Recent advancements in data science and artificial intelligence, as well as the development of clean and sustainable energy sources, have created numerous opportunities for energy researchers to conduct their studies. Artificial Intelligence (AI) and Machine Learning (ML) techniques have been applied to Renewable Energy Systems (RES) for several years, and their intensity and scope have grown in recent years. Since artificial intelligence and machine learning have a wide range of applications, it may be difficult to select and implement suitable methods for future research. In order to address this issue, this study examines several of the most popular and well-known AI techniques in renewable energy. This paper describes over ten of the most prevalent RES modeling and optimization algorithms, including Artificial Neural Networks (ANN), Long and Short-Term Memory (LSTM), Recurrent and Convolutional Neural Networks (RNNs and CNNs), the Genetic Algorithm (GA), and the Particle Swarm Optimization algorithm (PSO). More than a hundred different studies between 2020 and 2022 have been compiled and organized in the results section according to the method used and the field of application. At the end, the results are discussed, and the limitations of the current study will be mentioned, followed by some suggestions to complete our work and make it more useful.

中文翻译：

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人工智能在可再生能源系统中的应用综述：基于方法的研究

数据科学和人工智能的最新进展，以及清洁和可持续能源的发展，为能源研究人员创造了大量的研究机会。人工智能（AI）和机器学习（ML）技术多年来一直应用于可再生能源系统（RES），并且近年来其强度和范围不断扩大。由于人工智能和机器学习具有广泛的应用范围，因此可能很难为未来的研究选择和实施合适的方法。为了解决这个问题，本研究研究了可再生能源领域几种最流行和最知名的人工智能技术。本文描述了十多种最流行的 RES 建模和优化算法，包括人工神经网络 (ANN)、长短期记忆 (LSTM)、循环和卷积神经网络（RNN 和 CNN）、遗传算法 (GA) ，以及粒子群优化算法（PSO）。结果部分根据所使用的方法和应用领域整理和组织了 2020 年至 2022 年间的一百多项不同的研究。最后，讨论结果，并指出当前研究的局限性，然后提出一些建议，以完成我们的工作并使其更有用。