10 Recommended Papers for GEFCom2014 Contestants

Update 9/20/2014:

Rafal Weron's review on price forecasting is available on Science Direct with open access. The probabilistic load forecasting review paper written by Shu Fan and myself is currently under review by IJF. The link to the working paper is listed below.

Update 8/14/2014:

Thanks to Rob Hyndman, who generously put Rafal Weron's forthcoming IJF paper on the web. Now we have a super well-written review paper on price forecasting on the list as Ref [11]. This paper will be published in the coming issue of International Journal of Forecasting.

The Global Energy Forecasting Competition 2014 (GEFCom2014) is the first probabilistic forecasting competition in the power and energy industry. As of today, over 200 people from more than 40 countries have signed up the interest list. To help the folks quickly get use to the theme of this competition, we organizers collaboratively picked up 10 recommended papers for GEFCom2014 contestants.

Ref [1-3] are general readings on probabilistic forecasting, energy forecasting and the previous competition respectively. Ref [4-6], Ref [7-8] and Ref [9-10] are for load, wind and solar forecasting respectively. We did not recommend any specific paper on probabilistic price forecasting. The contestants on the price forecasting track may refer to the recommended papers on load and wind forecasting. The literature on probabilistic wind power forecasting is dominantly more extensive and mature than the other three categories. Therefore, we would highly recommend the contestants to read Ref [7-8] regardless which track to work on. Shu and I are preparing the tutorial review on probabilistic load forecasting (Ref [4]), which will serve as a general guideline as well.

1. Gneiting T. and Katzfuss M. (2014). Probabilistic Forecasting, Annual Review of Statistics and Its Application, 1, pp.125-151.  
2. Hong, T. (2014). Energy Forecasting : Past, Present, and Future. Foresight: The International Journal of Forecasting, (32), 43-49. 
3. Hong, T., Pinson, P., & Fan, S. (2014). Global Energy Forecasting Competition 2012. International Journal of Forecasting, 30(2), 357-363.  
4. Hong, T., and Fan, S. (2014). “Probabilistic Electric Load Forecasting: A Tutorial Review”, working paper.
5. Hong, T., Wilson, J., & Xie, J. (2014). Long Term Probabilistic Load Forecasting and Normalization With Hourly Information, IEEE Transactions on Smart Grid, 5(1), 456-462.
6. Hyndman, R. J., and Fan, S. (2010). Density Forecasting for Long-Term Peak Electricity Demand. IEEE Transactions on Power Systems, 25(2), 1142-1153. (access to the current version of the model)
7. Pinson, P. (2013). Wind energy: Forecasting Challenges for its Operational Management. Statistical Science, 28(4), 564-585. 
8. Zhang, Y., Wang, J., Wang, X. (2014). Review on Probabilistic Forecasting of Wind Power Generation. Renewable and Sustainable Energy Reviews, 32, 255-270. 
9. Bacher, P., Madsen, H. and Nielsen, H. A. (2009). Online Short-term Solar Power Forecasting, Solar Energy, 83(10), 1772-1783.  
10. Lorenz, E., Scheidsteger T., Hurka, J., Heinemann, D., Kurz, C. (2011) Regional PV power prediction for improved grid integration. Progress in Photovoltaics: Research and Applications, 19(7), 757-771. 
11. Weron, R. (2014). Electricity Price Forecasting: A Review of the State-of-the-Art with a Look into the Future. International Journal of forecasting, 30(4), 1030-1081.

Note that the link to Ref [4] is missing, because Shu and I are still working on it, with the hope that we complete the draft before the competition launching date. I will update the link as soon as we get the draft done.

If you see any other good papers in this area, please feel free to make your recommendation in the comments field below.