Overview of Startups Developing Artificial Intelligence for the Energy Sector

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This is a comprehensive overview of the startups in the energy sector based on AI, and it will definitely serve a wide audience, including investors. What I liked the most in this manuscript is the reviews of startups doing quantum science and technology.

Also, the manuscript is prepared very well in general.

In more detail, the main question addressed by the manuscript is the recent developments, potential impacts, and future directions of emerging companies at the forefront of AI and quantum computing innovations in the energy sector.

Since this is an important field but such reviews, especially considering the quantum technologies within this context are limited, I believe it is filling a substantial gap in the field.

To the best of my knowledge, and searches during the review, previous works were limited to AI within the classical technologies. Hence, including quantum technologies is adding value to the field.

I find the methodology solid, so I don’t see any necessary improvements.

The conclusions are consistent with the rest of the manuscript, and references are appropriate. Though, I guess there is a typo referring to a source on line 125, which could be fixed in the revision or in the production stage.

In Fig.1 and Fig.2, font sizes are too small, and the resolution is low.

In Fig.2, “Funding Amount (USD)” could be better something like “Funding Amount (Billion USD)”, so that all those 9 zeros can be omitted in the y axis.

Style of Table 1 could be improved. For example, Column 3 could be wider. Another option is to separate Column 3, and make a new table only with the 1st and 3rd column.

In summary, I recommend the publication of this manuscript following some minor revision.

Author Response

Comments 1: The conclusions are consistent with the rest of the manuscript, and references are appropriate. Though, I guess there is a typo referring to a source on line 125, which could be fixed in the revision or in the production stage.

Response 1: Concerning line 125: We acknowledge your observation and confirm that using references [15] and [16] to support the information in that paragraph was deliberate, not a typographical error. We value your meticulous review of our work.

 

Comments 2: In Fig.1 and Fig.2, font sizes are too small, and the resolution is low.2. Please give the full name of the abbreviation the first time appears in the text.

Response 2: Thank you for informing me about the revisions to Figures 1 and 2. Both figures now feature enlarged font sizes and improved resolution for better legibility. In Figure 2, we've modified the y-axis label to "Funding Amount (Billion USD)" for clarity, and importantly, we've reclassified the fields to create a clearer observation of funding distribution across different sectors. These changes should significantly enhance the visual presentation and interpretability of the data.

Comments 3: Style of Table 1 could be improved. For example, Column 3 could be wider. Another option is to separate Column 3, and make a new table only with the 1st and 3rd column.

Response 3: Thank you for addressing our feedback on Table 1. We appreciate the improvements made to enhance its readability and usefulness. The table has been summarized for clarity, and the fourth column has been expanded to better accommodate its contents. While we suggested separating the first and third columns into a new table, we understand your decision to maintain all information in a single table for ease of reference. The improved formatting should effectively address the readability concerns, making the table more accessible and informative for readers.

 

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

The paper contains a wealth of relevant information and is well-structured. However, it falls short in several areas. The content is largely descriptive, offering minimal analysis and lacking in actionable conclusions, such as identifying emerging pathways for new companies. There is insufficient depth or detail regarding the applications of AI and Quantum Computing (QC), and the use of QC by startups is not clearly articulated. Given the title of the paper, the focus should be narrowed to energy sector startups specifically, rather than any company with an energy-related use case.

In particular:

·       Line 30 – c;arify what is meant by “…assisting the carry…”

·       Line 39 – clarify the meaning of “Nature Energy”, if this a journal.

·       Line 71 – need a reference for the data.

·       All figures need to reference sources for the data shown.

·       Update cases of the text “错误!未找到引用源”

·       Figure 2 – change the colours for Non-energy and energy as they are too similar to distinguish easily.

·       Reference 16 doesn’t seem to be used.

·       Line 190 – Is misleading, change the statement that QC has the potential to revolutionise some industries.

·       Figure 3 – Change the position of the graph inset to not hide the data.

·       Section 3 - Update references for more recent QC achievements.

·       Line 33 – maybe be clearer to say “…using…” rather than “…focusing on…”.

·       Figure 4 – the data may be more interesting to have the number of startups normalised for size of the country.

·       Line 430 – define “success”

·       Line 494 – which 10 are selected and explain why those ones.

·       Table 1 – highlight the companies that use QC.

·       QC companies such as 34, 35, 37, 38 may have energy related use cases but are not startups focussing on the energy sector and should not be included without mentioning this in detail because the funding is for QC in general and not specifically for the energy sector.

·       Line 577 – should this be “Future” not “Feature”

·       Line 583 – refer to examples of how much enhancement and what exactly.

·       Line 599 – refer to examples of generative AI and how much enhancement and what exactly.

·       Line 602 – refer to examples of federated learning how much enhancement and what exactly.

·       Line 298 -  – refer to examples of QML and how much enhancement and what exactly.

·       Lines 608, 621 – format as a sub-headings

Comments on the Quality of English Language

The quality of English is generally good.

Author Response

Comments 1: Line 30: The text has been revised for clarity.

Response 1: Section 3 on quantum computing has been extended to provide a more comprehensive review. While we maintain a primary focus on AI applications in the energy sector, we have added more discussion on the interplay between AI and quantum computing in current research.

 

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 re-search in quantum computing. Quantum computers are distinguished from classical ones by their use of qubits, which leverage superposition and entanglement to perform compu-tations more efficiently. These capabilities could greatly impact fields such as AI, where quantum computing may enhance computational tasks and optimization, though practi-cal 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 res-ponsibly [67].

 

Comments 2. Line 39: We've confirmed that "Nature Energy" is indeed a journal name.

Response 2: We appreciate your attention to the Author Contributions section. The current description follows standard academic conventions and clearly outlines each author's role in the preparation of this review article. N.M.L. was responsible for the original draft and gathering resources, D.N. and G.K. contributed to reviewing and editing the manuscript, while G.J. and N.R. focused on visualization aspects. This division of tasks ensures a comprehensive and well-rounded review. All authors have read and approved the final version, indicating their agreement with the content and their respective contributions. We believe this breakdown adequately reflects each author's unique input to the paper.

Comments 3. Line 71: A reference has been added for the data.

Response 3: We appreciate your comment about the broad perspective of the energy industry. To clarify, our review does indeed cover various aspects of the energy sector, including production, distribution, and network management, not just energy consumption. We've made sure this comprehensive approach is more clearly communicated throughout the paper.

Comments 4. All figures: Sources for the data have been included in the figure captions.

Response 4: All figures: Sources for the data have been included in the figure captions.

 

Comments 5. The instances of "错误**!**未找到引用源" have been resolved.

Response 5: The instances of "错误**!**未找到引用源" have been resolved.

 

Comments 6. Figure 2: The colors for Non-energy and Energy categories have been updated for better distinction.

Response 6: In Figure 2, we've modified the y-axis label to "Funding Amount (Billion USD)" for clarity, and importantly, we've reclassified the fields to create a clearer observation of funding distribution across different sectors. These changes should significantly enhance the visual presentation and interpretability of the data.

 

Comments 7. References 15 and 16: Both references are used to support the information in that paragraph.

Response 7: References 15 and 16: Both references are used to support the information in that paragraph.

Comments 8. Line 190: As this statement relates to AI rather than QC, we've maintained the original text to accurately reflect the source material.

Response 8: Line 190: As this statement relates to AI rather than QC, we've maintained the original text to accurately reflect the source material.

The text proposes ways to enhance intelligent technologies for sustainable energy systems by examining AI technologies and their potential in the energy system [25].

 

Comments 9. Figure 3: The graph inset has been repositioned to avoid obscuring data.

Response 9: The graph inset has been repositioned to avoid obscuring data.

 

Comments 10. Section 3: While we've extended this section, we've maintained a focus on AI applications in line with the paper's scope.

Response 10: Section 3: While we've extended this section, we've maintained a focus on AI applications in line with the paper's scope.

Add this part to section 1:

Michela and Lorenzo review 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].

and this part to section 3:

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 re-search in quantum computing. Quantum computers are distinguished from classical ones by their use of qubits, which leverage superposition and entanglement to perform compu-tations more efficiently. These capabilities could greatly impact fields such as AI, where quantum computing may enhance computational tasks and optimization, though practi-cal 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 res-ponsibly [67].

Comments 11. Line 334 (assuming you meant this instead of 33): Changed to "using" for clarity.

Response 11: Line 334 (assuming you meant this instead of 33): Changed to "using" for clarity. Highlighted.

Comments 12. Figure 4: The visualization has been updated as suggested.

Response 12: Figure 4: The visualization has been updated as suggested and updated the explanation of it.
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 notewor-thy 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].

Comments 13. Line 430: We've included a definition of "success" in the figure explanation.

Response 13: We've addressed your feedback by including a definition of "success" in the figure explanation. The definition highlights, Most firms transition out of the startup phase after approximately three years, marked by indicators such as acquisition, revenue exceeding $20 million, multi-office presence, substantial employment, expanded board membership, and founder share sales upon achieving profitability. This definition provides clear, measurable criteria for startup success. The additional context about success factors, including entrepreneurial experience, market alignment, and geographical location, offers valuable insights into the complex landscape of startup ecosystems across different countries.

 

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 op-portunities in the startup world.

Most firms transition out of the startup phase after approximately three years, marked by indicators such as acquisition, revenue exceeding $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 suc-cess rate of 30%, compared to 18% for newcomers. Over half of startup failures can be at-tributed 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 ex-hibiting 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].

Comments 14. Line 494: We've updated this section to explain the selection criteria for the highlighted companies.

Response 14: Line 494: We've updated this section to explain the selection criteria for the highlighted companies.
Consequently, entrepreneurs and corporations progressively use AI to address in-dustry difficulties and improve sustainability. Discover a meticulously selected compila-tion of the foremost startups pioneering AI in the realm of energy savings and manage-ment. Based on the current discussions, we can confidently state that to maintain a com-petitive 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 inves-tigating 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 uti-lizing AI to improve effectiveness and environmental friendliness in areas such as ad-ministration, upkeep, and energy efficiency. This carefully selected compilation showcas-es 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 study-ing more than 79 startups from across the world. Initially, our research focused on com-panies 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 in-stances, 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 com-petition, AI2Business, the Event Horizon summit, and thorough market research. The pri-oritization of geographic diversity led to the inclusion of representatives from multiple countries.

Comments 15. Table 1: Companies utilizing QC have been highlighted.

Response 15: Table 1: Companies utilizing QC have been highlighted.

Comments 16. Regarding QC companies: We acknowledge that some listed companies may have broader applications beyond the energy sector. This limitation has been noted in the text.

Response 16: Yes, we acknowledge that the quantum computing companies listed (34, 35, 37, 38) may not be startups primarily focused on the energy sector. However, they are included because they are listed as startups in that field due to their potential energy-related applications. We've noted this limitation in the text to provide a more accurate representation of their role and to avoid overstating their direct focus on energy sector innovations.

Comments 17. Line 577: Corrected "Feature" to "Future".

Response 17:  Thank you for your concentration, Corrected "Feature" to "Future".

 

 

Comments 18. Lines 583, 599, 602, and 298: Due to limited specific data on enhancements in these areas, we were unable to provide detailed examples as requested. However, we've added more general information where possible.

Response 18: Lines 583, 599, 602, and 298: Due to limited specific data on enhancements in these areas, we were unable to provide detailed examples as requested. However, we've added more general information where possible. 

Comments 19. Lines 608 and 621: These have been reformatted as sub-headings for better structure

Response 19: Lines 608 and 621: These have been reformatted as sub-headings for better structure.

 

Regarding the companies in Table 1, we've clarified that while some may have grown beyond the startup phase, they originated as startups in the energy sector.

 

We appreciate your detailed feedback, which has helped improve the manuscript's clarity and accuracy. If you have any further questions or concerns, please don't hesitate to ask.

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

The proposed article seems to review the changes in the energy industry due to the development of AI and QC. The following issues should be resolved for the publication of the article.

1. Although the importance of quantum computing is mentioned, the review of quantum computing in Section 3 is too insufficient, so sufficient reviews should be added, and discussions on related research with AI, which is currently being studied, are needed.

2. A clearer explanation of the differential contributions of the authors that readers can obtain from the proposed review article is needed.

3. If the authors focus on the energy industry, they should look at the importance of the industry from a broad perspective by mentioning not only energy consumption but also production and distribution according to various types of energy.

4. The paper needs to be revised in terms of its structure. 1) The paragraphs need to be organized larger. 2) Table 1 is difficult to read, so it needs to be organized by field, and there is a lack of data on QC. 3) The readability of the figures is poor. 4) Errors in the references should be checked.

Comments on the Quality of English Language

Moderate editing of English language required.

Author Response

Comments 1: Although the importance of quantum computing is mentioned, the review of quantum computing in Section 3 is too insufficient, so sufficient reviews should be added, and discussions on related research with AI, which is currently being studied, are needed.

Thank you for your detailed review and suggestions. I'm pleased to address the changes and clarifications made in response to your comments:

 

Response 1: Section 3 on quantum computing has been extended to provide a more comprehensive review. While we maintain a primary focus on AI applications in the energy sector, we have added more discussion on the interplay between AI and quantum computing in current research.

 

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 re-search in quantum computing. Quantum computers are distinguished from classical ones by their use of qubits, which leverage superposition and entanglement to perform compu-tations more efficiently. These capabilities could greatly impact fields such as AI, where quantum computing may enhance computational tasks and optimization, though practi-cal 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].

Comments 2: A clearer explanation of the differential contributions of the authors that readers can obtain from the proposed review article is needed.

Response 2: N.M.L. conceptualized the review, prepared the original draft, and gathered primary resources. D.N. and G.K. contributed equally to reviewing and editing the manuscript, with D.N. and G.K. on AI& QC. G.J. and N.R. collaborated on data for Table 1 visualization and figure preparation,

All authors participated in regular discussions throughout the review process, contributing to the refinement of ideas and critical analysis of the literature. N.M.L. and D.N. were responsible for project administration and coordination among authors.

All authors have read and agreed to the published version of the manuscript, and they approve of their respective contributions as outlined above.

Comments 3: If the authors focus on the energy industry, they should look at the importance of the industry from a broad perspective by mentioning not only energy consumption but also production and distribution according to various types of energy.

Response 3: We appreciate your comment about the broad perspective of the energy industry. To clarify, our review does indeed cover various aspects of the energy sector, including production, distribution, and network management, not just energy consumption. We've made sure this comprehensive approach is more clearly communicated throughout the paper.

Comments 4: The paper needs to be revised in terms of its structure. 1) The paragraphs need to be organized larger. 2) Table 1 is difficult to read, so it needs to be organized by field, and there is a lack of data on QC. 3) The readability of the figures is poor. 4) Errors in the references should be checked.

Response 4: We've made several structural improvements to the paper:

 

   1) The paragraphs have been reorganized and consolidated for better flow and readability.

  

   2) Table 1 has been restructured to improve readability. It is now organized by field, making it easier for readers to understand the different areas of focus within the energy sector. We've also added more data related to quantum computing applications where available.

  

   3) All figures have been revised to improve their readability. This includes adjusting font sizes, improving color contrasts, and ensuring that all data points are visible.

  

   4) We've conducted a thorough check of all references to ensure accuracy and consistency.

We've noted your comment about the need for moderate editing of the English language. A thorough editing process has been undertaken to improve the overall language quality of the paper.

 

These revisions should address the main concerns you've raised, enhancing the paper's clarity, comprehensiveness, and overall quality. We appreciate your valuable feedback in helping us improve this work.

Author Response File: Author Response.docx

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

Comments are adequately addressed in the new version except I don't see the change for reformatting Lines 648 and 661 as a sub-headings.

 

Reviewer 3 Report

Comments and Suggestions for Authors

The revised manuscript appears to have been well-edited and improved based on the reviewers' comments, and appears ready for publication in its current form after minor English corrections.

Comments on the Quality of English Language

Minor editing of English language required.