Making Energy-transition headway: A Data driven assessment of German energy startups

Singh, Mahendra; Jiao, Jiao; Klobasa, Marian; Frietsch, Rainer

doi:10.1016/j.seta.2021.101322

Cited by 10 publications

(8 citation statements)

References 19 publications

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“…Moreover, energy management, which deals with the goal of optimizing efficient energy use, has shown a good connectivity to "Battery". This is in line with the recent observation that the preferred technology focus of energy startups lies on "Energy Management", and they enjoy a high digital-technology penetration [67].…”

Section: Network Analysissupporting

confidence: 87%

Exploring and Predicting the Knowledge Development in the Field of Energy Storage: Evidence from the Emerging Startup Landscape

Song

2021

Energies

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The distribution and deployment of energy storage systems on a larger scale will be a key element of successfully managing the sustainable energy transition by balancing the power generation capability and load demand. In this context, it is crucial for researchers and policy makers to understand the underlying knowledge structure and key interaction dynamics that could shape the future innovation trajectory. A data-driven approach is used to analyze the evolving characteristics of knowledge dynamics from static, dynamic and future-oriented perspective. To this end, a network analysis was performed to determine the influence of individual knowledge areas. Subsequently, an interaction trend analysis based on emergence indicators was conducted to highlight the promising relations. Finally, the formation of new knowledge interactions is predicted using a link prediction technique. The findings show that ensuring the energy efficiency is a key issue that has persisted over time. In future, knowledge areas related to digital technologies are expected to gain relevance and lead the transformative change. The derived insights can assist R&D managers and policy makers to design more targeted and informed strategic initiatives to foster the adoption of energy storage solutions.

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Section: Network Analysissupporting

confidence: 87%

Exploring and Predicting the Knowledge Development in the Field of Energy Storage: Evidence from the Emerging Startup Landscape

Song

2021

Energies

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“…Much of the previous NLP-powered energy transition research has focused on using unsupervised machine learning methods, specifically topic modeling, to uncover latent topics in the material [Dehler-Holland et al, 2021;Repo et al, 2021;Rizzoli et al, 2024;Saheb et al, 2022;Tie and Zhu, 2022]. Alternatively, dictionary-based NLP has been employed to analyse specific aspects of data, such as gender perspectives [Carroll et al, 2024], involvement of startups in renewable energy [Singh et al, 2021], and renewable energy investor sentiment [Herrera et al, 2022].…”

Section: An Anonymous Commenter On An Online News Site In January 2023mentioning

confidence: 99%

Applying computational approaches to energy discourse: a comparative methodological study of rule-based and large language model qualitative content analysis

Kousa

2024

Journal of Data Mining &Amp; Digital Humanities

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The objective of this study is to conduct a comparative analysis of a rule-based ontological classification tool and a large language model (LLM) chatbot as qualitative content analysis tools. The focus is on assessing their strengths and limitations when applied to the study of the discourse surrounding the energy transition. To achieve this, I used the tools to analyse two different types of corpora: citizens' social media discussions and politicians' parliamentary speeches. In the analysis, I evaluated the differences in the methods' recall and precision levels. Additionally, I assessed the extent to which these methods align with scientific principles, including reliability, transparency, and research integrity. The results reveal a classic trade-off: LLM's precision is high, but its recall is comparatively low, suggesting its strength in generating accurate but potentially incomplete analyses. On the other hand, the rule-based method outperforms in recall at the expense of precision, capturing more data points, but with varying accuracy levels. I discuss the implications of the results and outline ideas for leveraging the strengths of both methods in future studies. This article provides researchers with insights into the selection and application of computational tools in the social sciences and humanities, as well as in multifaceted research topics, such as energy transition.

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“…In addition, with the spread of distributed energy and the increase of new entrants, the competition among energy industry players has become intensifying. The efforts of energy industry players to innovate their business models and find new growth engines have become more critical (Palmié et al, 2021; Singh et al, 2021). In order to respond to such a global environmental regime and gain an edge in fierce competition, servitization, which has been triggered in the existing manufacturing industry, is being actively carried out in the energy industry (Helms, 2016).…”

Section: Changes and Future Challenges Related To The Spread Of As‐a‐...mentioning

confidence: 99%

Expansion of servitization in the energy sector and its implications

Park

2022

WIREs Energy & Environment

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The business model of as‐a‐service is rapidly spreading in the energy industry. This study aims to examine how servitization in the energy sector is progressing, what sectors are in the spotlight, and what their implications are. By understanding the servitization aspect of the energy sector, it is possible to gauge the direction of change in the energy industry. This study has reviewed various as‐a‐service types in the energy sector through new articles analytics. For a total of 6 years from 2015 to 2020, news articles containing keywords related to the energy industry and the term “as a service” were extracted from Google News using the R package. As a result, well‐known as‐a‐service models such as software‐as‐a‐service and platform‐as‐a‐service are being emphasized in the energy sector. Also, new types of services such as zero‐carbon‐as‐a‐service and resilience‐as‐a‐service have been emerging recently. With the spread of as‐a‐service, the value chain structure of the energy industry is expected to shift towards various types of services and platforms. Servitization, represented by as‐a‐service, is becoming a trend in the energy industry, and policy and regulatory support to foster the data industry and expand opportunities to demonstrate innovative technologies and services is required. This article is categorized under: Policy and Economics > Governance and Regulation Policy and Economics > Research and Development

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Making Energy-transition headway: A Data driven assessment of German energy startups

Cited by 10 publications

References 19 publications

Exploring and Predicting the Knowledge Development in the Field of Energy Storage: Evidence from the Emerging Startup Landscape

Exploring and Predicting the Knowledge Development in the Field of Energy Storage: Evidence from the Emerging Startup Landscape

Applying computational approaches to energy discourse: a comparative methodological study of rule-based and large language model qualitative content analysis

Expansion of servitization in the energy sector and its implications

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