Wednesday, May 31, 2017

Tao Hong - Energy Education Leader of the Year

Earlier this month, I was honored to be named as the Education Leader of the Year by Charlotte Business Journal (CBJ) at the Energy Inc. Summit.

My friend Alyssa Farrell took a video of the award reception speech, where I gave a "forecast" about the energy industry:
The energy companies will be moving more Gigabytes of data than GWh of electricity. 
Here is the 1-minute speech:


When I was first informed about this award, I didn't realize its prestige. Then I started getting congrats from friends, colleagues, and even the dean. I guessed that it must be something big. After the award ceremony, CBJ put my profile in print and online (Energy Leadership Awards: Putting big data to work for energy). UNC Charlotte also featured the story in its campus news letter.

Since the CBJ article is behind a paywall, I'm sharing the interview with the audience here.

What drew you to a career in education? How long have you been in that field?

Before coming back to the academia, I was working at SAS, one of the very best employers in the world. Part of that job was to teach classes internationally. My primary audience was industry professionals. Through that experience, I found a big gap between what the industry needed in terms of analytics and data science and what universities were offering through various academic programs. I thought I could be that person to help bridge the gap, so I took a mission of producing the finest data scientists for the energy industry and joined UNC Charlotte. I've been on this academic job for almost 4 years.

What’s the most important part of what you do?

I would say students are the most important part of what I do. I consider students as my products. I want to make the finest products for the industry, so everything I do is centered around the students: I try to pick the best raw materials, perfect them as much as possible, and then put them in the best place of the market. As a professor, my job can be mainly categorized in three pieces, research, teaching and services. These three closely tie together. The industry partners bring me their problems to work on; I help them solve these problems and then bring the research findings to the class; then they keep sending me new problems and hiring my students.

How do you see energy education evolving?

I think the evolution of the energy education is two-fold. First, it has to be interdisciplinary. It's no longer the job of one department, such as electrical engineering or mechanical engineering. We have to involve many academic departments to educate the workforce for the energy industry. Some of them should even go beyond the college of engineering, such as policy, economics, statistics and meteorological science.

Talk a little about the BigDEAL Lab. What does that mean for students?

It is the best place to be if you want to be the elite data scientists in the energy industry. BigDEAL students have the opportunity to solve the most challenging analytic problems in the industry; they have access to the state-of-the-art software donated by our industry partners; they can leverage many data sources that no other universities have access to. As a result, BigDEAL students have been taking top places in many international competitions and been chased by many renowned employers in the industry.

What role does UNCC play in the energy industry - both locally and nationally?

UNCC have been training many energy professionals in Carolinas and delivering many fresh graduates to the local energy industry. Nationally, UNCC sets a great example of industry-university collaboration.

What makes UNCC’s research so valuable?

We are fortunate to be located in a large city and surrounded by many enthusiastic industry partners. The research problems we work on are from the realworld rather than ivory tower. They tend to be very practical and meaningful to the industry.

Is there a key initiative you’re working on? 

During the recent few years, I've been experimenting a crowdsourcing approach to energy analytics research. I started the Global Energy Forecasting Competition in 2012. These competitions have attracted hundreds of contestants from more than 60 countries. Many of them are outside the power and energy field. In each competition, we try to tackle a challenging and emerging problem. Right now we are in the middle of the third one, GEFCom2017. The theme is energy forecasting in the big data world. We have also organized the first International Symposium on Energy Analytics this June in Cairns, Australia, to host the researchers and practitioners interested in this subject.

What are the advantages of working with industrial partners?

They bring in meaningful research problems, fund projects, hire graduates, and help broadcast our research findings through their network. Isn't it a sweet deal?

Are educational institutions able to educate enough workers, or does the industry face a shortage?

In my domain, which is energy analytics, there is definitely higher demand (jobs) than supply (workers). I get calls all the time asking for my students, but I don't have enough students to fill in all those job openings.

What’s fun about your job?

Teaching students to solve the most challenging problems for the industry. I very much enjoy both the analytical challenge and the success of the students. 

Wednesday, May 17, 2017

Wind Speed for Load Forecasting Models

One way to categorize the load forecasting papers is based on the variables used in those forecasting models. Because many people who wrote load forecasting papers only had access to the load data with time stamps, they had to propose the models based on the load series only. The representative techniques include exponential smoothing and the ARIMA family. Sometimes people also include the calendar information to come up with some regression models with classification variables. Although these are good and powerful techniques, their real-world applications in load forecasting are very limited. I have criticized those "load-only" models in some of my papers, such as the IJF2016 paper on recency effect:
Both seasonal naïve models perform very poorly compared with the other four models. Seasonal naïve models are used commonly for benchmarking purposes in other industries, such as the retail and manufacturing industries. In load forecasting, the two applications in which seasonal naïve models are most useful are: (1) benchmarking the forecast accuracy for very unpredictable loads, such as household level loads; and (2) comparisons with univariate models. In most other applications, however, the seasonal naïve models and other similar naïve models are not very meaningful, due to the lack of accuracy. 
Weather is must-have in most of the real-world load forecasting models. The most frequently used weather variable in the load forecasting literature is temperature. Some system operators, such as ISO New England, publish temperature data along with the load information. The recent load forecasting competitions, such as GEFCom2012 and GEFCom2014, have also released several years of hourly load and temperature data for benchmarking purpose.

Although non-temperature weather variables have some presence in the load forecasting literature, they are rarely studied in the context of variable selection. Recently we published a TSG paper Relative Humidity for Load Forecasting Models, discussing how to use humidity information to improve load forecasting accuracy. As a sister of that humidity paper, this paper discusses how to include wind speed information in load forecasting models.

Another comment I want to make is on the open access publication. I personally had no interest in publishing my paper with those open access publishers. This is my first try, which turns out to be a good surprise. The reviews were returned to me rather quickly, within 10 days. There were no non-sense comments, so I didn't need to deal with the personal attacks as I normally had to do. Before the final publication, the copy editor helped clean up some typos we had in the submission. From our first submission to the final pagerized version, the whole process took two weeks!

Anyway, hope that you enjoy reading this open access paper!

Citation

Jingrui Xie and Tao Hong, "Wind speed for load forecasting models", Sustainability, vol 9, no 5, pp 795, May, 2017 (open access).


Wind Speed for Load Forecasting Models

Jingrui Xie and Tao Hong

Abstract

Temperature and its variants, such as polynomials and lags, have been the most frequently-used weather variables in load forecasting models. Some of the well-known secondary driving factors of electricity demand include wind speed and cloud cover. Due to the increasing penetration of distributed energy resources, the net load is more and more affected by these non-temperature weather factors. This paper fills a gap and need in the load forecasting literature by presenting a formal study on the role of wind variables in load forecasting models. We propose a systematic approach to include wind variables in a regression analysis framework. In addition to the Wind Chill Index (WCI), which is a predefined function of wind speed and temperature, we also investigate other combinations of wind speed and temperature variables. The case study is conducted for the eight load zones and the total load of ISO New England. The proposed models with the recommended wind speed variables outperform Tao’s Vanilla Benchmark model and three recency effect models on four forecast horizons, namely, day-ahead, week-ahead, month-ahead, and year-ahead. They also outperform two WCI-based models for most cases.

Thursday, May 11, 2017

RTE Day-ahead Load Forecasting Competition 2017

For many years, the Transmission System Operator RTE has been building electricity demand forecasts, ensuring the ability to match supply and demand at all times and, consequently, guaranteeing power system reliability.

The emergence of new factors related to the energy transition are impacting the electricity demand and making forecasting a more challenging task: self-consumption, growth of new uses (electric vehicles, heat pumps…), regulation of building insulation, new supply offers, possibility for consumers to monitor and control their consumption…

In this context of increasing flexibility and market rule harmonisation at the European level, RTE wants to conduct a review of current forecasting methods and assess the performance of new dynamic and adaptive approaches brought by Data Science.

A first challenge will focus on the deterministic short-term forecast of national and 12 regional electricity demands, a second one will focus on a forecast with associated uncertainty.

RTE will launch its first international public challenge in Data Science mid-May, running till mid-July. The second challenge will take place during winter 2017-2018.
 
KEY POINTS OF THE CHALLENGE ORGANISATION
 
Registration

Registration will be open from the opening date to the 24th of May on the platform Datascience.net : https://www.datascience.net/fr/challenge/32/details#

All the information related to the challenge will be available on the platform mid-May. A discussion forum will allow participants to ask any questions they may have.     
 
Challenge rules

Participants will be given access to meteorological data provided by Météo France for RTE’s operational forecasting activities, and they will be able to retrieve national and regional demand data on RTE’s Eco2mix platform. Participants are allowed to use any other data, provided that source and nature are specified.

The models will be assessed on their ability to forecast demand of ten days (among which bank holidays) between the 25th of May and the 14th of July 2017.

These days will be announced since the challenge’s opening and will be used for the final ranking. Forecasts for day d must be submitted at 9pm on day d-1 at the latest.

In order to practice, participants will have the opportunity to submit forecasts on three consecutive days, from the 18th to the 20th of May.

The same rules regarding time of submission will apply.
 
Rewards

Following the final ranking, the top three participants will have to submit a one page methodology document before being awarded their prize.

This document will describe the main principles of the method and the data used.
1st prize:      €10,000
2nd prize:     €5,000
3rd prize:      €3,000

Thursday, May 4, 2017

7 Reasons to Attend ISEA2017

The International Symposium on Energy Analytics (ISEA2017) is coming in 7 weeks. If you are still wondering whether you should join the event or not, here are 7 reasons for you to attend ISEA2017:

1. Grow your international network

ISEA2017 is truly international. The early registrations came from 16 countries. As a conference attendee, you will hear 20+ presentations describing methodologies and insights gained from various places in the world. You will also share your experience and expertise with this diverse audience and get their critique and compliment.

2. Check out the winning methods of GEFCom2017

Selected GEFCom2017 teams will be presenting their methodologies at ISEA2017. You will witness the recognition of GEFCom2017 winners and have the face-to-face discussion with them. Rather than reading thousands of energy forecasting papers published every year and wondering which ones work well, you can grasp the secret sauce of the most effective methods during ISEA2017.

3. Experience a novel peer review process 

Whether we like today's peer review system or not, we have to live with it, at least for the next few years before a better one is in place. We have tied ISEA2017 to an IJF special section on energy forecasting, where we try to implement a new peer review process. The ISEA2017 attendees will have the opportunity to experience this new process and help improve it.

4. Peek and shape the future of energy analytics 

If you are struggling with the topic for your next paper, ISEA2017 is a must-attend conference for you. We will discuss the emerging topics as well as the research agenda for the future. Rather than guessing where the future goes, you can contribute to the plan!

5. Attend International Symposium on Forecasting

The 37th International Symposium on Forecasting (ISF) will be held two days after ISEA2017, right at the same location. ISF is the only major scientific forecasting conference I know of. I find it very rewarding to attend ISF, where I hear forecasting topics from various industries, as well as the methodological breakthroughs in general. Many of them could be applied to the energy forecasting problems. Extending the trip to include ISF in your travel plan would be a wise choice.

6. Two world heritage sites in one place

The World Heritage Centre has a list of about 1000 world heritage sites around the globe. Two of them (Great Barrier Reef and Daintree Rainforest) are in Cairns, Australia, making Cairns the only place in this planet with two world heritage sites side by side. ISF organizers have planned the social program including numerous social events and tour opportunities for delegates, their friends and family.

7. Low registration fees

Our sponsors, the International Institute of Forecasters, Tangent Works and Journal of Modern Power Systems and Clean Energy, have generously contributed to the organization of ISEA2017, helping significantly subsidize the registration fees. If you attend both ISEA and ISF, there is an additional discount. To register both ISEA and ISF, click HERE. To register ISEA only, click HERE.

ISEA2017 will be held in Cairns, Australia, June 22-23, 2017. Look forward to seeing you there!

Monday, March 20, 2017

GEFCom2014 Load Forecasting Data

The load forecasting track of GEFCom2014 was about probabilistic load forecasting. We asked the contestants to provide one-month ahead hourly probabilistic forecasts on a rolling basis for 15 rounds. In the first round, we provided 69 months of hourly load data and 117 months of hourly temperature data. Incremental load and temperature data was provided in each of the future rounds.

Where to download the data?

The complete data was published as the appendix of our GEFCom2014 paper. If you don't have access to Science Direct, you can downloaded from my Dropbox link HERE. Regardless where you get the data, you should cite this paper to acknowledge the source:

  • Tao Hong, Pierre Pinson, Shu Fan, Hamidreza Zareipour, Alberto Troccoli and Rob J. Hyndman, "Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond", International Journal of Forecasting, vol.32, no.3, pp 896-913, July-September, 2016.


What's in the package?

Unzip the file, you will see the folder "GEFCom2014 Data", which includes five zip files. The data for the probabilistic load forecasting track of GFECom2014 is in the file "GEFCom2014-L_V2.zip". Unzip it, you will see the folder "load", which includes an "Instructions.txt" file and 15 other subfolders. In each folder named as "Task n", there are two files, Ln-train.csv and Ln-benchmark.csv. The train file, together with the train files released in previous rounds, can be used to generate forecasts. The benchmark file includes the forecast generated from the benchmark method.

How to use the data?

Apparently the most straightforward way of using this dataset is to replicate the competition setup and compare results directly with the top entries. Because the data published through GEFCom2014 is quite long (totally 7 years of matching load and temperature data), we can also use this dataset to test methods and models for short term load forecasting.

GEFCom2014-E data

After GEFCom2014, I organized an in-class probabilistic load forecasting competition in Fall 2015 that was open to external participants. My in-class competition setup was very similar to that of GEFCom2014, so I denoted the data for this in-class load forecasting competition as GEFCom2014-E, where E is the abbreviation of "extended". In total, this dataset covers 11 years of hourly temperature and 9 years of hourly load. A top team Florian Ziel was invited to contribute a paper to IJF (see HERE). The readers may replicate the same competition setup and compare results with Ziel's.

Caution

Note that the data I used for GEFCom2014-E was created using ISO New England data. If you want to validate a method using two independent sources, you should not use GEFCom2014-E together with ISO New England data.

Back to Datasets for Energy Forecasting.

Monday, March 6, 2017

Leaderboard for GEFCom2017 Qualifying Match!!!

[Update 5/18/2017]: ISO NE just released the April load data two days ago. Jingrui and I have updated the leaderboard for the qualifying match. Please check the rankings and let us know by 5/26/2017 if there is any issue.

The six rounds of GEFCom2017 qualifying match just ended last week. I'm sure that the contestants are anxiously waiting for the leaderboard. Here is a brief report. I'll update this post as ISO New England releases its recent load data.

Out of 177 registered teams, 73 have submitted entries to the defined track, and 26 to the open track. After six rounds, 53 teams completed the defined track with at least 4 submissions, while 20 completed the open track. 

The due date of report and code is on March 10th, 2017. Please send them to hong.bigdeal@gmail.com. Follow the same protocol as the forecast submissions. Please follow THIS GUIDE to prepare the report.

Jingrui Xie created two benchmarks:
  • Vanilla Benchmark, which has been used to calculate the scores of the teams in each round. See Q7 of THIS FAQ for more information.
  • Rain Benchmark, which will be used to select the teams being advanced to the final match.  
(As an organizer of GEFCom2017, Jingrui Xie is not eligible for the prize.)

The spreadsheet with detailed scores can be accessed HERE. The higher the score is, the higher the rank is. 

Stay tuned :)

Tuesday, February 14, 2017

Call For Papers: Forecasting in Modern Power Systems | Journal of Modern Power Systems and Clean Energy

Journal of Modern Power Systems and Clean Energy

Special Section on Forecasting in Modern Power Systems 

Power systems have been evolving over the past century. The grid is getting more and more sophisticated due to modern technologies and business requirements, such as implementation of smart grid technologies, deployment of utra-high voltage transmission systems, and integration of ultra-high levels of renewable resources. All of these factors are challenging today’s energy forecasting practice. This special section of the Journal of Modern Power Systems and Clean Energy is aimed at answering the following question: How to better forecast the supply, demand and prices to accommodate the changes in modern power systems?

The topics of interests include, but are not limited

  • Probabilistic energy forecasting
  • Forecasting in multiple energy systems
  • High dimensional wind and solar power forecasting
  • Load forecasting with temporal and/or geographic hierarchies
  • Combination methods for energy forecasting

Submission Guidelines

http://www.editorialmanager.com/mpce or link via
http://www.springer.com/40565
http://www.mpce.info

The article templates can be downloaded from http://www.mpce.info.

Important Dates

Paper Submission Deadline:    June 30, 2017
Acceptance Notification:         December 31, 2017
Date of Publication:                 March 2018

Guest Editorial Board

Guest Editors-in-Chief
Wei-Jen Lee, University of Texas at Arlington, USA
E-mail: wlee@uta.edu
Tao Hong, University of North Carolina at Charlotte, USA
E-mail: hongtao01@gmail.com

Guest Editors
Jing Huang, Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia
Duehee Lee, Arizona State University, USA
Franklin Quilumba, National Polytechnic School, Ecuador
Jingrui Xie, SAS Institute, USA
Ning Zhang, Tsinghua University, China
Florian Ziel, University of Duisburg-Essen, Germany

Editor-In-Chief and Deputy

Professor Yusheng Xue (State Grid Electric Power Research Institute, Nanjing, China)
Professor Kit Po Wong (The University of Western Australia)


CONTACT INFORMATION
For more information, please do not hesitate to contact
Ms. Ying ZHENG
Tel: 86 25 8109 3060 Fax: 86 25 8109 3040
E-mail: zhengying1@sgepri.sgcc.com.cn; mpce@alljournals.cn

About Journal of Modern Power Systems and Clean Energy (MPCE)

MPCE sponsored by State Grid Electric Power Research Institute (SGEPRI) is Golden Open Accessed, peer-reviewed and bimonthly published journal in English. It is published by SGEPRI Press and Springer-Verlag GmbH Berlin Heidelberg commencing from June, 2013.It is indexed in SCIE, Scopus, Google Scholar, CSAD, DOAJ, CSA, OCLC, SCImago, ProQuest, etc. It is the first international power engineering journal originated in mainland China. MPCE publishes original papers, short letters and review articles in the field of modern power systems with focus on smart grid technology and renewable energy integration, etc. MPCE is dedicated to presenting top-level academic achievements in the fields of modern power systems and clean energy by international researchers and engineers, and endeavors to serve as a bridge between Chinese and global researchers in the power industry.

Monday, February 6, 2017

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.


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


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:


Look forward to seeing you in these fantastic events!

Sunday, January 1, 2017

Energy Forecasting @2016

Happy New Year! As a tradition of this blog, it's time to look at the statistics of Energy Forecasting in 2016.

Where are the readers?

They are from 147 countries and SARs.


They are from 2660 cities.


Comparing with Energy Forecasting @2015.


All-time top 10 most viewed posts (from 4478 views to 2731 views):
Top 10 most-viewed classic posts (from 3914 views to 1525 views):
Thank you very much for your support! Happy Forecasting in 2017!

Wednesday, December 21, 2016

2016 Greetings from IEEE Working Group on Energy Forecasting

Another Christmas is coming in few days. It's time to look back at 2016 and see what IEEE Working Group on Energy Forecasting has done:

Next year will be even more exciting:
  • We will hold the International Symposium on Energy Analytics (ISEA2017), the first-ever gathering of world-wide energy forecasters in Cairns, Australia, the only place on earth with two World Heritage sites side-by-side, Great Barrier Reef and the Daintree Rainforest.  
  • We will conclude GEFCom2017 at ISEA2017 with the winner presentations and prizes. 
  • A PESGM2017 panel session on multiple energy systems is being organized by Ning Zhang and myself. 
  • I will be editing a special issue for the Power & Energy Magazine on big data analytics. The papers are by invitation only. If you have any good idea and would like to present it to thousands of PES members through this special issue, please let me know. 
  • We didn't have the bandwidth for JREF this year. We will try to conduct the JREF survey next year. 

Happy Holidays and Happy Forecasting!