Hour-ahead wind power forecast based on random forests
Due to its chaotic nature, the wind behavior is difficult to forecast. Predicting wind power is a
real challenge for dispatchers who need to estimate renewable generation in advance to …
real challenge for dispatchers who need to estimate renewable generation in advance to …
A novel wavenets long short term memory paradigm for wind power prediction
Wind power prediction is essentially important for smooth integration of wind power into the
national grid pertained to its inherent fluctuations. To facilitate the wind energy production …
national grid pertained to its inherent fluctuations. To facilitate the wind energy production …
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 …
Wind speed and wind direction forecasting using echo state network with nonlinear functions
Wind turbines are among the most popular sources of renewable energy. The energy
available from wind varies widely because wind energy is highly dependent on continually …
available from wind varies widely because wind energy is highly dependent on continually …
A data-driven approach for estimating the power generation of invisible solar sites
H Shaker, H Zareipour, D Wood - IEEE Transactions on Smart …, 2015 - ieeexplore.ieee.org
Roof-top solar photovoltaic systems are normally invisible to system operators, meaning that
their generated power is not monitored. If a significant number of systems are installed …
their generated power is not monitored. If a significant number of systems are installed …
[PDF][PDF] A review of wind speed estimation for wind turbine systems based on Kalman filter technique
This paper presents a review of wind speed estimation based on Kalman filter technique
applied to wind turbine systems. Generally, wind speed measurement is performed by …
applied to wind turbine systems. Generally, wind speed measurement is performed by …
Electrical microgrid optimization via a new recurrent neural network
MEG Urias, EN Sanchez, LJ Ricalde - IEEE Systems Journal, 2014 - ieeexplore.ieee.org
This paper presents the development and implementation of a new recurrent neural network
for optimization as applied to optimal operation of an electrical microgrid, which is …
for optimization as applied to optimal operation of an electrical microgrid, which is …
Neural networks for power management optimal strategy in hybrid microgrid
This paper proposes a more reasonable objective function for combined economic emission
dispatch problem. To solve it, Lagrange programming neural network (LPNN) is utilized to …
dispatch problem. To solve it, Lagrange programming neural network (LPNN) is utilized to …
Wavelet neural network based multiobjective interval prediction for short-term wind speed
As a source of clean and pollution-free renewable energy, wind power has attracted much
attention and has been increasingly integrated into the existing power system. However, the …
attention and has been increasingly integrated into the existing power system. However, the …
Long-term groundwater-level forecasting in shallow and deep wells using wavelet neural networks trained by an improved harmony search algorithm
G Rakhshandehroo, H Akbari… - Journal of Hydrologic …, 2018 - ascelibrary.org
This study proposes a model using wavelet neural networks (WNNs) trained by a novel
improved harmony search (IHS) algorithm to forecast daily groundwater level (GWL) in a …
improved harmony search (IHS) algorithm to forecast daily groundwater level (GWL) in a …