Numéro |
Sci. Tech. Energ. Transition
Volume 79, 2024
Emerging Advances in Hybrid Renewable Energy Systems and Integration
|
|
---|---|---|
Numéro d'article | 85 | |
Nombre de pages | 14 | |
DOI | https://doi.org/10.2516/stet/2024060 | |
Publié en ligne | 23 octobre 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]
Les statistiques affichées correspondent au cumul d'une part des vues des résumés de l'article et d'autre part des vues et téléchargements de l'article plein-texte (PDF, Full-HTML, ePub... selon les formats disponibles) sur la platefome Vision4Press.
Les statistiques sont disponibles avec un délai de 48 à 96 heures et sont mises à jour quotidiennement en semaine.
Le chargement des statistiques peut être long.