Artificial Intelligence And Data Science For Sustainability Applications And Methods

Artificial Intelligence and Data Science for Sustainability: Applications and Methods

The integration of artificial intelligence (AI) and data science (DS) into sustainability efforts is increasingly crucial as the world faces environmental and resource challenges. By leveraging advanced AI and DS techniques, we can create innovative solutions for sectors like energy, agriculture, and waste management, promoting long-term sustainability. These technologies have the potential to optimize processes, reduce environmental footprints, and support smarter decision-making across industries. Ultimately, AI and DS are key to advancing both the efficiency and impact of sustainability efforts, contributing to a more sustainable future for society at large. Artificial Intelligence and Data Science for Sustainability: Applications and Methods explores the application of AI and DS techniques and methodologies in addressing various sustainability challenges. It brings together cutting-edge research, case studies, and practical applications that demonstrate how AI can be leveraged to promote sustainable development across diverse domains. Covering topics such as structural optimization, drone imagery, and development financing, this book is an excellent resource for computer scientists, computer engineers, urban planners, sustainability professionals, government officials, policymakers, researchers, professionals, scholars, academicians, and more.

ARTIFICIAL INTELLIGENCE AND DATA SCIENCE FOR SUSTAINABILITY.

Artificial Intelligence for Smart and Sustainable Energy Systems and Applications

Energy has been a crucial element for human beings and sustainable development. The issues of global warming and non-green energy have yet to be resolved. This book is a collection of twelve articles that provide strong evidence for the success of artificial intelligence deployment in energy research, particularly research devoted to non-intrusive load monitoring, network, and grid, as well as other emerging topics. The presented artificial intelligence algorithms may provide insight into how to apply similar approaches, subject to fine-tuning and customization, to other unexplored energy research. The ultimate goal is to fully apply artificial intelligence to the energy sector. This book may serve as a guide for professionals, researchers, and data scientists—namely, how to share opinions and exchange ideas so as to facilitate a better fusion of energy, academic, and industry research, and improve in the quality of people's daily life activities.