This book provides research that explores the theoretical and practical aspects of implementing new and innovative artificial intelligence (AI) techniques for renewable energy systems. The contributions offer coverage on economic and promotion policies of renewable energy and energy-efficiency technologies, the emerging fields of neuro-computational models and simulations under uncertainty (such as fuzzy-based computational models and fuzzy trace theory), evolutionary computation, metaheuristics, machine learning applications, advanced optimization, and stochastic models.

What is artificial intelligence? What are the Sustainable Development Goals (SDGs)? How does AI affect the SDGs? Artificial Intelligence has a real impact on our lives and on our environment, and the Sustainable Development Goals enable us to evaluate these impacts in a systematic manner. This book shows that doing so requires us to understand the context of AI - the infrastructure it is built on, who develops it, who owns it, who has access to it, who uses it, and what it is used for - rather than relying on an isolationist theory of technology. By doing so, we can analyze not only the direct effects of AI on sustainability, but also the indirect - or second-order - effects. AI for the Sustainable Development Goals shows how AI potentially affects all SDGs - both positively and negatively.

This book examines the role of bureaucracy in modern technologically advanced societies, the traditional models of governance, and the potential of information technology to fundamentally change and improve governance. In the area of public-domain governance, information and communication technologies (ICTs) have empowered public agencies to improve their activities and to strengthen the efficiency of their operations.

This book explores the use of AI tools for improving the management of energy systems and providing sustainability with smart cities, smart facilities, smart buildings, smart transportation, and smart houses. Featuring research from International Federation for Information Processing's (IFIP) “AI in Energy and Sustainability” working group, this book provides new models and algorithms for AI applications in energy and sustainability fields. Any short-term, mid-term and long-term forecasting, optimization models, trend foresights and prescriptions based on scenarios are studied in the energy world and the smart systems for sustainability.

Artificial Intelligence (AI) is everywhere, yet it causes damage to society in ways that can't be fixed. Instead of helping to address our current crises, AI causes divisions that limit people's life chances, and even suggests fascistic solutions to social problems. This book provides an analysis of AI's deep learning technology and its political effects and traces the ways that it resonates with contemporary political and social currents, from global austerity to the rise of the far right. Dan McQuillan calls for us to resist AI as we know it and restructure it by prioritising the common good over algorithmic optimisation. He sets out an anti-fascist approach to AI that replaces exclusions with caring, proposes people's councils as a way to restructure AI through mutual aid and outlines new mechanisms that would adapt to changing times by supporting collective freedom. Academically rigorous, yet accessible to a socially engaged readership, this unique book will be of interest to all who wish to challenge the social logic of AI by reasserting the importance of the common good.

This book provides a theoretical definition, a precise measurement system, and best practice examples for the implementation of sustainable corporate governance. It includes theoretical foundations based on stakeholder and institutional theories from different environments and contexts, such as developed and developing economies, large publicly traded companies and SMEs, and family businesses.