# Overview of Startups Developing Artificial Intelligence for the Energy Sector
## 2 Artificial Intelligence  
This section provides a thorough exploration of AI and its various subfields, particularly emphasizing its significance in the energy industry. ML stands out as a key method for achieving AI, leveraging algorithms and data to train systems for task performance. Deep learning, a subset of ML, further enhances AI capabilities by fostering collaboration among algorithms. Given the energy sector's wealth of data resources, it emerges as a promising domain for AI implementation, offering the potential for market optimization and addressing industry challenges. Artificial intelligence technology encompasses several fields, such as computer science, cybernetics, information theory, neurophysiology, psychology, and linguistics. It involves knowledge representation, reasoning, search, and planning, making it an integrated and sophisticated topic. Researchers from several disciplines have conducted numerous studies using diverse approaches, methodologies, and application areas. AI technology may be categorized into distinct schools of thought based on varying interpretations of the nature of intelligence. These schools mostly include Symbolism, Connectionism, and Actionism [28]. Artificial intelligence comprises a range of methods that enable robots to display intelligent behavior, including the ability to imitate human acts. Machine Learning (ML), a key sub-field of AI, encompasses several approaches such as computer vision, Natural Language Processing (NLP), neural networks, and Reinforcement Learning (RL). This section offers a comprehensive review of AI approaches and specifically emphasizes the integration of electric vehicles into energy infrastructures [29]. The energy sector is experiencing a transformative digital shift [30,31]. However, this evolution goes beyond mere digitalization; contemporary computer science offers a plethora of technologies to enhance efficiency across energy generation, distribution, and consumption [32, 33, 34, 35, 36, 37, 38]. The integration of the IoT, cyber-physical technologies, and embedded systems has been pivotal in advancing the smart grid concept. The smart grid, a modern power grid, seamlessly integrates traditional energy infrastructure with advanced information and telecommunications technology [39]. AI is set to revolutionize future electrical systems, offering essential support for managing complexities in solar and wind forecasting, dispatch optimization, battery management, and smart meter data analysis. By leveraging various computer science methodologies and technologies, AI enables systems to exhibit intelligent behaviors, effectively processing external data and adapting to achieve objectives. Recent studies by Microsoft and PricewaterhouseCoopers (PwC) project that integrating AI into the energy sector could lead to a significant reduction in global emissions by up to 4% by 2030, alongside a potential GDP growth boost of 4.4%. These findings highlight AI's pivotal role in driving sustainable energy practices and economic prosperity [40]. Figure 3, presented by Thunder Said Energy, titled \"Impact of AI on Various Industries\" illustrates the significant improvements AI brings to various sectors, particularly Carbon Capture and Storage (CCS) and Carbon Capture and Utilization (CCU), Buildings (Energy Systems and HVAC), and Photovoltaic (PV) solar systems. The current efficiency levels in these sectors vary, but AI contributes to notable enhancements by optimizing operations, maintenance, and energy management. For CCS and CCU and HVAC systems, there is substantial improvement potential, with AI playing a crucial role in achieving these gains. Although PV systems already demonstrate high efficiency, AI further optimizes their performance, albeit to a lesser extent compared to other sectors. The term TCE vehicles likely refers to Turbo Control Efficiency vehicles, which could see significant efficiency improvements through AI applications, optimizing engine performance and fuel consumption.