Center of Excellence in Artificial Intelligence for Renewable Energy (CEAIRE)

• Grant number: 22/00720-2
• Support Opportunities: Research Grants - Applied Research Centers Program
• Start date: August 01, 2024
• End date: July 31, 2029
• Field of knowledge: Physical Sciences and Mathematics - Computer Science - Computer Systems
• Agreement: MCTI/MC
• Principal Investigator: Alvaro Luiz Gayoso de Azeredo Coutinho
• Grantee: Alvaro Luiz Gayoso de Azeredo Coutinho
• Host Institution: Instituto Alberto Luiz Coimbra de Pós-Graduação e Pesquisa (COPPE). Universidade Federal do Rio de Janeiro (UFRJ)
• City of the host institution: Rio de Janeiro
• Partner institutions: EMBRAPA SUINOS AVES/EMBRAPA; UNIDADE NAO MAPEADA
• Principal investigators: Antonio Andre Novotny ; Argimiro Resende Secchi ; Doris Regina Aires Veleda ; Fernando Alves Rochinha ; Frederic Gerard Christian Valentin ; Gerson Zaverucha ; Suzana Kahn Ribeiro
• Associated researchers: Adriano Mauricio de Almeida Cortes ; Airton Kunz ; Aline Scarpetta ; André Luiz Vizine Pereira ; Andre Rodrigues Goncalves ; Antonio Tadeu Azevedo Gomes ; Bruna de Souza Moraes ; Bruno Didier Olivier Capron ; Carlos Eduardo Borba ; Daniel Augusto Cantane ; Daniel Cardoso Moraes de Oliveira ; Dorival Leão Pinto Júnior ; Eduardo Cunha de Almeida ; Eduardo Soares Ogasawara ; Enio Bueno Pereira ; Fabio André Machado Porto ; Fabio Pereira dos Santos ; Felipe Souza Marques ; Fernando Ramos Martins ; George Victor Brigagão ; Gustavo de Novaes Pires Leite ; Helton José Alves ; Jose Luiz de Medeiros ; Leonardo Shiguemi Dinnouti ; Marcelo Dutra Fragoso ; Marcos Garcia Todorov ; Mário Augusto Bezerra da Silva ; Mariza Ferro ; Marta Lima de Queiros Mattoso ; Maurício Antônio Lopes ; Maurício Bezerra de Souza Júnior ; Melissa Braga ; Michèle Schubert Pfeil ; Milad Shadman ; Moisés Bastos Neto ; Nelson Francisco Favilla Ebecken ; Ofelia de Queiroz Fernandes Araujo ; Olga de Castro Vilela ; Paula Dornhofer Paro Costa ; Paulo de Tarso Themistocles Esperança ; Príamo Albuquerque Melo Junior ; Priscila Seixas Sabaini ; Ramiro Brito Willmersdorf ; Regina de Fatima Peralta Muniz Moreira ; Ricardo José Ferracin ; Rodrigo Santos Costa ; Rogério Pinto Espíndola ; Rossano Gambetta ; Saul de Castro Leite ; Segen Farid Estefen ; Thiago Gamboa Ritto ; Thierry Pinheiro Moreira ; Wellington Rangel dos Santos

Abstract

The great challenge of sustainable development in this century is to balance the increase in demand for energy with the restrictions imposed by carbon emissions and climate change. In this context, recent advances in artificial intelligence, data science, and high-performance processing play a key role in improving mathematical models' computational efficiency and effectiveness, focusing on renewable energy. This proposal aims to create a nationwide Center of Excellence for the development of new techniques and the application of recent results of artificial intelligence, data science, and high-performance computing, with the support of fundamental techniques from applied mathematics and software engineering, for renewable energy applications such as wind, solar, biogas and hydrogen. The Center is organized into working groups (WGs) with a transversal working group (WG0) and four vertical working groups (WG1-4):WG0 - Artificial Intelligence, High-Performance Computing, Data Science: Dedicated to leveraging the development of solutions based on artificial intelligence, data science, and high-performance computing (HPC) techniques in Renewable Energy. WG0 aims to provide advanced artificial intelligence as data science modeling tools for other WGs in Renewable Energy applications and develop conceptually new artificial intelligence methods, exploiting the HPC resources of CEAIRE members, which includes the most advanced supercomputer centers in Brazil.WG1 - Wind energy: dedicated to combining data monitoring, modeling, forecasting, control, and structural health monitoring (SHM) techniques to produce IA solutions to enhance production efficiency, increase Remaining Useful Life (RUL), mitigating intermittency, and contributing to power system flexibility.WG2 - Solar Energy: dedicated to the use of surface and satellite observed databases combined with AI techniques to address complex issues related to resource characterization and its intrinsic relationship with climate, climate variability impacts, resource forecasting in different time horizons, detection and fault diagnosis of distributed and centralized generation systems, O&M management and intermittency mitigation. The problems will be addressed both from the point of view of the generators and the system operator.WG3 - Biogas: dedicated to biogas energy generated from biomass - such as dedicated energy crops, agricultural crop residues, animal production waste, forestry residues, algae, municipal waste, among others, incorporating AI to support a) spatially explicit predictions of biomass production in the Brazilian territory; b) predictions of biomass properties; c) prediction and monitoring of process performance of biomass conversion; d) process control and improvement with an emphasis in biodigestion, energy production, gas pretreatment, sludge disposal, and fertilizer production; e) prediction of the performance of biogas/biomethane end-use systems; f) supply chain modeling and optimization with an emphasis in life-cycle analysis (LCA), scale and logistics to connect biogas to sustainable bioenergy systems.WG4 - Hydrogen: the production of hydrogen using energy sources such as wind and solar is one of the pillars to decarbonize the world economy. Thus, AI Techniques will be fundamental to increasing hydrogen production efficiency, mainly through the sizing and integration of used equipment, establishing O&M procedures to increase the equipment's useful life, and optimizing preventive and predictive maintenance. Each working group will develop research, development, and innovation projects that will be related in the context of the thematic network, with the support of large and medium-sized companies and the participation of technology-based companies. (AU)