Del Ser, J.Casillas-Perez, D.Cornejo-Bueno, L.Prieto-Godino, L.Sanz-Justo, J.Casanova-Mateo, C.Salcedo-Sanz, S.2022-03Del Ser , J , Casillas-Perez , D , Cornejo-Bueno , L , Prieto-Godino , L , Sanz-Justo , J , Casanova-Mateo , C & Salcedo-Sanz , S 2022 , ' Randomization-based machine learning in renewable energy prediction problems : Critical literature review, new results and perspectives ' , Applied Soft Computing Journal , vol. 118 , 108526 . https://doi.org/10.1016/j.asoc.2022.1085261568-4946Publisher Copyright: © 2022 Elsevier B.V.In the last few years, methods falling within the family of randomization-based machine learning models have grasped a great interest in the Artificial Intelligence community, mainly due to their excellent balance between performance in prediction problems and their computational efficiency. The use of these models for prediction problems related to renewable energy sources has been particularly notable in recent times, including different ways in which randomization is considered, their hybridization with other modeling techniques and/or their multi-layered (deep) and ensemble arrangement. This manuscript comprehensively reviews the most important features of randomization-based machine learning methods, and critically examines recent evidences of their application to renewable energy prediction problems, focusing on those related to solar, wind, marine/ocean and hydro-power renewable sources. Our study of the literature is complemented by an extensive experimental setup encompassing three real-world problems dealing with solar radiation prediction, wind speed prediction in wind farms and hydro-power energy. In all these problems randomization-based methods are reported to achieve a better predictive performance than their corresponding state-of-the-art solutions, while demanding a dramatically lower computational effort for its learning phases. Finally, we pause and reflect on important challenges faced by these methods when applied to renewable energy prediction problems, such as their intrinsic epistemic uncertainty, or the need for explainability. We also point out several research opportunities that arise from this vibrant research area.enginfo:eu-repo/semantics/openAccessjournal article10.1016/j.asoc.2022.108526Hydro-powerMachine LearningMarine EnergyRandomization-based algorithmsRenewable resourcesSolar EnergyWind energySoftwareSDG 7 - Affordable and Clean Energyhttp://www.scopus.com/inward/record.url?scp=85124176894&partnerID=8YFLogxK