Department of Engineering, University of Exeter, EX4 4QJ, United Kingdom.
Department of Engineering, University of Exeter, EX4 4QJ, United Kingdom.
J Environ Manage. 2024 Jun;362:121275. doi: 10.1016/j.jenvman.2024.121275. Epub 2024 Jun 4.
The depletion of fossil energy reserves and the environmental pollution caused by these sources highlight the need to harness renewable energy sources from the oceans, such as waves and tides, due to their high potential. On the other hand, the large-scale deployment of ocean energy converters to meet future energy needs requires the use of large farms of these converters, which may have negative environmental impacts on the ocean ecosystem. In the meantime, a very important point is the volume of data produced by different methods of collecting data from the ocean for their analysis, which makes the use of advanced tools such as different machine learning algorithms even more colorful. In this article, some environmental impacts of ocean energy devices have been analyzed using machine learning and quantum machine learning. The results show that quantum machine learning performs better than its classical counterpart in terms of calculation accuracy. This approach offers a promising new method for environmental impact assessment, especially in a complex environment such as the ocean.
化石能源储备的枯竭以及这些能源所造成的环境污染，凸显了利用海洋（如波浪和潮汐）等可再生能源的必要性，因为它们具有巨大的潜力。另一方面，为了满足未来的能源需求，大规模部署海洋能源转换器需要使用这些转换器的大型农场，这可能对海洋生态系统产生负面影响。与此同时，一个非常重要的问题是，不同的海洋数据采集方法所产生的数据量，这使得先进工具的使用（如不同的机器学习算法）更加多样化。在本文中，使用机器学习和量子机器学习分析了海洋能源装置的一些环境影响。结果表明，在计算精度方面，量子机器学习优于传统机器学习。这种方法为环境影响评估提供了一种有前途的新方法，特别是在海洋等复杂环境中。
