A proposed intelligent short-term load forecasting hybrid models of ANN, WNN and KF based on clustering techniques for smart grid

HHH Aly - Electric power systems research, 2020 - Elsevier
Smart grid is one of the most important topics to be covered with the increasing penetration
of renewable energy in the power system grid to improve grid energy efficiency by managing …

A hybrid optimized model of adaptive neuro-fuzzy inference system, recurrent Kalman filter and neuro-wavelet for wind power forecasting driven by DFIG

HHH Aly - Energy, 2022 - Elsevier
Renewable energy resources are playing a compromising role in the new generation of
sustainable energy and smart grid. Wind power is playing a crucial role these days to …

A novel deep learning intelligent clustered hybrid models for wind speed and power forecasting

HHH Aly - Energy, 2020 - Elsevier
Wind energy is playing a compromising role in the new generation of sustainable energy
and promising to increase more. Forecasting of the fluctuated wind speed and its output …

An intelligent hybrid model of neuro Wavelet, time series and Recurrent Kalman Filter for wind speed forecasting

HHH Aly - Sustainable Energy Technologies and Assessments, 2020 - Elsevier
Wind speed Forecasting is the first step to integrate wind power into the main grid. It is
important to improve the accuracy of wind speed forecasting to improve the load …

A novel approach for harmonic tidal currents constitutions forecasting using hybrid intelligent models based on clustering methodologies

HHH Aly - Renewable energy, 2020 - Elsevier
Forecasting of renewable energy resources and their output power is playing a key role to
improve the grid energy efficiency by making some load generation management. Tidal …

Intelligent optimized deep learning hybrid models of neuro wavelet, Fourier Series and Recurrent Kalman Filter for tidal currents constitutions forecasting

HHH Aly - Ocean Engineering, 2020 - Elsevier
Harmonic tidal currents constitutions forecasting is the first step to integrate tidal power into
the main grid. It is important to improve the accuracy of tidal current forecasting models to …

Comparison of artificial neural network, linear regression and support vector machine for prediction of solar PV power

AM Kuriakose, DP Kariyalil, M Augusthy… - 2020 IEEE Pune …, 2020 - ieeexplore.ieee.org
Solar Photo voltaic (PV) system's usage is increasing day by day as a substitute of energy
considering about the environmental factors. But at the same time its performance have a …

Stability and optimisation of direct drive permanent magnet synchronous generator based tidal turbine

A Mohanty, M Viswavandya, PK Ray, TK Panigrahi… - Vacuum, 2019 - Elsevier
Magnetic materials have become more and more user friendly in recent days particularly in
an electronic based infrastructure. The study proposes a direct drive permanent magnet …

A Proposed Hybrid Machine Learning Model Based on Feature Selection Technique for Tidal Power Forecasting and Its Integration

HH Aly - Electronics, 2024 - mdpi.com
Renewable energy resources are playing a crucial role in minimizing fossil fuel emissions.
Integrating machine learning techniques with tidal power forecasting could greatly enhance …

A novel approach for seasonality and trend detection using fast fourier transform in box-jenkins algorithm

H Musbah, HH Aly, TA Little - 2020 IEEE Canadian Conference …, 2020 - ieeexplore.ieee.org
Forecasting is the first step to deal with the new generation of renewable energy systems.
The accuracy of the forecasting techniques is very important. Time series technique is one of …