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 …
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 …
Short-term offshore wind power forecasting-A hybrid model based on Discrete Wavelet Transform (DWT), Seasonal Autoregressive Integrated Moving Average …
Short-term time series wind power predictions are extremely essential for accurate and
efficient offshore wind energy evaluation and, in turn, benefit large wind farm operation and …
efficient offshore wind energy evaluation and, in turn, benefit large wind farm operation and …
Design engineering, synthesis protocols, and energy applications of MOF-derived electrocatalysts
The core reactions for fuel cells, rechargeable metal–air batteries, and hydrogen fuel
production are the oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and …
production are the oxygen reduction reaction (ORR), oxygen evolution reaction (OER), and …
Decomposition-based hybrid wind speed forecasting model using deep bidirectional LSTM networks
The goal of sustainable development can be attained by the efficient management of
renewable energy resources. Wind energy is attracting attention worldwide due to its …
renewable energy resources. Wind energy is attracting attention worldwide due to its …
A review on renewable energy and electricity requirement forecasting models for smart grid and buildings
The benefits of renewable energy are that it is sustainable and is low in environmental
pollution. Growing load requirement, global warming, and energy crisis need energy …
pollution. Growing load requirement, global warming, and energy crisis need energy …
A convolutional Transformer-based truncated Gaussian density network with data denoising for wind speed forecasting
Y Wang, H Xu, M Song, F Zhang, Y Li, S Zhou, L Zhang - Applied Energy, 2023 - Elsevier
Wind speed forecasting plays an important role in the stable operation of wind energy power
systems. However, accurate and reliable wind speed forecasting faces four challenges: how …
systems. However, accurate and reliable wind speed forecasting faces four challenges: how …
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 …
Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks
Recently, the boom in wind power industry has called for the accurate and stable wind
speed forecasting, on which reliable wind power generation systems depend heavily. Due to …
speed forecasting, on which reliable wind power generation systems depend heavily. Due to …
Evaluating the performance of various algorithms for wind energy optimization: a hybrid decision-making model
Wind resource is one of the most promising renewable energy, which has become a suitable
replacement for fossil fuels. Optimizing the transferring wind energy from a wind turbine is …
replacement for fossil fuels. Optimizing the transferring wind energy from a wind turbine is …