Ágnes Fűrész
PhD Student, Hungarian University of Agriculture and Life Sciences,
Doctoral School of Economic and Regional Sciences, Hungary
fureszagi@gmail.com
Abstract
Artificial intelligence (AI) is emerging as a key enabler of energy efficiency, a fundamental pillar of sustainable energy use and climate action. Leveraging real-time data analysis, predictive modeling, and automated decision-making, AI-powered technologies offer new ways to optimize energy consumption. The energy crisis of 2021 made evident that boosting efficiency is not only an economic necessity but also a societal demand, as rising prices drove consumers to adopt more deliberate and resource-conscious behaviors. This study is based on a quantitative online survey involving over 400 Hungarian participants, examining how AI-driven digital tools—such as adaptive platforms, automated feedback systems, and intelligent energy interfaces—can contribute to the spread of sustainable energy consumption practices and the deepening of energy awareness. The paper explores the potential of AI-integrated digital tools to foster more sustainable consumption patterns and raise public energy awareness. The study shows that such technologies can help reduce reliance on fossil fuels, support the integration of renewable sources, and align with long-term energy transition strategies. Scalable, cost-effective, and behaviorally impactful, AI-driven solutions may play a transformative role in shaping the future of energy use.
Keywords: energy efficiency, consumer behavior, renewable energy, artificial intelligence, energy consciousness
JEL: D91; Q41; Q55
DOI: 10.52244/c2025.10
References
Ajzen, I. – Fishbein, M. (1975): Belief, attitude, intention and behavior. London: Addison-Wesley Reading, MA.
Ágnes Csiszárik-Kocsir, & Varga, J. (2023). Innovation and factors leading to innovative behaviour according to Hungarian businesses1. 000291–000296. https://doi.org/10.1109/saci58269.2023.10158548
András Szeberényi, & Ferenc Bakó. (2023). Electricity Market Dynamics and Regional Interdependence in the Face of Pandemic Restrictions and the Russian–Ukrainian Conflict. Energies, 16(18), 6515–6515. https://doi.org/10.3390/en16186515
Bahman Huseynli. (2024). Gamification in Energy Consumption: A Model for Consumers’ Energy Saving. International Journal of Energy Economics and Policy, 14(1), 312–320. https://doi.org/10.32479/ijeep.14395
Bartiaux, F., Gram-Hanssen, K., Fonseca, P., Ozoliņa, L., & Christensen, T. H. (2014). A practice–theory approach to homeowners’ energy retrofits in four European areas. Building Research & Information, 42(4), 525–538. https://doi.org/10.1080/09613218.2014.900253
Bennagi, A., AlHousrya, O., Cotfas, D. T., & Cotfas, P. A. (2024). Comprehensive study of the artificial intelligence applied in renewable energy. Energy Strategy Reviews, 54, 101446. https://doi.org/10.1016/j.esr.2024.101446
Bouzarovski, S., & Tirado Herrero, S. (2016). The energy divide: Integrating energy transitions, regional inequalities and poverty trends in the European Union. European Urban and Regional Studies, 24(1), 69–86. https://doi.org/10.1177/0969776415596449
Bozsik, N., Szeberényi, A., & Bozsik, N. (2024). Impact of Climate Change on Electric Energy Production from Medium-Size Photovoltaic Module Systems Based on RCP Climate Scenarios. Energies, 17(16), 4009. https://doi.org/10.3390/en17164009
Chang, S., & Nam, K. (2021). Smart Home Adoption: The Impact of User Characteristics and Differences in Perception of Benefits. Buildings, 11(9), 393. https://doi.org/10.3390/buildings11090393
Davis, F. D. (1989): Perceived usefullness, perceived ease of use, and user acceptance ofinformation technology. MIS Quarterly, 13, 3, 319–340. o.
European Commission. (2023, January 26). A European approach to Artificial intelligence | Shaping Europe’s digital future. Digital-Strategy.ec.europa.eu; European Commission. https://digital-strategy.ec.europa.eu/en/policies/european-approach-artificial-intelligence
Gadenne, D., Sharma, B., Kerr, D., & Smith, T. (2011). The influence of consumers’ environmental beliefs and attitudes on energy saving behaviours. Energy Policy, 39(12), 7684–7694. December 2011. Energy Policy 39 (12):7684-7694. DOI:10.1016/j.enpol.2011.09.002
Gielen, D., Boshell, F., Saygin, D., Bazilian, M. D., Wagner, N., & Gorini, R. (2019). The role of renewable energy in the global energy transformation. Energy Strategy Reviews, 24, 38–50. https://doi.org/10.1016/j.esr.2019.01.006
Gorina, L., Korneeva, E., Kovaleva, O., & Wadim Strielkowski. (2024). Energy‐saving technologies and energy efficiency in the post‐COVID era. Sustainable Development. https://doi.org/10.1002/sd.2978
Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572–2593. Wiley. https://doi.org/10.1111/bjet.12864
Gróf, G., Sárvári, B., & Várgedő, B. (2024). Az energiahatékonyság szerepe a jelzáloghitelek csődvalószínűségében és a tőkekövetelmények meghatározásában. Közgazdasági Szemle, 71(6), 653–670. https://doi.org/10.18414/ksz.2024.6.653 (hungarian)
Haring, T., Ahmadiahangar, R., Rosin, A., Korotko, T., & Biechl, H. (2020). Accuracy Analysis of Selected Time Series and Machine Learning Methods for Smart Cities based on Estonian Electricity Consumption Forecast. 2020 IEEE 14th International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG). https://doi.org/10.1109/cpe-powereng48600.2020.9161690
Henryk Dzwigol, Aleksy Kwilinski, Oleksii Lyulyov, & Tetyana Pimonenko. (2024). Digitalization and Energy in Attaining Sustainable Development: Impact on Energy Consumption, Energy Structure, and Energy Intensity. Energies, 17(5), 1213–1213. https://doi.org/10.3390/en17051213
Holczinger, N., & Sárvári, B. (2025). A zöld átmenet szerepe a Visegrádi országokban a KAYA-azonosság alapján. Észak-Magyarországi Stratégiai Füzetek, 22(01), 5–16. https://doi.org/10.32976/stratfuz.2025.1 (hungarian)
Iorgovan, D. (2024). Artificial intelligence and renewable energy utilization. Proceedings of the . . . International Conference on Business Excellence, 18(1), 2776–2783. https://doi.org/10.2478/picbe-2024-0231
Káposzta, J., & Honvári, P. (2019). A smart falu koncepciójának főbb összefüggései és kapcsolódása a hazai vidékgazdaság fejlesztési stratégiájához. Tér és Társadalom, 33(1), 83–97. https://doi.org/10.17649/TET.33.1.3091 (hungarian)
Káposzta, J., & Szeiner, Zs. (2023). Társadalmi egyenlőtlenségek hatása a területi különbségek kialakulására. Comitatus Önkormányzati Szemle, 33(246), 93–102. https://doi.org/10.59809/comitatus.2023.33-246.93 (hungarian)
Keszey, T., & Zsukk, J. (2017). Az új technológiák fogyasztói elfogadása. A magyar és nemzetközi szakirodalom áttekintése és kritikai értékelése. Vezetéstudomány / Budapest Management Review, 48(10), 38–47. https://doi.org/10.14267/veztud.2017.10.05 (hungarian)
Kolozsi, P. P., Ladányi, S., & Straubinger, A. (2022). Measuring the Climate Risk Exposure of Financial Assets : Methodological Challenges and Central Bank Practices. Financial and Economic Review, 21(1), 113–140. https://doi.org/10.33893/fer.21.1.113
Kolny, B. (2022). Young Consumers Towards Smart Homes. Marketing of Scientific and Research Organizations, 44(2), 105–125. https://doi.org/10.2478/minib-2022-0011
Kozma, D. E., Molnárné Barna, K., & Molnár, T. (2021). Rangsoroljunk vagy nem? A körforgásos gazdaság mérési lehetőségei és azok összehasonlítása az EU-tagországokban. Vezetéstudomány – Budapest Management Review, 52(8-9), 63–77. https://doi.org/10.14267/veztud.2021.09.05 (hungarian)
László Radácsi, & Szigeti, C. (2024). The illusion of the Holy Grail of decoupling: Are there countries with relatively high SDGI and moderately low ecological footprint? Environmental and Sustainability Indicators, 22, 100379–100379. https://doi.org/10.1016/j.indic.2024.100379
Li, L., & Yuan, X. (2024). The influence of energy-saving information in online reviews on green home appliance purchase behavior based on machine learning. Energy and Buildings, 314, 114296. https://doi.org/10.1016/j.enbuild.2024.114296
Li, X., Li, S., Cao, J., & Spulbar, A. C. (2025). Does artificial intelligence improve energy efficiency? Evidence from provincial data in China. Energy Economics, 142, 108149. https://doi.org/10.1016/j.eneco.2024.108149
Milev, I., Prislan, L., & Zajc, M. (2022). Energy Portal Design and Evaluation for Consumer Active Participation in Energy Services: Seven-Month Field Study with 234 Slovenian Households. Electronics, 11(21), 3452. https://doi.org/10.3390/electronics11213452
Mišík, M., Oravcová, V., & Vicenová, R. (2024). Energy efficiency of buildings in Central and Eastern Europe: room for improvement. Energy Efficiency, 17(4). https://doi.org/10.1007/s12053-024-10215-y
Nándor Bozsik, András Szeberényi, & Bozsik, N. (2024). Impact of Climate Change on the Performance of Household-Scale Photovoltaic Systems. HighTech and Innovation Journal, 5(1), 1–15. https://doi.org/10.28991/hij-2024-05-01-01
Nemeskéri, Z., Sebők, M., Zádori, I., & Szabó, B. (2023). Az OVHR modell vizsgálata a magyar közműszolgáltatóknál. Kultúratudományi Szemle, 4(3-4), 135–151. https://doi.org/10.15170/ksz.2022.04.03-04.09 (hungarian)
Némediné Kollár, K. (2022). A hazai okos és autonóm falvak területi összefüggései. Studia Mundi – Economica, 9(1), 57–67. https://doi.org/10.18531/studia.mundi.2022.09.01.57-67 (hungarian)
Patterson, M. G. (1996). What is energy efficiency? Energy Policy, 24(5), 377–390. https://doi.org/10.1016/0301-4215(96)00017-1
Rozite, V., Miller, J., & Oh, S. (2023). Why AI and energy are the new power couple. IEA.
State of implementation of the oecd ai principles insights from national ai policies oecd digital economy papers. (2021). https://www.oecd.org/content/dam/oecd/en/publications/reports/2021/06/state-of-implementation-of-the-oecd-ai-principles_38a4a286/1cd40c44-en.pdf
Steg, L., & Vlek, C. (2009). Encouraging pro-environmental behaviour: An integrative review and research agenda. Journal of Environmental Psychology, 29(3), 309–317. https://doi.org/10.1016/j.jenvp.2008.10.004
Szakály, Z., Soós, M., & Berke, Sz. (2021). The level of energy consciousness among Hungarian consumers and its influencing factors. Hungarian Journal of Marketing and Management, 55(1), 3–15.
Szeberényi, A., Rokicki, T., & Papp-Váry, Á. (2022). Examining the Relationship between Renewable Energy and Environmental Awareness. Energies, 15(19), 7082. https://doi.org/10.3390/en15197082
Szennay, Á., Szigeti, C., Beke, J., & Radácsi, L. (2021). Ecological Footprint as an Indicator of Corporate Environmental Performance-Empirical Evidence from Hungarian SMEs. Sustainability, 13(2), 1000. https://doi.org/10.3390/su13021000
Ukoba, K., Olatunji, K. O., Adeoye, E., Jen, T.-C., & Madyira, D. M. (2024). Optimizing renewable energy systems through artificial intelligence: Review and future prospects. Energy & Environment, 35(7). https://doi.org/10.1177/0958305×241256293
Varga, J., & Csiszárik-Kocsir, Á. (2024). The Impact of Human Activity on Environmental Elements Based on the Results of a Primary Research. Acta Polytechnica Hungarica, 21(12), 147–168. https://doi.org/10.12700/aph.21.12.2024.12.9
Yussuf, R. O., & Asfour, O. S. (2024). Applications of artificial intelligence for energy efficiency throughout the building lifecycle: An overview. Energy and Buildings, 305, 113903. https://doi.org/10.1016/j.enbuild.2024.113903