Abstract
The dynamic and rapidly developing European landscape of solar photovoltaic (PV) small and medium-sized enterprises (SMEs) calls for the adoption of artificial intelligence (AI)-based solutions harnessing the power of data. Currently, many SMEs face challenges in putting this approach into practice due to the lack of resources (financial, human, strategic). To aid SMEs in this endeavour, AI maturity assessments have been developed to evaluate the current state of an SME’s AI transformation on multiple dimensions along the established maturity stages. However, recommendations on how to advance between the successive maturity stages are fragmented in contemporary literature. In this exploratory study, we conduct thirteen semi-structured interviews and three AI maturity assessments with solar PV plant-operating SMEs in the Netherlands, concluding that the Dutch solar PV industry can be classified as being in the first maturity stage. To transition towards the second stage, our framework emphasizes the need to develop realistic AI strategies, data requirement specifications and human‒AI symbiosis. The outcomes of our study may be used by PV energy SMEs as a guide to understand and implement the complex AI maturity stage transitions, as well as by future researchers to build on the proposed framework.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
Notes
- 1.
SMEs are businesses with less than 250 employees and an annual turnover of no more than €50 million.
References
Ahmad, T., Zhang, D., Huang, C., Zha, H., Dai, N., Song, Y., & Chen, H. (2021). Artificial intelligence in sustainable energy industry: Status Quo, challenges and opportunities. Journal of Cleaner Production, 289. https://doi.org/10.1016/j.jclepro.2021.125834
Alsheibani, S., Cheung, Y., & Messom, C. (2019). Towards an artificial intelligence maturity model: From science fiction to business facts. Pacific Asia Conference on Information Systems, 46.
Alzahrani, A., Shamsi, P., Dagli, C., & Ferdowsi, M. (2017). Solar irradiance forecasting using deep neural networks. Procedia Computer Science, 114. https://doi.org/10.1016/j.procs.2017.09.045
Bacchetti, E. (2017). A design approach with methods and tools to support SMEs in designing and implementing Distributed Renewable Energy (DRE) solutions based on Sustainable Product-Service System (S.PSS). Procedia CIRP, 64, 229–234. https://doi.org/10.1016/j.procir.2017.03.064
Baarsma, J. (2019). EU market outlook for solar power 2019–2023. Retrieved on May 21, 2021, from https://www.solarpowereurope.org/wp-content/uploads/2019/12/SolarPower-Europe_EU-Market-Outlook-for-Solar-Power-2019-2023_.pdf?cf_id=7181
Brundtland, G. (1987). Report of the World Commission on Environment and Development: Our Common Future. United Nations General Assembly document A/42/427.
Charran, E., & Sweetman, S. (2020). AI maturity and organizations—Understanding AI maturity. Retrieved on May 4, 2021, from https://query.prod.cms.rt.microsoft.com/cms/api/am/binary/RE4Divg
Culot, G., Nassimbeni, G., Orzes, G., & Sartor, M. (2020). Behind the definition of Industry 4.0: Analysis and open questions. International Journal of Production Economics, 226. https://doi.org/10.1016/j.ijpe.2020.107617
DDMA. (2021). DDMA AI maturity test. Retrieved on May 5, 2021, from https://aimaturitytest.nl
European Commission. (2020). On artificial intelligence—A European approach to excellence and trust. Retrieved on February 3, 2021, from https://ec.europa.eu/info/sites/info/files/commission-white-paper-artificial-intelligence-feb2020_en.pdf
Holden, E., Linnerud, K., & Banister, D. (2014). Sustainable development: Our common future revisited. Global Environmental Change, 26, 130–139. https://doi.org/10.1016/j.gloenvcha.2014.04.006
Interreg. (2018). Industry 4.0—opportunities and challenges for SMEs in the North Sea Region. Retrieved on February 10, 2021, from https://northsearegion.eu/growin4/desk-study-on-industry-40-in-the-north-sea-region/
Jäger-Waldau, A. (2019). PV Status Report 2019. EUR 29938 EN, Publications Office of the European Union. https://doi.org/10.2760/326629
Jimenez, A. A., Munoz, C. Q. G., Marquez, F. P. G., & Zhang, L. (2017). Artificial intelligence for concentrated solar plant maintenance management. Advances in Intelligent Systems and Computing, 502, 125–134. https://doi.org/10.1007/978-981-10-1837-4_11
Kaur, A., Nonnenmacher, L., Pedro, H., & Coimbra, C. (2016). Benefits of solar forecasting for energy imbalance markets. Renewable Energy, 86, 819–830. https://doi.org/10.1016/j.renene.2015.09.011
Lichtenthaler, U. (2020). Five maturity levels of managing AI: From isolated ignorance to integrated intelligence. Journal of Innovation Management, 8. https://doi.org/10.24840/2183
Lorenz, E., Ruiz-Arias, J., & Wilbert, S. (2017). Forecasting Solar Radiation. NREL Technical Report. National Renewable Energy Laboratory.
Lupangu, C., & Bansal, R. (2018). Retraction notice to “a review of technical issues on the development of solar photovoltaic systems.” Renewable and Sustainable Energy Reviews, 94. https://doi.org/10.1016/j.rser.2018.08.016
Madeti, S. R., & Singh, S. N. (2018). Modeling of PV system based on experimental data for fault detection using kNN method. Solar Energy, 173,. https://doi.org/10.1016/j.solener.2018.07.038
Matt, D., Modrak, V., & Zsifkovits, H. (2020). Industry 4.0 for SMEs: Challenges, opportunities and requirements. Palgrave Macmillan.
McCrone, A., Moslener, U., d’Estais, F., Grüning, C., & Emmerich, M. (2020). Global trends in renewable energy investment 2020. Frankfurt School of Finance & Management. Retrieved on May 24, 2021, from https://www.fs-unep-centre.org/wp-content/uploads/2020/06/GTR_2020.pdf
Meijer, L., Huijben, B., Boxstael, A., & Romme, G. (2019). Barriers and drivers for technology commercialization by SMEs in the Dutch sustainable energy sector. Renewable and Sustainable Energy Reviews, 112, 114–126. https://doi.org/10.1016/j.rser.2019.05.050
Mellit, A., & Kalogirou, S. (2021). Artificial intelligence and internet of things to improve efficacy of diagnosis and remote sensing of solar photovoltaic systems: Challenges, recommendations and future directions. Renewable and Sustainable Energy Reviews, 143. https://doi.org/10.1016/j.rser.2021.110889
Miller, C. (2014). The ethics of energy transitions. IEEE International Symposium on Ethics in Science, Technology and Engineering (pp. 1‒5). https://doi.org/10.1109/ETHICS.2014.6893445
MIT Sloan. (2019). Data, analytics & AI: How trust delivers value. MIT Sloan Management Review. Retrieved on May 5, 2021, from https://sloanreview.mit.edu/sponsors-content/data-analytics-and-ai-how-trust-delivers-value/
Molengraaf, P. (2020). EU market outlook for solar power 2020–2024. Retrieved on May 24, 2021, from https://www.solarpowereurope.org/european-market-outlook-for-solar-power-2020-2024/
Neelamegam, P., & Amirtham, V. A. (2016). Prediction of solar radiation for solar systems by using ANN models with different back propagation algorithms. Journal of Applied Research and Technology, 14. https://doi.org/10.1016/j.jart.2016.05.001
Nishant, R., Kennedy, M., & Gorbett, J. (2020). Artificial intelligence for sustainability: Challenges, opportunities and a research agenda. International Journal of Information Management, 53. https://doi.org/10.1016/j.ijinfomgt.2020.102104
Ramakrishnan, K. (2020). The AI maturity framework. Retrieved on May 27, 2021, from https://www.elementai.com/products/ai-maturity
Roddis, P., Roelich, K., Tran, K., Carver, S., Dallimer, M., & Ziv, G. (2020). What shapes community acceptance of large-scale solar farms? A case study of the UK’s first ‘nationally significant’ solar farm. Solar Energy, 209, 235–244. https://doi.org/10.1016/j.solener.2020.08.065
Saari, L., Kuusisto, O., & Pirttikangas, S. (2019). AI maturity web tool helps organisations proceed with AI. VTT Technical Research Centre of Finland, 1‒7. https://doi.org/10.32040/Whitepaper.2019 AIMaturity.
Santos, C., Mehrsai, A., Barros, A., Araújo, M., & Enrique, A. (2017). Towards Industry 4.0: An overview of European strategic roadmaps. Procedia Manufacturing, 13, 972–979. https://doi.org/10.1016/j.promfg.2017.09.093
Saunders, M. N. K., Lewis, P., & Thornhill, A. (2016). Research methods for business students. Financial Times/Prentice Hall.
Schumacher, A., Erol, S., & Sihn, W. (2016). A maturity model for assessing Industry 4.0 readiness and maturity of manufacturing enterprises. Procedia CRIP, 52, 161–166. https://doi.org/10.1016/j.procir.2016.07.040
Vakkuri, V., Janutnen, M., Halme, E., Kemmel, K. K., Ngyuen-Duc, A., Mikkonen, T., & Abrahamsson, P. (2021). Time for AI (ethics) maturity model is now. University of Jyväskylä.
Veile, J., Kiel, D., Müller, J. & Voigt, K.-I. (2019). Lessons learned from Industry 4.0 implementation in the German manufacturing industry. Journal of Manufacturing Technology Management. https://doi.org/10.1108/JMTM-08-2018-0270.
Verhoef, P., Broekhuizen, T., Bart, Y., Bhattacharya, A., Dong, J., Fabian, N., & Haenlein, M. (2019). Digital transformation: A multidisciplinary reflection and research agenda. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2019.09.022
Verzijlbergh, R. A., Vries, L. J., Dijkema, G. P. J., & Herder, P. (2016). Institutional challenges caused by the integration of renewable energy sources in the European electricity sector. Renewable and Sustainable Energy Reviews, 75,. https://doi.org/10.1016/j.rser.2016.11.039
Werther, M., van Hooff, W., Coijn, K. & Blokdijk, R. (2019). Next-generation solar power: Dutch technology for the solar energy revolution. Retrieved on May 24, 2021, from https://www.rvo.nl/sites/default/files/2020/08/NL-Solar-Guide-2020.pdf.
Yang, T., Zhao, L., Li, W., & Zomaya, A. (2020). Reinforcement learning in sustainable energy and electric systems: A survey. Annual Reviews in Control, 49. https://doi.org/10.1016/j.arcontrol.2020.03.001
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this chapter
Cite this chapter
Schmidt, M., Marrone, S., Paraschakis, D., Singh, T. (2022). Artificial Intelligence in the Energy Transition for Solar Photovoltaic Small and Medium-Sized Enterprises. In: Bertolaso, M., Capone, L., Rodríguez-Lluesma, C. (eds) Digital Humanism. Palgrave Macmillan, Cham. https://doi.org/10.1007/978-3-030-97054-3_7
Download citation
DOI: https://doi.org/10.1007/978-3-030-97054-3_7
Published:
Publisher Name: Palgrave Macmillan, Cham
Print ISBN: 978-3-030-97053-6
Online ISBN: 978-3-030-97054-3
eBook Packages: Social SciencesSocial Sciences (R0)