Forecasting renewable energy generation with machine learning and deep learning: Current advances and future prospects
This article presents a review of current advances and prospects in the field of forecasting
renewable energy generation using machine learning (ML) and deep learning (DL) …
renewable energy generation using machine learning (ML) and deep learning (DL) …
A Review on Sustainable Energy Sources Using Machine Learning and Deep Learning Models
A Bhansali, N Narasimhulu, R Pérez de Prado… - Energies, 2023 - mdpi.com
Today, methodologies based on learning models are utilized to generate precise conversion
techniques for renewable sources. The methods based on Computational Intelligence (CI) …
techniques for renewable sources. The methods based on Computational Intelligence (CI) …
Metaheuristic-based hyperparameter tuning for recurrent deep learning: application to the prediction of solar energy generation
As solar energy generation has become more and more important for the economies of
numerous countries in the last couple of decades, it is highly important to build accurate …
numerous countries in the last couple of decades, it is highly important to build accurate …
[HTML][HTML] The usage of 10-fold cross-validation and grid search to enhance ML methods performance in solar farm power generation prediction
It is essential to have accurate projections of the quantity of solar energy that will be
generated in the future to improve the competitiveness of solar power plants in the energy …
generated in the future to improve the competitiveness of solar power plants in the energy …
A review of the applications of artificial intelligence in renewable energy systems: An approach-based study
Recent advancements in data science and artificial intelligence, as well as the development
of clean and sustainable energy sources, have created numerous opportunities for energy …
of clean and sustainable energy sources, have created numerous opportunities for energy …
NOA-LSTM: An efficient LSTM cell architecture for time series forecasting
The application of Machine learning and deep learning techniques for time series
forecasting has gained significant attention in recent years. Numerous endeavors have been …
forecasting has gained significant attention in recent years. Numerous endeavors have been …
Smart brain tumor diagnosis system utilizing deep convolutional neural networks
Y Anagun - Multimedia Tools and Applications, 2023 - Springer
The early diagnosis of cancer is crucial to provide prompt and adequate management of the
diseases. Imaging tests, in particular magnetic resonance imaging (MRI), are the first …
diseases. Imaging tests, in particular magnetic resonance imaging (MRI), are the first …
Machine learning-and artificial intelligence-derived prediction for home smart energy systems with PV installation and battery energy storage
Overview: Photovoltaic (PV) systems are widely used in residential applications in Poland
and Europe due to increasing environmental concerns and fossil fuel energy prices. Energy …
and Europe due to increasing environmental concerns and fossil fuel energy prices. Energy …
Short-term wind power forecasting based on VMD and a hybrid SSA-TCN-BiGRU network
Y Zhang, L Zhang, D Sun, K Jin, Y Gu - Applied Sciences, 2023 - mdpi.com
Wind power generation is a renewable energy source, and its power output is influenced by
multiple factors such as wind speed, direction, meteorological conditions, and the …
multiple factors such as wind speed, direction, meteorological conditions, and the …
A Solar and Wind Energy Evaluation Methodology Using Artificial Intelligence Technologies
V Simankov, P Buchatskiy, A Kazak, S Teploukhov… - Energies, 2024 - mdpi.com
The use of renewable energy sources is becoming increasingly widespread around the
world due to various factors, the most relevant of which is the high environmental …
world due to various factors, the most relevant of which is the high environmental …