Overview of Startups Developing Artificial Intelligence for the Energy Sector

Abstract

The energy industry is experiencing a major change due to fast progress in artificial intelligence (AI). Startup companies in this revolution use AI technologies like Machine Learning (ML), predictive analytics, and optimization algorithms to improve energy efficiency, optimize grid management, and incorporate renewable energy sources. AI-powered solutions allow for a more accurate prediction of demand, immediate monitoring, and automated decision-making processes, significantly enhancing operational efficiency and sustainability. Through promoting a more effective energy system, these advancements play a vital role in the worldwide battle against climate change and carbon dioxide emissions. Adding to the progress of AI, quantum computing (QC) shows great potential despite being a nascent area. The collaboration of AI and QC is poised to transform the energy industry by offering unmatched computational capabilities. This blend of technologies can tackle intricate energy obstacles like enhancing power grids and enhancing battery storage, which traditional computers cannot currently handle. Combining QC with AI speeds up innovation, providing advanced solutions that improve the resilience and efficiency of energy networks. This paper discusses the latest advancements, possible effects, and upcoming paths of new companies leading in AI and QC innovations within the energy industry. Their joint responsibility is highlighted in advancing a sustainable and intelligent energy future, as well as tackling crucial environmental issues and lessening the impact of climate change.

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 optimize AI 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].

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. Rogers’ 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 energy storage suggests a movement towards renewable energy sources. An increase in funding for energy and digital technologies demonstrates a focus on investing in digital advancements in the energy industry. In addition, there has been a significant increase in funding for electric mobility and hydrogen fuel cells, demonstrating a shift towards sustainable transportation solutions. A diverse investment landscape is evident in the wide variety of sectors receiving funding for advancements in energy and technology. In general, the chart shows an increasing financial dedication to energy innovation and sustainability, especially in the past ten years.

Economic opportunities, technological breakthroughs, supportive regulations, and business sustainability targets all contribute to an increase in sustainable energy expenditure. It reflects increased investor trust in renewable energy’s profitability and reliability, shifting risk perceptions away from fossil fuels. However, while positive, current investment levels may still fall short of global climate targets. To fill this disparity, stronger financial investments are required to speed up the switch to a low-carbon energy system. Addressing these patterns is essential for developing successful energy policies and strategies to tackle climate change while fostering long-term economic prosperity [16].

Figure 2. Number of startups by country in 2024 (data taken from the [17] and figure developed by the authors).

Figure 2. Number of startups by country in 2024 (data taken from the [17] and figure developed by the authors).

The integration of AI into the energy sector not only promotes economic and social advancement but also environmental sustainability. This approach focuses on creating transparent, accountable, and human-centered AI systems. Despite limitations in terms of breadth, progress, and sustainability, the partnership between academics and investors has expedited advancements in fostering sustainability through the creation and use of AI technology, promoting a “Sustainable Energy Systems” strategy [17,18].

The term “Artificial Intelligence” (AI) was initially coined by computer scientist McCarthy in 1954. During the meeting he and his colleagues arranged, he said that all aspects of learning and intelligence might be articulated in a manner that can be replicated by a computer. AI refers to the capacity to imitate the cognitive processes of humans, including learning and problem-solving, which are unique characteristics of the human mind [19]. AI is an extensive and rapidly growing domain that is permeating all branches of science. Currently, it is being utilized in several domains such as marketing, finance, agriculture, healthcare, security, robots, voice recognition, chatbots, manufacturing, and several more fields [20,21]. AI applications in energy systems have received more attention in recent years [22].

The field of AI is dedicated to developing intelligent machines that mimic human behavior and cognition. By adding intelligence to applications, AI represents the next frontier in technological advancement, enabling computers to possess human-like skills. It is being utilized more and more recently because of the widespread availability of powerful computers and the significant growth in data quantities. The IoT drives the rise of AI by enabling the management of data through cloud platforms when devices are connected to the internet. This connectivity allows for effective and quick decision-making [23].

Fabian Heymann et al. explored the potential of AI in the power industry, analyzing 258,919 papers from 1982 to 2023. They identified six AI fields and 19 specific applications in the power supply chain. The research found that AI applications are primarily focused on power retail (55%), transmission (14%), and generation (13%). The study also found a lack of clarity on the definition of AI domains and the practical use and integration of AI in power system use cases [24].

The advancement of energy structures, scheduling models, and user participation have led to a rapid transition towards a synergistic energy system. This system requires advanced data processing capabilities, specialized expertise, collaboration across locations, and real-time monitoring. AI technology has gained attention for its superior performance. This research examines the progress of AI in energy systems by categorizing independent energy units (IEUs) and interconnected energy units (IEUSs). The primary components of IEUs can be analyzed from three perspectives: perception, decision-making, and execution. The most effective technique for using AI technologies in IEUs is examined, while the AI used for IEUSs’ interaction faces coordination and adversarial relationship challenges. The text proposes ways to enhance intelligent technologies for sustainable energy systems by examining AI technologies and their potential in the energy system [25].

Quantum computing and simulations are revolutionizing several industries by using quantum mechanics concepts to produce and analyze data. Initial studies indicate that these innovations could hasten the implementation of novel approaches to satisfy energy demand while protecting the ecosystem. Applications involve manufacturing materials, optimizing traffic patterns, locating energy generation facilities, constructing pipeline networks, and accelerating processes such as image processing and modeling of reservoirs [26].

Michela and Lorenzo reviewed recent challenges in the energy sector that can be addressed with quantum computing, focusing on key areas like forecasting, grid management, and the production of batteries, solar cells, green hydrogen, ammonia, and carbon capture. These areas are crucial for energy companies aiming for a net-zero economy. The review highlights that quantum computing could significantly reduce CO2 emissions in these sectors and improve optimization in energy forecasting, power demand management, and grid stability. The paper also discusses current methodologies and suggests directions for future research in this field [27].

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. Overall, the chart underscores AI’s critical role in driving technological advancements and sustainability across multiple industries [41].

Artificial intelligence, closely linked with big data analysis, is increasingly being recognized as a pivotal emerging technology. Governments and businesses alike acknowledge its potential applications in the energy sector. Over the past two decades, research has surged in combining big data, AI, and energy across various disciplines. Big data serves as the fundamental basis for AI, enabling computers to emulate human intelligence through algorithmic training. The integration of distributed processing, cloud storage, and IoT technologies has fueled a substantial rise in data influx within the energy sector, driven by technologies like sensors and cloud computing [42].

Artificial intelligence is increasingly being recognized for its transformative potential in the energy sector, enabling analyses of historical data, optimization of current operations, and prediction of future trends. As the utility industry leans towards a technology-driven environment, AI holds promise for revolutionizing energy provision and other related industries [43].

Power systems’ intricate nature exposes them to security vulnerabilities, particularly with the integration of complex algorithms and real-time operating equipment. Mitigating these risks is crucial, as underscored by historical events like national blackouts [44]. Integrating AI technology into power system development presents an avenue for enhancing decision-making across all stages of power grid development. In the energy market, intelligent systems incorporating ML are utilized for various activities such as alarm processing, problem diagnostics, forecasting, and security evaluation [45]. ML finds extensive application in energy analytics and computing, with utilities employing it for customer segmentation, pricing anticipation, fraud detection, and maintenance estimation [46]. ML techniques are also deployed by utilities like Pacific Gas & Electric and San Diego Gas & Electric for load estimation and anomaly detection [47].

Deep learning (DL), a specific AI branch, plays a crucial role in enhancing energy planning efficiency and predicting energy usage. Brazilian researchers propose leveraging deep learning models for energy usage prediction, yielding significantly improved precision compared to conventional methods [48].

3. Quantum Computing

Quantum theory, a significant scientific achievement of the last century, was combined with computer technology to produce quantum computation. Feynman [49] initially proposed quantum computers in 1982, observing that classical computers could not effectively imitate some quantum phenomena. Deutsch [50] formalized these ideas in 1985, describing quantum parallelism and arguing that quantum computers could outperform classical computers in certain calculations. Shor’s [51] polynomial-time technique for prime factorization and Grover’s [52] Quicker database search algorithms were major advances in 1994 and 1996, respectively. These advances triggered a surge in QC research. QC’s influence on computer science extends to AI. AI has two primary objectives: creating intelligent robots and comprehending intelligent behaviors [53].

McCarthy [54] proposed “computational intelligence” as a more appropriate word for AI, stressing the computer’s role. QC is intended to help AI engineers achieve their goal of speeding up computational operations. However, developing more efficient quantum algorithms for AI issues than existing classical ones is difficult. It is unknown how quantum processing can help AI achieve its scientific goals, and there has been minimal research in this field. Interestingly, there is a large body of research on the applications of quantum theory in AI and vice versa, rather than quantum computation. Quantum theory’s probabilistic character appears to be more compatible with numerical AI than logical AI. Furthermore, recent developments in quantum machine learning (QML) show promise [55].

Quantum computers, driven by quantum physics, work differently than traditional computers by handling atomic particles [56]. They are expected to transform technology and business, with big corporations investing in their advancement [57]. Quantum superposition and entanglement enable quantum computers to process information more quickly and effectively than classical computers [58]. They have conceivable uses in security and healthcare [59], such as unassailable encryption and enhanced molecular research. Unlike classical bits, quantum bits (qubits) can be in multiple positions concurrently, allowing for substantially more complicated computations [60,61]. In terms of business intelligence processing [62,63], QC has the potential to dramatically improve data processing, analysis, and decision-making skills [64,65], though it raises data security concerns, prompting the creation of quantum-resistant encryption algorithms. Ultimately, QC has the potential to disrupt several businesses by enabling faster, more accurate data processing and decision-making processes. The integration of QC with AI also raises philosophical and ethical questions. As AI systems become more powerful with QC, issues related to control, privacy, and the ethical use of AI become more pressing. It is crucial to address these concerns alongside the technical advancements to ensure that the development of quantum AI benefits society as a whole [66].

Quantum computers, built on quantum mechanics, were initially proposed by Feynman in 1982 and later formalized by Deutsch in 1985. Significant advances, such as Shor’s prime factorization and Grover’s database search algorithms, further fueled research in quantum computing. Quantum computers are distinguished from classical ones by their use of qubits, which leverage superposition and entanglement to perform computations more efficiently. These capabilities could greatly impact fields such as AI, where quantum computing may enhance computational tasks and optimization, though practical applications and theoretical energy savings are still under exploration.

The integration of quantum computing with AI could lead to faster and more effective data processing, but it also raises ethical and security concerns. As quantum technology advances, addressing these concerns will be crucial to ensure its benefits are realized responsibly [67].

4. Business Models and Risk of Investing in Startups

In this part, we review the innovative business models (BMs) in the existing research to provide an introduction to the review of the existing startup businesses. A critical component of Business Model Innovation (BMI) is its emphasis on generating value for various stakeholders. Teece (2010) explains that BMs detail how a company generates and retains value. Through partnering with other businesses and suppliers, companies can tap into fresh resources and capabilities that strengthen their value offerings. In recent years, BMI has gained significant attention due to companies like Apple and Uber revolutionizing entire sectors with digital technology and intricate inter-organizational networks. Organizations now engage in collaborative innovation, relying on partners for resources and value creation. BMs strategically guide operations and value generation by spanning boundaries and fostering shared value among suppliers, collaborators, integrators, and customers. Effective BMs implementation requires clear resource identification, strong relationships with collaborators, and ongoing monitoring to drive innovation [68].

Business models research has evolved from fixed descriptions to flexible frameworks focused on advancement and novelty [69,70,71]. BMI, still subject to debate [72], involves mapping processes, evaluating options, and implementing new models to enhance performance and competitiveness [73,74,75]. BMI leverages advanced technology and responds to market shifts, with large corporations emphasizing continual innovation and new competitors employing disruptive strategies integrating technology and BMs [76]. Two main types of BMIs exist: technology-driven (utilizing R&D outcomes) and market-driven (responding to value proposition shifts or role changes in the value chain) [77]. BMI fosters creativity through analogical thinking and conceptual exploration [78].

Startup investments have received plenty of attention lately as a possible source of large profits. However, this asset type is associated with significant risks that should be taken into account. This study attempts to give an in-depth review of the key risks associated with startup investments, using open-access academic and governmental resources. While there are great potential rewards, investing in startups also carries a considerable risk. Using reports and academic research as a basis, the following subsections summarize the main risks.

4.1. Failure Rate Risk

The exceptionally high failure rate of startups constitutes a significant risk to investors. According to the latest statistics gathered by the United States Bureau of Labor Statistics (2021), around 20% of new enterprises fail in their first year, and 50% fail within five years. This figure highlights the inherent instability of the startup landscape and the value of rotation for investors [79].

4.2. Financial Risks

The capital dimension is the least emphasized in the textual corpus, representing 36.4% of citations, and pertains to the direct financial implications of investing in startups. While all investment risks have financial elements, the capital dimension is distinctly categorized to highlight purely monetary aspects in analyzing investment risks, including financial and liquidity risks [80].

Financial risk refers to the potential for investors to lose their entire invested capital [81]. Therefore, startups managed by experienced entrepreneurs with a track record of success, operating in high-growth markets, and offering a substantial return on investment, can mitigate the risk of a total capital loss. Financial risk adversely impacts the investment decision, increasing investor risk aversion due to information asymmetry in the investment process [82]. Even if the entire capital is not lost, there remains a risk that the return on investment will fall short of investor expectations [83].

4.3. Market Risks

Market risk involves the chance that a product will not achieve adequate sales due to competition or barriers to market entry. While Venture Capital (VC) investors generally grasp this risk well, entrepreneurs might either underestimate or overestimate it, resulting in a misaligned valuation of the venture. Technology risk, on the other hand, pertains to the potential for the technology to fail or to work only after considerable delays and/or at higher costs. Entrepreneurs typically have a good understanding of these risks, whereas many VC investors are hesitant to assume them [84].

4.4. Operational Risk

Startups often lack the operational experience of established companies, facing challenges in scaling and maintaining quality. The journal “Risks” outlines dependencies on key personnel and inadequate processes [85]. Performance risk concerns the likelihood that the startup may not progress as anticipated, thereby disappointing investor expectations [86]. As noted by Widyasthana et al. (2017), a startup’s engagement with other ecosystem stakeholders can lower investment risk, enhance performance, and boost the startup’s chances of success. Furthermore, setting performance objectives can serve as a strategy to mitigate performance risk [87,88].

4.5. Regulatory Risk

Regulatory obstacles can impede the expansion of startups, especially in the financial and healthcare industries. According to the World Bank, stable regulatory frameworks encourage more investment. The substantial influence of regulatory changes on startup viability is discussed in the Journal of Financial Economics [89]. To reduce regulatory risks, the Harvard Business Review emphasizes the importance of compliance strategy and foresight [90].

4.6. Technological Risks

Since startups frequently rely on novel technologies, there is a chance that they will function poorly or become outdated. Risks associated with development and scaling are described in the Journal of Technology Transfer [91]. According to technological studies, obsolescence may result from quick changes in technology [92]. Investors need to think about a startup’s ability for innovation as well as technological lifecycles.

5. Startups

The concept of a “startup” holds crucial significance in economic policy, as it is viewed as a driver of regional economic growth through technology and knowledge-driven entrepreneurship. Startups epitomize a modern, innovative, and imaginative approach to work.

Empirical observations reveal that defining the notion of a startup poses significant challenges. Daniel Cockayne’s interviews with individuals highlighted a lack of consensus and conflicting definitions, reflecting the practical complexity of this term in economic geography research [93].

The term “startup” evolved into a distinct category or method of working within economic geography during the 1980s. Previously, “startup” broadly described a company’s initial phases, focusing on establishment time, startup costs, and funding challenges [94,95,96]. By the 1980s, economic geographers began associating “startups” with specific sectors or activities, such as semiconductor firms in Silicon Valley or emerging financing trends. This shift coincided with increased usage of the term alongside technological and Venture Capital (VC) developments [97,98,99].

Take, for example, Tesla, which completely changed the electric vehicle industry and significantly impacted the shift to clean energy in places such as California.

In Figure 4, the bar chart titled “Top 20 Countries by Total Startup Output” provides a striking visual representation of the global startup landscape. Dominating the chart is the United States, with a towering blue bar that dwarfs all others, showcasing its unparalleled startup ecosystem. China follows in a distant second place, represented by a green bar roughly one-fifth the height of the US. The United Kingdom secures the third spot, leading a cluster of nations including India, Germany, and Canada. What’s particularly noteworthy is the presence of smaller nations like Israel, Singapore, and Switzerland in this elite group, punching well above their weight in the global startup arena. The alternating blue and green bars create a visually engaging pattern, with each country’s total startup output clearly labeled, allowing for quick comparisons. This chart not only highlights the leaders in the startup world but also reveals the global distribution of innovation hubs, spanning North America, Europe, Asia, and beyond [100].

Figure 5 illustrates stark differences in startup success rates across various countries. Switzerland stands out with the highest success rate, where over a third of startups succeed. Close behind are the United Kingdom, Germany, Singapore, and Australia, each boasting that nearly one-third of their startups achieve success. These high performers contrast sharply with countries like South Africa, where only about one in seven startups succeed.

These figures offer valuable insights for entrepreneurs and investors. Countries with higher success rates likely provide more favorable ecosystems for startup growth, potentially due to factors like strong regulatory frameworks, access to capital, and supportive innovation hubs. The data allow for direct comparisons between nations, revealing which environments might be more conducive to entrepreneurial success.

While the chart does not show industry-specific data, it is important to consider that success rates may vary significantly across different sectors within each country. This variation could reflect local expertise, market conditions, or industry-specific challenges.

The disparities in success rates also hint at differences in cultural attitudes towards entrepreneurship and risk-taking. Countries with higher success rates might have cultures that are more accepting of entrepreneurial endeavors and failure as part of the innovation process.

For policymakers, these data are crucial. They can guide decisions on how to foster environments that support startup growth and sustainability. For entrepreneurs, they provide a glimpse into the relative risks of starting a business in different countries, helping inform location decisions for new ventures [101].

This visualization effectively highlights the varied landscape of startup ecosystems across different countries, suggesting that factors such as economic environment, support systems, and market conditions play crucial roles in determining startup success. The chart provides valuable insights for entrepreneurs and investors considering global opportunities in the startup world.

Most firms transition out of the startup phase after approximately three years, marked by indicators such as acquisition, revenue exceeding USD 20 million, multi-office presence, substantial employment, expanded board membership, and founder share sales upon achieving profitability. Statistical evidence highlights various critical elements that influence startup success. Entrepreneurs with prior experience demonstrate a higher success rate of 30%, compared to 18% for newcomers. Over half of startup failures can be attributed to inadequate research, poor product–market alignment, and ineffective marketing strategies. Other essential components include assembling a skilled team, implementing sound financial practices, and possessing relevant expertise. The geographical location of a startup also plays a significant role, with nations such as Switzerland and the UK exhibiting higher success rates. To thrive in the competitive business environment, startups must combine these factors while effectively addressing technical, legal, and operational hurdles. Ultimately, the interplay of these elements significantly enhances a startup’s prospects for success [101].

Innovation theory studies how startups gain competitive advantages through innovative practices, including sustainable development, disruptive innovation, and incremental innovation. Innovation, as famously attributed to Thomas Edison, involves addressing societal needs through inventive solutions, emphasizing dedication and tenacity among entrepreneurs. Innovation extends beyond technology to encompass product, process, service, and BM enhancements [102].

These numbers provide a comprehensive picture of the worldwide startup scene in 2024, offering light on not just where companies develop but also their viability in a variety of economic and cultural environments. This information is crucial for entrepreneurs looking to enter international markets, investors assessing opportunities, and politicians creating favorable settings for startup growth and innovation. Understanding these global trends enables stakeholders to discover potential areas for growth and improvement within startup ecosystems, resulting in a more robust and sustainable entrepreneurial landscape globally.

Vollenbroek (2002) emphasizes that successful innovation responds to societal demands and integrates social, economic, and environmental sustainability pillars, contributing to societal progress and sustainable development [103]. Startups are using creativity to tackle complicated issues in the energy industry. For instance, startups utilizing AI are creating predictive analytics tools to enhance energy efficiency and predict renewable energy generation levels. These innovations not only support the expansion of businesses but also aid in promoting sustainable development through reducing carbon emissions and encouraging clean energy usage [104].

6. Top Startups Developing Energy Sector with Artificial Intelligence

The worldwide AI industry is expected to have a Compound Annual Growth Rate (CAGR) of 38.1% and reach around USD 1600 billion by 2030. Big data and technological advancements in hardware and software contribute to this remarkable ascent [105]. Statista reports that the data generated by the modern digital economy is increasing by 40% each year. It is projected to reach 163 trillion gigabytes by 2025, contributing to the expansion of AI [105].

Consequently, entrepreneurs and corporations progressively use AI to address industry difficulties and improve sustainability. We provide a meticulously selected compilation of the foremost startups pioneering AI in the realm of energy savings and management. Based on the current discussions, we can confidently state that to maintain a competitive edge, it is crucial to stay ahead of technological advancements. Startups generate data-driven insights to promote innovation in the energy market. Presently, we are investigating 65 meticulously chosen AI and 14 QC enterprises that are exerting a substantial impact on the energy industry [105].

Within the energy and utilities industry, a multitude of emerging companies are utilizing AI to improve effectiveness and environmental friendliness in areas such as administration, upkeep, and energy efficiency. This carefully selected compilation showcases leading startups and enterprises that are using information technology to achieve cost savings and enhance performance [105].

We analyze the emerging business models that are being developed in response to the potential created by digitalization in the energy sector. Our concentration is on studying more than 79 startups from across the world. Initially, our research focused on companies using AI technology. Later, we broadened our scope to cover businesses that use AI and/or QC in the energy industry as a whole. This study consolidates 79 prominent instances, covering a wide range of services including energy commerce, electric mobility, efficiency, grid surveillance, CO2 credit exchange, and other related areas.

The chosen firms were obtained via a variety of respected platforms and events, such as Ofgem’s regulatory sandbox, the Free Electrons World’s Best Energy Startup 2018 competition, AI2Business, the Event Horizon summit, and thorough market research. The prioritization of geographic diversity led to the inclusion of representatives from multiple countries.

Table 1. Startups that use AI and QC in energy sectors (data taken from [106,107,108,109,110,111,112,113,114,115,116,117,118,119,120,121,122,123,124,125]).

NO	Company Name	Country	Funding (USD)	Business Focus, Technology Focus, and Primary Activity Areas
1	Octopus Energy	UK	2.9B	AI-powered smart grid solutions and power distribution for energy/utilities sector
2	Stem	USA	582.6M	AI-powered energy storage solutions for cost savings in energy industry
3	Zenobe	UK	522M	Electric vehicle fleet management and battery storage services
4	SparkCognition	USA	300M	AI- software for safety and reliability in energy and security sectors
5	ClimaCell	USA	270.9M	Weather intelligence and optimization for energy operations
6	Xanadu	Canada	250M	Photonic quantum computing solutions for energy, finance, and chemistry
7	Nest Labs	USA	230M	Smart home energy management products using hardware and IoT
8	Tibber	France	181.2M	AI-driven dynamic power management for electricity supply
9	CIRCTEC	UK	172M	Green tech solutions focusing on tire recycling
10	AutoGrid	USA	161M	Energy data analytics and predictions for big data in energy industry
11	Beyond Limits	USA	158.5M	Enterprise-grade AI solutions for industrial applications including energy
12	PASQAL	France	152M	Quantum computing for complex simulations in material science and energy optimization
13	Riverlane	UK	125M	Quantum- software for materials science in energy and pharmaceuticals
14	On. Energy	USA	120M	AI-driven energy storage management for grid-scale projects
15	Urbint	USA	109M	Predictive analytics for infrastructure and energy operations
16	Multiverse Computing	Spain	108M	Quantum computing, AI, and optimization for energy sector
17	Zapata Computing	USA	82.1M	Quantum- software solutions for energy, finance, and pharmaceuticals
18	Kayrros	Canada	78.6M	Data analytics for energy market investment decisions
19	BrainBox AI	Canada	75.1M	AI for HVAC optimization in buildings
20	Q-CTRL	Australia	74M	Quantum control solutions for energy, aerospace, and finance
21	Foghorn Systems	USA	72.5M	IoT and ML platform for energy applications
22	Carbon Relay	USA	68M	AI for data center cooling optimization in energy sector
23	Greenly	France	52M	Carbon accounting platform for climate tech sector
24	Verdigris Technologies	USA	51.5M	AI-powered energy consumption optimization (SaaS)
25	Cambridge QC	UK	45M	Quantum- software for energy, cybersecurity, and drug discovery
26	1QBit	Canada	45M	Quantum-inspired software for energy, finance, and life sciences
27	METRON	France	43M	AI-powered energy intelligence platform
28	Worlds	USA	38M	IoT and AI solutions for physical space analysis in energy sector
29	QC Ware	Canada	33.2M	Quantum algorithms for energy, finance, and aerospace industries
30	C12 Quantum Electronics	France	29.4M	Quantum computing hardware for energy applications
31	Ambyint	Canada	29.1M	AI-driven optimization for oil and gas production
32	SmartCat	Serbia	28.9M	AI-powered heating and cooling device optimization
33	QiO Technologies	UK	27.18M	AI-driven sustainability solutions for emissions reduction
34	Phasecraft	UK	26.3M	Quantum algorithms to optimize energy grids
35	Osperity	USA	25.6M	AI-driven visual monitoring for industrial operations in energy sector
36	Energy X	South Korea	25.4M	AI-driven platform for renewable energy investment
37	Innowatts	USA	24.3M	AI platform for energy providers
38	Strangeworks	USA	24M	Quantum computing ecosystem for energy, finance, and aerospace
39	Jua	Switzerland	21.5M	AI for weather-dependent energy trading
40	Grid4C	Israel	13M	AI and ML for smart grid optimization
41	LightSolver	Israel	13.7M	Energy-efficient supercomputer development using quantum computing
42	Dexter Energy	Netherlands	12.5M	AI-based energy forecasting and dispatching solutions
43	BluWave-ai	Canada	10.92M	AI solutions for energy grids and renewable energy
44	Raycatch	Israel	10.2M	AI-driven asset management for solar energy
45	Buzz Solutions	USA	9.5M	AI for power line maintenance in energy infrastructure
46	Quantum Benchmark	Canada	9.16M	Quantum error diagnostics for energy, finance, and defense sectors
47	Flutura	India	8.5M	AI platform for industrial asset optimization in energy sector
48	Myst AI	USA	8M	AI-based electricity demand and supply forecasting
49	Blue Wave AI Labs	USA	6.9M	AI for nuclear reactor operations
50	Skyqraft	Sweden	6.9M	ML-powered aerial inspection for power lines
51	Limejump	UK	5.5M	AI platform for renewable energy aggregation
52	Invenia	Canada	5M	ML platform for electric utility optimization
53	Dabbel	Germany	4.4M	AI-based energy management systems for buildings
54	ANNEA	Germany	4.142M	AI-driven maintenance for renewable energy assets
55	Ogre AI	Romania	2.834M	ML-based decision support for energy and utilities
56	Qu & Co	Netherlands	2.7M	Quantum computational- software for energy, chemistry, and finance
57	R8tech	Estonia	2.616M	AI add-ons for building automation systems
58	COI Energy Services	USA	2.5M	AI and blockchain for energy efficiency improvement
59	Ossus Biorenewables	India	2.364M	AI-powered green hydrogen production from waste carbon
60	Tablepointer	Singapore	2.25M	AI for commercial energy usage optimization
61	SMPnet	UK	1.4M	AI-powered grid control software for energy service providers
62	Nnergix	Spain	1M	AI-powered weather analytics for renewable energy forecasting
63	Quant Co	-	555.2K	AI as a Service for home energy management and smart grid
64	Capalo AI	Finland	545K	AI platform for the energy industry
65	Quadrical. Ai	Canada	450K	AI-based solar plant monitoring and forecasting
66	Sync Energy	USA	376K	AI-based analytics for electrical utilities
67	Rezlytix Technologies	India	200K	AI solutions for oil and gas optimization
68	Hank	USA	150K	AI-powered HVAC management in the energy sector
69	Kapacity.io	Finland	125K	AI/ML for HVAC energy consumption optimization
70	NRGI.ai	Ireland	120K	AI-based B2B energy trading marketplace
71	Eneryield	Sweden	120K	ML solutions for electric power system fault prediction
72	Sobolt	Netherlands	56.5K	AI for building heat loss mapping in the energy efficiency sector
73	Kagera AI	Serbia	-	ML and DL for oil and gas production optimization
74	DeepMind	UK	-	AI research and applications in energy efficiency
75	Evolve Energy	USA	-	AI and IoT solutions for consumer energy cost savings
76	Neurons Lab	Ukraine	-	AI solutions for energy station optimization
77	AIDI.solar	Ukraine	-	AI and ML for solar power plant management
78	Leanheat	Finland	-	AI for climate control optimization in buildings
79	Nesh	USA	-	AI assistant for oil and gas industry decision support

provides a detailed overview of startups at the forefront of applying AI and QC in the energy sector. It lists companies sourced from platforms like Ofgem’s regulatory sandbox and global startup competitions.

The Company Name column identifies pioneers in AI and QC applications specific to energy systems and networks. Each startup’s funding level reflects market confidence and investment in innovative energy solutions. Descriptions under Services/Products detail how startups leverage AI and QC for grid optimization, predictive maintenance, and other advanced energy technologies. The Country column illustrates global participation and regional innovation hubs, underscoring geographic diversity. Industries targeted by these startups range from renewable energy to smart grids, highlighting diverse sector interests.

This comprehensive selection process ensures the representation of leading global innovators in AI and QC-driven energy solutions.

Figure 6 visually ranks the top 25 startups from Table 1 based on their funding levels. It provides a comparative view of financial backing, highlighting leaders in AI and QC applications within the energy industry. Identified companies in this figure are pivotal in driving innovation.

By showcasing their ranking based on funding amounts, Figure 6 underscores the scale of investment attracted by these startups. The company names featured represent industry leaders recognized for their contributions to advancing energy technologies. Insights from this figure can reveal correlations between funding levels and factors such as startup origin, technology focus, and industry specialization.

Table 1 and Figure 6 offer a comprehensive view of the dynamic landscape where AI and QC intersect with energy innovation. They provide essential insights for investors, policymakers, and industry stakeholders to understand emerging trends, identify market leaders, and seize opportunities in the rapidly evolving energy technology sector.

The bar chart in Figure 7 summarizes the adoption of AI and QC technologies across various energy sectors according to our dataset, which reflects real-world data. This shows that AI adoption is widespread across sectors such as gas, oil, renewable, and smart grids, with some nearing full adoption. This indicates that companies in these sectors leverage AI to enhance their efficiency, predictive maintenance, and data analytics.

By contrast, QC adoption is minimal across all sectors. Several factors contribute to this limited usage: QC is an emerging technology with few practical applications in the energy sector; the high costs associated with building and maintaining quantum computers present significant barriers to entry; QC requires specialized skills that may not be readily available within many energy companies; a lack of proven use cases specific to the energy industry deters investment; and the widespread adoption of AI might currently fulfill the technological needs of these companies, reducing the immediate drive to explore QC.

The chart also highlights interesting disparities between sectors. For example, the gas sector shows a moderate number of companies, but nearly complete AI adoption, indicating a strong focus on utilizing AI for operational improvements. Similarly, despite having fewer companies, the renewable and smart grid sectors also demonstrate very high AI adoption rates, reflecting a commitment to technological innovation in these forward-looking areas.

Overall, this visualization depicts an energy industry that has widely embraced AI while remaining cautious about quantum computing. Low QC adoption across all sectors suggests significant potential for future growth as the technology matures and proves its value for addressing energy-related challenges. As the industry evolves, it will be interesting to observe how these adoption patterns shift, particularly with the potential of QC to revolutionize areas such as the optimization of energy distribution, advanced simulations, and solving complex problems that are beyond the capabilities of classical computers.

7. Discussion and Future Outlook

The combination of AI and QC marks the beginning of an age of innovation in the energy industry, particularly for enterprises working to address global warming and switch to renewable energy. These forefront technologies promise to transform energy generation, delivery, and consumption while drastically lowering environmental impact and carbon emissions.

Artificial intelligence is already making tremendous progress in enhancing renewable energy infrastructure. Looking ahead, we will likely see entirely self-sufficient smart networks that incorporate various renewable energy sources smoothly. Innovative startups are creating AI systems to forecast and optimize renewable energy generation based on climate trends as well as improved carbon capture technology and real-time energy distribution and consumption.

As AI technology advances, several key features have emerged as game changers. Advanced NLP capabilities can transform user interactions into energy systems. AI-powered virtual assistants allow consumers to manage their energy consumption using simple voice commands and optimize their usage while maintaining comfort. AI-enabled computer vision systems have transformed infrastructure management. Drones that use high-resolution sensors and AI algorithms can automatically monitor power lines, solar panels, and wind turbines, discovering possible problems before they become crucial. RL systems use historical data and real-time inputs to make complex decisions regarding energy distribution, storage, and generation. This leads to increasingly efficient strategies for balancing supply and demand across the grid.

Generative AI models are accelerating innovation in the design of energy systems. By inputting the desired parameters, these models can produce multiple design options for solar panels, wind turbines, and entire power-plant layouts, leading to more efficient energy solutions. Federated learning techniques provide valuable insights into large-scale energy consumption patterns while protecting personal data, allowing for AI models to be trained on distributed datasets without compromising individual privacy.

As QC technology matures, it promises to solve complex optimization problems in the green energy sector. Potential applications include designing more efficient solar cells, optimizing wind farm layouts, and developing advanced energy storage solutions.

Innovative Concepts Emerging from AI and QC Convergence

The synergy between AI and QC is a groundbreaking spawning concept. An AI-powered “Ecosystem Guardian” can use vast amounts of environmental data and QC to predict the long-term impacts of energy projects. Quantum-enhanced bioenergy leverages QC to improve photosynthesis for large-scale efficient biofuel production. Understanding this quantum process in photosynthesis can be applied to other technologies. For instance, solar cells could be designed using quantum mechanical principles to enhance their conversion efficiency. Overall, quantum mechanics may play a crucial role in explaining many natural phenomena in the future [126]. AI-accelerated fusion energy development could potentially unlock an endless, clean energy source. Climate-adaptive buildings can dynamically adjust their energy use and configuration to minimize their carbon footprints. Carbon-neutral urban ecosystems can optimize citywide systems to reduce emissions and actively remove carbon from the atmosphere.

Broader Impacts and Interdisciplinary Applications

The influence of AI and QC extends beyond energy into fields such as medicine and earth sciences. AI can forecast climate change-related disease outbreaks, whereas quantum simulations can be used to model complex biological processes. Quantum algorithms can be used to discover new catalysts for enhanced hydrogen production and storage [127].

AI and QC are also revolutionizing climate modeling, enabling more accurate predictions and effective mitigation strategies. In the realm of a circular economy, these technologies optimize the recycling processes and aid in designing easily recyclable and energy-efficient materials.

As these technologies mature, we envision a future in which AI and QC work in tandem to create smarter and more efficient energy systems that fully utilize renewable sources and employ advanced materials for improved energy generation and storage.

Several key factors are required to achieve this goal. Substantial financial investment from both the private and public sectors is crucial. Interdisciplinary collaboration among information specialists, mathematicians, and engineers is essential. Partnerships between startups, universities, government bodies, and tech giants are needed to commercialize innovations.

AI-powered social networks can promote sustainable behavior and enhance energy demand forecasting, whereas AI risk assessment tools can revolutionize energy sector financing with personalized options.

The outlook for energy-sector startups that leverage AI and QC is exceptionally promising. These technologies can potentially be used to create more efficient, resilient, and sustainable energy systems. As AI and QC continue to evolve, they will not only enhance the efficiency of our energy infrastructure but also pave the way for a more sustainable and prosperous future.

8. Conclusions

The energy industry is undergoing significant transformation owing to the incorporation of advanced technologies such as artificial intelligence (AI) and quantum computing (QC). These technologies enable startups to drive substantial advancements and tackle critical challenges in the sector by focusing on optimizing energy efficiency, reinventing grid monitoring, and facilitating electric transportation. When AI dominates the landscape, it is evident that it is pivotal in the current wave of energy innovation. However, the emerging field of QC has also shown promise in complementing AI by providing unparalleled computational capabilities to solve complex energy problems. This synergy between AI and QC fosters a robust and intelligent energy ecosystem, contributing to more sustainable and resilient energy infrastructures. The aggregation of more than 74 startups from various countries, gathered via reliable platforms and events, highlights the extensive global influence of AI and QC on transforming the energy sector. By leveraging AI-driven and QC-enhanced solutions, these emerging companies not only improve operational effectiveness but also lay the foundation for a more environmentally friendly and robust energy future. As we delve into the intricacies of their business models and technical solutions, it becomes clear that collaboration between AI and QC is leading the way in bringing about significant changes in the energy sector. To expedite the implementation of these technologies and to shape a more environmentally friendly, intelligent, and sustainable energy system, it is crucial to maintain ongoing support and collaboration with these pioneering firms.

Table in Abbreviations part includes a thorough list of abbreviations utilized in this study. Every acronym is explained to guarantee clarity and help the reader comprehend the terms and ideas being discussed.