Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm
- Data is now available to registered users.
- Registrants should use an official academic, government, or industry instituition email. Personal emails will not be accepted. Please allow 24 hours for your email to be registered in the system.
The ongoing COVID-19 related shutdowns have had a profound impact on the electric demand profiles worldwide, as governments put strict mitigation and/or suppression measures in place. The global electrical demand plummeted around the planet in March, April, and May 2020, with countries such as Spain and Italy experiencing more than 20% decrease in their usual electric consumption. In view of such massive electric demand changes, electricity network operators are facing unprecedented challenges in scheduling energy resources, as energy forecasting systems struggle to provide an accurate demand prediction. In fact, power systems operational reliability highly depends on an accurate projection of the future demand and scheduling an appropriate mixture of generation resources accordingly. Particularly, day-ahead forecasts are critical in managing market operation uncertainty. Thus, recent changes expose operators to technical and financial risks, further reinforcing the adverse economic impacts of the pandemic.
This competition aims at a detailed analysis of the impacts of the COVID-19 related measures on electricity demand, calling for strategies to mitigate the impact on day-ahead forecasting techniques’ performance. In particular, the competition is focused on day-ahead prediction of city-wide demand. The competition includes one-track only, deterministic forecasting of hourly load, 16 to 40 hours ahead.
Data includes historical demand, historical weather observations, and historical weather forecasts. Data is obtained from a real-world utility and weather service providers, and thus may be contaminated with issues such as missing periods, anomalies, etc. Details of data will be provided to registered participants.
The test data will cover 30 batches of 1-day each. Evaluation takes place in 30 phases. Contestant must provide a day-ahead forecast based on the most recent released data. More details with respect to submission rules will be released closer to the evaluation period.
The final winners will be determined by the Technical Committee based on predictions mean absolute error (MAE) per phase, documentation, reproducibility, etc.
- Dec. 14, 2020: Historical data release
- Mar. 1, 2021: Registration deadline
- Mar. 15, 2021: Evaluation period starts – first batch of test data release
- Apr. 13, 2021: Evaluation period ends – last batch of test data release
- Apr. 19, 2021: Final report and code submission deadline
- May 3, 2021: Winners announced
- 1st place: $5000 USD
- 2nd place: $3500 USD
- 3rd place: $1500 USD
The Technical Committee has currently submitted a Special Issue application to an IEEE PES journal, aimed at pre-approving the competition winners for publishing in the Special Issue. The Spcial Issue is currently pending approval from the IEEE PES Publication.
Registrants should use an official academic, government, or industry instituition email. Personal emails will not be accepted.
- Dr. Jethro Browell, University of Strathclyde, Scotland
- Dr. Mostafa Farrokhabadi, BluWave-ai, Canada
- Dr. Stephen Makonin, Simon Fraser University, Canada
- Dr. Wencong Su, University of Michigan-Dearborn, US
- Dr. Yi Wang, ETH Zurich, Switzerland
- Dr. Hamidreza Zareipour, University of Calgary, Canada
- IEEE DataPort
- IEEE PES PSOPE WG on Energy Forecasting and Analytics
- IEEE Foundation Donor Supported Program
- Data is provided courtesy of BluWave-ai.