Optimization Method of Teaching Program under the Concept of Sustainable Environmental Development of Renewable Energy Based on Artificial Intelligence Internet

Authors

  • Bevl Naidu

    Department of Management Studies, Aditya Degree & PG Colleges, Andhra Pradesh 533001, India
  • Krishna Babu Sambaru

    Department of Digital Marketing, Aditya Degree & PG College, Kakinada, Andhra Pradesh 533001, India
  • Guru Prasad Pasumarthi

    Department of Research and Analytics, PB Siddhartha Arts and Science College,  Vijayawada, Andhra Pradesh 521108, India
  • Romala Vijaya Srinivas

    Department of Research and Analytics, Business School, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram  A.P 522302, India
  • K. Srinivasa Krishna

    Department of Research and Analytics, Business School, Koneru Lakshmaiah Education Foundation, Green Fields, Vaddeswaram  A.P 522302, India
  • V. Purna Kumari Pechetty

    Department of Research and Analytics, SR University, Anantha Sagar, Hasanparthy, Hanamkonnda 506371, India

DOI:

https://doi.org/10.30564/jees.v7i7.9015
Received: 8 March 2025 | Revised: 7 May 2025 | Accepted: 21 May 2025 | Published Online: 1 July 2025

Abstract

The increasing global demand for energy, coupled with concerns about environmental sustainability, has underscored the need for a transition toward renewable energy sources. A well-structured teaching program under the framework of sustainable development in renewable energy seeks to give students the information, abilities, and critical thinking needed to solve energy-related problems sustainably. This research proposes AI-powered personalized learning, innovative real-time integration of diverse data, and adaptive teaching strategies to enhance student understanding regarding renewable energy concepts. The sheep flock-optimized innovative recurrent neural network (SFO-IRNN) will recommend relevant topics and resources based on students' performance. Renewable energy teaching data from assessments are combined with real-time IoT-based renewable energy data. This dataset contains renewable energy education using AI-driven teaching methods and internet-based learning. The data was preprocessed by handling missing values and min-max scaling. The data features were extracted using Fourier Transform (FT). Further application of 10-fold cross-validation will increase the reliability of the model as it can evaluate its performance metrics like accuracy, F1-score, recall, and precision on different subsets of student data, which improves its robustness and prevents overfitting. The findings showed that the proposed method is significantly better, which ensures that the students have a deeper theoretical and practical understanding of renewable energy technologies. In addition, integrating real-time IoT data from renewable energy sources gives students a chance to do live simulations and problems that would enhance analytical thinking and hands-on learning. The research shows that AI provides context-aware guidance on sustainable energy infrastructure, enhancing interactive and personalized learning.

Keywords:

Teaching Program; Artificial Intelligence (AI); Sustainability; Sheep Flock Optimized Innovative Recurrent Neural Network (SFO-IRNN); Renewable Energy; Environmental

References

[1] Correa-Jullian, C., Cardemil, J.M., Droguett, E.L., et al., 2020. Assessment of deep learning techniques for prognosis of solar thermal systems. Renewable Energy. 145, 2178–2191. DOI: https://doi.org/10.1016/j.renene.2019.07.100

[2] De Las Heras, A., Luque-Sendra, A., Zamora-Polo, F., 2020. Machine learning technologies for sustainability in smart cities in the post-COVID era. Sustainability. 12(22), 9320. DOI: https://doi.org/10.3390/su12229320

[3] Chen, A., Zhang, X., Zhou, Z., 2020. Machine learning: accelerating materials development for energy storage and conversion. InfoMat. 2(3), 553–576. DOI: https://doi.org/10.1002/inf2.12094

[4] Magazzino, C., Mele, M., Morelli, G., 2021. The relationship between renewable energy and economic growth in a time of COVID-19: a machine learning experiment on the Brazilian economy. Sustainability. 13(3), 1285. DOI: https://doi.org/10.3390/su13031285

[5] Asadikia, A., Rajabifard, A., Kalantari, M., 2021. Systematic prioritization of SDGs: Machine learning approach. World Development. 140, 105269. DOI: https://doi.org/10.1016/j.worlddev.2020.105269

[6] Jabeur, S.B., Khalfaoui, R., Arfi, W.B., 2021. The effect of green energy, global environmental indexes, and stock markets in predicting oil price crashes: Evidence from explainable machine learning. Journal of Environmental Management. 298, 113511. DOI: https://doi.org/10.1016/j.jenvman.2021.113511

[7] Sharma, P., Said, Z., Kumar, A., et al., 2022. Recent advances in machine learning research for nanofluid-based heat transfer in renewable energy systems. Energy & Fuels. 36(13), 6626–6658. DOI: https://doi.org/10.1021/acs.energyfuels.2c01006

[8] Sanjeevikumar, P., Samavat, T., Nasab, M.A., et al., 2022. Machine learning-based hybrid demand-side controller for renewable energy management. In: Kumar, K., Rao, R.S., Kaiwartya, O., et al. (eds.). Sustainable developments by artificial intelligence and machine learning for renewable energies. Elsevier: Amsterdam, Netherlands. pp. 291–307. DOI: https://doi.org/10.1016/B978-0-323-91228-0.00003-3

[9] Liang, Y., Wang, H., Hong, W.C., 2021. Sustainable development evaluation of innovation and entrepreneurship education of clean energy major in colleges and universities based on SPA-VFS and GRNN optimized by chaos bat algorithm. Sustainability. 13(11), 5960. DOI: https://doi.org/10.3390/su13115960

[10] Rao, A., Talan, A., Abbas, S., et al., 2023. The role of natural resources in the management of environmental sustainability: Machine learning approach. Resources Policy. 82, 103548. DOI: https://doi.org/10.1016/j.resourpol.2023.103548

[11] Prayogi, S., Verawati, N.N.S.P., 2024. Physics learning technology for sustainable development goals (SDGs): a literature study. International Journal of Ethnoscience and Technology in Education. 1(2), 155–191. DOI: https://doi.org/10.33394/ijete.v1i2.12316

[12] Benti, N.E., Chaka, M.D., Semie, A.G., 2023. Forecasting renewable energy generation with machine learning and deep learning: Current advances and prospects. Sustainability. 15(9), 7087. DOI: https://doi.org/10.3390/su15097087

[13] Saheb, T., Dehghani, M., Saheb, T., 2022. Artificial intelligence for sustainable energy: A contextual topic modeling and content analysis. Sustainable Computing: Informatics and Systems. 35, 100699. DOI: https://doi.org/10.1016/j.suscom.2022.100699

[14] Aggarwal, D., 2023. Green Education: A Sustainable Development Initiative with the Power of Artificial Intelligence (AI). Journal of Image Processing and Intelligent Remote Sensing. 35, 39–44. DOI: https://doi.org/10.55529/jipirs.35.39.44

[15] Madabathula, C.T., Agrawal, K., Mehta, V., et al., 2025. Smart Green Energy Management for Campus: An Integrated Machine Learning and Reinforcement Learning Model. Smart Cities. 8(1), 30. DOI: https://doi.org/10.3390/smartcities8010030

[16] Garay-Rondero, C.L., Zadeh, S.I., 2024. Shaping a sustainable future at sea: an exploration in maritime education sustainability competencies. Proceedings of the 16th International Conference on Education and New Learning Technologies; 1-3 July, 2024; Palma, Spain. IATED: Valencia, Spain. 7481–7488. DOI: https://doi.org/10.21125/edulearn.2024.1758

[17] Liu, X.Q., Zhang, C., Zhou, Y., et al., 2021. Temperature change and electricity consumption of the group living: a case study of college students. Science of the Total Environment. 781, 146574. DOI: https://doi.org/10.1016/j.scitotenv.2021.146574

[18] Edsand, H.E., Broich, T., 2020. The impact of environmental education on environmental and renewable energy technology awareness: Empirical evidence from Colombia. International Journal of Science and Mathematics Education. 18(4), 611–634. DOI: https://doi.org/10.1007/s10763-019-09988-x

[19] Hwang, R.L., Liao, W.J., Chen, W.A., 2022. Optimization of energy use and academic performance for educational environments in hot-humid climates. Building and Environment. 222, 109434. DOI: https://doi.org/10.1016/j.buildenv.2022.109434

[20] Du, J., Pan, W., 2021. Examining energy saving behaviors in student dormitories using an expanded theory of planned behavior. Habitat International. 107, 102308. DOI: https://doi.org/10.1016/j.habitatint.2020.102308

[21] Toli, G., Kallery, M., 2021. Enhancing student interest to promote learning in science: The case of the concept of energy. Education Sciences. 11(5), 220. DOI: https://doi.org/10.3390/educsci11050220

[22] Ozkan, G., Umdu Topsakal, U., 2020. Investigating the effectiveness of STEAM education on students' conceptual understanding of force and energy topics. Research in Science & Technological Education. 39(4), 441–460. DOI: https://doi.org/10.1080/02635143.2020.1769586

[23] Szeberényi, A., Rokicki, T., Papp-Váry, Á., 2022. Examining the relationship between renewable energy and environmental awareness. Energies. 15(19), 7082. DOI: https://doi.org/10.3390/en15197082

[24] Lee, Y.F., Nguyen, H.B.N., Sung, H.T., 2022. Energy literacy of high school students in Vietnam and determinants of their energy-saving behavior. Environmental Education Research. 28(6), 907–924. DOI: https://doi.org/10.1080/13504622.2022.2034752

[25] Abdurrahman, A., Maulina, H., Nurulsari, N., et al., 2023. Impacts of integrating engineering design process into STEM makerspace on renewable energy unit to foster students' system thinking skills. Heliyon. 9(4), e15100. DOI: https://doi.org/10.1016/j.heliyon.2023.e15100

[26] Kaggle, 2023. Renewable energy teaching dataset. Available from: https://www.kaggle.com/datasets/programmer3/renewable-energy-teaching-dataset (cited 22 January 2025).

Downloads

How to Cite

Bevl Naidu, Krishna Babu Sambaru, Guru Prasad Pasumarthi, Romala Vijaya Srinivas, K. Srinivasa Krishna, & V. Purna Kumari Pechetty. (2025). Optimization Method of Teaching Program under the Concept of Sustainable Environmental Development of Renewable Energy Based on Artificial Intelligence Internet. Journal of Environmental & Earth Sciences, 7(7), 171–184. https://doi.org/10.30564/jees.v7i7.9015

Issue

Vol. 7 , Iss. 7 (July 2025): In Progress

Article Type

Article

License

Copyright © 2025 Bevl Naidu, Krishna Babu Sambaru, Guru Prasad Pasumarthi, Romala Vijaya Srinivas, K. Srinivasa Krishna, V. Purna Kumari Pechetty

Creative Commons License

This is an open access article under the Creative Commons Attribution-NonCommercial 4.0 International (CC BY-NC 4.0) License.

Crossref
Scopus
Google Scholar
Europe PMC