A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems
The optimal co-planning of the integrated energy system (IES) and machine learning (ML)
application on the multivariable prediction of IES parameters have mostly been carried out …
application on the multivariable prediction of IES parameters have mostly been carried out …
Optimal design, operational controls, and data-driven machine learning in sustainable borehole heat exchanger coupled heat pumps: Key implementation challenges …
The integration of technologies has made it possible to develop optimal operating conditions
at reduced costs, which results in a more sustainable energy transition away from fossil fuels …
at reduced costs, which results in a more sustainable energy transition away from fossil fuels …
Large-scale integration of renewable energies by 2050 through demand prediction with ANFIS, Ecuador case study
The growing reliance on hydroelectric power and the risk of future droughts pose significant
challenges for power systems, especially in developing countries. To address these …
challenges for power systems, especially in developing countries. To address these …
Spatial–temporal prediction of minerals dissolution and precipitation using deep learning techniques: An implication to Geological Carbon Sequestration
Abstract In Geological Carbon Sequestration (GCS), mineralization is a secure carbon
dioxide (CO 2) trapping mechanism to prevent possible leakage at a later stage of the GCS …
dioxide (CO 2) trapping mechanism to prevent possible leakage at a later stage of the GCS …
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 …
Assessing the impact of hydropower projects in Brazil through data envelopment analysis and machine learning
M Bortoluzzi, M Furlan, JF dos Reis Neto - Renewable Energy, 2022 - Elsevier
The aim of this study was to assess the environmental impact of hydroelectric power
generation projects and classify them according to their scale of environmental impact. To …
generation projects and classify them according to their scale of environmental impact. To …
Predicting hydropower production using deep learning CNN-ANN hybridized with gaussian process regression and salp algorithm
M Ehtearm, H Ghayoumi Zadeh, A Seifi… - Water Resources …, 2023 - Springer
The hydropower industry is one of the most important sources of clean energy. Predicting
hydropower production is essential for the hydropower industry. This study introduces a …
hydropower production is essential for the hydropower industry. This study introduces a …
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 …
[HTML][HTML] A taxonomy of earth observation data for sustainable finance
S Rapach, A Riccardi, B Liu, J Bowden - Journal of Climate Finance, 2024 - Elsevier
Abstract Corporate Environmental, Social and Governance (ESG) reporting has been
subject to heightened attention and demand within the financial sector, with the objective of …
subject to heightened attention and demand within the financial sector, with the objective of …
Machine Learning Applications for Renewable-Based Energy Systems
G Graditi, A Buonanno, M Caliano, M Di Somma… - Advances in Artificial …, 2023 - Springer
Abstract Machine learning is becoming a fundamental tool in current energy systems. It
helps to obtain accurate predictions of the variable renewable energy (VRE) generation …
helps to obtain accurate predictions of the variable renewable energy (VRE) generation …