Machine learning approaches to predict electricity production from renewable energy sources
Bearing in mind European Green Deal assumptions regarding a significant reduction of
green house emissions, electricity generation from Renewable Energy Sources (RES) is …
green house emissions, electricity generation from Renewable Energy Sources (RES) is …
A survey on advanced machine learning and deep learning techniques assisting in renewable energy generation
B Sri Revathi - Environ Sci Pollut Res, 2023 - Springer
The sustainability of the earth depends on renewable energy. Forecasting the output of
renewable energy has a big impact on how we operate and manage our power networks …
renewable energy has a big impact on how we operate and manage our power networks …
A research landscape bibliometric analysis on climate change for last decades: Evidence from applications of machine learning
Climate change (CC) is one of the greatest threats to human health, safety, and the
environment. Given its current and future impacts, numerous studies have employed …
environment. Given its current and future impacts, numerous studies have employed …
Hydropower production prediction using artificial neural networks: an Ecuadorian application case
J Barzola-Monteses, J Gomez-Romero… - Neural Computing and …, 2022 - Springer
Hydropower is among the most efficient technologies to produce renewable electrical
energy. Hydropower systems present multiple advantages since they provide sustainable …
energy. Hydropower systems present multiple advantages since they provide sustainable …
Machine learning solutions for renewable energy systems: Applications, challenges, limitations, and future directions
Abstract The Paris Agreement, a landmark international treaty signed in 2016 to limit global
warming to 2° C, has urged researchers to explore various strategies for achieving its …
warming to 2° C, has urged researchers to explore various strategies for achieving its …
Multiple-depth soil moisture estimates using artificial neural network and long short-term memory models
Accurate prediction of soil moisture is important yet challenging in various disciplines, such
as agricultural systems, hydrology studies, and ecosystems studies. However, many data …
as agricultural systems, hydrology studies, and ecosystems studies. However, many data …
[HTML][HTML] Recent advances and applications of machine learning in the variable renewable energy sector
Abstract Machine learning (ML) plays an essential role in various scientific fields. ML
streamlines renewable energy systems, boosting efficiency and production, as global …
streamlines renewable energy systems, boosting efficiency and production, as global …
Case study: Development of the CNN model considering teleconnection for spatial downscaling of precipitation in a climate change scenario
Global climate models (GCMs) are used to analyze future climate change. However, the
observed data of a specified region may differ significantly from the model since the GCM …
observed data of a specified region may differ significantly from the model since the GCM …
A systematic review of methods for investigating Climate Change impacts on Water-Energy-Food Nexus
Water, energy and food are important for human survival and sustainable development. With
climate change, investigating climate change impacts on Water-Energy-Food nexus has …
climate change, investigating climate change impacts on Water-Energy-Food nexus has …
Impact of climatic factors on the prediction of hydroelectric power generation: a deep CNN-SVR approach
M Özbay Karakuş - Geocarto International, 2023 - Taylor & Francis
This study, which aims to make predictions using a previously unused deep hybrid
Convolutional Neural Network-Support Vector Regression approach for hydropower …
Convolutional Neural Network-Support Vector Regression approach for hydropower …