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All issues Volume 616 (2025) E3S Web Conf., 616 (2025) 02027 References
Open Access
Issue
E3S Web Conf.
Volume 616, 2025
2nd International Conference on Renewable Energy, Green Computing and Sustainable Development (ICREGCSD 2025)
Article Number 02027
Number of page(s) 12
Section Green Computing
DOI https://doi.org/10.1051/e3sconf/202561602027
Published online 24 February 2025
  1. N. Kushwahal and A. A. Waoo, “Energy Consumption Prediction by Using Machine Learning,” E-ISSN: 2582-2160, 2024. [Google Scholar]
  2. N. I. Vatin and V. Manasa, “Predictive machine learning for optimal energy management in sustainable transportation systems,” MATEC Web of Conferences, vol. 392, no. 01169, 2024. [Google Scholar]
  3. D. K. Moulla, D. Attipoe, E. Mnkandla, and A. Abran, “Predictive Model of Energy Consumption Using Machine Learning: A Case Study of Residential Buildings in South Africa,” Sustainability, vol. 16, no. 11, p. 4365, 2024. [CrossRef] [Google Scholar]
  4. A. Javed, H. Larijani, and A. Wixted, “Improving Energy Consumption of a Commercial Building with IoT and Machine Learning,” IEEE Transactions on Industry Applications, vol. 20, no. 5, pp. 1–5, Sep.-Oct. 2018. [Google Scholar]
  5. D. Chakraborty and H. Elzarka, “Performance testing of energy models: Are we using the right statistical metrics,” Energy and Buildings, vol. 159, pp. 433–448, 2017. [Google Scholar]
  6. W. F., R. Ghazali, A. S. Shah, and M. U. Fayaz, “Prediction of Energy Consumption in Buildings Using Multi-Layer Perceptron,” Energy and Buildings, vol. 101, pp. 13–22, 2017. [Google Scholar]
  7. I. Priyadarshini, S. Sahu, R. Kumar, and D. Taniar, “A Machine-Learning Ensemble Model for Predicting Energy Consumption in Smart Homes,” Internet of Things, vol. 20, Article 100636, 2022. [CrossRef] [Google Scholar]
  8. R. Banik and P. Das, “Prediction of Electrical Energy Consumption Based on Machine Learning Technique,” Springer-Verlag GmbH Germany, vol. 103, pp. 909–920, 2021. [Google Scholar]
  9. D. Mhlanga, “Artificial Intelligence and Machine Learning for Energy Consumption and Production in Emerging Markets,” Energies, vol. 16, no. 2, p. 745, 2023. [CrossRef] [Google Scholar]
  10. J. A. Bohlmann and R. Inglesi-Lotz, “Analysing the South African Residential Sector’s Energy Profile,” Renewable and Sustainable Energy Reviews, vol. 96, pp. 240–252, 2018. [CrossRef] [Google Scholar]
  11. Y. Hu and J. Li, “Short-Term Electric Load Forecasting Model and Its Verification for Process Industrial Enterprises Based on Hybrid GA-PSO-BPNN Algorithm: A Case Study of Papermaking Process,” Energy and Buildings, vol. 170, pp. 1215–1227, 2019. [Google Scholar]
  12. A. D. Pham and N. T. Ngo, “Predicting Energy Consumption in Multiple Buildings Using Machine Learning for Improving Energy Efficiency and Sustainability,” Energy, vol. 260, Article 121082, 2020. [Google Scholar]
  13. I. Atik, “A New CNN-Based Method for Short-Term Forecasting of Electrical Energy Consumption in the COVID-19 Period: The Case of Turkey,” IEEE Access, vol. 10, pp. 1–70, 2022. [Google Scholar]
  14. S. Bourhnane and M. R. Abid, “Machine Learning for Energy Consumption Prediction and Scheduling in Smart Buildings,” Springer, vol. 2, Article 297, 2020. [Google Scholar]
  15. J. Q. Wang, Y. Du, and J. Wang, “LSTM-Based Long-Term Energy Consumption Prediction With Periodicity,” Energy, vol. 197, Article 121084, 2020. [Google Scholar]
  16. G. V. Reddy and L. J. Aitha, “Electricity Consumption Prediction Using Machine Learning,” E3S Web of Conferences, vol. 391, no. 01048, 2023. [Google Scholar]
  17. T. Y. Kim and S. B. Cho, “Predicting Residential Energy Consumption Using CNN- LSTM Neural Networks,” Energy and Buildings, vol. 182, pp. 72–81, Sep. 2019. [Google Scholar]
  18. A. W. Salam and A. El Hibaoui, “Comparison of Machine Learning Algorithms for the Power Consumption Prediction: Case Study of Tetouan City,” Energy and Buildings, vol. 191, pp. 35–47, 2020. [Google Scholar]
  19. T. Y. Kim and S. B. Cho, “Predicting the Household Power Consumption Using CNN- LSTM Hybrid Networks,” 19th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL), Madrid, Spain, 2018, pp. 481–490. [Google Scholar]
  20. A. Mosavi and S. Bahmani, “Energy Consumption Prediction Using Machine Learning: A Review,” Energy and Buildings, vol. 184, pp. 1–19, 2019. [CrossRef] [Google Scholar]
  21. Keating et al., “Energy Consumption Prediction by Using Machine Learning for Smart Buildings: Case Study in Malaysia,” Developments in the Built Environment, vol. 5, Article 100037, 2021. [Google Scholar]
  22. A. González-Briones, G. Hernández, and J. M. Corchado, “Machine Learning Models for Electricity Consumption Forecasting: A Review,” Energy, vol. 27, pp. 23631–23642, 2020. [Google Scholar]
  23. R. Bhardwaj, A. R. Nambiar, and D. Dutta, “A Study of Machine Learning in Healthcare,” Energy and Buildings, vol. 183, pp. 1–12, 2017. [Google Scholar]
  24. A. Raihan, “A Comprehensive Review of Artificial Intelligence and Machine Learning Applications in the Energy Sector,” Journal of Technology Innovations and Energy, vol. 2, no. 4, pp. 1–26, 2023. [CrossRef] [Google Scholar]
  25. M. Soori, B. Arezoo, and R. Dastres, “Artificial Intelligence, Machine Learning and Deep Learning in Advanced Robotics: A Review,” Robotics and Automation Systems, vol. 3, pp. 54–70, 2023. [Google Scholar]

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