Friday, April 17, 2015

Language for Rejecting Energy Forecasting Papers

As a volunteer in the peer review system, I'm handling (as an editor & editor-in-chief) and reviewing (as a reviewer) at least 10 papers per month. While I try to perform thorough review for each paper following these four steps, the consequence is that I spend more time helping others improve and publish papers than working on my own papers. As the editorial workload keeps growing, I have been trying to figure out a more efficient way of performing editorial services. One phenomenon I have observed is that most papers (about 80% or more) get rejected at the end. Most of my editorial services are for top journals such as TSG and IJF, of which the rejection rate is even higher than 80%. In fact many papers share similar reasons for rejection. I guess one way is to standardize the rejection language, so that I don't have to type the same reasons each time. To save time for other editors and reviewers in the same peer review system, I would be more than happy to share the following rejection language.

Thursday, April 16, 2015

Jiali Liu - The First Alumna of BigDEAL

Today (April 16, 2015), Jiali Liu just defended her MS thesis on combining sister load forecasts, which made her the second graduate of BigDEAL. Since the first BigDEAL graduate Rain is continuing her PhD study here, Jiali becomes the first alumna of BigDEAL.

Jiali received her bachelor degree in International Business and Trade from Wuhan University of Technology, and her second bachelor degree in Business Administration from University of Pittsburgh, both in 2012. In August 2013, Jiali joined the Master of Science in Engineering Management program of UNC Charlotte. In May 2014. She joined BigDEAL to conduct MS thesis research under my supervision. She received her SAS Base Programmer and SAS Advanced Programmer certifications in summer 2014. She was part of the BigDEAL team winning the Analytics2014 poster contest. She was also a key contributor to the EISPC/NARUC Load Forecasting Case Study. She participated in the Global Energy Forecasting Competition 2014 with a top 8 place in the probabilistic wind power forecasting track.

Jiali received her Graduate Certificate in Energy Analytics in December 2014. This May, she will receive her MS in Engineering Management degree with a concentration in Energy Systems. After that, Jiali will move to Raleigh working as a Business Analyst at Evalueserve.

Congratulations, Jiali, and all the best!

Thursday, April 9, 2015

Crystal Ball Lessons in Predictive Analytics

For a long time, I have had the idea of writing an article about "how much benefit are we getting from reducing load forecast errors". A few months ago I got a request from EnergyBiz to contribute an article. I thought this "valuation" topic would be a good fit. So here is Crystal Ball Lessons in Predictive Analytics.

Monday, April 6, 2015

Job Opening at SAS: Solutions Architect

Posting a job for my friends and former colleagues at SAS. SAS is well-known to be a best place to work. As a former SAS employee enjoying the SAS benefits in person, I have to say that whatever you read from the web are underestimating the comfort working for SAS. (BTW, this is why I left SAS.) If you are interested in joining SAS, please apply HERE.

Solutions Architect (Commercial Business Unit - Energy & Utilities)-20006794

Description

Thursday, April 2, 2015

Jingrui Xie - The First Graduate of BigDEAL

Today (April 2, 2015), Jingrui (Rain) Xie just defended her MS thesis on retail energy forecasting, which made her the first graduate of BigDEAL.

Rain received her B.A. in Finance from Sun Yat-sen University in 2009, and her M.A. in Economics from Duke University in 2011. In August 2013, Rain joined the Master of Science in Engineering Management program of UNC Charlotte as my first student. Since then she has published two journal papers, one on long term probabilistic load forecasting, and the other on retail energy forecasting. She was a key contributor to the EISPC/NARUC Load Forecasting Case Study. She was part of the BigDEAL team that topped the NPower Forecasting Challenge 2015.  As one of the top teams of the Global Energy Forecasting Competition 2014, she was invited by International Journal of Forecasting to write a paper describing her winning methodology. This summer, Rain will be attending two conferences, International Symposium on Forecasting and IEEE Power and Energy Society General Meeting, to present her work on probabilistic load forecasting.

Rain will continue her study in the Ph.D. Program in Infrastructure and Environmental Systems (INES) here at UNC Charlotte. Her dissertation topic is on probabilistic load forecasting.

Congratulations, Rain!

Monday, March 30, 2015

Call For Papers: GlobalSIP'15 Symposium on Signal and Information Processing for Optimizing Future Energy Systems

Posting this CFP per request of Dr. Hao Zhu.

GlobalSIP'15 Symposium on 
Signal and Information Processing for Optimizing Future Energy Systems
Orlando, Florida, USA, December 14-16, 2015

Wednesday, March 25, 2015

Job Opening at SCE: Data Science Analyst

Posting a job for my friends at SCE. If interested, please visit www.edison.com/careers and click on Southern California Edison to apply and submit your resume.

Data Science Analyst (Job # 71010850)

POSITION OVERVIEW:

Thursday, March 19, 2015

EISPC Update: Electric Load Forecasting Finds Its New Life in the Smart-Grid Era

Source: NARUC Bulletin 3/16/2015

A new report sheds light on how regulators and the utility industry can develop and evaluate load forecasts in an era of uncertainty with the electricity sector. The report also offers insights and findings from three case studies in 10 States.

The Energy Production and Infrastructure Center of University of North Carolina at Charlotte and the Robert W. Galvin Center for Electricity Innovation of Illinois Institute of Technology collaborated on this report, entitled ‘”Load Forecasting Case Study.” Commissioned by the Eastern Interconnection States’ Planning Council, the report provides a comprehensive review of load forecasting topics for States, planning coordinators, and others.

Additionally, the report presents three case studies in different jurisdictions (ISO New England, Exelon and North Carolina Electric Membership Corporation) to assist planning coordinators and their relevant States with applying innovative concepts, tools, and analysis to their forecasting regime.

“Load forecasting has been an integral part of the utility business for over a century, though the practice has not changed much since 1990s,” said Dr. Tao Hong, the lead author of the report and Chair of IEEE Working Group on Energy Forecasting, “Smart grid technologies bring a great opportunity for improvement of the load forecasting practice.”

The Eastern Interconnection States’ Planning Council and the National Association of Regulatory Utility Commissioners commissioned the study. EISPC is a consortium of State-level government agencies responsible for siting electric transmission across the 39 States, including the District of Columbia and City of New Orleans, located within the Eastern Interconnection. The group is funded by the U.S. Department of Energy.

The report demystifies the many complex concepts, terms, and statistics that are used in load forecasting. It serves as both a primer on load forecasting and also provides an in-depth discussion of load forecasting topics with a real-world demonstration that will be useful to state commissioners, planning coordinators, utilities, legislators, researchers, and others.

Some of the key takeaways from the study include:
  1. Load forecasting is the foundation for utility planning, but utilities still face challenges in getting accurate load forecasts. Particularly in light of significant change in the resource mix resulting from environmental regulation, aging infrastructure, the projected low cost of natural gas, and decreasing costs of renewable technologies, it is crucial for utilities to have accurate load forecasts for resource planning, rate cases, designing rate structures, and financial planning.   
  2. Many factors influence the load forecasting accuracy, such as geographic diversity, data quality, forecast horizon, forecast origin, and customer segmentation. A model that works well in one region may not be the best model for another.   
  3. Deployment of smart-grid technologies has made high granular data available for load forecasting. An emerging topic, hierarchical load forecasting, which provides forecasts at various levels in the system, is of great importance in the smart grid era. Each sub-region or utility may need a customized model to maximize the forecast accuracy. Meanwhile, the accuracy gained at a lower level can be often translated to the enhanced forecasts at the aggregated levels.  
  4. Within the same utility, a model that forecasts well in one year may not generate a good forecast for another year. In order to establish the credibility in load forecasting, utilities have to follow forecasting principles to develop a consistent load forecasting methodology.   
  5. Long-term load forecasts should be in the form of scenarios, intervals or density functions, rather than point estimates (one number per time interval throughout the forecast horizon). The evaluation should also be based on probabilistic scoring rules.   
  6. All forecasts are wrong. It would be ideal to predict the future with as much accuracy as possible, but it is more realistic to predict the future while taking into consideration the insights on various risks that a utility may be faced with. Load forecasting is not a static process. Rather, utilities and policymakers should be continually looking for ways to improve the process, the databases, and advance the state-of-the-art in forecasting tools. It is imperative that utilities devote substantial time and resources to the effort to develop credible load forecasts.
The report (in PDF) can be found HERE.

Monday, March 16, 2015

BigDEAL Students Topping NPower Forecasting Challenge 2015 with The Most Robust and Accurate Forecasts

A month ago, I posted NPower Forecasting Challenge 2015. Then four of my students (Bidong Liu, Xiaoqian Lyu, Jingrui Xie, Lili Zhang) formed a team to have fun in the competition. The competition attracted many teams worldwide. 33 teams completed the compeition, of which 23 are from U.K. universities.

This morning, the final leaderboard was just released. I was again very pleased to see the outstanding performance of the BigDEAL team:
  • Ranking #3 on the average MAPE of the three rounds. The MAPE values of the three teams in the top cluster were extremely close: 2.42%, 2.47% and 2.48%. BTW, all top three teams were GEFCom2014 contestants, but not from U.K. universities. 
  • The only team managing to have less than 3% MAPE in all three rounds. The highest MAPE of BigDEAL's three rounds was 2.84%, which was the lowest one among all 33 teams. 
  • The only team ranking top 2 twice; and one of the only three teams ranking top 10 in all three rounds. BigDEAL's rankings in the three rounds were #2, #7 and #2, respectively. 
  • One of the only two teams with consistently improved MAPE throughout the three rounds. BigDEAL's MAPEs were 2.84%, 2.39% and 2.21%, respectively. 
Many thanks to NPower for organizing this fantastic competition. And congratulations, BigDEAL students

Saturday, February 28, 2015

Error Analysis in Load Forecasting: The Second Growing List

Over the past few years, THE most frequently asked question I have received is like the following:
My forecast error is x percent, how is the accuracy compared with my peers'?
Of course, each question was packaged with details about the jurisdictions and maybe model setups. At first I was trying to provide a customized response every time. Three years ago, I gave a webinar to cover very basic error measures used in load forecasting, which was apparently not enough to cover all the load forecast error/accuracy questions. Last week, after getting another email inquiry about forecasting accuracy, I decided to start this new series of blog posts. It will be the same style as the growing list of load forecasting terminology. The first 8 topics are listed below:
  1. How accurate is my forecast? (recorded webinar)
  2. Factors affecting load forecast accuracy
  3. More about weather variables
  4. How to measure load forecast errors?
  5. Accuracy of ISO/RTO short-term forecasts
  6. What a mess! Problems with reporting long term forecast errors
  7. Models for benchmarking
  8. Which techniques are "more accurate"?
  9. How much are we saving from 1% error reduction? 
I will add the links as I finish the posts. If you have any feedback, just email me or leave your thoughts in the comment field below. I will update the list accordingly.