Bioinspired Machine Learning
Akshaya Kumar Mandal, Pankaj Kumar Deva Sarma, Satchidananda Dehuri, Nayanjyoti Mazumdar · 2024
Bioinspired machine learning (Bio-ML) is emerging as a game-changing technique in renewable energy systems. These approaches involve merging computer science and biological analytics to apply machine learning in renewable energy contexts. To propel this technology forward, diverse strategies have been employed to tackle human challenges and expedite progress. These approaches have allowed us to assess the potential of wind and solar energy. Researchers are striving to understand core machine learning concepts and apply them to address difficulties in the power and energy industries, which also involve analytical approaches of computer and biological sciences. Diverse tactics have been used to handle human difficulties and accelerate development with this technology. The entire chapter emphasizes on Bio-ML applications in solar, wind, and other renewable energy sources. Furthermore, it emphasizes the fundamentals of various machine learning technique and their application in solar and wind renewable energy systems. Present trends and future possibilities of Bio-ML technologies are also examined along with highlighting notable achievements in acceptance, modeling, and simulation research in sustainable energy system. The emergence of these technologies have allowed the invention of solutions that limit the hazards of human errors in complex systems, with many innovations in technology based on inspiration drawn from natural systems. These approaches are an interdisciplinary and promising approach that encourages the incorporation of biological activities into machines to improve their intelligence. Bioinspired computing is a branch of computer science which mimics nature to serve a variety of objectives, such as analyzing electric circuits, solving differential equations, designing complicated chemical compounds, and detecting and treating illnesses. Thus, bioinspired computing's potential resides in its ability to handle common issues in a cost-effective and sustainable manner. In conclusion, this chapter delves into Bio-ML applications in solar, wind, and other renewable energy sectors. It highlights various bioinspired approaches, exemplified by solar and wind energy systems, showcasing a pivotal advancement in “Bioinspired Machine Learning: A New Era in Sustainable Energy Systems.” It outlines present trends and future potentials of bioinspired methods. In essence, these techniques lower energy usage, stabilize energy load, enhance user comfort, and lower emissions. In addition, the Internet of Energy and bioinspired approaches can enhance distributed and hybrid renewable energy control systems and improve the capabilities of advanced intelligent energy management systems.