A generous donation from Professor Tao Hong has funded this award for papers on energy forecasting published in the International Journal of Forecasting. The award was first given in 2016 for papers published within 2013–2014. The award decision for 2020, hence covering the period 2017-2018, was made by a committee consisting of Professors Rafal Weron, James Mitchell and Pierre Pinson.
The third IIF – Tao Hong Award goes to Kevin Berk, Alexander Hoffmann and Alfred Müller for their 2018 paper “Probabilistic forecasting of industrial electricity load with regime-switching behaviour”, International Journal of Forecasting 34(2), 147-162.
The paper by Berk, Hoffmann and Müller concentrates on an important problem in energy forecasting, related to electricity demand. While a majority of the electric load forecasting works, until a few years ago, dealt with electric load prediction at an aggregate level, the deployment of smart meters and new challenges in electric energy management (renewables, demand response, liberalization, etc.) makes that it is of utmost importance to now focus on also predicting load at very detailed levels, e.g. for households and industry. This is what the paper delivers, by rightly appraising the underlying characteristics of industrial load and their potential regime-switching nature. The paper elegantly blends approaches time-series modelling, regime-switching (with time-varying transition probabilities) and a dedicated estimation. Eventually, the forecasts generated are probabilistic in nature and evaluated as such.
Congratulations to the award winners. We hope that this work will inspire many others to invest in new aspects of energy forecasting, from methods to applications!
2018: Pierre Gaillard, Yannig Goude and Raphael Nedellec for their 2016 paper “Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecasting”, International Journal of Forecasting 32(3), 1038-1050.
2016: Rafal Weron for his 2014 paper “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.