Zobrazeno 1 - 10
of 121
pro vyhledávání: '"Temporal Fusion Transformer"'
Autor:
Lorenz Kapral, Christoph Dibiasi, Natasa Jeremic, Stefan Bartos, Sybille Behrens, Aylin Bilir, Clemens Heitzinger, Oliver Kimberger
Publikováno v:
EClinicalMedicine, Vol 75, Iss , Pp 102797- (2024)
Summary: Background: During surgery, intraoperative hypotension is associated with postoperative morbidity and should therefore be avoided. Predicting the occurrence of hypotension in advance may allow timely interventions to prevent hypotension. Pre
Externí odkaz:
https://doaj.org/article/428d4f2c41c74c8fbbbd06df6905d4cf
Publikováno v:
IEEE Access, Vol 12, Pp 115895-115904 (2024)
This paper introduces the Multi-Dimensional Spatio-Temporal Fusion Transformer (MDSTFT), a state-of-the-art deep learning framework designed to enhance multi-variate time series forecasting. The MDSTFT diverges from traditional models by integrating
Externí odkaz:
https://doaj.org/article/ca154757873f4087a228ba02f0baaa16
Autor:
Sadman Sakib, Mahin K. Mahadi, Samiur R. Abir, Al-Muzadded Moon, Ahmad Shafiullah, Sanjida Ali, Fahim Faisal, Mirza M. Nishat
Publikováno v:
Heliyon, Vol 10, Iss 6, Pp e27795- (2024)
Bangladesh's subtropical climate with an abundance of sunlight throughout the greater portion of the year results in increased effectiveness of solar panels. Solar irradiance forecasting is an essential aspect of grid-connected photovoltaic systems t
Externí odkaz:
https://doaj.org/article/d9adb43744b341aba1d3300b393e3946
Publikováno v:
Results in Engineering, Vol 21, Iss , Pp 101776- (2024)
Forecasting fluid flow in subsurface resources such as groundwater, geothermal, and oil and gas is essential to maximize project economics and maximize resource recovery. We propose a novel workflow for a data-driven surrogate flow model for subsurfa
Externí odkaz:
https://doaj.org/article/b289826b48e342f194316250631c8a0c
Publikováno v:
Energies, Vol 17, Iss 13, p 3061 (2024)
Short-term load forecasting plays a crucial role in managing the energy consumption of buildings in cities. Accurate forecasting enables residents to reduce energy waste and facilitates timely decision-making for power companies’ energy management.
Externí odkaz:
https://doaj.org/article/1267422201d44c438dabded062ede5c2
Publikováno v:
Mechanical Engineering Journal, Vol 11, Iss 2, Pp 23-00465-23-00465 (2024)
Japanese geothermal power plants are expected to be significant renewable energy sources owing to the abundance of geothermal sources in Japan. The plant capacity factor in geothermal power plants is low. One reason is frequent sudden pressure drops
Externí odkaz:
https://doaj.org/article/123b873fe8d44bad8bb72c836588be9e
Publikováno v:
Frontiers in Nuclear Engineering, Vol 3 (2024)
Introduction: The accurate prognosis of reactor accidents is essential for deploying effective strategies that prevent radioactive releases. However, research in the nuclear sector is limited. This paper introduces a novel Temporal Fusion Transformer
Externí odkaz:
https://doaj.org/article/e2b42f83797d422686a81c3375a30482
Publikováno v:
Hydrology, Vol 11, Iss 3, p 41 (2024)
This study employs a temporal fusion transformer (TFT) for predicting overflow from sewer manholes during heavy rainfall events. The TFT utilised is capable of forecasting overflow hydrographs at the manhole level and was tested on a sewer network wi
Externí odkaz:
https://doaj.org/article/311b2720b4a34d6fbe4f67c0dd38374a
On Forecasting Cryptocurrency Prices: A Comparison of Machine Learning, Deep Learning, and Ensembles
Publikováno v:
Forecasting, Vol 5, Iss 1, Pp 196-209 (2023)
Traders and investors are interested in accurately predicting cryptocurrency prices to increase returns and minimize risk. However, due to their uncertainty, volatility, and dynamism, forecasting crypto prices is a challenging time series analysis ta
Externí odkaz:
https://doaj.org/article/19b39db0e1be4143835ac0dd0065100c
Publikováno v:
Energies, Vol 16, Iss 24, p 8105 (2023)
Solar power is a clean and sustainable energy source that does not emit greenhouse gases or other atmospheric pollutants. The inherent variability in solar energy due to random fluctuations introduces novel attributes to the power generation and load
Externí odkaz:
https://doaj.org/article/8a9d45313a454a0a958b2ed804b9396d