Critical Review of Machine Learning Applications for Energy Efficiency: State of the Art and Implementation Perspectives in El Salvador

Applying machine learning (ML) techniques for enhancing energy efficiency has garnered considerable attention worldwide, offering the potential to revolutionize industrial processes and promote sustainable practices. This paper critically reviews the prospects and challenges of implementing ML for energy efficiency within the context of El Salvador's industrial landscape. Based on a literature review and in-depth interviews with governmental and industry leaders, the study reveals a significant knowledge gap and a general lack of awareness regarding the capabilities and benefits of ML in the Salvadoran industry. Decision-makers often prioritize short-term gains over long-term sustainability, resulting in limited investments in ML technologies. Moreover, the absence of practical use cases and success stories within the country further exacerbates skepticism and hinders the adoption of these transformative technologies. To address these challenges, the paper highlights the urgent need for targeted training and capacity-building programs to educate professionals and industry leaders about the potential of ML. It emphasizes the importance of balancing short-term profit objectives and long-term sustainability goals. Additionally, the review calls for increased awareness campaigns and strategic investments to bridge the knowledge gap and unlock the substantial benefits of energy efficiency in El Salvador's industrial sector. In conclusion, this critical review underscores the untapped potential of ML in energy efficiency for El Salvador's industries. By addressing the current knowledge deficit and fostering a culture of awareness and investment, the country can position itself at the forefront of sustainable industrial practices, contributing to economic growth and environmental preservation.