Friday, November 20, 2015

Job Opening at UNC Charlotte: Assistant/Associate/Full Professor

The Energy Production and Infrastructure Center (EPIC) at the University of North Carolina at Charlotte (UNC Charlotte) invites applications for a tenure-track or tenured faculty position at all levels in the area of energy analytics and control systems.

EPIC was formed in response to the need for highly trained engineers qualified to meet the demands of the energy industry. EPIC is an industry/education partnership that produces a technical workforce, creates advancements in technology for the global energy industry, and supports the Carolinas’ multi-state economic and energy security. The EPIC initiative includes a 200,000 square foot, $76 million LEED Certified building that opened in July 2012. It houses classrooms, lecture halls, conference rooms, a clean room, a smart grid facility, and offices and laboratories for electrical, civil, environmental and computer research related to energy and energy delivery infrastructure; space for industrial partners is also available. The EPIC Building features several specialized laboratories in the different focus areas.

Candidates at the level of assistant/associate/full professor will be considered, commensurate with professional experience and academic record. Candidates for all levels must hold a doctoral degree in electrical engineering, control engineering, analytics, or a closely-related field of study. The ideal candidate has expertise to build a successful research program in the area of control systems and energy analytics.  Applicants with relevant experience in power systems control and energy analytics will be considered, but particular consideration will be given to applicants demonstrating a desire and potential for interdisciplinary research. Of particular interest are candidates with the potential to contribute to power system control, diagnostics and forecasting. The successful candidate will be expected to teach at both the undergraduate and graduate levels, with significant contributions to the graduate energy courses in Applied Energy and Electrical or Systems Engineering and contribute to EPIC, departmental, college, and university service.
Candidates that fit within EPIC’s mission ( are particularly encouraged to apply.

Application: For complete job descriptions and application procedures, see and search for position 004346. As an EOE/AA employer and an ADVANCE Institution that strives to create an academic climate in which the dignity of all individuals is respected and maintained, the University of North Carolina at Charlotte encourages applications from all underrepresented groups. All finalists are subject to criminal background checks.

Monday, November 16, 2015

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?
This is also one of the most frequent question I have been asked from our community. When IEEE Working Group on Energy Forecasting was founded 5 years ago, the mission was to bridge the gap between the industry needs and academic research and education. This mission has been embedded in all the tasks we have performed, including the renowned Global Energy Forecasting Competition. Now it's time to find out the venues that host the best energy forecasting papers.

To figure out a credible answer, I again took a crowd-sourcing approach. I surveyed 20 research groups that are currently active in energy forecasting, asking them to nominate and rank the journals based on the energy forecasting papers published since 2011. 14 of them responded to my survey in time. From the survey responses, I can summarize the grade and score for energy forecasting in general as well as the three subject areas, including demand forecasting, price forecasting and generation forecasting.

Here comes the A+ journals from the 2015 Journal Rankings in Energy Forecasting with their A+ ranked subject areas:
  • International Journal of Forecasting (energy; demand; price)
  • IEEE Transactions on Smart Grid (energy; demand; generation)
  • IEEE Transactions on Power Systems (energy; price)
  • IEEE Transactions on Sustainable Energy (energy)
  • Solar Energy (generation)
  • Wind Energy (generation)
The complete list of all the journals nominated during this survey is available HERE.

I would like to gratefully acknowledge the valuable input and comments from the following scholars (including myself):
  • Bello Morales, Antonio | Comillas Pontifical University, Spain
  • Dowell, Jethro | University of Strathclyde, UK
  • Fan, Shu | Monash University, Australia
  • Haben, Stephen | University of Oxford, UK
  • Hong, Tao | University of North Carolina at Charlotte, USA
  • Jeon, Jooyoung | University of Bath, UK
  • Kang, Chongqing | Tsinghua University, China
  • Lee, Duehee | KPMG, USA
  • Lee, Wei-Jen | University of Texas at Arlington, USA
  • Monteiro, Cláudio | University of Porto, Portugal
  • Pinson, Pierre | Denmark University of Technology, Denmark
  • Povinelli, Richard | Marquette University, USA
  • Wang, Jianxue | Xi'an Jiaotong University, China
  • Weron, Rafal | Wroclaw University of Technology, Poland
  • Zareipour, Hamidreza | University of Calgary, Canada
I would also like to continue this effort going forward. If you would like to have your opinion counted in the next survey, please sign up HERE by putting JREF in the comment field. You will need at least one forecasting paper published in one of the A+ journals over the past three years to be eligible to vote. 

Saturday, November 7, 2015

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.

This course offers a comprehensive and in-depth treatment to long-term load forecasting. The content includes a review of the fundamental concepts and classical methods, a statistical approach that leverages high resolution data and modern computing power, probabilistic forecasting that helps better quantify the uncertainties of the unpredictable future, and several advanced and emerging challenges triggered by big data, renewable energy integration and demand side management programs. Real-world examples and case studies are embedded throughout the course when introducing the theories and methodologies.

Load Forecasting in MS Excel

This post-course workshop brings the long term load forecasting methodologies introduced in the course to the most widely-used business forecasting tool, spreadsheets. The workshop starts with an overview of various options available in the market, and then zooms into spreadsheet operations. We will demonstrate five different ways of doing forecasting in spreadsheets, and help the participants understand the limitations of each. The participants will gain hands on experience with building models and generating load forecasts in spreadsheets, while competing and collaborating with others.

Key References
Related Courses

Wednesday, October 28, 2015

Call for Nominations: IEEE PSPI Prize Paper Award

IEEE Power Systems Planning and Implementation Committee (PSPI) is calling for nominations for various awards. I am serving as the coordinator for the IEEE Working Group on Energy Forecasting to turn in the nominations. Here I would like to encourage you to nominate some energy forecasting papers for the PSPI Prize Paper Award.

Here are the criteria for the nominated papers:
  • High-quality standard. 
  • A journal paper in the energy forecasting area (see the scope HERE).
  • Published in an IEEE PES journal (PETSJ, TEC, TPWRD, TPWRS, TSG and TSTE) or magazine (Power and Energy Magazine). 
  • Published within previous three years. 
Some sample benchmarks for a prize paper:
  • A paper focusing on both issues and methods should be superior to a paper focusing on algorithm only.
  • A paper focusing on both theoretical method and practical solution should be superior to a paper focusing ononly theoretical method or only practical solution.
  • A paper with an actual example of utility system should be superior to a paper with a hypothetical example or test system. 
  • A paper focusing on a new idea or resolving a new issue should be superior to a paper focusing on improvement of an existing method or problem.
If you would like to make a nomination, please email me the following info by Nov 4, 2015:
  • Names of Authors:
  • Institutions/Addresses/emails:
  • Title of Paper:
  • Publication Data (Vol., Page, Date):
  • Most Significant Contribution(s) of Paper:
  • Nominator’s name (also indicating WG if nominated by a WG):
  • Email of nominator:
Look forward to your nominations!

Sunday, October 25, 2015

Winners Announced for Global Energy Forecasting Competition 2014

At 2015 PES General Meeting held in Denver, CO, we announced the winning universities and teams of Global Energy Forecasting Competition 2014 (GEFCom2014). The picture below was taken right before the award reception.

I'm delighted to post the winners here.

Three winning universities for the institute prize:

Siberian State Aerospace University, Russia
  • Faculty advisor: Olesya Shesterneva
  • Contributing teams: E.S. Mangalova (Load #5, Price #8, Wind #3); Arkadiy Strelnikov (Price #6); SAOR (Load #15); Power Team SAOR (Solar #15)
University of North Carolina at Charlotte, USA
  • Faculty advisor: Tao Hong
  • Contributing teams: Jingrui (Rain) Xie (Load #3); Yanghai Cong (Price #7); Bidong Liu (Load #8);  Jiali Liu (Wind #8); Florencio Gonzalez (Price #9); Ying Chen (Solar  #9); Christopher Benfield (Load #11); Mohamed Abuella (Solar #12); Nikolina (Load  #13)
Tsinghua University, China
  • Faculty advisor: Chongqing Kang
  • Contributing teams: T_morning (Solar #6); THU_EILAB#6 (Solar #8); Sniper (Load #10)

Five winning teams from each of the four tracks:

Probabilistic Electric Load Forecasting Track
  • Pierre Gaillard, Yannig Goude, and  Raphaël Nedellec (EDF R&D, France)
  • Virginie Dornonnat, Audrey Pichavant, and Amandine Pierrot (EDF R&D, France)
  • Jingrui Xie (University of North Carolina at Charlotte and SAS Institute Inc, USA).
  • Georgios Giasemidis (Countinglab Limited, UK) and Stephen Haben (University of Oxford, UK)
  • Ekaterina Mangalova (Siberian State Aerospace University, Russia)
Probabilistic Electricity Price Forecasting Track
  • Pierre Gaillard, Yannig Goude, and  Raphaël Nedellec (EDF R&D, France)
  • Katarzyna Maciejowska and Jakub Nowotarski (Wroclaw University of Technology, Poland)
  • Grzegorz Dudek (Czestochowa University of Technology, Poland)
  • Zico Kolter (C3 Energy and Carnegie Mellon University, USA), Romain Juban (C3 Energy, USA), Henrik Ohlsson (C3 Energy and University of California, Berkeley, USA), and Mehdi Maasoumy (C3 Energy, USA)
  • Frank Lemke (KnowledgeMiner Software, Germany)
Probabilistic Wind Power Forecasting Track
  • Mark Landry, Thomas P Erlinger, David Patschke, and Craig Varrichio (Eigen Analytics, USA)
  • Gabor Nagy (Budapest University of Technology and Economics, Hungary), Gyula Borbely (DMLab, Hungary), Gabor Simon (DMLab, Hungary), and Gergo Barta (Budapest University of Technology and Economics, Hungary)
  • Ekaterina Mangalova (Siberian State Aerospace University, Russia)
  • Zico Kolter (C3 Energy and Carnegie Mellon University, USA), Romain Juban (C3 Energy, USA), Henrik Ohlsson (C3 Energy and University of California, Berkeley, USA), and Mehdi Maasoumy (C3 Energy, USA)
  • Yao Zhang (Xi'an Jiaotong University, China)
Probabilistic Solar Power Forecasting Track
  • Jing Huang and Matthew Perry (CSIRO, Australia)
  • Gabor Nagy (Budapest University of Technology and Economics, Hungary), Gyula Borbely (DMLab, Hungary), Gabor Simon (DMLab, Hungary), and Gergo Barta (Budapest University of Technology and Economics, Hungary)
  • Zico Kolter (C3 Energy and Carnegie Mellon University, USA), Romain Juban (C3 Energy, USA), Henrik Ohlsson (C3 Energy and University of California, Berkeley, USA), and Mehdi Maasoumy (C3 Energy, USA)
  • Duehee Lee (University of Texas at Austin, USA), Zhi Zhou (Argonne National Laboratory, USA), Yongsung Kwon (University of Texas at Austin, USA)
  • Giuseppe Casalicchio (Ludwig-Maximilians University of Munich, Germany)
The original press release from IEEE PES is HERE. The detailed leaderboard is HERE. For more information about GEFCom2014, please visit the competition website and the related blog posts

Wednesday, October 21, 2015

Ying Chen - From Meteorology to Energy Forecasting

Today (October 21, 2015), Ying Chen just defended her MS thesis "How Does Relative Humidity Affect Electricity Demand?"

Ying received her B.S. in Atmospheric Science from Nanjing University of Information Science and Technology in 2006, and her M.S. in Meteorology from University of Hawaii at Manoa in 2010. In August 2014, Ying joined the Master of Science in Engineering Management program of UNC Charlotte as a member of BigDEAL to conduct MS thesis research under my supervision.  She received her SAS Base Programmer and SAS Advanced Programmer certifications in fall 2014. She participated in the Global Energy Forecasting Competition 2014 with a top 9 place in the probabilistic solar power forecasting track. This past summer, Ying presented her research work at the 3rd International Conference on Energy & Meteorology.

Since October 2014, Ying has been working at North Carolina Electric Membership Corporation as a Load Forecasting Analyst Intern. This December, She will receive her MS in Engineering Management.

Congratulations, Ying!

GEFCom2014 Presentations at PESGM2015

Over the past few months, I have received numerous requests asking for the GEFCom2014 data, papers and presentation files. Recently I managed to compile all the GEFCom2014 Presentations at PESGM2015. Here is the LINK to the ZIP file on OneDrive. I will compile and upload the data by the end of the year. The papers will be published in 2016.

Stay tuned...

Thursday, October 15, 2015

Job Openings at Nexant: Energy Efficiency Project Analyst

Posting several job openings for a good, old friend at Nexant. The head of the group is one of the best known experts in the load research community. It is a great opportunity for any junior consultant in this area to work with experts like him. If interested, please search "analyst" via Nexant career site and submit your application over there.

Project Analyst (Energy Efficiency)

About the Job

Nexant is currently seeking energy efficiency Project Analysts for our Cary, NC, Louisville, CO and Malvern, PA offices for our Customer Strategy Planning and Analysis Group. The successful candidate will be responsible for providing statistical, analytical, and technical support services for Nexant’s analysis of energy efficiency, renewables, and demand response technologies and programs.

Monday, October 12, 2015

Fall 2015 In-class Probabilistic Load Forecasting Competition

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:

Thursday, October 8, 2015

NPower Forecasting Challenge, Again

RWE npower is hosting its forecasting challenge again to recruit summer interns for the next year. Same as the previous challenge, they are going to recruit students from UK but would like to open the competition the entire world. 

In the previous challenge, BigDEAL students did a great job. We also wrote a paper to summarize the winning method. I will again encourage my students to participate in this challenge. Meanwhile, I would encourage your participation as well. The registration is OPEN now. Look forward to seeing your name on the leaderboard!