Zobrazeno 1 - 10
of 46
pro vyhledávání: '"Chia Sheng Tu"'
Publikováno v:
Applied Sciences, Vol 14, Iss 18, p 8229 (2024)
This paper combines self-organizing mapping (SOM) and a long short-term memory network (SOM-LSTM) to construct an audio-based motor-fault diagnosis system for identifying the operating states of a rotary motor. This paper first uses an audio signal c
Externí odkaz:
https://doaj.org/article/a6305d62275d490285a84e5280c1b6fd
Publikováno v:
Energies, Vol 16, Iss 14, p 5436 (2023)
Accurately predicting the power produced during solar power generation can greatly reduce the impact of the randomness and volatility of power generation on the stability of the power grid system, which is beneficial for its balanced operation and op
Externí odkaz:
https://doaj.org/article/8639f0d8b7fc4fdfb82c40e838e3846f
Publikováno v:
Energies, Vol 16, Iss 1, p 156 (2022)
This paper mainly discusses the demand bidding and risk management of user aggregators by considering profit and risk. The covariance matrix of demand price was used to analyze the risk model under an uncertain demand price. By considering revenue an
Externí odkaz:
https://doaj.org/article/77161faedf654b55a62b425210a227df
Short-Term Solar Power Forecasting via General Regression Neural Network with Grey Wolf Optimization
Publikováno v:
Energies, Vol 15, Iss 18, p 6624 (2022)
With the increasing awareness of environmental protection and the support of national policy, as well as the maturing of solar power generation technology, solar power generation has become one of the most promising renewable energies. However, due t
Externí odkaz:
https://doaj.org/article/2572a1fdd9a943bb889be60d54e00ce0
The prediction of wind power output is part of the basic work of power grid dispatching and energy distribution. At present, the output power prediction is mainly obtained by fitting and regressing the historical data. The medium- and long-term power
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3be97a17a4fc83036c413dfc34305e69
https://doi.org/10.20944/preprints202304.0917.v1
https://doi.org/10.20944/preprints202304.0917.v1
Publikováno v:
Energies, Vol 13, Iss 23, p 6319 (2020)
This paper presents a short-term wind power forecasting model for the next day based on historical marine weather and corresponding wind power output data. Due the large amount of historical marine weather and wind power data, we divided the data int
Externí odkaz:
https://doaj.org/article/b885c0e28e7c4f94acda69d2a90be6c6
Publikováno v:
Energies, Vol 13, Iss 21, p 5852 (2020)
This paper presents a phase arrangement procedure for distribution transformers to improve system unbalance and voltage profile of distribution systems, while considering the location and uncertainties of the wind turbine (WT) and photovoltaics (PV).
Externí odkaz:
https://doaj.org/article/416fd5515c78499dbee0b55f9aa6f73d
Autor:
Chia-Sheng Tu, Ming-Tang Tsai
Publikováno v:
Energies, Vol 13, Iss 3, p 545 (2020)
This paper presents an efficient strategy for transformer planning to reduce the system losses by means of transformer rearrangement. The customer connected to the distribution transformer are first investigated by the field survey, and the loads of
Externí odkaz:
https://doaj.org/article/b19326b08ef14a4cbbbf2c20b24fd71e
Publikováno v:
Energies, Vol 8, Iss 9, Pp 10504-10521 (2015)
This paper proposes a hybrid algorithm to solve the optimal energy dispatch of an ice storage air-conditioning system. Based on a real air-conditioning system, the data, including the return temperature of chilled water, the supply temperature of chi
Externí odkaz:
https://doaj.org/article/4cea87c487274882b9a5134ce2769cae
Publikováno v:
Processes; Volume 10; Issue 11; Pages: 2321
Traditional economic dispatch methods, which are used to minimize fuel costs, have become inadequate because they do not consider the environmental impact of emissions in the optimization process. By taking into account the horizon year load and carb
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5d054591c951e1073a6ebe5d99f7f929