دانلود کتاب انرژی‌های تجدیدپذیر -گذار با راهکارهای چالش‌محور هوش مصنوعی

Global warming and the increasing effects of climate change are one of the most pressing challenges of our time. The adoption of renewable energy in a clean, scalable and cost-effective manner is key to a net zero transition globally, besides playing a pivotal role in many national agendas around the world. At the same time, we have seen a revolution in artificial intelligence (AI) in recent years, powered by generative AI models that match human performance in multiple domains, driven by advances in machine learning (ML) and often Internet of Things (IoT) sensor networks. Similarly, it is expected that AI can play a crucial role in scaling and optimizing renewable energy systems, enhancing predictive capabilities, supporting decisions in Operations & Maintenance (O&M), enabling smart structural health monitoring and accelerating deployment at scale. This book sits at the intersection of these two pressing themes: the global shift towards renewable energy and the evolving capabilities of AI. Our primary focus is on showcasing how AI research, often developed in academic settings, can be successfully translated into real-world applications in the renewable energy sector. We present case studies of successful collaborations between academia and industry, or the public sector, and focus specifically on how barriers to innovation can be overcome, enabling impactful knowledge exchange and transfer. While most case studies are centered around offshore wind and solar energy, we also incorporate perspectives on life cycle assessment, socioeconomic barriers to adoption and training needs for a growing renewables workforce. This book was developed from the scope of a workshop on “Facilitating the Renewables Transition” held at ICLR in 2022 (see https://iclr.cc/virtual/2022/ social/8733), which brought together leaders from academia, industry, and public sector organizations to identify bottlenecks and pathways for the effective integration of AI in renewable energy. One of the key takeaways from the event, which has formed the basis for much of this book, is that a considerable gap remains between cutting-edge AI research and its deployment in industrial practice. While we summarized initial results in a short contribution to Patterns, this edited volume aims to look beyond highlighting challenges and offering solutions. A key reason for this gap is the lack of transparency associated with state-ofthe- art neural network architectures. While often highly accurate in their predictions, leading deep learning models still operate as black boxes, which leads to a reluctance by stakeholders to adopt them in safety-critical or regulatory environments. A second bottleneck is the lack of high-quality datasets that are openly available for academic research, which creates a shortage of high-TRL prototypes, performance benchmarks, and agreed standards. These issues are exacerbated in newer or geographically remote renewable energy sites, where data collection is logistically difficult and often costly. This book aims to go beyond highlighting these challenges and contributing to a roadmap to overcoming them. We present success stories of how barriers like model opacity, data scarcity, or lack of trust in academic tools were addressed through collaboration, transparency, and knowledge sharing. Each case study is designed to be transferable, offering not only insights into what was done, but how and why it worked, and how similar methods might be applied in other settings. The chapters in this volume fall into two broad categories. First, we present detailed accounts of successful collaborations between academia and industry or third-sector stakeholders. These chapters discuss the state of research prior to collaboration, the methodologies adopted to address a shared challenge, and the resulting outcomes and impacts. We focus on bringing out the aspects that made the collaboration successful and discuss pathways to transfer. Second, we feature forward-looking contributions that explore promising new AI methodologies. These chapters emphasize the need for AI systems that are not only high-performing but also safe, reliable, transparent, and trustworthy, which we see as core requirements for practical adoption. Here, we explore aspects like explainable AI, multimodal algorithms, cybersecurity, and other recent advances that hold promise for greater real-world adoption. We hope that this book will serve not only as a resource but also as a catalyst, inspiring researchers, engineers, policymakers, and business leaders to come together and push the boundaries of research and technology in renewable energy, enabled by the ever-growing field of AI. By bridging the divide between research and practice, we move one step closer to a sustainable energy future—powered by both clean technologies and intelligent systems.