Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian …
M Sharifzadeh, A Sikinioti-Lock, N Shah - Renewable and Sustainable …, 2019 - Elsevier
Renewable energy from wind and solar resources can contribute significantly to the
decarbonisation of the conventionally fossil-driven electricity grid. However, their seamless …
decarbonisation of the conventionally fossil-driven electricity grid. However, their seamless …
Energy conversion strategies for wind energy system: Electrical, mechanical and material aspects
A Chaudhuri, R Datta, MP Kumar, JP Davim… - Materials, 2022 - mdpi.com
Currently, about 22% of global electricity is being supplemented by different renewable
sources. Wind energy is one of the most abundant forms of renewable energy available in …
sources. Wind energy is one of the most abundant forms of renewable energy available in …
Wind power forecasting based on daily wind speed data using machine learning algorithms
Wind energy is a significant and eligible source that has the potential for producing energy
in a continuous and sustainable manner among renewable energy sources. However, wind …
in a continuous and sustainable manner among renewable energy sources. However, wind …
Short-term wind speed forecasting over complex terrain using linear regression models and multivariable LSTM and NARX networks in the Andes Mountains, Ecuador
G López, P Arboleya - Renewable Energy, 2022 - Elsevier
Wind speed forecasting systems over complex terrain at high altitude are very complex and
conventional forecasting systems are unable to be applied due to wind variability. This study …
conventional forecasting systems are unable to be applied due to wind variability. This study …
Long-term wind power forecasting using tree-based learning algorithms
The intermittent and uncertain nature of wind places a premium on accurate wind power
forecasting for the reliable and efficient operation of power grids with large-scale wind power …
forecasting for the reliable and efficient operation of power grids with large-scale wind power …
New ridge regression, artificial neural networks and support vector machine for wind speed prediction
Y Zheng, Y Ge, S Muhsen, S Wang… - … in Engineering Software, 2023 - Elsevier
For wind energy conversion systems (WECS), forecasting wind speed is crucial for meeting
customer demands while monitoring, controlling, planning, and dispatching the electricity …
customer demands while monitoring, controlling, planning, and dispatching the electricity …
Deep learning-based multistep ahead wind speed and power generation forecasting using direct method
Long-term effective and accurate wind power potential prediction, especially for wind farms,
facilitates planning for the sustainable development of renewable energy. Accurate wind …
facilitates planning for the sustainable development of renewable energy. Accurate wind …
Models for short-term wind power forecasting based on improved artificial neural network using particle swarm optimization and genetic algorithms
As sources of conventional energy are alarmingly being depleted, leveraging renewable
energy sources, especially wind power, has been increasingly important in the electricity …
energy sources, especially wind power, has been increasingly important in the electricity …
Wind turbine data analysis and LSTM-based prediction in SCADA system
I Delgado, M Fahim - Energies, 2020 - mdpi.com
The number of wind farms is increasing every year because many countries are turning their
attention to renewable energy sources. Wind turbines are considered one of the best …
attention to renewable energy sources. Wind turbines are considered one of the best …
Review of estimating and predicting models of the wind energy amount
V Simankov, P Buchatskiy, S Teploukhov… - Energies, 2023 - mdpi.com
Obtaining wind energy for the production of electric energy plays a key role in overcoming
the problems associated with climate change and the dwindling reserves of traditional types …
the problems associated with climate change and the dwindling reserves of traditional types …