Load forecasting is essential for the efficient, reliable, and cost-effective management of power systems. Load forecasting performance can be improved by learning the similarities among multiple entities (e.g., regions, buildings). Techniques based on multi-task learning obtain predictions by leveraging consumption patterns from the historical load demand of multiple entities and their relationships. However, existing techniques cannot effectively assess inherent uncertainties in load demand or cannot account for dynamic changes in consumption patterns. This talk proposes a multi-task learning technique for online and probabilistic load forecasting. This technique provides accurate probabilistic predictions for loads of multiple entities by leveraging their dynamic similarities. The method's performance is evaluated using datasets that register the load demand of multiple entities and contain diverse and dynamic consumption patterns. The experimental results show that the proposed method can significantly enhance the effectiveness of current multi-task learning approaches across a wide variety of load consumption scenarios.

 In this talk we will look at the interplay between Shannon Classical Information Theory and Quantum Information.

The concepts of Classical  Compression and Channel Coding will be related with their Quantum counterparts.

El uso de herramientas de inteligencia artificial (IA) en todos los campos de la actividad humana plantea cuestiones cruciales sobre la responsabilidad ética profesional. ¿En qué medida las técnicas de IA representan un desafío ético adicional comparado con herramientas tradicionales de cómputo? La charla se centra en la problemática de la explicabilidad y la comprensión del trabajo con IA mediante procesos de «caja negra»: ¿cómo un ingeniero, un médico, un abogado, puede asumir responsabilidad ética si no puede explicar racionalmente su trabajo, tanto a sí mismo como a los demás? Se demuestra así necesaria una nueva comprensión de la ética en el diseño y uso de las tecnologías de inteligencia artificial.