In addition to the conventional natural resources such as petroleum extracts, natural gas and charcoal, and particularly with the current worldwide condition of economy of the energy sector, renewable energy sources are increasingly gaining attention. On the basis of recent researches, experts and environmentalists suggest that the renewable energy sources contribute for the major energy consumption. Rapid development of the renewable energy and energy‐efficient technologies results in significant energy security and economic benefits, which lead to reduction in capital investment for electricity systems. Therefore, through power system planning, we can augment the quality and efficiency of the power supply. The challenges in meeting the power requirements can be addressed by machine learning (ML) technique, an interdisciplinary field that allows using the statistical techniques to solve the energy‐related problems using pattern recognition, artificial neural network, and fuzzy and hybrid combinations. The recent innovations in web‐based and mobile technologies led to the application of ML in energy sector.

中文翻译：

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基于机器学习的混合可再生能源在工程教育中的可行性研究

除了常规的自然资源，例如石油提取物，天然气和木炭，尤其是当前世界范围内能源行业的经济状况，可再生能源也越来越受到关注。根据最近的研究，专家和环保主义者认为可再生能源是主要能源消耗的原因。可再生能源和节能技术的快速发展带来了巨大的能源安全和经济效益，从而减少了电力系统的资本投资。因此，通过电源系统规划，我们可以提高电源的质量和效率。满足电源要求的挑战可以通过机器学习（ML）技术解决，一个跨学科领域，允许使用统计技术通过模式识别，人工神经网络以及模糊和混合组合来解决与能源有关的问题。基于Web和移动技术的最新创新导致机器学习在能源领域的应用。