A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Artificial intelligence in renewable systems for transformation towards intelligent buildings
Carbon-neutrality transition in building sectors requires combinations of renewable systems and artificial intelligence (AI) for robustness, reliability, automation, and flexibility. In this study, a comprehensive review on AI applications in renewable systems, have been conducted, to report the current progress, tendency and challenges. Underlying learning mechanisms of AI-based various applications have been studied, in terms of modelling techniques on solar power forecasting, multi-level stochastic uncertainty analysis, smart controls, fault detection and diagnosis, single and multi-objective optimisations and intelligent buildings. Furthermore, AI techniques in renewable energy utilisations have been clearly reviewed, with respect to solar potential evaluation, multi-level stochastic uncertainty analysis, smart controls, fault detection and diagnosis, single and multi-objective optimisations. Results showed that, compared to physical models, the data-driven models show superiority in modelling simplification and modification, high prediction accuracy, and computational efficiency. In order to improve the system operational robustness under multi-level scenario uncertainty, a generic approach with artificial intelligence was proposed, including nonlinear data-driven model development, uncertainty-based energy performance prediction, stochastic uncertainty and statistical analysis. Furthermore, artificial intelligence can also be applied in renewable systems for security, reliability and stability. This study provides a clear roadmap on historical development, recent advances, cutting-edge techniques and progress, and future challenges of AI applications in renewable energy systems, paving path for developing smart and resilient renewable energy systems for decarbonization in buildings.
Artificial intelligence in renewable systems for transformation towards intelligent buildings
Carbon-neutrality transition in building sectors requires combinations of renewable systems and artificial intelligence (AI) for robustness, reliability, automation, and flexibility. In this study, a comprehensive review on AI applications in renewable systems, have been conducted, to report the current progress, tendency and challenges. Underlying learning mechanisms of AI-based various applications have been studied, in terms of modelling techniques on solar power forecasting, multi-level stochastic uncertainty analysis, smart controls, fault detection and diagnosis, single and multi-objective optimisations and intelligent buildings. Furthermore, AI techniques in renewable energy utilisations have been clearly reviewed, with respect to solar potential evaluation, multi-level stochastic uncertainty analysis, smart controls, fault detection and diagnosis, single and multi-objective optimisations. Results showed that, compared to physical models, the data-driven models show superiority in modelling simplification and modification, high prediction accuracy, and computational efficiency. In order to improve the system operational robustness under multi-level scenario uncertainty, a generic approach with artificial intelligence was proposed, including nonlinear data-driven model development, uncertainty-based energy performance prediction, stochastic uncertainty and statistical analysis. Furthermore, artificial intelligence can also be applied in renewable systems for security, reliability and stability. This study provides a clear roadmap on historical development, recent advances, cutting-edge techniques and progress, and future challenges of AI applications in renewable energy systems, paving path for developing smart and resilient renewable energy systems for decarbonization in buildings.
Artificial intelligence in renewable systems for transformation towards intelligent buildings
Yuekuan Zhou (author)
2022
Article (Journal)
Electronic Resource
Unknown
Metadata by DOAJ is licensed under CC BY-SA 1.0
Artificial intelligence in smart buildings
| Taylor & Francis Verlag | 2022
Artificial Intelligence and Business Strategy towards Digital Transformation: A Research Agenda
| DOAJ | 2021
Artificial intelligence for energy conservation in buildings
| Taylor & Francis Verlag | 2010