Big data analytics for AI renewable energy networks

The chapter addresses the application of big data analytics in renewable energy. The importance of big data in renewable energy has grown as the application of artificial intelligence (AI)-based methods, such as machine learning (ML) and deep learning (DL), has gained popularity. This chapter talks about the application of AI and big data analytics in renewable energy, and some of its applications, which include renewable energy forecasting, predictive maintenance, energy storage optimization, renewable energy production optimization, demand response, and load forecasting, and effective and efficient operations of microgrids. Besides, the influence of big data on renewable energy, such as optimization of resources, grid, predictive maintenance, optimization of energy storage, consumer outreach, financial decisions, and policy and planning, is also reported. It also discusses the big data applications in renewable energy, such as predictive analytics to predict energy and optimization algorithms to manage energy, grid management, and demand response, and measurement of renewable resources and selection of sites. The issue of challenges and opportunities to big data analytics in renewable energies can also be found in this chapter to include the problem of data quality and integration, concerns of security and privacy, and computational complexity and scalability. Finally, the chapter also addresses shortcomings and research directions of big data and AI in renewable energy.