A review of wind speed and wind power forecasting with deep neural networks

Y Wang, R Zou, F Liu, L Zhang, Q Liu - Applied energy, 2021 - Elsevier
The use of wind power, a pollution-free and renewable form of energy, to generate electricity
has attracted increasing attention. However, intermittent electricity generation resulting from …

Deep learning in smart grid technology: A review of recent advancements and future prospects

M Massaoudi, H Abu-Rub, SS Refaat, I Chihi… - IEEE …, 2021 - ieeexplore.ieee.org
The current electric power system witnesses a significant transition into Smart Grids (SG) as
a promising landscape for high grid reliability and efficient energy management. This …

Near real-time wind speed forecast model with bidirectional LSTM networks

LP Joseph, RC Deo, R Prasad, S Salcedo-Sanz… - Renewable Energy, 2023 - Elsevier
Wind is an important source of renewable energy, often used to provide clean electricity to
remote areas. For optimal extraction of this energy source, there is a need for an accurate …

Deep spatio-temporal graph network with self-optimization for air quality prediction

XB **, ZY Wang, JL Kong, YT Bai, TL Su, HJ Ma… - Entropy, 2023 - mdpi.com
The environment and development are major issues of general concern. After much
suffering from the harm of environmental pollution, human beings began to pay attention to …

Deep learning based optimal energy management for photovoltaic and battery energy storage integrated home micro-grid system

MM Alam, MH Rahman, MF Ahmed, MZ Chowdhury… - Scientific reports, 2022 - nature.com
The development of the advanced metering infrastructure (AMI) and the application of
artificial intelligence (AI) enable electrical systems to actively engage in smart grid systems …

Ultra-short-term interval prediction of wind power based on graph neural network and improved bootstrap technique

W Liao, S Wang, B Bak-Jensen, JR Pillai… - Journal of Modern …, 2023 - ieeexplore.ieee.org
Reliable and accurate ultra-short-term prediction of wind power is vital for the operation and
optimization of power systems. However, the volatility and intermittence of wind power pose …

An overview of deterministic and probabilistic forecasting methods of wind energy

Y **e, C Li, M Li, F Liu, M Taukenova - Iscience, 2023 - cell.com
In recent years, a variety of wind forecasting models have been developed, prompting
necessity to review the abundant methods to gain insights of the state-of-the-art …

An ensemble hybrid forecasting model for annual runoff based on sample entropy, secondary decomposition, and long short-term memory neural network

W Wang, Y Du, K Chau, D Xu, C Liu, Q Ma - Water Resources …, 2021 - Springer
Accurate and consistent annual runoff prediction in a region is a hot topic in management,
optimization, and monitoring of water resources. A novel prediction model (ESMD-SE-WPD …

Wind and wave energy prediction using an AT-BiLSTM model

D Song, M Yu, Z Wang, X Wang - Ocean Engineering, 2023 - Elsevier
Wind and wave energy have substantial potential as renewable sources of electricity. With
the development of various power-generating options, wind and wave energy are expected …

A wavelet-assisted deep learning approach for simulating groundwater levels affected by low-frequency variability

SKR Chidepudi, N Massei, A Jardani, A Henriot… - Science of the Total …, 2023 - Elsevier
Groundwater level (GWL) simulations allow the generation of reconstructions for exploring
the past temporal variability of groundwater resources or provide the means for generating …