Enhancing energy consumption prediction in smart homes: a convergence-aware federated transfer learning approach | Science and Technology for Energy Transition (STET)

Emerging Advances in Hybrid Renewable Energy Systems and Integration

Open Access

Issue		Sci. Tech. Energ. Transition Volume 79, 2024 Emerging Advances in Hybrid Renewable Energy Systems and Integration


Article Number		85
Number of page(s)		14
DOI		https://doi.org/10.2516/stet/2024060
Published online		23 October 2024

Khan A.N., Iqbal N., Ahmad R., Kim D.H. (2021) Ensemble prediction approach based on learning to statistical model for efficient building energy consumption management, Symmetry 13, 3, 405. https://doi.org/10.3390/sym13030405. [CrossRef] [Google Scholar]
Khan A.N., Iqbal N., Rizwan A., Ahmad R., Kim D.H. (2021) An ensemble energy consumption forecasting model based on spatial-temporal clustering analysis in residential buildings, Energies 14, 11, 3020. https://doi.org/10.3390/en14113020. [CrossRef] [Google Scholar]
Notton G., Voyant C. (2018) Chapter 3 – Forecasting of intermittent solar energy resource, in: Yahyaoui I. (ed), Advances in renewable energies and power technologies, Elsevier, pp. 77–114. https://doi.org/10.1016/B978-0-12-812959-3.00003-4. [CrossRef] [Google Scholar]
Hurst W., Montañez C.A.C., Shone N. (2020) Time-pattern profiling from smart meter data to detect outliers in energy consumption, IoT 1, 92–108. https://doi.org/10.3390/iot1010006. [CrossRef] [Google Scholar]
Arif A., Javaid N., Anwar M., Naeem A., Gul H., Fareed S. (2020) Electricity load and price forecasting using machine learning algorithms in smart grid: a survey, in: Barolli L., Amato F., Moscato F., Enokido T., Takizawa M. (eds), Web, artificial intelligence and network applications, Springer International Publishing, Cham, pp. 471–483. https://doi.org/10.1007/978-3-030-44038-1_43. [CrossRef] [Google Scholar]
Taïk A., Cherkaoui S. (2020) Electrical load forecasting using edge computing and federated learning, in: ICC 2020 – 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 7–11 June, IEEE, pp. 1–6. https://doi.org/10.1109/ICC40277.2020.9148937. [Google Scholar]
Truong N., Sun K., Wang S., Guitton F., Guo Y. (2021) Privacy preservation in federated learning: An insightful survey from the GDPR perspective, Comput. Secur. 110, 102402. https://doi.org/10.1016/j.cose.2021.102402. [CrossRef] [Google Scholar]
Bonawitz K., Eichner H., Grieskamp W., Huba D., Ingerman A., Ivanov V., Kiddon C., Konečný J., Mazzocchi S., McMahan B. (2019) Towards federated learning at scale: System design, Proc. Mach. Learn. Syst. 1, 374–388. https://doi.org/10.48550/arXiv.1902.01046. [Google Scholar]
Yang Q., Liu Y., Chen T., Tong Y. (2019) Federated machine learning: Concept and applications, ACM Trans. Intell. Syst. Technol. 10, 2, 1–19. https://doi.org/10.48550/arXiv.1902.04885. [Google Scholar]
Ahmed K.M., Imteaj A., Amini M.H. (2021) Federated deep learning for heterogeneous edge computing, in: 2021 20th IEEE International Conference on Machine Learning and Applications (ICMLA), Pasadena, CA, USA, 13–16 December, IEEE, pp. 1146–1152. https://doi.org/10.1109/ICMLA52953.2021.00187. [Google Scholar]
Liu W., Chen L., Chen Y., Zhang W. (2020) Accelerating federated learning via momentum gradient descent, IEEE Trans. Parallel Distrib. Syst. 31, 8, 1754–1766. https://doi.org/10.48550/arXiv.1910.03197. [CrossRef] [Google Scholar]
Wu X., Liang Z., Wang J. (2020) Fedmed: a federated learning framework for language modeling, Sensors 20, 14, 4048. https://doi.org/10.3390/s20144048. [CrossRef] [PubMed] [Google Scholar]
Brisimi T.S., Chen R., Mela T., Olshevsky A., Paschalidis I.C., Shi W. (2018) Federated learning of predictive models from federated electronic health records, Int. J. Med. Inform. 112, 59–67. https://doi.org/10.1016/j.ijmedinf.2018.01.007. [CrossRef] [Google Scholar]
Pokhrel S.R., Choi J. (2020) Federated learning with blockchain for autonomous vehicles: analysis and design challenges, IEEE Trans. Commun. 68, 8, 4734–4746. https://doi.org/10.1109/TCOMM.2020.2990686. [CrossRef] [Google Scholar]
Wang Y., Tong Y., Shi D. (2020) Federated latent dirichlet allocation: a local differential privacy based framework, Proc. AAAI Conf. Artif. Intell. 34, 4, 6283–6290. https://doi.org/10.1609/aaai.v34i04.6096. [Google Scholar]
Li L., Fan Y., Tse M., Lin K.Y. (2020) A review of applications in federated learning, Comput. Ind. Eng. 149, 106854. https://doi.org/10.1016/j.cie.2020.106854. [CrossRef] [Google Scholar]
Leroy D., Coucke A., Lavril T., Gisselbrecht T., Dureau J. (2019) Federated learning for keyword spotting, in: ICASSP 2019–2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Brighton, UK, 12–17 May, IEEE, pp. 6341–6345. [Google Scholar]
Liu Y., Huang A., Luo Y., Huang H., Liu Y., Chen Y., Feng L., Chen T., Yu H., Yang Q. (2020) Fedvision: an online visual object detection platform powered by federated learning, Proc. AAAI Conf. Artif. Intell. 34, 8, 13172–13179. https://doi.org/10.48550/arXiv.2001.06202. [Google Scholar]
Chen Y., Qin X., Wang J., Yu C., Gao W. (2020) Fedhealth: a federated transfer learning framework for wearable healthcare, IEEE Intell. Syst. 35, 4, 83–93. https://doi.org/10.48550/arXiv.1907.09173. [CrossRef] [Google Scholar]
Briggs C., Fan Z., Andras P. (2020) Federated learning with hierarchical clustering of local updates to improve training on non-IID data, in: 2020 International Joint Conference on Neural Networks, IJCNN 2020, Glasgow, United Kingdom, 19–24 July, IEEE, pp. 1–9. https://doi.org/10.48550/arXiv.2004.11791. [Google Scholar]
Smith V., Chiang C.-K., Sanjabi M., Talwalkar A.S. (2017) Federated multi-task learning, in: Guyon I., Von Luxburg U., Bengio S., Wallach H., Fergus R., Vishwanathan S., R. Garnett (eds), Advances in neural information processing systems, vol. 30, Curran Associates, Inc. Available at https://proceedings.neurips.cc/paper_files/paper/2017/file/6211080fa89981f66b1a0c9d55c61d0f-Paper.pdf. [Google Scholar]
Zhao Y., Li M., Lai L., Suda N., Civin D., Chandra V. (2018) Federated learning with non-IID data. Preprint. https://doi.org/10.48550/arXiv.1806.00582. [CrossRef] [Google Scholar]
Mohri M., Sivek G., Suresh A.T. (2019) Agnostic federated learning, in: Chaudhuri K., Salakhutdinov R. (eds), Proceedings of the 36th International Conference on Machine Learning, Long Beach, CA, USA, 9–15 June, PMLR, pp. 4615–4625. https://doi.org/10.48550/arXiv.1902.00146. [Google Scholar]
Konečný J., McMahan B., Ramage D. (2015) Federated optimization: distributed optimization beyond the datacenter, Preprint. https://doi.org/10.48550/arXiv.1511.03575. [Google Scholar]
Yildiz B., Bilbao J.I., Dore J., Sproul A.B. (2017) Recent advances in the analysis of residential electricity consumption and applications of smart meter data, Appl. Energy 208, 402–427. https://doi.org/10.1016/j.apenergy.2017.10.014. [CrossRef] [Google Scholar]
Kaytez F., Taplamacioglu M.C., Cam E., Hardalac F. (2015) Forecasting electricity consumption: a comparison of regression analysis, neural networks and least squares support vector machines, Int. J. Electr. Power Energy Syst. 67, 431–438. https://doi.org/10.1016/j.ijepes.2014.12.036. [CrossRef] [Google Scholar]
Amasyali K., El-Gohary N.M. (2018) A review of data-driven building energy consumption prediction studies, Renew. Sustain. Energy Rev. 81, 1192–1205. https://doi.org/10.1016/j.rser.2017.04.095. [CrossRef] [Google Scholar]
Gajowniczek K., Ząbkowski T. (2017) Electricity forecasting on the individual household level enhanced based on activity patterns, PLoS One 12, 4, e0174098. https://doi.org/10.1371/journal.pone.0174098. [CrossRef] [PubMed] [Google Scholar]
Raza M.Q., Khosravi A. (2015) A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings, Renew. Sustain. Energy Rev. 50, 1352–1372. https://doi.org/10.1016/j.rser.2015.04.065. [CrossRef] [Google Scholar]
Rizwan A., Khan A.N., Ahmad R., Kim D.H. (2022) Optimal environment control mechanism based on ocf connectivity for efficient energy consumption in greenhouse, IEEE Internet Things J. 10, 6. https://doi.org/10.1109/JIOT.2022.3222086. [Google Scholar]
Mahia F., Dey A.R., Masud M.A., Mahmud M.S. (2019) Forecasting electricity consumption using ARIMA model, in: 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI), Dhaka, Bangladesh, 24–25 December, IEEE, pp. 1–6. https://doi.org/10.1109/STI47673.2019.9068076. [Google Scholar]
Li K., Hu C., Liu G., Xue W. (2015) Building’s electricity consumption prediction using optimized artificial neural networks and principal component analysis, Energy Build. 108, 106–113. https://doi.org/10.1016/j.enbuild.2015.09.002. [CrossRef] [Google Scholar]
Briggs C., Fan Z., Andras P. (2021) Federated learning for short-term residential energy demand forecasting, Preprint. https://doi.org/10.48550/arXiv.2105.13325. [Google Scholar]
Wei N., Li C., Peng X., Zeng F., Lu X. (2019) Conventional models and artificial intelligence-based models for energy consumption forecasting: a review, J. Pet. Sci. Eng. 181, 106187. https://doi.org/10.1016/j.petrol.2019.106187. [CrossRef] [Google Scholar]
Edwards R.E., New J., Parker L.E. (2012) Predicting future hourly residential electrical consumption: a machine learning case study, Energy Build. 49, 591–603. https://doi.org/10.1016/j.enbuild.2012.03.010. [CrossRef] [Google Scholar]
Mocanu E., Nguyen P.H., Gibescu M., Kling W.L. (2016) Deep learning for estimating building energy consumption, Sustain. Energy Grids Netw. 6, 91–99. https://doi.org/10.1016/j.segan.2016.02.005. [CrossRef] [Google Scholar]
Tun Y.L., Thar K., Thwal C.M., Hong C.S. (2021) Federated learning based energy demand prediction with clustered aggregation, in: 2021 IEEE International Conference on Big Data and Smart Computing (BigComp), Jeju Island, Korea (South), 17–20 January, IEEE, pp. 164–167. https://doi.org/10.1109/BigComp51126.2021.0003.9 [Google Scholar]
Fekri M.N., Patel H., Grolinger K., Sharma V. (2021) Deep learning for load forecasting with smart meter data: online adaptive recurrent neural network, Appl. Energy 282, 116177. https://doi.org/10.1016/j.apenergy.2020.116177. [CrossRef] [Google Scholar]
Sehovac L., Grolinger K. (2020) Deep learning for load forecasting: sequence to sequence recurrent neural networks with attention, IEEE Access 8, 36411–36426. https://doi.org/10.1109/ACCESS.2020.2975738. [CrossRef] [Google Scholar]
Tian Y., Sehovac L., Grolinger K. (2019) Similarity-based chained transfer learning for energy forecasting with Big Data, IEEE Access 7, 139895–139908. https://doi.org/10.1109/ACCESS.2019.2943752. [CrossRef] [Google Scholar]
Li J.B., Ren Y.Q., Fang S.W., Li K.C., Sun M.Y. (2020) Federated learning-based ultra-short term load forecasting in power internet of things, in: 2020 IEEE International Conference on Energy Internet (ICEI), Sydney, NSW, Australia, 24–28 August, IEEE, pp. 63–68. https://doi.org/10.1109/ICEI49372.2020.00020. [Google Scholar]
Saputra Y.M., Hoang D.T., Nguyen D.N., Dutkiewicz E., Mueck M.D., Srikanteswara S. (2019) Energy demand prediction with federated learning for electric vehicle networks, in: 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, 9–13 December, IEEE, pp. 1–6. https://doi.org/10.48550/arXiv.1909.00907. [Google Scholar]
Fekri M.N., Grolinger K., Mir S. (2022) Distributed load forecasting using smart meter data: federated learning with recurrent neural networks, Int. J. Electr. Power Energy Syst. 137, 107669. https://doi.org/10.1016/j.ijepes.2021.107669. [CrossRef] [Google Scholar]

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