Numéro
Sci. Tech. Energ. Transition
Volume 79, 2024
Emerging Advances in Hybrid Renewable Energy Systems and Integration
Numéro d'article 57
Nombre de pages 10
DOI https://doi.org/10.2516/stet/2024062
Publié en ligne 26 août 2024
  • Keck F., Jütte Silke, Lenzen M., Li M. (2022) Assessment of two optimisation methods for renewable energy capacity expansion planning, Appl. Energy 306, Pt. A, 117988. [CrossRef] [Google Scholar]
  • López M., Sans C., Valero S. (2022) Automatic classification of special days for short-term load forecasting, Electr. Power Syst. Res. 202, 107533. [CrossRef] [Google Scholar]
  • Chu Z., Lakshminarayana S., Chaudhuri B., Teng F. (2023) Mitigating load-altering attacks against power grids using cyber-resilient economic dispatch, IEEE Trans. Smart Grid 14, 4, 3164–3175. [CrossRef] [Google Scholar]
  • Gorka J., Roald L. (2022) Efficient representations of radiality constraints in optimization of islanding and de-energization in distribution grids, Electr. Power Syst. Res. 213, 1–8. [Google Scholar]
  • Khosravi N., Echalih S., Baghbanzadeh R., Hekss Z., Hassani R., Messaoudi M. (2022) Enhancement of power quality issues for a hybrid AC/DC microgrid based on optimization methods, IET Renew. Power Gener. 16, 8, 1773–1791. [CrossRef] [Google Scholar]
  • Song X.H., Wag P., Niu D.X. (2023) Short-term power load forecasting based on heterogeneous data from multiple sources, Comput. Simul. 40, 9, 59–65. [Google Scholar]
  • Ahajjam M.A., Licea D.B., Ghogho M., Kobbane A., Yan J. (2022) Experimental investigation of variational mode decomposition and deep learning for short-term multi-horizon residential electric load forecasting, Appl. Energy 326, 492–506. [Google Scholar]
  • Evangelopoulos V.A., Georgilakis P.S. (2022) Probabilistic spatial load forecasting for assessing the impact of electric load growth in power distribution networks, Electr. Power Syst. Res. 207, 107847. [CrossRef] [Google Scholar]
  • Rafati A., Joorabian M., Mashhour E., Shaker H.R. (2022) Machine learning-based very short-term load forecasting in microgrid environment: evaluating the impact of high penetration of PV systems, Electr. Eng. 104, 4, 2667–2677. [CrossRef] [Google Scholar]
  • Kim J.H., Kim C.H., Lee B.S. (2023) A study on the development of long-term hybrid electrical load forecasting model based on MLP and statistics using massive actual data considering field applications, Electr. Power Syst. Res. 221, 109415. [CrossRef] [Google Scholar]
  • Ruíz-Guirola David E., Rodríguez-López Carlos A., Montejo-Sánchez Samuel, Souza R.D., López Onel L.A., Alves H. (2022) Energy-efficient wake-up signalling for machine-type devices based on traffic-aware long short-term memory prediction, IEEE Internet Things J. 9, 21, 21620–21631. [CrossRef] [Google Scholar]
  • Ahmad T., Zhang D. (2022) A data-driven deep sequence-to-sequence long-short memory method along with a gated recurrent neural network for wind power forecasting, Energy 239, Pt. B, 122109. [CrossRef] [Google Scholar]
  • Alizadeh Bidgoli M., Ahmadian A. (2022) Multi-stage optimal scheduling of multi-microgrids using deep- learning artificial neural network and cooperative game approach, Energy 239, Pt. B, 122036. [CrossRef] [Google Scholar]
  • Moutevelis D., Roldan-Perez J., Prodanovic M., Sanchez-Acevedo S. (2022) Bifurcation analysis of active electrical distribution networks considering load tap changers and power converter capacity limits, IEEE Trans. Power Electr. 37, 6, 7230–7246. [CrossRef] [Google Scholar]
  • Dumas J., Wehenkel A., Lanaspeze D., Cornélusse Bertrand, Sutera A. (2022) A deep generative model for probabilistic energy forecasting in power systems: normalizing flows, Appl. Energy 305, 117871. [CrossRef] [Google Scholar]
  • Vaishya S.R., Abhyankar A.R., Kumar P. (2023) A novel loss sensitivity based linearized OPF and LMP calculations for active balanced distribution networks, IEEE Syst. J. 17, 1, 1340–1351. [CrossRef] [Google Scholar]
  • Azeroual M., Boujoudar Y., Aljarbouh A., Moussaoui H.E., Markhi H.E. (2022) A multi-agent-based for fault location in distribution networks with wind power generator, Wind Eng. 46, 3, 700–711. [CrossRef] [Google Scholar]
  • Al-Qaness M.A.A., Ewees A.A., Fan H., Abualigah L., Elaziz M.A. (2022) Boosted ANFIS model using augmented marine predator algorithm with mutation operators for wind power forecasting, Appl. Energy 314, 118851. [CrossRef] [Google Scholar]
  • Bendaoud N.M.M., Farah N., Ahmed S.B. (2022) Applying load profiles propagation to machine learning based electrical energy forecasting, Electr. Power Syst. Res. 203, 107635. [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.