Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage

This study presents a broad view of the current state of the art of ML applications in the manufacturing sectors that have a considerable impact on sustainability and the environment, namely renewable energies (solar, wind, hydropower, and biomass), smart grids, the industry of catalysis and power storage and distribution. Artificial neural networks are the most preferred techniques over other ML algorithms because of their generalization capabilities. Demands for ML techniques in the energy sectors will increase considerably in the coming years, since there is a growing demand of academic programmes related to artificial intelligence in science, math, and engineering. Data generation, management, and safety are expected to play a key role for the successful implementation of ML algorithms that can be shared by major stakeholders in the energy sector, thereby promoting the development of ambitious energy management projects. New algorithms for producing reliable data and the addition of other sources of information (e.g., novel sensors) will enhance flow of information between ML and systems. It is expected that unsupervised and reinforcement learning will take a central role in the energy sector, but this will depend on the expansion of other major fields in data science such as big data analytics. Massive implementations, specialized algorithms, and new technologies like 5G will promote the development of sustainable applications of ML in non-industrial applications for energy management. (c) 2021 Institution of Chemical Engineers. Published by Elsevier B.V. All rights reserved.