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This book aims to explore the intersection of computational intelligence techniques and renewable energy technologies, serving as a valuable resource for researchers, engineers, and policymakers. By compiling cutting-edge research and innovative applications, it seeks to demonstrate how CI can contribute to a more sustainable future, highlighting both theoretical advancements, practical implementations, and future directions.
Nowadays, as the world faces the pressing challenges of climate change and the depletion of fossil fuel resources, the shift toward decarbonization and renewable energy systems has become a vital priority. Innovations in computational intelligence (CI) offer promising opportunities to optimize and manage energy production, distribution, and consumption. By leveraging techniques like nature-inspired optimization algorithms, fuzzy methods, machine learning, and clustering, researchers and practitioners can enhance the efficiency and management of renewable energy systems.
List of contents
A Review of the Genetic Algorithm Approach in Predictive Maintenance and Energy Forecasting.- Deep Reinforcement Learning in Energy Management System for Fuel Cell Hybrid Vehicles: A Review on Reward Design and Testing Framework.- Forecasting Renewable Energy and Electricity Consumption using Evolutionary Computation.- Enhancing EV Battery Safety: SOH Estimation with Machine Learning.- Short-Term Renewable Energy Forecasting Methods Using Artificial Neural Networks: A Comprehensive Review.- etc...
Summary
This book aims to explore the intersection of computational intelligence techniques and renewable energy technologies, serving as a valuable resource for researchers, engineers, and policymakers. By compiling cutting-edge research and innovative applications, it seeks to demonstrate how CI can contribute to a more sustainable future, highlighting both theoretical advancements, practical implementations, and future directions.
Nowadays, as the world faces the pressing challenges of climate change and the depletion of fossil fuel resources, the shift toward decarbonization and renewable energy systems has become a vital priority. Innovations in computational intelligence (CI) offer promising opportunities to optimize and manage energy production, distribution, and consumption. By leveraging techniques like nature-inspired optimization algorithms, fuzzy methods, machine learning, and clustering, researchers and practitioners can enhance the efficiency and management of renewable energy systems.
