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Short-term multi-hour ahead country-wide wind power prediction for Germany using gated recurrent unit deep learning
In recent years, wind power has emerged as an important source of renewable energy.
When onshore and offshore wind farm regions are connected to the grid for power …
When onshore and offshore wind farm regions are connected to the grid for power …
A review of critical challenges in MI-BCI: From conventional to deep learning methods
Brain-computer interfaces (BCIs) have achieved significant success in controlling external
devices through the Electroencephalogram (EEG) signal processing. BCI-based Motor …
devices through the Electroencephalogram (EEG) signal processing. BCI-based Motor …
HBO-LSTM: Optimized long short term memory with heap-based optimizer for wind power forecasting
The forecasting and estimation of wind power is a challenging problem in renewable energy
generation due to the high volatility of wind power resources, inevitable intermittency, and …
generation due to the high volatility of wind power resources, inevitable intermittency, and …
Wind power forecasting based on hybrid CEEMDAN-EWT deep learning method
A precise wind power forecast is required for the renewable energy platform to function
effectively. By having a precise wind power forecast, the power system can better manage its …
effectively. By having a precise wind power forecast, the power system can better manage its …
Boosted ANFIS model using augmented marine predator algorithm with mutation operators for wind power forecasting
There are several major available renewable energies, such as wind power which can be
considered one of the most potential energy resources. Thus, wind power is a vital green …
considered one of the most potential energy resources. Thus, wind power is a vital green …
[HTML][HTML] A review on deep learning models for forecasting time series data of solar irradiance and photovoltaic power
Presently, deep learning models are an alternative solution for predicting solar energy
because of their accuracy. The present study reviews deep learning models for handling …
because of their accuracy. The present study reviews deep learning models for handling …
A signal recovery method for bridge monitoring system using TVFEMD and encoder-decoder aided LSTM
J ** the
structural operation status. However, because of data missing and distortion induced by the …
structural operation status. However, because of data missing and distortion induced by the …
LSTM recurrent neural network classifier for high impedance fault detection in solar PV integrated power system
This paper presents the detection of High Impedance Fault (HIF) in solar Photovoltaic (PV)
integrated power system using recurrent neural network-based Long Short-Term Memory …
integrated power system using recurrent neural network-based Long Short-Term Memory …
A comprehensive review on deep learning approaches in wind forecasting applications
The effective use of wind energy is an essential part of the sustainable development of
human society, in particular, at the recent unprecedented pressure in sha** a low carbon …
human society, in particular, at the recent unprecedented pressure in sha** a low carbon …
Multi-sequence LSTM-RNN deep learning and metaheuristics for electric load forecasting
Short term electric load forecasting plays a crucial role for utility companies, as it allows for
the efficient operation and management of power grid networks, optimal balancing between …
the efficient operation and management of power grid networks, optimal balancing between …