The wide portfolio of modern techniques and algorithms that fall within the Artificial Intelligence (AI) field have proven to efficiently deal with many complex tasks and learning optimization paradigms. Many diverse activity sectors have harnessed the application of AI techniques to solve a plethora of practical problems, showcasing the potential of this technology realm to deal with such systems in an automated fashion. Among them, problems stemming from the Energy domain have become of capital importance given the progressive digitalization of their processes and assets. As a result, most of them have given a rise to learning tasks, such as data classification, clustering or prediction, as well as to the optimization problems of unprecedented complexity. In the light of this paradigm shift, a vibrant upsurge of research activity around Energy-related applications endowed with AI-powered functionalities has been noted in recent years. These include, but are not limited to Renewable Energy resource evaluations, design of energy-efficient systems, radically novel Energy system applications in Smart Grids, energy management strategies integrating renewable energy sources into conventional energy systems and many others alike.
This Special Session is focused on AI techniques for Energy-related problems, from a broad range of areas, covering both the algorithmic advances and the innovative applications with AI at their core. Articles discussing new algorithms with applications in energy-related problems, or revisited algorithms providing good solutions to practically difficult problems in energy-related applications, are very much welcome. Alternative applications with a close connection to Energy, such as problems related to renewable energy resources (wind, solar, marine, etc.), microgrids and smart grids designs (with renewable and non-renewable generation), Energy management and policy will also be welcome whenever AI algorithms are considered.
Session topics include but are not limited to:
- AI techniques and algorithmic tools for Energy-related problems (Machine Learning, Deep Learning, Soft Computing, Heuristic Optimization, other assorted techniques where automation and learning become essential features)
- Intelligent Demand Side Management
- Energy consumption characterization and forecasting
- Detection of non-technical losses in energy distribution networks
- Robustness and resilience techniques in power delivery networks under faults
- Problems related to renewable energy (wind, solar, marine, etc.)
- Multi-criteria design of smart grids and micro-grids, encompassing e.g. local energy storage, local renewable generation, etc.
- Energy efficiency in industrial systems and assets
- Climate change impacts on renewable energy systems
- Other applications that have a close connection with the Energy domain
- Sancho Salcedo-Sanz, Universidad de Alcalá (Spain)
- Javier del Ser, Fundación TECNALIA RESEARCH & INNOVATION and University of the Basque Country (Spain)
- Ravinesh C. Deo, University of Southern Queensland (Australia)
Dr. Sancho Salcedo Sanz,
Departamento de Teoría de la Señal y Comunicaciones, Universidad de Alcalá
(28805) Alcalá de Henares, Madrid (Spain)
Dr. Javier del Ser
Fundación TECNALIA RESEARCH & INNOVATION
University of the Basque Country