Название: Solar Energy Optimization Using Generative Artificial Intelligence
Автор: Abhishek Kumar, Pramod Singh Rathore, Arun Lal Srivastav, Ashutosh Kumar Dubey
Издательство: Wiley-Scrivener
Год: 2026
Страниц: 406
Язык: английский
Формат: pdf (true)
Размер: 31.4 MB

Lead the sustainable energy revolution with this guide to mastering the AI-driven algorithms and smart material innovations that are revolutionizing solar energy.

The integration of Artificial Intelligence into solar energy systems represents the next frontier in sustainable development, promising to improve efficiency, reduce costs, and increase the viability of solar energy as a mainstream energy source. This book will delve into the transformative role of Artificial Intelligence (AI) in enhancing various aspects of solar energy systems. It will begin by exploring how AI can significantly boost the energy efficiency of solar panels, showcasing innovative algorithms and techniques designed to optimize energy capture and conversion. The development of smart materials for enhanced energy storage will also be covered, emphasizing the latest advancements in material science driven by AI to improve the storage capabilities and longevity of solar panels. Further, it will address integrated waste management options for exhausted solar panels, providing insights into sustainable practices and AI-driven solutions for recycling and repurposing solar panel components. It will discuss the significance of AI in solar energy conservation and climate change management, illustrating how AI technologies are being harnessed to predict, monitor, and mitigate environmental impacts.

Additionally, the book will explore the future scope of photovoltaic-based solar energy in a changing environment, highlighting AI’s role in achieving sustainability and adapting to evolving climatic conditions. Using case studies and real-world applications, this book will demonstrate successful implementations of AI in the solar energy sector. Topics such as predictive maintenance, solar irradiance forecasting, optimal placement of solar panels, and AI-enhanced solar tracking systems will be featured to provide a comprehensive understanding of how AI is revolutionizing the solar energy landscape.

Chapter 1: This chapter presents a comprehensive review of machine learning and deep learning techniques used in solar energy forecasting. It critically analyzes statistical, neural, ensemble, and hybrid models, highlighting their strengths and limitations.

Chapter 2: This chapter explores the evolution of smart materials used in photovoltaic systems to improve energy storage and conversion efficiency. It discusses crystalline silicon, thin-film photovoltaics, perovskite solar cells, and carbon nanotube-based composites.

Chapter 3: This chapter investigates how Artificial Intelligence enhances solar panel efficiency through optimization of placement, orientation, and operational parameters. It reviews AI-driven techniques for energy conservation, system monitoring, and cost minimization. The chapter demonstrates how machine learning improves installation planning and maximizes energy yield.
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Chapter 13: This chapter presents AI-based methodologies for wind turbine site selection using geospatial and environmental data. It discusses machine learning models, GIS integration, and optimization algorithms.

Chapter 14: This chapter synthesizes AI-driven innovations in solar energy and climate management. It discusses smart grids, predictive maintenance, energy storage, and IoT integration.

Chapter 15: In this chapter, the author describes how the Internet of Things (IoT) and Artificial Intelligence (AI) played a revolutionary role in the sunlight energy system to transcend these limitations. The chapter is a useful contribution to the field of understanding that more intelligent solar energy management relies on technological advances, architectures, and intelligent approaches.