operational decisions that are fact-based and data-driven. A challenge, though, has been to compile a coherent approach for professionals having different skill sets within the utility to leveraging the multiple applications of analytics to achieve better key performance indicator.

This course provides an introduction to analytics in the context of the electric power systems and industry. The course is designed for engineers, planners, analysts and managers who are either new to the utility industry or looking to develop a better understanding of how to use analytics across the entire organization. Through a number of diverse case studies and hands-on exercises, the attendees will gain a fundamental understanding of how to apply analytics within the utility industry, the classical and emerging problems, and how to tackle those problems using the quantitative techniques in the enterprise environment.

For more information, such as course outline and registration link, please visit the course page HERE. 

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!

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.

Hong Analytics New Course: Long Term Load Forecasting

I recently developed a new course on long term load forecasting. The first offering is scheduled in Nashiville, TN, December 7-8, 2015. This offering includes a one-and-a-half days course and a half day post-course workshop. Below is a summary of the course and workshop. If you are interested, please find the registration link from Hong Analytics.

Long Term Load Forecasting

Load forecasting is a fundamental element in utility business operations and planning processes. During the past 120 plus years, load forecasting methodologies have evolved as the industry and related technologies progress. Consequently, many classical methods are no longer suitable in addressing today's challenges in the utility industry.

Fall 2015 In-class Probabilistic Load Forecasting Competition

Update: The final ranking is available HERE.

The second exam of my Energy Analytics course this semester is a probabilistic load forecasting competition. The competition rules are listed below:

• The competition will start on 10/22/2015, and end on 11/25/2015. 
• The historical data will be released on 10/22/2015.
• The year-ahead hourly probabilistic load forecast is due on 11:45am ET each Wednesday starting from 10/28/2015. 
• The exam is individual effort. Each student form a single-person team. No collaboration is allowed.
• The student can not use any data other than what's provided by Dr. Tao Hong and the U.S. federal holidays.
• Pinball loss function is the error measure in this competition. 
• The benchmark will be provided by Dr. Tao Hong. A student receive no credit if not beating the benchmark nor ranking top 6 in the class. 
• No late submission is allowed. 

I would like to open this competition to students and professionals outside my class. If you are interested in joining the competition, please contact me for detailed instructions.

Recommended readings:

• Probabilistic Electric Load Forecasting: A Tutorial Review
• Long Term Probabilistic Load Forecasting and Normalization with Hourly Information

Fall 2015 In-class Short Term Load Forecasting Competition

Update: The final ranking is available HERE.

This semester, I'm teaching my Energy Analytics course. The first exam is a short term load forecasting competition for the Dominion load zone under PJM. The competition rules are listed below:

1. The competition will start on 10/5/2015, and end on 10/9/2015. 
2. The exam is individual effort. Each student form a single-person team. No collaboration is allowed.
3. The day-ahead hourly load forecast is due on 11:45am ET each day. The first forecast of 24 hourly loads for 10/6/2015 is due on 11:45am 10/5/2015. The fifth and last forecast of 24 hourly loads for 10/10/2015 is due on 11:45am 10/9/2015.
4. The student can use any data (load, weather, calendar, economy, location, etc.) they can find to make the forecast as accurate as possible.
5. MAPE is the error measure in this competition. The preliminary MAPE is calculated on daily basis using preliminary hourly load data published by PJM. The final MAPE is calculated using historical metered load data published by PJM in November.The final rankings and scores are based on the final MAPE. 
6. The benchmark is the day-ahead load forecast released by PJM on 11:45am ET each day. A student receive no credit if not beating the benchmark nor ranking top 6 in the class. No late submission is allowed. 

I would like to open this competition to students and professionals outside my class. If you are interested in joining the competition, please contact me for detailed instructions. 

Tao Hong: Be Honest

Below is my recent interview with T&D World Magazine. The original version is HERE.

Tao Hong: Be Honest

Tao Hong always sticks to his integrity, especially when it comes to energy forecasting. As graduate program director and EPIC assistant professor at the Systems Engineering and Engineering Management Department at the University of North Carolina at Charlotte, he said the best advice he has ever received is to always be honest.

Sometimes we are pressured to make the forecasts following someone else' personal agenda. Rather than modeling other people's mind and making fraudulent forecasts, we should always stick to our integrity.

Hong will be presenting Energy Forecasting in the Smart Grid Era (blog post) at the 2015 IEEE PES General Meeting, being held July 26-30, in Denver, Colorado. The full-day tutorial covers how wide-range deployment of smart grid technologies enables utilities to monitor the power systems and gather data on a much more granular level than ever before. While the utilities can potentially better understand the customers, design the demand response programs, forecast and control the loads, integrate renewable energy and plan the systems, etc., they are facing analytic issues with making sense and taking advantage of the "big data".

Hong Analytics LLC

I started my career as a consultant when I was pursuing my first MS degree. Then I developed my MS thesis and PhD dissertation, each from a consulting project. Both of them were later commercialized and now being used by many utilities worldwide. Most of my research ideas were inspired by the consulting projects through working closely with the utility analysts, managers and executives. After testing and validating these research ideas through several field implementations, I converted them to teaching materials for my courses. These courses fully packed with solid fundamental knowledge, best industry practices, advanced topics and a wide range of case studies have been very well received by the industry (see what the clients are saying), generating even more consulting business.

As mentioned in why I left a great place to work - from industry to academia, majority of my academic job is made of consulting, teaching and research. Going through such a cycle has been quite rewarding for both my clients and myself. Nevertheless, some of my clients are still complaining about the lengthy and verbose contracting process. To make the cycle even more enjoyable and productive, I decided to incorporate Hong Analytics LLC to formally house my consulting practices outside the university.

Hong Analytics provides premium training and consulting services in the area of energy and retail analytics. While being a full-time professor, I can allocate up to one day per week of my time to Hong Analytics. I would like to take on those projects that are relatively small in scale (i.e., less than $100k) and relatively short in performance period (i.e., less than 6 months). While I'm happy to help with conventional short and long term load forecasting projects, I would give preference to the projects that are challenging and requiring heavy-duty analytics. Since revenue is not the a KPI of my business, I am also willing to provide the services free of charge for those projects with high value to the industry.

For more information, please visit www.honganalytics.com.

IIF Student Forecasting Awards for Energy Analytics

Today is Labor Day. Thanks to the International Institute of Forecasters (IIF), I received my first Labor Day gift, an email notification of the IIF student forecasting award for five years.

While the interested readers can get the details from the IIF website, here are a few highlights:

• The awards are offered by the IIF to the top-performing students in undergraduate and graduate level forecasting courses. 
• No more than 20 awards are available across all universities. 
• No more than one subject can be eligible for an IIF award at the same university. 
• Each star student will receive $100, a Certificate of Achievement from the IIF, and one year’s free membership of the Institute, with all its attendant benefits. 

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.

An Enjoyable Journey with 100+ Forecasters

The next offering of my load forecasting courses is scheduled on March 9-11, 2016 in Charlotte. Registration is open here: https://support.sas.com/edu/schedules.html?id=1326

Among all the professional commitments, I love teaching the most. I teach all over the places, from industry trainings to university lectures, from conference workshops to private onsite classes. This blog post is about an enjoyable journey of teaching my course, "Electric Load Forecasting: Fundamentals and Best Practices" over the last year.

1. An idea

After working on load forecasting projects for several years with many utilities, I realized that I had never got a comprehensive, formal and rigorous classroom training on this subject.

IEEE Tutorial: Energy Forecasting in the Smart Grid Era

Update 3/5/3014: We will offer this tutorial again at 2014 IEEE Power and Energy Society General Meeting in National Harbor, MD (Washington D.C. Metro Area).
-------------
Instructors:

Dr. Tao Hong, SAS, USA 

Dr. Shu Fan, Monash University, Australia

Dr. Hamidreza Zareipour, University of Calgary, Canada

Dr. Pierre Pinson, Technical University of Denmark, Denmark

Abstract:

Wide range deployment of smart grid technologies enables utilities to monitor the power systems and gather data on a much more granular level than ever before. While the utilities can potentially better understand the customers, design the demand response programs, forecast and control the loads, and plan the systems, etc., they are facing analytic issues with making sense and taking advantage of the “big data”. This tutorial offers a comprehensive overview of energy forecasting to utility analysts, planners, operators and their managers. The participants will learn the fundamentals and the state-of-the-art of load, price and wind forecasting.

Download outline, full abstract and instructors' bio (pdf)

The tutorial will be taught at 2013 IEEE Power and Energy Society General Meeting in Vancouver, Canada, July 21, 2013. For more information, please contact Dr. Tao Hong (hongtao01@gmail.com).