Battery degradation prediction against uncertain future conditions with recurrent neural network enabled deep learning
Accurate degradation trajectory and future life are the key information of a new generation of
intelligent battery and electrochemical energy storage systems. It is very challenging to …
intelligent battery and electrochemical energy storage systems. It is very challenging to …
Transformers in time-series analysis: A tutorial
Transformer architectures have widespread applications, particularly in Natural Language
Processing and Computer Vision. Recently, Transformers have been employed in various …
Processing and Computer Vision. Recently, Transformers have been employed in various …
A survey on deep learning tools dealing with data scarcity: definitions, challenges, solutions, tips, and applications
Data scarcity is a major challenge when training deep learning (DL) models. DL demands a
large amount of data to achieve exceptional performance. Unfortunately, many applications …
large amount of data to achieve exceptional performance. Unfortunately, many applications …
Streamflow prediction using an integrated methodology based on convolutional neural network and long short-term memory networks
Streamflow (Q flow) prediction is one of the essential steps for the reliable and robust water
resources planning and management. It is highly vital for hydropower operation, agricultural …
resources planning and management. It is highly vital for hydropower operation, agricultural …
Forecasting the dynamics of cumulative COVID-19 cases (confirmed, recovered and deaths) for top-16 countries using statistical machine learning models: Auto …
Most countries are reopening or considering lifting the stringent prevention policies such as
lockdowns, consequently, daily coronavirus disease (COVID-19) cases (confirmed …
lockdowns, consequently, daily coronavirus disease (COVID-19) cases (confirmed …
A hybrid VMD-LSTM/GRU model to predict non-stationary and irregular waves on the east coast of China
L Zhao, Z Li, L Qu, J Zhang, B Teng - Ocean Engineering, 2023 - Elsevier
Accurate wave forecasting is essential for the safety of port and offshore structure operations
and ship navigation. Computational fluid dynamics (CFD) and traditional time series models …
and ship navigation. Computational fluid dynamics (CFD) and traditional time series models …
Wind speed prediction of unmanned sailboat based on CNN and LSTM hybrid neural network
Z Shen, X Fan, L Zhang, H Yu - Ocean Engineering, 2022 - Elsevier
Wind speed is a key factor for unmanned sailboats, and accurate prediction of wind speed is
of great significance to the safety and performance of unmanned sailboats. In this study, a …
of great significance to the safety and performance of unmanned sailboats. In this study, a …
Air quality index forecast in Beijing based on CNN-LSTM multi-model
J Zhang, S Li - Chemosphere, 2022 - Elsevier
Accurate predicting the air quality trend can provide a theoretical basis for environmental
protection management and decision-making. This study proposed the convolutional neural …
protection management and decision-making. This study proposed the convolutional neural …
Carbon price forecasting system based on error correction and divide-conquer strategies
X Niu, J Wang, L Zhang - Applied Soft Computing, 2022 - Elsevier
Carbon price forecasting is an important component of a sound carbon price market
mechanism. The accurate prediction of carbon prices is an active topic of research …
mechanism. The accurate prediction of carbon prices is an active topic of research …
Short-term power load forecasting system based on rough set, information granule and multi-objective optimization
J Wang, K Wang, Z Li, H Lu, H Jiang - Applied Soft Computing, 2023 - Elsevier
Accurately forecasting power load is essential for utilities to effectively manage their
resources, reduce operational costs, and provide improved customer service. However, the …
resources, reduce operational costs, and provide improved customer service. However, the …