Friday, January 12, 2018

RTE Forecasting Challenge 2018

RTE, the French TSO is organizing its forecasting challenge for the second time. This short-term winter electricity demand challenge includes two parts, point forecasting and probabilistic forecasting. The registration will open until January 21, 2018, followed by both parts of the challenge running simultaneously from January 22 to February 10, 2018.


If you are interested, you can register at datascience.net. For non-French speakers, if you see the website in French, you may click the UK flag on the top to view the English version.

According to Geert Scholma, who took the 4th place in the first RTE forecasting challenge, it was "the most exciting competition so far". I guess this one will be very competitive too.

It's  nice to kick of the new year with such an interesting competition, isn't it? 

Thursday, December 21, 2017

Short-term Industrial Load Forecasting: a Case Study in an Italian Factory

Frequent readers of this blog know how much I dislike today's peer review system in the academic world. The experience this time was truly pleasant.

About two years ago, I submitted a paper to PMAPS2016, which later turned into a TSG paper. The quality of the review comments we received from PMAPS on the original submission was super high, way beyond my expectation. I managed to get the contact information from that reviewer, Antonio Bracale. I then reached out to him to express my appreciation. Later Antonio came back to me with a collaboration proposal on industrial load forecasting. This is the first paper from our collaboration. 

The load forecasting literature has been so dominated by forecasting at high voltage level. The smart grid initiatives stimulated many papers at medium or low voltage level. Nevertheless, industrial load forecasting is still an important area that has not yet been extensively studied. This is certainly not the first industrial load forecasting paper, but our findings from the real-world data collected at an Italian factory may be helpful to the others dealing with similar problems. 

Citation
Antonio Bracale, Guido Carpinelli, Pasquale De Falco and Tao Hong, "Short-term industrial load forecasting: a case study in an Italian factory," 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Torino, Italy, September 26-29, 2017

Short-term Industrial Load Forecasting: a Case Study in an Italian Factory

Antonio Bracale, Guido Carpinelli, Pasquale De Falco and Tao Hong

Abstract

Excellence in the planning and operations of power systems largely relies on accurate forecasts of loads. Although load forecasting has been extensively studied over the past several decades, the scientific community has not yet paid much attention to industrial load forecasting. The electricity demand of factories depends on many factors, of which some are uncommon or not as important in the classical load forecasting models. For instance, the scheduled processes and work shifts are very important to forecasting short-term industrial loads. In this paper, we offer some insights into modeling industrial loads. We develop a set of multiple linear regression models for an Italian factory that manufactures transformers. The proposed models outperform two other benchmark models for forecasting industrial loads 24 hours in advance.

Friday, December 8, 2017

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

Wednesday, December 6, 2017

UNC Charlotte Students Winning All Top 3 Spots of NPower Forecasting Challenge 2017

Every year, RWE npower, a large electricity generator and supplier of gas and electricity based in the United Kingdom hosts a forecasting competition to recruit summer interns. While the internships are only open to UK students, the competition is open to the world. Hundreds of students and working professionals have participated in these npower forecasting challenges in the past few years. Every time I sent a few students to the competition. Every time, they took a few the top spots (see 2015 electricity, 2015 gas, and 2016).

This year, 19 UK teams and 26 international teams joined the competition. Npower created a separate leaderboard for the UK students. The top 1 UK team would rank #9 among all teams. The screen shot below shows the top 8 teams. The official site is HERE.



For the first time, my students took all top 3 spots. They came from two of my classes: Technological Forecasting and Decision Making (Spring 2017) and Energy Analytics (Fall 2017). Most of them are currently enrolled in the master capstone projects under my supervision. My courses are among the most challenging ones in the college. The students had to spend tremendous amount of time to earn the credits. I'm glad that they have acquired some useful skills from the class and showed off their analytical capabilities through the competition. I asked the top teams to summarize their methodology in the comment field below.



Congratulations, 49ers!

Monday, November 27, 2017

Masoud Sobhani - From Petroleum Engineer to Load Forecaster

Today (November 27, 2017), Masoud Sobhani just defended his MS thesis on data cleansing, the first BigDEAL thesis authored by a non-Chinese student.

Masoud was a petroleum engineer in Iran. He migrated to the U.S. several years ago. He first came to my office in 2015 with inquiries about our MS Engineering Management program, when I was the program director. At that time he could barely speak English. Nevertheless, I admitted him to the program mainly because of his solid academic background and industry experience in the energy sector.

He started the program in Spring 2016 to pursue a non-thesis master degree, planning to graduate in Summer 2017. Due to the challenging nature of my courses (see some student comments HERE), most of the non-thesis master students in our MSEM program try their best to avoid them. Masoud is certainly an exception. He managed to take all my courses during his tenure in the program. At Npower forecasting challenge 2016, Masoud took a top 3 place.

In Spring 2017, he came to me to discuss the possibilities of pursuing a PhD under my supervision. Recognizing him as the top student in the program, I agreed to take him as my doctoral student with the condition that he completes a MS thesis by the end of the year. He took the challenge. From May to November, he passed SAS Advanced Programmer certification exam, identified his thesis topic, designed and implemented a novel data cleansing algorithm, and finished his 10,000-word thesis. The defense was very well done.

Congratulations, Masoud, and best luck with your PhD journey!

Tuesday, October 31, 2017

NPower Forecasting Challenge 2017

It's time for npower forecasting challenge 2017! The registration will close on Nov 2nd, 2017. You don't have to be a UK student or citizen to join the game, but the prizes and internship opportunities are for the UK people only. The organizer also told me that you may register as a single-person team if you like. Since the registration form asks for multiple names, you may put your own name and contact information twice.

This is the list of blog posts about the previous npower forecasting challenges, where you can find our winning methodologies. 

Look forward to seeing you in the competition!

Thursday, September 14, 2017

Who's Who in Energy Forecasting: Geert Scholma

I got to know Geert Scholma from NPower Forecasting Challenge 2015, where he outperformed my BigDEAL students on the leaderboard. Since then, he has been topping the NPower leaderboard every time. Recently, as a winner of the qualifying match of GEFCom2017, he presented his methodology at ISEA2017.

Geert lives in Rotterdam, The Netherlands. He has a strong focus on data science and the energy transition, with a masters degree in physics and 5 years experience as an Energy Forecaster for Energy Retail Company and E.On spin-off Uniper Benelux.

Sunday, September 10, 2017

TAO: The Analytics Officer

Today is the 7-year mark after my PhD defense. It happens to be the "Teachers' Day" in China, a holiday dedicated to the teachers. I just created a new label "9/10" to collect the posts published on this same date.

I'd like to announce a new blog on this special day, TAO: The Analytics Officer, a blog of data science for the current and future Chief Analytics Officers.

As you, my audience of this blog, are following me, I am following you too. I'm very happy to see that many of you have been promoted one or more times during the past five years. I'm sure that some of you are trying to get that promotion or climb up the career ladder. I started TAO to give you a hand by sharing some of my successful experience in helping others.

Thursday, August 31, 2017

Factors Affecting Load Forecast Accuracy

In some of my papers, I tried to present fairly comprehensive case studies that cover various load zones. I often use a primary case study to illustrate the flow or components of a proposed methodology. After that I apply the same methodology to a secondary case study to show that the same methodology works well on other zones. A by product of this publication process is a series of benchmark results on various of load zones. You may have realized that the same methodology or model typically results in different forecast errors on different load zones.

Friday, August 18, 2017

IEEE PES Announces Winning Teams for Global Energy Forecasting Competition 2017



More than 300 students and professionals from more than 30 countries formed 177 teams to compete on hierarchical probabilistic load forecasting, exploring opportunities from the big data world and tackling the analytical challenges.

PISCATAWAY, N.J., USA, August 18, 2017 – IEEE, the world's largest professional organization advancing technology for humanity, today announced the results of the Global Energy Forecasting Competition 2017 (GEFCom2017), which was organized and supported by the IEEE Power & Energy Society (IEEE PES) and the IEEE Working Group on Energy Forecasting (WGEF).