Computational Intelligence for a Greener Future: Innovations in Renewable Energy Systems : Integrating AI and Behavioral Insights to Drive Economic Efficiency and Sustainability / edited by Mohamed Arezki Mellal, Yusuke Nojima, Naoki Masuyama.

Format:

Book

Author/Creator:

Mellal, Mohamed Arezki.

Contributor:

Nojima, Yusuke.

Masuyama, Naoki.

Series:

Studies in Computational Intelligence, 1860-9503 ; 1240

Language:

English

Subjects (All):

Computational intelligence.

Energy policy.

Artificial intelligence.

Computational Intelligence.

Energy Policy, Economics and Management.

Artificial Intelligence.

Local Subjects:

Computational Intelligence.

Energy Policy, Economics and Management.

Artificial Intelligence.

Physical Description:

1 online resource (660 pages)

Edition:

1st ed. 2026.

Place of Publication:

Cham : Springer Nature Switzerland : Imprint: Springer, 2026.

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.

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...

Notes:

Description based on publisher supplied metadata and other sources.

ISBN:

3-032-06732-4

9783032067326

OCLC:

1569190010