# Overview of Startups Developing Artificial Intelligence for the Energy Sector
## 1 Introduction
Figure 1 illustrates the historical trend of energy usage alongside GDP growth from World Bank Data, highlighting the interplay between economic development and energy consumption. This graph likely shows a strong correlation between these two factors over time, reflecting the energy-GDP link. Historically, industrialization and economic growth have led to increased energy consumption, driven by technological advancement, changing consumer behavior, and urbanization. Recently, some developed economies have shown signs of decoupling, where GDP grows while energy consumption stabilizes or decreases. This decoupling can be attributed to improved energy efficiency, shifts to service-based economies, and the adoption of renewable energy sources [9]. The global energy landscape is unpredictable due to geopolitical conflicts and climate change, with fossil fuel prices decreasing since 2023. Addressing climate change is crucial due to rising temperatures and the negative effects of greenhouse gas emissions. Despite these challenges, there is optimism for a sustainable energy economy driven by renewable energy technology and electric vehicles. Financing for clean energy has increased due to environmental, economic, and security concerns. However, achieving ambitious climate goals requires further effort and financial commitment. The International Energy Agency (IEA) emphasizes the need for rapid, secure, cost-effective, and inclusive energy transitions to mitigate climate change's consequences [10]. In recent times, there has been a noticeable trend where improving the efficiency of systems and reducing energy consumption, along with economic progress and societal well-being, are leading to an increase in global power. As a result, energy systems worldwide are starting to distribute resources, decrease carbon emissions, and foster democracy, often starting from local community levels [11]. These, frequently known as the \"three Ds\", are driven by the need to manage energy expenditure, replace deteriorating infrastructure, enhance flexibility and reliability, reduce carbon dioxide emissions to mitigate global warming, and provide reliable electricity to areas without energy infrastructure [12]. The demand for reliable and affordable energy is growing in developing countries, necessitating the integration of AI in the energy industry. AI can facilitate optimization systems, customization, and parameterization of consumption and output. Envisioning smart agents driven by smart grid technologies and AI algorithms hinges on a deep understanding of social and economic elements. AI methods like structured data management, data mining, and ML can contribute to an AI ecosystem in the energy sector. Integrating AI at all levels facilitates energy network growth and expands renewable energy deployment [13]. The reliance on theoretical frameworks is crucial for both comprehending and facilitating sustainable energy transitions. [PERSON] theory of diffusion of innovations demonstrates how new concepts disseminate throughout communities and are impacted by benefits and alignment. The Technology Acceptance Model explores how individuals make decisions to adopt technology, focusing on how useful and easy to use it is perceived to be. Transition Management and the Multi-Level Perspective provide wider perspectives, highlighting systemic changes and socio-technical dynamics. These guidelines assist policymakers and stakeholders in creating successful plans for shifting to a low-carbon economy. Combining these theories can offer a deeper insight into sustainable energy paths [14; 15]. Figure 2 shows a rise in funding across different sectors from 2000 to 2020. The data show a substantial increase in funding, particularly starting in 2010, with a marked peak between 2015 and 2020. A sizable boost in funding for clean energy, solar, wind, and  
Figure 1: The historical trend of energy usage alongside GDP growth (data taken from [9] and figure developed by authors). energy storage suggests a movement towards renewable energy sources.