# Overview of Startups Developing Artificial Intelligence for the Energy Sector
## 1 Introduction  
Artificial intelligence is rapidly emerging as a crucial technology in the energy sector, facilitating the transition towards energy systems that are more efficient, environmentally sustainable, and resilient. Integrating AI into the energy sector could remodel the way energy is generated, distributed, and used. According to the Journal of Energy Storage, AI integration in the industry has the potential to transform energy generation, distribution, and consumption, with AI solutions making notable progress in predicting energy, managing grid operations, and overseeing renewable energy. It provides many advantages, including enhanced productivity and environmental friendliness. AI algorithms examine extensive datasets from smart meters, weather stations, and IoT devices to forecast consumption patterns and modify grid operations instantly, maximizing energy distribution and the utilization of renewable resources. Nature Energy Journal explores how AI can enhance the dependability of renewable energy systems by forecasting fluctuations in solar and wind power generation [1]. Nevertheless, incorporating AI encounters obstacles such as the requirement for top-notch data and worries regarding data security and privacy. Energy Informatics highlight the significance of strong data handling and cybersecurity practices to optimizeAI advantages and mitigate risks [2]. Energy companies need a strategic approach to leverage the transformative potential of AI. This involves funding research, collaborating with academic and tech organizations, and consistently assessing AI options. AI has the potential to enhance efficiency, sustainability, and resilience through optimizing energy consumption, improving grid operations, and integrating renewable energy sources. This study intends to shed light on the creative strategies created by AI-driven startups and their significant ability to transform energy systems. The research will offer valuable information for new businesses, decision-makers, and industry players as they navigate the dynamic world of AI, focusing on the potential benefits, obstacles, and approaches to leveraging the revolutionary capabilities of AI [3]. Energy is seen as a significant economic and social indicator, with energy resources playing a prominent role in the economy and serving as a symbol of a nation's riches [4; 5]. The correlation between economic progress and rising affluence is regularly linked to the escalating utilization of energy resources. Increasing energy production may not be the most effective strategy, as it is not acceptable to deplete energy reserves that are meant for future generations [6]. However, the impediment may be overcome by enhancing current energy systems and methods by implementing innovative and contemporary ideas to achieve a higher efficiency. It is logical that increased efficiency results in reduced consumption. However, it is worth noting that many of these systems can be turned off or operated at lower capacities. Thus, adopting a more adaptable approach may offer a solution that is very close to being optimum. AI and Machine Learning (ML) are emerging concepts in energy systems that can improve system efficiency by analyzing previous and future events [7]. The World Bank uses Vector Autoregression (VAR) models to predict the relationships between global energy efficiency, Gross Domestic Product (GDP) per capita, and global energy prices. These models show strong causal relationships up to a three-period delay, accounting for 78% to 99% of the variable variation [8]. The energy consumption in previous years has a significant impact on current global energy use, increasing inefficiency. Increases in global GDP per capita have been proven to enhance energy utilization efficiency. The current year's world GDP per capita shows a rise in both the previous year's GDP per capita and energy use, while cumulative energy prices show an increase in last year's GDP per capita and energy costs. Unit root tests confirm these relationships [8].