Physics-informed machine learning meets renewable energy systems: A review of advances, challenges, guidelines, and future outlooks

Seyed Masoud Parsa,
Physics-informed machine learning meets renewable energy systems: A review of advances, challenges, guidelines, and future outlooks,
Applied Energy,
Volume 402, Part A,
2025,
126925,
ISSN 0306-2619,
https://doi.org/10.1016/j.apenergy.2025.126925.
(https://www.sciencedirect.com/science/article/pii/S0306261925016551)
Abstract: Physics-informed machine learning (PIML) has emerged as a powerful paradigm to combine machine learning techniques with physical laws to enhance the accuracy, reliability and interpretability of machine learning (ML) models. This review presents recent advances in the application of PIML in renewable energy systems (RESs) through different aspects from macro-scale to micro-scale including wind energy (farm modeling, blade analysis, power output, spatiotemporal analysis, fault detection), solar energy (flat-plate collectors, photovoltaic, parabolic trough, heat transfer coefficients), biomass energy (kinetic reaction, syngas composition, biofuel production), geothermal (energy extraction, heat-exchanger performance), ocean current, and hybrid RESs. Following a discussion on different integration approaches of ML models and physics, the review critically analyzes diverse PIML approaches and architectures adopted in RESs by highlighting the progress in well-established technologies such as wind energy and emerging areas like ocean energy where PIML applications are still in early development. Moreover, insights on how a wide range of algorithms and optimization strategies have been adopted to solve domain-specific challenges within RESs using PIML are provided. Furthermore, current tools and open-source packages that support PIML implementations are also reviewed. Additionally, it outshines key criteria, indicators and guidelines for novel strategies to implement PIML in RESs. Finally, current challenges and future prospects are outlined.
Keywords: Artificial intelligence; Physics-embedded machine learning (PEML); Physics-informed neural network (PINN); Renewable energy technology; Sustainable energy technologies; Scientific machine learning