IJF Tao Hong Award for the best paper in energy forecasting 2013-2014

Professor Tao Hong has generously funded a new prize for the best IJF paper on energy forecasting, to be awarded every two years. The first award will be for papers published in the International Journal of Forecasting during the period 2013-2014. The prize will be US$1000 plus an engraved plaque. The award committee is Rob J Hyndman, Pierre Pinson and James Mitchell.

Nominations are invited from any reader of the IJF. Each person may nominate up to three papers, but you cannot nominate a paper that you have coauthored yourself. Papers coauthored by Tao Hong or one of the award committee are not eligible for the prize. All nominations are to be accompanied by a short statement (up to 200 words) from the nominator, explaining why the paper deserves an award.

You can see the relevant papers published in the period 2013-2014 on Google Scholar. Of course, a good paper does not always get noticed, so don’t let the citation count sway you too much in nominating what you consider to be the best IJF paper from this period.

Nominations should be sent to me by email by 8 February 2017.

GEFCom2017: Hierarchical Probabilistic Load Forecasting

After the great success of the previous two energy forecasting competitions we have run (GEFCom2012 and GEFCom2014), we are holding another one, this time focused on hierarchical probabilistic load forecasting. Check out all the details over on Tao Hong’s blog.

The previous GEFComs have led to some major advances in forecasting methodology, available via IJF papers by the winning teams. I expect similar developments to arise out of this competition. Winners get to present their work in Cairns, Australia at ISEA2017.

The latest IJF issue with GEFCom2014 results

The latest issue of the IJF is a bumper issue with over 500 pages of forecasting insights.

The GEFCom2014 papers are included in a special section on probabilistic energy forecasting, guest edited by Tao Hong and Pierre Pinson. This is a major milestone in energy forecasting research with the focus on probabilistic forecasting and forecast evaluation done using a quantile scoring method. Only a few years ago I was having to explain to energy professionals why you couldn’t use a MAPE to evaluate a percentile forecast. With this special section, we now have a tutorial review on probabilistic electric load forecasting by Tao Hong and Shu Fan, which should help everyone get up to speed with current forecasting approaches, evaluation methods and common misunderstandings. The section also contains a large number of very high quality articles showing how to do state-of-the-art density forecasting for electricity load, electricity price, solar and wind power. Moreover, we have some benchmarks on publicly available data sets so future researchers can easily compare their methods against these published results.

In addition to the special section on probabilistic energy forecasting, there is an invited review paper on call centre forecasting by Ibrahim, Ye, L’Ecuyer and Shen. This is an important area in practice, and this paper provides a helpful review of the literature, a summary of the key issues in building good models, and suggests some possible future research directions.

There is also an invited paper from Blasques, Koopman, Łasak and Lucas on “In-sample confidence bands and out-of-sample forecast bands for time-varying parameters in observation-driven models” with some great discussion from Catherine Forbes and Pierre Perron. This was the subject of Siem Jan Koopman’s ISF talk in 2014.

Finally, there are 18 regular contributed papers, more than we normally publish in a whole issue, on topics ranging from forecasting excess stock returns to demographics of households, from forecasting food prices, to evaluating forecasts of counts and intermittent demand. Check them all out on ScienceDirect.

2017 International Symposium on Energy Analytics

Predictive Energy Analytics in the Big Data World

Cairns, Australia, June 22-23, 2017



This will be a great conference, and it is in a great location — Cairns, Australia, right by the Great Barrier Reef. Even better, if you stay on you can attend the International Symposium on Forecasting which immediately follows the International Symposium on Energy Analytics.

So block out 22-28 June 2017 on your calendars so you can enjoy a tropical paradise in one of the most beautiful parts of Australia, while attending two awesome conferences.

Continue reading →

Electricity price forecasting competition

The GEFCom competitions have been a great success in generating good research on forecasting methods for electricity demand, and in enabling a comprehensive comparative evaluation of various methods. But they have only considered price forecasting in a simplified setting. So I’m happy to see this challenge is being taken up as part of the European Energy Market Conference for 2016, to be held from 6-9 June at the University of Porto in Portugal. Continue reading →

North American seminars: June 2015

For the next few weeks I am travelling in North America and will be giving the following talks.

  • 19 June: Southern California Edison, Rosemead CA.
    “Probabilistic forecasting of peak electricity demand”.
  • 23 June: International Symposium on Forecasting, Riverside CA.
    “MEFM: An R package for long-term probabilistic forecasting of electricity demand”.
  • 25 June: Google, Mountain View, CA.
    “Automatic algorithms for time series forecasting”.
  • 26 June: Yahoo, Sunnyvale, CA.
    “Exploring the boundaries of predictability: what can we forecast, and when should we give up?”
  • 30 June: Workshop on Frontiers in Functional Data Analysis, Banff, Canada.
    “Exploring the feature space of large collections of time series”.

The Yahoo talk will be streamed live.

I’ll post slides on my main site after each talk.

New R package for electricity forecasting

Shu Fan and I have developed a model for electricity demand forecasting that is now widely used in Australia for long-term forecasting of peak electricity demand. It has become known as the “Monash Electricity Forecasting Model”. We have decided to release an R package that implements our model so that other people can easily use it. The package is called “MEFM” and is available on github. We will probably also put in on CRAN eventually.

The model was first described in  Hyndman and Fan (2010). We are continually improving it, and the latest version is decribed in the model documentation which will be updated from time to time.

The package is being released under a GPL licence, so anyone can use it. All we ask is that our work is properly cited.

Naturally, we are not able to provide free technical support, although we welcome bug reports. We are available to undertake paid consulting work in electricity forecasting.


Generating quantile forecasts in R

From today’s email:

I have just finished reading a copy of ‘Forecasting:Principles and Practice’ and I have found the book really interesting. I have particularly enjoyed the case studies and focus on practical applications.

After finishing the book I have joined a forecasting competition to put what I’ve learnt to the test. I do have a couple of queries about the forecasting outputs required. The output required is a quantile forecast, is this the same as prediction intervals? Is there any R function to produce quantiles from 0 to 99?

If you were able to point me in the right direction regarding the above it would be greatly appreciated.

Many Thanks,

Continue reading →

GEFCom 2014 energy forecasting competition is underway

GEFCom 2014 is the most advanced energy forecasting competition ever organized, both in terms of the data involved, and in terms of the way the forecasts will be evaluated.

So everyone interested in energy forecasting should head over to the competition webpage and start forecasting: www.gefcom.org.

This time, the competition is hosted on CrowdANALYTIX rather than Kaggle.

Highlights of GEFCom2014:

  • An upgraded edition from GEFCom2012
  • Four tracks: electric load, electricity price, wind power and solar power forecasting.
  • Probabilistic forecasting: contestants are required to submit 99 quantiles for each step throughout the forecast horizon.
  • Rolling forecasting: incremental data sets are being released on weekly basis to forecast the next period of interest.
  • Prizes for winning teams and institutions: up to 3 teams from each track will be recognized as the winning team; top institutions with multiple well-performing teams will be recognized as the winning institutions.
  • Global participation: 200+ people from 40+ countries have already signed up the GEFCom2014 interest list.

Tao Hong (the main organizer) has a few tips on his blog that you should read before starting.