Zobrazeno 1 - 10
of 617
pro vyhledávání: '"forecasting theory"'
Publikováno v:
IET Generation, Transmission & Distribution, Vol 18, Iss 20, Pp 3200-3220 (2024)
Abstract To address the challenge of insufficient comprehensive extraction and fusion of meteorological conditions, temporal features, and power periodic features in short‐term power prediction for distributed photovoltaic (PV) farms, a TPE‐CBiGR
Externí odkaz:
https://doaj.org/article/9e7bbb46c3604aaa8109fcee4ea419e8
Publikováno v:
Electronics Letters, Vol 60, Iss 16, Pp n/a-n/a (2024)
Abstract Because of the intermittency and randomness of wind power generation, constructing an accurate wind power generation forecasting model is of great necessity for stable operation and optimal scheduling of modern power systems. Considering the
Externí odkaz:
https://doaj.org/article/c347da070cb04ca380520590c2d75a01
Publikováno v:
IET Renewable Power Generation, Vol 18, Iss 6, Pp 1019-1039 (2024)
Abstract In response to the suboptimal efficiency observed in the network configuration and administration of 5G photovoltaic base stations (PVBSs), as well as the inherent limitations in accurately forecasting photovoltaic power during inclement wea
Externí odkaz:
https://doaj.org/article/f4812b6af5964fa2b334f90a1cafe04d
Publikováno v:
IET Generation, Transmission & Distribution, Vol 18, Iss 5, Pp 941-951 (2024)
Abstract In this study, a new short‐term wind power prediction model based on a temporal convolutional network (TCN) and the Informer model is proposed to solve the problem of low prediction accuracy caused by large wind speed fluctuations in short
Externí odkaz:
https://doaj.org/article/c764abc7fa3043cd8d6d012a6d7a6b7f
Publikováno v:
IET Renewable Power Generation, Vol 18, Iss 3, Pp 331-347 (2024)
Abstract With the increasing penetration of grid‐scale wind energy systems, accurate wind power forecasting is critical to optimizing their integration into the power system, ensuring operational reliability, and enabling efficient system asset uti
Externí odkaz:
https://doaj.org/article/1f08779d7b5f4233b610a85af0687a4a
Publikováno v:
IET Renewable Power Generation, Vol 18, Iss 3, Pp 348-360 (2024)
Abstract While wave energy is regarded as one of the prominent renewable energy resources to diversify global low‐carbon generation capacity, operational reliability is the main impediment to the wide deployment of the related technology. Current e
Externí odkaz:
https://doaj.org/article/582d0e13e7c74f06ad628e33c14ba5d5
Publikováno v:
IET Renewable Power Generation, Vol 18, Iss 2, Pp 238-260 (2024)
Abstract With the increasing installation of photovoltaic (PV) systems, the impact of their randomness and volatility on power system has become a significant concern. To effectively quantify the uncertainty of PV output, it is crucial to develop rel
Externí odkaz:
https://doaj.org/article/92565244021b4a569875a3b523fdde9a
Publikováno v:
IET Generation, Transmission & Distribution, Vol 18, Iss 2, Pp 337-352 (2024)
Abstract Accurately forecasting regional distributed photovoltaic (DPV) power is crucial in mitigating the negative impact of high DPV penetration on the reliability and resilience of the distribution network. However, most of the current photovoltai
Externí odkaz:
https://doaj.org/article/1dec20fc649d41ec98cba2793bd4c25f
Publikováno v:
IET Renewable Power Generation, Vol 17, Iss 15, Pp 3624-3637 (2023)
Abstract In order to further improve the accuracy of distributed photovoltaic (DPV) power prediction, this paper proposes a support vector machine (SVM) model based on hybrid competitive particle swarm optimization (HCPSO) with consideration of spati
Externí odkaz:
https://doaj.org/article/778a48f8efa3401d857b3fcd0a5bc546
Publikováno v:
Energy Reports, Vol 8, Iss , Pp 14643-14657 (2022)
Carbon dioxide emissions are the main cause of global warming. At present, how to reduce carbon dioxide emissions while promoting energy savings and emission reduction is a hot research topic. Hence, China’s carbon dioxide emissions must be reasona
Externí odkaz:
https://doaj.org/article/0749bd8ec3d742aaacdc9474c57f1d84