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[HTML][HTML] Distributed energy systems: A review of classification, technologies, applications, and policies
The sustainable energy transition taking place in the 21st century requires a major
revam** of the energy sector. Improvements are required not only in terms of the …
revam** of the energy sector. Improvements are required not only in terms of the …
A review of wind speed and wind power forecasting with deep neural networks
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 …
has attracted increasing attention. However, intermittent electricity generation resulting from …
Short-term multi-step wind power forecasting based on spatio-temporal correlations and transformer neural networks
Spatio-temporal wind power forecasting is significant to the stability of electric power
systems. However, the accuracy of power forecasting results is easily impaired by the …
systems. However, the accuracy of power forecasting results is easily impaired by the …
A review of deep learning for renewable energy forecasting
As renewable energy becomes increasingly popular in the global electric energy grid,
improving the accuracy of renewable energy forecasting is critical to power system planning …
improving the accuracy of renewable energy forecasting is critical to power system planning …
[HTML][HTML] A review and taxonomy of wind and solar energy forecasting methods based on deep learning
G Alkhayat, R Mehmood - Energy and AI, 2021 - Elsevier
Renewable energy is essential for planet sustainability. Renewable energy output
forecasting has a significant impact on making decisions related to operating and managing …
forecasting has a significant impact on making decisions related to operating and managing …
A hybrid attention-based deep learning approach for wind power prediction
Renewable energy, especially wind power, is a practicable and promising solution to
mitigate the existing dilemma associated with climate change. Efficient and accurate …
mitigate the existing dilemma associated with climate change. Efficient and accurate …
[HTML][HTML] A survey of machine learning models in renewable energy predictions
The use of renewable energy to reduce the effects of climate change and global warming
has become an increasing trend. In order to improve the prediction ability of renewable …
has become an increasing trend. In order to improve the prediction ability of renewable …
Probabilistic spatiotemporal wind speed forecasting based on a variational Bayesian deep learning model
Y Liu, H Qin, Z Zhang, S Pei, Z Jiang, Z Feng, J Zhou - Applied Energy, 2020 - Elsevier
Reliable and accurate probabilistic forecasting of wind speed is of vital importance for the
utilization of wind energy and operation of power systems. In this paper, a probabilistic …
utilization of wind energy and operation of power systems. In this paper, a probabilistic …
Electrical load forecasting: A deep learning approach based on K-nearest neighbors
Y Dong, X Ma, T Fu - Applied Soft Computing, 2021 - Elsevier
Deep learning approaches have shown superior advantages than shallow techniques in the
field of electrical load forecasting; however, their applications in existing studies encounter …
field of electrical load forecasting; however, their applications in existing studies encounter …
Degradation curve prediction of lithium-ion batteries based on knee point detection algorithm and convolutional neural network
Estimating the capacity degradation curve and the remaining useful life (RUL) of lithium-ion
batteries is of great importance for battery manufacturers and customers. Lithium iron …
batteries is of great importance for battery manufacturers and customers. Lithium iron …