Call for the 2022-3 Competition on solar generation forecasting
Benchmarking of artificial intelligence methods for solar generation forecasting to address the increasing importance of energy resources forecasting in current and future power and energy systems.
NEW DEADLINE: 15th January 2023
I. Scope and Topics
Energy resources forecasting is increasingly important in current and future power and energy systems. Due to the high uncertainty of generation based on renewable energy sources, which results from their dependence on weather conditions, such as wind speed or solar intensity, the need to develop suitable solutions to deal with such variability increases considerably.
Relevant effort is being put on the development of energy consumption and generation forecasting methods, able to deal with different forecasting circumstances, e.g., the prediction time horizon, the available data, the frequency of data, or even the quality of data measurements. The main conclusion is that different methods are more suitable for different prediction circumstances, and it is not clear that a certain method can outperform all others in all situations.
This competition fosters the benchmarking of artificial intelligence methods for solar generation forecasting. Authors of methods that present the best results in this competition will be invited to present their work.
II. Submission Instructions
The deadline for the results submission has been postponed to 15th January, 2023. The participants that already made their submission, are invited to improve their results until the new deadline.
The forecasts for each hour of the 7 days must be submitted in XLS format. After submission, the teams with the best results will be requested to present their solution in a remote session with the jury.
Competition Website: https://www2.gecad.isep.ipp.pt/smartgridcompetition-forecast/
Other competitions: https://www2.gecad.isep.ipp.pt/smartgridcompetitions/
Organizers
Luis Gomes, Zita Vale, and Tiago Pinto (Polytechnic of Porto, Portugal)
Supported by Working Group of Intelligent Data Mining and Analysis (IDMA) and IEEE PES Task Force on Open Data Set.
Supported by IEEE CIS Task Force on ‘Computational Intelligence in the Energy Domain’.
