A review on deep learning models for forecasting time series data of solar irradiance and photovoltaic power
RA Rajagukguk, RAA Ramadhan, HJ Lee - Energies, 2020 - mdpi.com
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 …
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 …
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 …
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 …
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 shaping a low carbon …
human society, in particular, at the recent unprecedented pressure in shaping a low carbon …
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 …
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 hybrid LSTM neural network for energy consumption forecasting of individual households
Irregular human behaviors and univariate datasets remain as two main obstacles of data-
driven energy consumption predictions for individual households. In this study, a hybrid …
driven energy consumption predictions for individual households. In this study, a hybrid …
A signal recovery method for bridge monitoring system using TVFEMD and encoder-decoder aided LSTM
J Xin, C Zhou, Y Jiang, Q Tang, X Yang, J Zhou - Measurement, 2023 - Elsevier
Accurate monitoring data in bridge health monitoring systems are critical for grasping 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 …
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 …