Thursday, June 20, 2019

Energy Forecasting in the Big Data World

All papers for the International Journal of Forecasting special section on energy forecasting in the big data world have been published online. Out of 14 papers collected for this special section, eight are from GEFCom2017 documenting winning methods, while the other six non-GEFCom2017 papers cover diverse topics in the areas of energy supply, demand and price forecasting.

The guest editorial is HERE. Below is the list of special section papers:
  1. Tao Hong, Jingrui Xie, and Jonathan Black. Global Energy Forecasting Competition 2017: Hierarchical probabilistic load forecasting
  2. Florian Ziel. Quantile regression for the qualifying match of GEFCom2017 probabilistic load forecasting.
  3. I. Dimoulkas, P. Mazidi, and L. Herre. Neural networks for GEFCom2017 probabilistic load forecasting.
  4. Slawek Smyl and N. Grace Hua. Machine learning methods for GEFCom2017 probabilistic load forecasting.
  5. Andrew J. Landgraf. An ensemble approach to GEFCom2017 probabilistic load forecasting.
  6. Cameron Roach. Reconciled boosted models for GEFCom2017 hierarchical probabilistic load forecasting.
  7. Julian de Hoog and Khalid Abdulla. Data visualization and forecast combination for probabilistic load forecasting in GEFCom2017 final match.
  8. Isao Kanda and J.M. Quintana Veguillas. Data preprocessing and quantile regression for probabilistic load forecasting in the GEFCom2017 final match.
  9. Stephen Haben, Georgios Giasemidis, Florian Ziel, and Siddharth Arora. Short term load forecasting and the effect of temperature at the low voltage level.
  10. Jakob W. Messner and Pierre Pinson. Online adaptive lasso estimation in vector autoregressive models for high dimensional wind power forecasting
  11. Dazhi Yang, Elynn Wu, Jan Kleissl. Operational solar forecasting for the real-time market.
  12. Grzegorz Marcjasz, Bartosz Uniejewski, and Rafał Weron. On the importance of the long-term seasonal component in day-ahead electricity price forecasting with NARX neural networks.
  13. Bartosz Uniejewski, Grzegorz Marcjasz, and Rafał Weron. Understanding intraday electricity markets: Variable selection and very short-term price forecasting using LASSO.
  14. Xuerong Li, Wei Shang, and Shouyang Wang. Text-based crude oil price forecasting: A deep learning approach.

Citation 

Tao Hong and Pierre Pinson, "Energy forecasting in the big data world," International Journal of Forecasting, in press, 2019. 

Energy Forecasting in the Big Data World

Tao Hong and Pierre Pinson

Modern information and communication technologies have brought big data to virtually every segment of the energy and utility industries. While forecasting is an important and necessary step in the data-driven decision-making process, the problem of generating better forecasts in the world of big data is an emerging issue and a challenge to both industry and academia. This special section aims to collect top-quality forecasting articles that document cutting-edge research findings and best practices on a wide range of important business problems in the energy industry. Our emphasis is on big data, such as forecasting with high resolution data, the use of high-dimensional processes, forecasting in real-time, and the use of non-traditional data and variables. 

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