Performance evaluation of artificial intelligence methods for energy consumption forecasting using open data sets (slides)
Creators: * 21PESGM2426
Introduction of the Competition on Performance evaluation of artificial intelligence methods for energy consumption forecasting using open data sets: Z. VALE
Polytechnic of Porto * 21PESGM2427
Announcement of the Competition winners: T.  PINTO
Polytechnic of Porto
Portugal * 21PESGM2428
Presentation of the Competition results: L. GOMES
School of Engineering
Polytechnic of Porto * 21PESGM2429
Presentation of the winners solutions: H. MORI
Meiji University
Publisher: name: IEEE
Year: 2021

Energy consumption forecasting is crucial in current and future power and energy systems. With the increasing penetration of renewable energy sources, with high associated uncertainty due to the dependence on natural conditions, such as wind speed or solar intensity, the need to balance the fluctuation of generation with the flexibility from the consumer side increases considerably. In this way, significant work has been done on the development of energy consumption 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 there it is not clear that a certain method can outperform all others in all situations. This panel fosters discussion on the most recent advances in consumption forecasting methods, by bringing together different perspectives from panelists of diverse backgrounds. This panel will take place benefiting from the Competition on energy consumption forecasting, organized by the Open Data Sets (ODS) Task Force, to take place in 2021. Authors of methods that present the best results in this competition will be invited as panelists to present and discuss their work.
Energy consumption forecasting is crucial in current and future power and energy systems. With the increasing penetration of renewable energy sources, with high associated uncertainty due to the dependence on natural conditions, such as wind speed or solar intensity, the need to balance the fluctuation of generation with the flexibility from the consumer side increases considerably. In this way, significant work has been done on the development of energy consumption 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 there it is not clear that a certain method can outperform all others in all situations. This panel fosters discussion on the most recent advances in consumption forecasting methods, by bringing together different perspectives from panelists of diverse backgrounds. This panel will take place benefiting from the Competition on energy consumption forecasting, organized by the Open Data Sets (ODS) Task Force, to take place in 2021. Authors of methods that present the best results in this competition will be invited as panelists to present and discuss their work.