Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Nailya Maitanova"'
Publikováno v:
Energy Science & Engineering, Vol 10, Iss 12, Pp 4496-4511 (2022)
Abstract The high share of power generation based on fluctuating renewable energy sources, especially wind and solar, has increased the levels of variability and uncertainty in power systems. The aim of this study is to develop a method for quantifyi
Externí odkaz:
https://doaj.org/article/73d7bcb1fc864d9da76071f84abc335b
Autor:
Nailya Maitanova, Jan-Simon Telle, Benedikt Hanke, Matthias Grottke, Thomas Schmidt, Karsten von Maydell, Carsten Agert
Publikováno v:
Energies, Vol 13, Iss 3, p 735 (2020)
A fully automated transferable predictive approach was developed to predict photovoltaic (PV) power output for a forecasting horizon of 24 h. The prediction of PV power output was made with the help of a long short-term memory machine learning algori
Externí odkaz:
https://doaj.org/article/16b3becb1e564149af80f9133da02217
Autor:
Benedikt Hanke, Matthias Grottke, Karsten von Maydell, Jan-Simon Telle, Thomas Steens, Nailya Maitanova
Publikováno v:
EVER
In order to successfully integrate renewable energy technologies, the requirements of local energy management systems are becoming increasingly complex, as is the sector integration of electricity, heat and transportation. To address this, this study
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::85f9b692ba58095bf231ba2eb8395a31
https://doi.org/10.1109/ever48776.2020.9243026
https://doi.org/10.1109/ever48776.2020.9243026
Autor:
Agert, Nailya Maitanova, Jan-Simon Telle, Benedikt Hanke, Matthias Grottke, Thomas Schmidt, Karsten von Maydell, Carsten
Publikováno v:
Energies; Volume 13; Issue 3; Pages: 735
A fully automated transferable predictive approach was developed to predict photovoltaic (PV) power output for a forecasting horizon of 24 h. The prediction of PV power output was made with the help of a long short-term memory machine learning algori
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=multidiscipl::c310c7ba0cbf68edb513416c6cb661db