[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 on the selected applications of forecasting models in renewable power systems
This paper presents a literature review on the selected applications of renewable resource
and power forecasting models to facilitate the optimal integration of renewable energy (RE) …
and power forecasting models to facilitate the optimal integration of renewable energy (RE) …
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 hybrid deep learning-based neural network for 24-h ahead wind power forecasting
Wind power generation is always associated with uncertainties as a result of fluctuations of
wind speed. Accurate predictions of wind power generation are important for the efficient …
wind speed. Accurate predictions of wind power generation are important for the efficient …
A wind speed correction method based on modified hidden Markov model for enhancing wind power forecast
Short-term wind power forecast (WPF) depends highly on the wind speed forecast (WSF),
which is the prime contributor to the forecasting error. To achieve more accurate WPF …
which is the prime contributor to the forecasting error. To achieve more accurate WPF …
Deep belief network based k-means cluster approach for short-term wind power forecasting
K Wang, X Qi, H Liu, J Song - Energy, 2018 - Elsevier
Wind energy is the intermittent energy and its output has great volatility. How to accurately
predict wind power output is a problem that many researchers have been paying attention to …
predict wind power output is a problem that many researchers have been paying attention to …
Transfer learning for short-term wind speed prediction with deep neural networks
Q Hu, R Zhang, Y Zhou - Renewable Energy, 2016 - Elsevier
As a type of clean and renewable energy source, wind power is widely used. However,
owing to the uncertainty of wind speed, it is essential to build an accurate forecasting model …
owing to the uncertainty of wind speed, it is essential to build an accurate forecasting model …
Learning temporal and spatial correlations jointly: A unified framework for wind speed prediction
Leveraging both temporal and spatial correlations to predict wind speed remains one of the
most challenging and less studied areas of wind speed prediction. In this paper, the problem …
most challenging and less studied areas of wind speed prediction. In this paper, the problem …
Forecasting wind speed using empirical mode decomposition and Elman neural network
J Wang, W Zhang, Y Li, J Wang, Z Dang - Applied soft computing, 2014 - Elsevier
Because of the chaotic nature and intrinsic complexity of wind speed, it is difficult to describe
the moving tendency of wind speed and accurately forecast it. In our study, a novel EMD …
the moving tendency of wind speed and accurately forecast it. In our study, a novel EMD …
Wind power forecast using wavelet neural network trained by improved Clonal selection algorithm
With the integration of wind farms into electric power grids, an accurate wind power
prediction is becoming increasingly important for the operation of these power plants. In this …
prediction is becoming increasingly important for the operation of these power plants. In this …