Applications of machine learning on machinery efficiency and reliability in clean energy projects

Eziefula, Bennett Ikechukwu and Ekechukwu, Darlington Eze and Ibekwe, Kenneth Ifeanyi (2025) Applications of machine learning on machinery efficiency and reliability in clean energy projects. World Journal of Advanced Research and Reviews, 25 (3). pp. 1226-1241. ISSN 2581-9615

Clean energy resources have been considered globally as the energy of the future due to its low or no carbon footprint. Clean energy is the energy that replenished itself after use. To get most of these energy resources, constant improvement in manufacturing and process is highly needed. One of the key technologies revolutionizing the clean energy power generation is machine learning. Machine learning is an algorithm that examine enormous volumes of data in order to spot trends, generate predictions, and improve decision-making. This article therefore, using descriptive approach explored the application of machine learning in clean energy machinery efficiency and reliability as well as clean energy projects. The energy systems and projects considered in the article include, solar, wind, tidal, geothermal, biomass and hydroelectric energy sources. Based on this article findings, machine learning algorithm can be utilized to initiate predictive maintenance, provide real-time data for decision-making, and optimize process and overall improve the efficiency and reliability of these energy machines. In terms of clean energy project delivery, machine learning can be utilized in project planning, site selection and resources allocation. Although, machine learning has quite a number of benefits, no doubt it has its own drawbacks. These drawbacks include the quality and availability of data, which might be irregular or scarce in some clean energy resource which will determine how accurate machine learning models can be developed. Widespread adoption may be hampered by computational complexity and the requirement for a strong hardware foundation.