| Issue |
Sci. Tech. Energ. Transition
Volume 79, 2024
Emerging Advances in Hybrid Renewable Energy Systems and Integration
|
|
|---|---|---|
| Article Number | 57 | |
| Number of page(s) | 10 | |
| DOI | https://doi.org/10.2516/stet/2024062 | |
| Published online | 26 August 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]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.