Day-Ahead Electricity Demand Forecasting: Post-COVID Paradigm

The ongoing COVID-19 related shutdowns has 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 the market operation uncertainty. Thus, recent changes expose operators to technical and financial risks, further reinforcing the adverse economic impacts of the pandemic.ObjectiveThis 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 the load 16 to 40 hour ahead in 15-minute granularity.DataData 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. Data is provided courtesy of BluWave-ai.Evaluation FormatThe test data will cover 12 batches of 7-day each. Evaluation takes place in 12 phases; for each phase, the data up to the 7- day batch will be released. Contestant must predict the 7-day period using the forecasts of the first day as pseudo-actuals for the second day, continuing forward until 7 day-ahead predictions are made for the week.Evaluation MetricThe final winners will be determined by the Technical Committee based on predictions mean absolute error (MAE) per phase, documentation, reproducibility, etc.Important Dates:07/12/2020: Historical data release01/03/2021: Registration deadline15/03/2021: Evaluation period starts – first batch of test data release09/04/2021: Evaluation period ends – last batch of test data release18/04/2021: Final report and code submission deadline03/05/2021: Winners announcedPrizes1st place: $5000 USD2nd place: $3500 USD3rd place: $1500 USDPublicationThe 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.Technical CommitteeDr. Jethro Browell, University of Strathclyde, ScotlandDr. Mostafa Farrokhabadi, BluWave-ai, CanadaDr. Stephen Makonin, Simon Fraser University, CanadaDr. Wencong Su, University of Michigan-Dearborn, USDr. Yi Wang, ETH Zurich, SwitzerlandAdvisory Committee:Dr. Hamidreza Zareipour, University of Calgary, CanadaSponsorsIEEE DataPortIEEE PES PSOPE WG on Energy Forecasting and AnalyticsIEEE Foundation Donor Supported ProgramContactDr. Mostafa Farrokhabadi, [e-mail]. Melissa Handa, [e-mail]