Artificial intelligence in renewable systems for transformation towards intelligent buildings

Abstract

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.