Thursday, September 22, 2016

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.

Monday, September 19, 2016

Announcing GEFCom2017: Join the Interest List

I'm sure readers of this blog are anxious for the next Global Energy Forecasting Competition. Today I'm pleased to announce the GEFCom2017, an upgraded version over GEFCom2012 and GEFCom2014.

To bring together contestants with diverse background and to dive deep into the challenging problems, GEFCom2017 will feature a bi-level setup: a three-month qualifying match and a one-month final match. The qualifying match means to attract and educate a large number of contestants with diverse background. The final match will be open to the top entries from the qualifying match, tackling a more challenging, larger scale problem.

Many contestants who joined GEFCom2012 also participated in GEFCom2014. To encourage the continuous investments in energy forecasting and recognize those who excel in these competitions, we will start building the World Rankings in Energy Forecasting.

We will release the competition problems and the formal registration on 10/14/2016. Please join the interest list HERE to get timely updates about GEFCom2017.

Stay tuned!

Sunday, September 11, 2016

Call For Sponsors: 2017 International Symposium on Energy Analytics (ISEA2017)

The first International Symposium on Energy Analytics (ISEA2017) will be held in Cairns, Australia, June 22-23, 2017. Cairns is the only place in the world where two World Heritage listed areas are side-by-side: The Great Barrier Reef and The Daintree Rainforest, For more information about Cairns, please visit the Cairns visitors information guide.

ISEA2017 features the theme "Predictive Energy Analytics in the Big Data World". The topics of interest can be found HERE. We expect about 50 attendees, 1/3 from academia and 2/3 from the industry. ISEA2017 is right before the 37th International Symposium on Forecasting (ISF2017), the flagship conference of the International Institute of Forecasters (IIF). Attendees of ISEA2017 will also get a discounted registration to ISF2017.

IIF is a major sponsor of ISEA2017. We are also looking for additional sponsors to keep the cost down for attendees. The sponsor information is highly visible at ISEA2017 and its website, as well as through the email and social media campaigns. This is a great opportunity to support the energy forecasting community, promote your organization and show off your products and services. For your energy analysts, this symposium would be a great venue to learn from and network with peers from other organizations.

The sponsorship can be on any of the four levels as listed below. If you are interested in sponsoring the event, please contact me via email: hongtao01 AT gmail DOT com.

Wednesday, August 24, 2016

Guest Editorial: Big Data Analytics for Grid Modernization

IEEE Transactions on Smart Grid just published our special section on Big Data Analytics for Grid Modernization. The guest editorial is on IEEE Xplore with open access. The original Call for Papers is HERE.

While "big data" is quickly becoming a buzz word (see THIS POST), in this guest editorial we discussed our interpretation from four aspects:
  1. The data involved in the analysis is big in at least one of its three defining dimensions: volume, variety or velocity. The big data used in the utility industry includes but is not limited to smart meter data, phasor measurement unit data, weather data, and social media data. 
  2. The problem under investigation is to prepare for analyzing the big data, such as data compression and data security issues. 
  3. The methodology requires customized modeling of individual components of a system, or leads to in-depth understanding of the individual components. For instance, estimating the invisible solar generation belongs to this category. 
  4. The technology can be used to help reach the answer faster, or answer the questions otherwise difficult to answer. For example, a distributed platform can be used to speed up the analytic tasks.
Thanks to the diligent work from our guest editors, reviewers and the authors, we are able to present a high-quality collection of papers to the community. Below is the list of 17 special section papers:
  1. D. Zhou, J. Guo, Y. Zhang, J. Chai, H. Liu, Y. Liu, C. Huang, X. Gui and Y. Liu, "Distributed Data Analytics Platform for Wide-Area Synchrophasor Measurement Systems," IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2397-2405, Sept. 2016
  2. P. H. Gadde, M. Biswal, S. Brahma and H. Cao, "Efficient Compression of PMU Data in WAMS," IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2406-2413, Sept. 2016
  3. X. Tong, C. Kang and Q. Xia, "Smart Metering Load Data Compression Based on Load Feature Identification," IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2414-2422, Sept. 2016
  4. J. Hu and A. V. Vasilakos, "Energy Big Data Analytics and Security: Challenges and Opportunities," IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2423-2436, Sept. 2016
  5. Y. Wang, Q. Chen, C. Kang and Q. Xia, "Clustering of Electricity Consumption Behavior Dynamics Toward Big Data Applications," IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2437-2447, Sept. 2016
  6. S. Ben Taieb, R. Huser, R. J. Hyndman and M. G. Genton, "Forecasting Uncertainty in Electricity Smart Meter Data by Boosting Additive Quantile Regression," IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2448-2455, Sept. 2016
  7. H. Shaker, H. Zareipour and D. Wood, "Estimating Power Generation of Invisible Solar Sites Using Publicly Available Data," IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2456-2465, Sept. 2016
  8. H. Shaker, H. Zareipour and D. Wood, "A Data-Driven Approach for Estimating the Power Generation of Invisible Solar Sites," IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2466-2476, Sept. 2016
  9. X. Zhang and S. Grijalva, "A Data-Driven Approach for Detection and Estimation of Residential PV Installations," IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2477-2485, Sept. 2016
  10. H. Wang and J. Huang, "Cooperative Planning of Renewable Generations for Interconnected Microgrids," IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2486-2496, Sept. 2016
  11. J. Peppanen, M. J. Reno, R. J. Broderick and S. Grijalva, "Distribution System Model Calibration With Big Data From AMI and PV Inverters," IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2497-2506, Sept. 2016
  12. Y. C. Chen, J. Wang, A. D. Domínguez-García and P. W. Sauer, "Measurement-Based Estimation of the Power Flow Jacobian Matrix," IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2507-2515, Sept. 2016
  13. H. Sun, Z. Wang, J. Wang, Z. Huang, N. Carrington and J. Liao, "Data-Driven Power Outage Detection by Social Sensors," IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2516-2524, Sept. 2016
  14. H. Jiang, X. Dai, D. W. Gao, J. J. Zhang, Y. Zhang and E. Muljadi, "Spatial-Temporal Synchrophasor Data Characterization and Analytics in Smart Grid Fault Detection, Identification, and Impact Causal Analysis," IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2525-2536, Sept. 2016
  15. M. Rafferty, X. Liu, D. M. Laverty and S. McLoone, "Real-Time Multiple Event Detection and Classification Using Moving Window PCA," IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2537-2548, Sept. 2016
  16. T. Jiang, Y. Mu, H. Jia, N. Lu, H. Yuan, J. Yan and W. Li, "A Novel Dominant Mode Estimation Method for Analyzing Inter-Area Oscillation in China Southern Power Grid," IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2549-2560, Sept. 2016
  17. B. Wang, B. Fang, Y. Wang, H. Liu and Y. Liu, "Power System Transient Stability Assessment Based on Big Data and the Core Vector Machine," IEEE Transactions on Smart Grid, vol. 7, no. 5, pp. 2561-2570, Sept. 2016


Tao Hong, Chen Chen, Jianwei Huang, Ning Lu, Le Xie and Hamidreza Zareipour, "Guest Editorial: big data analytics for grid modernization", IEEE Transactions on Smart Grid, vol.7, no.5, pp 2395-2396, September, 2016

Guest Editorial: Big Data Analytics for Grid Modernization

Tao Hong, Chen Chen, Jianwei Huang, Ning Lu, Le Xie and Hamidreza Zareipour

Saturday, July 30, 2016

Southwest Forecasting and Customer Analytics Forum 2016

Southwest Forecasting and Customer Analytics Forum 

Hosted By Tucson Electric Power, September 15-16, 2016

Tucson Electric Power is pleased to host the Southwest Forecasting and Customer Analytics Conference for the utility industry at its downtown Tucson headquarters.  Topics and events covered in the program:
  • Perspective on Using Forecasts by David G. Hutchens, President and CEO, TEP and its parent company, UNS Energy Corporation
  • The Evolving Regulated Utility Industry – Prof. Stanley Reynolds, University of Arizona
  • Energy Forecasting: Past, Present, and Future – Prof. Tao Hong, UNC at Charlotte
  • How Will Battery Storage Deployment Affect Load Forecasting? – Jason Burwen, ESA
  • Impact and Value of Plug-in Electric Vehicle Load & Managed Charging – R. Graham, U.S. Energy Department and Dr. Hongyan Sheng, Southern California Edison
  • Location-Specific Probabilistic Forecasting and Planning Methods – Josh Bode, Nexant
  • Home Appliances: Historical Declines in Energy Use, Future Potential Savings, and an Update on Efficiency Standards – Joanna Mauer, Appliance Standards Awareness Project
  • Likelihood of Customer Participation in Utility Programs – Dr. Erin Boyd, Pacific Gas & Electric
  • Networking Dinner
The only cost of this program is your own travel to Tucson, Arizona and the cost of dinner.  For details, see the program HERE.

Tuesday, July 26, 2016

Temperature Scenario Generation for Probabilistic Load Forecasting

When using weather scenarios to generate probabilistic load forecasts, a frequently asked question is
How many years of weather history do we need? 
This paper gives an answer based on an empirical study.

Most of my papers were accepted after two or more revisions. This time it only took one revision to have this paper accepted. In the first round of review, We received 40 comments from 6 reviewers. Our first revision was accepted after 4 of the reviewers recommended acceptance. In this blog post, I'm attaching the submitted version of the revision including our response letter. Some of our responses were rebuttals to one of the reviewers who made a personal attack on me.


Jingrui Xie and Tao Hong, "Temperature scenario generation for probabilistic load forecasting", Transactions on Smart Grid, accepted.

The working paper is available HERE.

Temperature Scenario Generation for Probabilistic Load Forecasting

Jingrui Xie and Tao Hong


In today’s dynamic and competitive business environment, probabilistic load forecasting (PLF) is becoming increasingly important to utilities for quantifying the uncertainties in the future. Among the various approaches to generating probabilistic load forecasts, feeding simulated weather scenarios to a point load forecasting model is being commonly accepted by the industry for its simplicity and interpretability. There are three practical and widely used methods for temperature scenario generation, namely fixed-date, shifted-date, and bootstrap methods. Nevertheless, these methods have been used mainly on ad hoc basis without being formally compared or quantitatively evaluated. For instance, it has never been clear to the industry how many years of weather history is sufficient to adopt these methods. This is the first study to quantitatively evaluate these three temperature scenario generation methods based on the quantile score, a comprehensive error measure for probabilistic forecasts. Through a series of empirical studies on both linear and nonlinear models with three different levels of predictive power, we find that 1) the quantile score of each method shows diminishing improvement as the length of available temperature history increases; 2) while shifting dates can compensate short weather history, the quantile score improvement gained from the shifted-date method diminishes and eventually becomes negative as the number of shifted days increases; and 3) comparing with the fixed-date method, the bootstrap method offers the capability of generating more comprehensive scenarios but does not improve the quantile score. At the end, an empirical formula for selecting and applying the temperature scenario generation methods is proposed together with a practical guideline.  

Thursday, July 7, 2016

GEFCom2012 Load Forecasting Data

The load forecasting track of GEFCom2012 was about hierarchical load forecasting. We asked the contestants to forecast and backcast (check out THIS POST for the definitions of forecasting and backcasting) the electricity demand for 21 zones, of which the Zone 21 was the sum of the other 20 zones.

Where to download the data?

You can also download an incomplete dataset from Kaggle, which does not have the solution data. The complete data was published as the appendix of our GEFCom2012 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 and Shu Fan, "Global energy forecasting competition 2012", International Journal of Forecasting, vol.30, no.2, pp 357-363, April-June, 2014. 

What's in the package?

Unzip the file, and navigate to "GEFCOM2012_Data\Load\" folder, you will see 6 files:
  • load_history
  • temperature_history
  • holiday_list
  • load_benchmark
  • load_solution
  • temperature_solution
Our GEFCom2012 paper has introduced the first five datasets but not the last one. The "temperature_solution" dataset includes the temperature data from 2008/6/30 7:00 to 2008/7/7 24:00, while the "load_solution" dataset does not include the load data from 2008/6/30 7:00 to 2008/6/30 24:00.

What's not working?

Before using the data, please understand that
there is no way to restore the exact Kaggle setup for you to make direct comparison on the error score. 
The main reason is that Kaggle pick a random subset of the solution data to calculate the scores for public leaderboard, and the rest for private leaderboard. We do not know which data was used for which leaderboard.

Nevertheless, it was never our intention to let you make comparisons in a Kaggle way. It is because the GEFCom2012 was set up more like a data mining competition than a forecasting competition. The contestants can submit their forecasts many times, while Kaggle was picking the best score. This is not a realistic forecasting process.

How to use the data?

Instead, we encourage you to use these 4.5 years of hourly data without considering the Kaggle setup. You can even keep 4 full calendar years and get rid of the last half a year in your case studies. With four years of data, you can perform one-year ahead ex post forecasting (see my weather station selection paper). You can also perform short term ex post forecasting on rolling basis (see my recency effect paper).

Then the question is whether the accuracy is "good enough". According to Table 3 of our GEFCom2012 paper, the winning teams improved the benchmark by about 30% - see the "test" column, which is the private leaderboard of Kaggle. In other words, if your model is getting about 30% error reduction comparing to the Vanilla benchmark on this dataset, it is a decent model.

Please also understand that this 30% is gained from a forecasting system with many bells and whistles, such as detailed modeling of temperature, and special treatment of holidays. If your research is focus on one components, the error reduction may be much smaller than 30%. You can find a more detailed arguments in my response to the second review comment in THIS POST.

It's been over two years since we publish the GEFCom2012 data. Many researchers have already used it to test their models. You can also replicate the experiment setup in the recently published papers that used this GEFCom2012 data, and compare your results with the results on those papers.

Saturday, July 2, 2016

Datasets for Energy Forecasting

Reproducible research is a key to advancing knowledge. In energy forecasting, it is necessary and crucial that researchers compare their models and methods using the same datasets. Five years ago when we founded the IEEE Working Group on Energy Forecasting, "lack of benchmark data pool" was one of the issues we identified. Fortunately, things have been changing toward the right direction over the past few years. More and more datasets are being made available to and recognized by the energy forecasting community.

This post will serve as the starting point of a blog series on datasets. In each post, I will feature a dataset and discuss how to use it. I will also host the datasets on Dropbox and provide the links in these posts. Meanwhile, I would like to take a crowd-sourcing approach to making a comprehensive and widely accessible data pool:
  • If you can host the datasets through other channels, please contact me. 
  • If you know of some public datasets that are not on my list, please contact me. 
  • If you have some private datasets that can be made available to the energy forecasting community, please contact me. 
Here is a list of 9 posts with the publicly available data that I have used in my papers. I will update the list with links and additional data sources, so check this page from time to time to see if there is something you need.

Electric load forecasting
  1. GEFCom2012
  2. GEFCom2014
  3. ISO New England
  4. RWE npower forecasting challenge 2015
Gas load forecasting
  1. RWE npower forecasting challenge 2015
Electricity price forecasting
  1. GEFCom2014
Wind power forecasting
  1. GEFCom2012
  2. GEFCom2014
Solar power forecasting
  1. GEFCom2014
Stay tuned...

Saturday, May 21, 2016

Call For Papers: 2017 International Symposium on Energy Analytics (ISEA2017)

2017 International Symposium on Energy Analytics
Cairns, Australia, June 22-23, 2017
Predictive Energy Analytics in the Big Data World

Modern information and communication technologies have brought big data to virtually every segment of the energy and utility industries. While predictive analytics is an important and necessary step in the data-driven decision-making process, how to generate better forecasts in the big data world is an emerging issue and challenge to both industry and academia.

This symposium aims at bringing forecasting experts and practitioners together to share experiences and best practices on a wide range of important business problems in the energy industry. Here the energy industry broadly covers utilities, oil, gas and mining industries. The subjects to be forecasted range from supply, demand and price, to asset/system condition and customer count.

The topics of interest include but are not limited to:
  • Probabilistic energy forecasting
  • Hierarchical energy forecasting
  • High-dimensional energy forecasting
  • High-frequency and high-resolution energy forecasting
  • Equipment failure prediction
  • Power systems fault prediction
  • Automatic outlier detection
  • Load profiling
  • Customer segmentation
  • Customer churn prediction
If you are interested in contributing a presentation to this symposium, please submit a one-page extended abstract to both guest editors via email with the subject line “ISEA2017 Abstract Submission”. Authors of selected abstracts will be invited to submit full papers to the International Journal of Forecasting (IJF) or Power and Energy Magazine.

Important ISEA2017 dates
  • Abstract submission open - November 15, 2016
  • Abstract submission due - January 15, 2017
  • Abstract acceptance - February 15, 2017
  • Early registration deadline - April 14, 2017
  • Paper submission for consideration of journal/magazine publication - May 31, 2017
  • ISEA2017 - June 22-23, 2017
  • ISF2017 - June 25-28, 2017
Important publication dates
  • First round review completion - August 31, 2017
  • Final version ready for Power and Energy Magazine - October 31, 2017
  • Final version ready for IJF - December 31, 2017
  • Power and Energy Magazine special issue publication - May/June, 2018
  • IJF special section publication - 2018

Guest Editors:
Tao Hong, University of North Carolina at Charlotte, USA (
Pierre Pinson, Technical University of Denmark, Denmark (

Rob J Hyndman, Monash University, Australia
International Journal of Forecasting

Thursday, May 19, 2016

Job Openings for Energy Analysts and Forecasters

During the past two years or so, the largest tag in this blog has been "jobs". From February 2013 to February 2015, I posted 56 jobs. Due to the increased demand for energy forecasters, I can no longer respond to the job posting requests in time. Therefore, I have decided to take a new approach to job posting.

You are invited to post your job openings using the Name/URL option in the comments field. Please be brief about your job postings. Rather than pasting the whole job description, I would recommend you just listing the job title, company and a link to the job description or application site. In addition, you can also provide your contact information for the readers to contact you if you are the hiring manager or recruiter. I will moderate the comment field.

I'm posting the BigDEAL recruiting message first as an example.

Happy recruiting and job hunting!