Renewable Energy and New Computational Intelligence

The transition to renewable energy is vital for achieving sustainable economic growth and addressing climate change. This chapter explores the role of hydroelectric and biomass energy sources in reducing environmental degradation and improving energy security. While hydroelectricity offers affordable, low-emission power, its benefits depend on geographical conditions and may disrupt ecosystems. Biomass, as a versatile and abundant energy source, provides a cleaner alternative to fossil fuels, supporting economic growth and job creation. The chapter also highlights the increasing importance of Computational Intelligence (CI) and Machine Learning (ML) in renewable energy research. By leveraging Big Data, CI and ML enable accurate energy forecasting, optimize grid operations, and address the variability of renewable sources like wind and solar. Techniques such as Artificial Neural Networks (ANNs), Support Vector Machines (SVMs), and ensemble learning improve prediction accuracy, while optimization algorithms like genetic algorithms and swarm intelligence enhance energy distribution. Empirical analysis using SVM and Bagging demonstrates their superiority over traditional regression methods in predicting ecological impacts. Despite advancements, challenges such as data quality, scalability, and integration still persist. Addressing these issues and fostering supportive policies will maximize CI’s potential, driving innovation and efficiency in renewable energy systems for a sustainable future.