TY - GEN
T1 - Hybrid Collaborative Networks in Energy Ecosystems
AU - Adu-Kankam, Kankam Okatakyie
AU - Camarinha-Matos, Luís M.
AU - Obeng, Eric
N1 - Publisher Copyright:
© IFIP International Federation for Information Processing 2024.
PY - 2024/9
Y1 - 2024/9
N2 - Human-AI collaboration in renewable energy ecosystems can revolutionize the way communities achieve sustainable and efficient energy solutions. This synergistic approach combines the analytical prowess of AI with the expertise of human decision-making, fostering a comprehensive strategy for energy management. AI excels in tasks that are repetitive and mundane, while humans excel at decision-making tasks that reflect their preferences and community dynamics, ensuring that AI-driven solutions are aligned with societal values. This collaborative approach, representing a case of hybrid collaborative network, can lead to optimizing energy use, reducing dependency on the grid, and empowering communities to lead the energy transition, fostering more resilient and sustainable energy ecosystems. In this context, we explore possibilities of achieving “meaningful” energy conservation practices in a Collaborative Energy Ecosystem (CEE) using human-AI collaboration. As such, we expand and discuss the CEE model from the perspective of hybrid human-AI collaboration, present pilot implementation results, and discuss future research directions.
AB - Human-AI collaboration in renewable energy ecosystems can revolutionize the way communities achieve sustainable and efficient energy solutions. This synergistic approach combines the analytical prowess of AI with the expertise of human decision-making, fostering a comprehensive strategy for energy management. AI excels in tasks that are repetitive and mundane, while humans excel at decision-making tasks that reflect their preferences and community dynamics, ensuring that AI-driven solutions are aligned with societal values. This collaborative approach, representing a case of hybrid collaborative network, can lead to optimizing energy use, reducing dependency on the grid, and empowering communities to lead the energy transition, fostering more resilient and sustainable energy ecosystems. In this context, we explore possibilities of achieving “meaningful” energy conservation practices in a Collaborative Energy Ecosystem (CEE) using human-AI collaboration. As such, we expand and discuss the CEE model from the perspective of hybrid human-AI collaboration, present pilot implementation results, and discuss future research directions.
KW - collaborative energy ecosystem
KW - collaborative intelligence
KW - collaborative networks
KW - Hybrid collaborative Networks
KW - meaningful energy conservation
UR - http://www.scopus.com/inward/record.url?scp=85205124835&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-71739-0_1
DO - 10.1007/978-3-031-71739-0_1
M3 - Conference contribution
AN - SCOPUS:85205124835
SN - 978-3-031-71738-3
T3 - IFIP Advances in Information and Communication Technology
SP - 3
EP - 21
BT - Navigating Unpredictability: Collaborative Networks in Non-linear Worlds
A2 - Camarinha-Matos, Luís M.
A2 - Ortiz, Angel
A2 - Boucher, Xavier
A2 - Barthe-Delanoë, Anne-Marie
PB - Springer
CY - Cham
T2 - 25th IFIP WG 5.5 Working Conference on Virtual Enterprises, PRO-VE 2024
Y2 - 28 October 2024 through 30 October 2024
ER -