Blue wind energy, a pioneering concept, amalgamates offshore wind energy generation with cutting-edge technologies. There is a dire need to address the growing energy demands of urbanization while mitigating the environmental impact of conventional energy sources. Urban areas are substantial energy consumers, and traditional methods are both unsustainable and environmentally damaging. Blue wind energy, alongside AI and IoT integration, presents a compelling solution, but the challenge is in effectively implementing and optimizing these technologies to ensure sustainable, efficient, and eco-friendly energy management in cities. This chapter introduces a set of essential performance indicators to assess the advantages of diverse AI-accelerated workflows in blue-wind energy and the latest developments in applications of computer vision and employing AI in the enhancement of energy harvesting like as photovoltaics, storage including batteries, conversion, and management in smart grids.
Top1. Introduction
Blue-wind energy is to comprehend Smart Energy Management, its start by getting acquainted with the fundamentals of energy management and then explore how it seamlessly fits into the framework of smart cities (Moroke & Makatjane, 2022). The green energy solution essentially, is the science that encompasses the planning, directing and controlling of energy supply and consumption (Makatjaneet al., 2021). The goal is to maximize productivity and comfort while minimizing both energy costs and pollution through mindful, strategic, and efficient energy usage. In simpler terms, energy management is all about saving energy (Makatjane, 2022). When it talks about energy savings, it involves the ongoing process of monitoring, controlling and conserving energy in any given scenario (Kasinathan et al. 2022).
The Internet of Things (IoT) is an important data source for data science technologies, providing benefits such as quick discovery of trends and patterns, enhanced automation, continuous development, effective handling of multidimensional data, and cheap computing cost (Elrahmani et al. 2021). Predicting energy consumption is critical for promoting sustainable cities and urban planning, given that buildings are the world's top energy users, driven by variables such as population increase, development, and economic structural shifts. This study looked at the use of deep learning algorithms to forecast energy usage in smart residential buildings. It was discovered that the optimal window size has a substantial impact on prediction performance, determining the best N window size, and calculating model uncertainty (Li et al. 2019).
Artificial intelligence, big data, Internet of Things (IoT) devices, and blockchain are examples of technologies that have experienced widespread research and use in a variety of technical sectors. While previous review papers frequently focus on specific elements and deployments in the energy sector, these technologies, which are based on communication, information, and data analysis, have intrinsic coherence (ElFar et al. 2021). This encompasses optimizing energy utilization, managing energy resources effectively, and actively promoting energy efficiency. As cities rapidly expand, dealing with the challenge of energy consumption becomes a pivotal goal. As existing cities evolve into smart cities, the role of blue-wind energy management becomes indispensable in the urban transformation process (Singh, 2023). The smart cities in essence strive to be a sustainable and efficient hub, aiming to enhance the quality of life for its residents by optimizing available resources. The vision for smart cities includes achieving greater autonomy and improved efficiency in managing their energy consumption, all while considering local resources and the diverse needs of their communities. In this context, Smart Energy Management emerges as a fundamental building block for realizing the potential of smart cities (Eljack & Kazi, 2021).