Zobrazeno 1 - 10
of 1 311
pro vyhledávání: '"Load forecast"'
Autor:
Youzhong Wu, Lei Tang, Zhitian Liu, Xia Li, Qingqing Pang, Mingfang Yang, Yanyan Gong, Mengran Zhou, Feng Hu, Guangyao Zhou
Publikováno v:
Engineering Reports, Vol 6, Iss 12, Pp n/a-n/a (2024)
Abstract In smart grid and smart building environments, accurate forecasting of load demand in residential buildings is of critical importance. This helps to enhance the stability of the power system, facilitate the integration of distributed renewab
Externí odkaz:
https://doaj.org/article/a38c2d5950544bd4a71f08189d338575
Publikováno v:
IEEE Access, Vol 12, Pp 186856-186871 (2024)
The rationale for using enhanced Deep Neural Networks (DNNs) in the power distribution system for short-term load forecasting (STLF) originates from a thorough analysis of current trends, the emergence of the state-of-the-art use cases and approaches
Externí odkaz:
https://doaj.org/article/52315e5e555b422b8de6bf2758c5856a
Publikováno v:
Energy Reports, Vol 11, Iss , Pp 637-650 (2024)
It is known that load forecasting plays an important role for the smart grids and mostly, the users have the combinational load forecast demands with different time-scales. However, due to various internal or external factors, such as poor management
Externí odkaz:
https://doaj.org/article/501af0b8d304494c9277a0f851bdc28e
Publikováno v:
南方能源建设, Vol 11, Iss 1, Pp 143-156 (2024)
[Introduction] Accurate load forecasting underpins the operation optimization of the electricity systems and is an indispensable aspect of energy management within such systems. Given the low accuracy and high computational complexity inherent in tra
Externí odkaz:
https://doaj.org/article/d6188baa10b24126adbb1a3a30295bfc
Publikováno v:
Energies, Vol 17, Iss 20, p 5183 (2024)
This paper presents a novel methodology for short-term load forecasting in the context of significant shifts in the daily load curve due to the rapid and extensive adoption of Distributed Energy Resources (DERs). The proposed solution, built upon the
Externí odkaz:
https://doaj.org/article/be442e90d6d641658170aa02037c15bd
Autor:
K. Naveena, Murugaperumal Krishnamoorthy, N. Karuppiah, Pramod Kumar Gouda, Shanmugasundaram Hariharan, K. Saravanan, Ajay Kumar
Publikováno v:
Measurement: Sensors, Vol 33, Iss , Pp 101192- (2024)
The global energy landscape is rapidly shifting toward cleaner, lower-carbon electricity generation, necessitating a transition to alternate energy sources. Hydrogen, particularly green hydrogen, looks to be a significant solution for facilitating th
Externí odkaz:
https://doaj.org/article/824ccb75e5bb4639b5bc88e14ac40d85
Publikováno v:
Energy Reports, Vol 9, Iss , Pp 971-981 (2023)
Industrial customers consume a large part of the total electricity demand. In the operation of industrial energy systems, accurate prediction of electric loads is a prerequisite to help industrial users adjust their electric load dispatch and improve
Externí odkaz:
https://doaj.org/article/4986cf70535b464d9c310b1bba219c0f
Publikováno v:
Energy Reports, Vol 9, Iss , Pp 1-5 (2023)
Power system dispatch (PSD) greatly depends on load forecast (LF) with high accuracy. However, since load curve evolving along time axis is affected by long-term trendy and short-term stochastics electricity consumption modes, it is not easy to forec
Externí odkaz:
https://doaj.org/article/bf8e99fbdbdd4d14a18258aa6da5b70b
Publikováno v:
Energy Reports, Vol 9, Iss , Pp 388-395 (2023)
High penetration of renewable energy generation and intensive application of new-type load control measures in power system greatly increase the difficulty of load forecast (LF). Long-term dependencies in load series limit LF accuracy improvement of
Externí odkaz:
https://doaj.org/article/3ea2d18b01b0476aaa83dc50fbf0b93c
Autor:
Kun Yu
Publikováno v:
Energies, Vol 17, Iss 15, p 3709 (2024)
Special load customers such as electric vehicles are emerging in modern power systems. They lead to a higher penetration of special load patterns, raising difficulty for short-term load forecasting (STLF). We propose a hierarchical STLF framework to
Externí odkaz:
https://doaj.org/article/f841ff0bce1d40efba2ea3832789652b