skip to main content
Language:
Search Limited to: Search Limited to: Resource type Show Results with: Show Results with: Search type Index

Results 1 - 20 of 445  for All Library Resources

Sorted by: Sorted by: RelevanceOr hit Enter to replace sort method
Results 1 2 3 4 5 next page
Refined by: Journal Title: Energies remove subject: Machine Learning remove
Result Number Material Type Add to My Shelf Action Record Details and Options
1
State of the Art of Machine Learning Models in Energy Systems, a Systematic Review
State of the Art of Machine Learning Models in Energy Systems, a Systematic Review
Material Type:
Article 		Add to My Research

State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

Energies (Basel), 2019-04, Vol.12 (7), p.1301 [Peer Reviewed Journal]

2019. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en12071301

Full text available

Citations Cited by
2
A Novel Wind Power Outlier Detection Method with Support Vector Machine Optimized by Improved Harris Hawk
A Novel Wind Power Outlier Detection Method with Support Vector Machine Optimized by Improved Harris Hawk
Material Type:
Article 		Add to My Research

A Novel Wind Power Outlier Detection Method with Support Vector Machine Optimized by Improved Harris Hawk

Energies (Basel), 2023-12, Vol.16 (24), p.7998 [Peer Reviewed Journal]

2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en16247998

Full text available

Citations Cited by
3
Thin Reservoir Identification Based on Logging Interpretation by Using the Support Vector Machine Method
Thin Reservoir Identification Based on Logging Interpretation by Using the Support Vector Machine Method
Material Type:
Article 		Add to My Research

Thin Reservoir Identification Based on Logging Interpretation by Using the Support Vector Machine Method

Energies (Basel), 2023-02, Vol.16 (4), p.1638 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en16041638

Full text available

Citations Cited by
4
A Data-Driven Predictive Prognostic Model for Lithium-Ion Batteries based on a Deep Learning Algorithm
A Data-Driven Predictive Prognostic Model for Lithium-Ion Batteries based on a Deep Learning Algorithm
Material Type:
Article 		Add to My Research

A Data-Driven Predictive Prognostic Model for Lithium-Ion Batteries based on a Deep Learning Algorithm

Energies (Basel), 2019-02, Vol.12 (4), p.660 [Peer Reviewed Journal]

2019. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;2019. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en12040660

Full text available

Citations Cited by
5
Forecasting Daily Electricity Consumption in Thailand Using Regression, Artificial Neural Network, Support Vector Machine, and Hybrid Models
Forecasting Daily Electricity Consumption in Thailand Using Regression, Artificial Neural Network, Support Vector Machine, and Hybrid Models
Material Type:
Article 		Add to My Research

Forecasting Daily Electricity Consumption in Thailand Using Regression, Artificial Neural Network, Support Vector Machine, and Hybrid Models

Energies (Basel), 2022-05, Vol.15 (9), p.3105 [Peer Reviewed Journal]

2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en15093105

Full text available

Citations Cited by
6
Enhanced Machine-Learning Techniques for Medium-Term and Short-Term Electric-Load Forecasting in Smart Grids
Enhanced Machine-Learning Techniques for Medium-Term and Short-Term Electric-Load Forecasting in Smart Grids
Material Type:
Article 		Add to My Research

Enhanced Machine-Learning Techniques for Medium-Term and Short-Term Electric-Load Forecasting in Smart Grids

Energies (Basel), 2023-01, Vol.16 (1), p.276 [Peer Reviewed Journal]

COPYRIGHT 2022 MDPI AG ;2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en16010276

Full text available

Citations Cited by
7
Research on Vertical SEC Centrifugal Pump Multi-Fault Diagnosis Based on WPT–SVM
Research on Vertical SEC Centrifugal Pump Multi-Fault Diagnosis Based on WPT–SVM
Material Type:
Article 		Add to My Research

Research on Vertical SEC Centrifugal Pump Multi-Fault Diagnosis Based on WPT–SVM

Energies (Basel), 2023-11, Vol.16 (22), p.7653 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en16227653

Full text available

Citations Cited by
8
An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand
An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand
Material Type:
Article 		Add to My Research

An Advanced Machine Learning Based Energy Management of Renewable Microgrids Considering Hybrid Electric Vehicles’ Charging Demand

Energies (Basel), 2021-02, Vol.14 (3), p.569 [Peer Reviewed Journal]

2021. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en14030569

Full text available

Citations Cited by
9
Building Energy Consumption Prediction: An Extreme Deep Learning Approach
Building Energy Consumption Prediction: An Extreme Deep Learning Approach
Material Type:
Article 		Add to My Research

Building Energy Consumption Prediction: An Extreme Deep Learning Approach

Energies (Basel), 2017-10, Vol.10 (10), p.1525 [Peer Reviewed Journal]

2017. This work is licensed under https://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en10101525

Full text available

Citations Cited by
10
A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments
A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments
Material Type:
Article 		Add to My Research

A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments

Energies (Basel), 2021-08, Vol.14 (16), p.5196 [Peer Reviewed Journal]

2021 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en14165196

Full text available

Citations Cited by
11
Machine Learning-Based Optimization of Synchronous Rectification Low-Inductance Current Secondary Boost Converter (SLIC-QBC)
Machine Learning-Based Optimization of Synchronous Rectification Low-Inductance Current Secondary Boost Converter (SLIC-QBC)
Material Type:
Article 		Add to My Research

Machine Learning-Based Optimization of Synchronous Rectification Low-Inductance Current Secondary Boost Converter (SLIC-QBC)

Energies (Basel), 2023-09, Vol.16 (18), p.6690 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en16186690

Full text available

Citations Cited by
12
Automated Classification of Pipeline Defects from Ultrasonic Phased Array Total Focusing Method Imaging
Automated Classification of Pipeline Defects from Ultrasonic Phased Array Total Focusing Method Imaging
Material Type:
Article 		Add to My Research

Automated Classification of Pipeline Defects from Ultrasonic Phased Array Total Focusing Method Imaging

Energies (Basel), 2022-11, Vol.15 (21), p.8272 [Peer Reviewed Journal]

COPYRIGHT 2022 MDPI AG ;2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en15218272

Full text available

Citations Cited by
13
Prediction of TOC Content in Organic-Rich Shale Using Machine Learning Algorithms: Comparative Study of Random Forest, Support Vector Machine, and XGBoost
Prediction of TOC Content in Organic-Rich Shale Using Machine Learning Algorithms: Comparative Study of Random Forest, Support Vector Machine, and XGBoost
Material Type:
Article 		Add to My Research

Prediction of TOC Content in Organic-Rich Shale Using Machine Learning Algorithms: Comparative Study of Random Forest, Support Vector Machine, and XGBoost

Energies (Basel), 2023-05, Vol.16 (10), p.4159 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en16104159

Full text available

Citations Cited by
14
Development of a Short-Term Electrical Load Forecasting in Disaggregated Levels Using a Hybrid Modified Fuzzy-ARTMAP Strategy
Development of a Short-Term Electrical Load Forecasting in Disaggregated Levels Using a Hybrid Modified Fuzzy-ARTMAP Strategy
Material Type:
Article 		Add to My Research

Development of a Short-Term Electrical Load Forecasting in Disaggregated Levels Using a Hybrid Modified Fuzzy-ARTMAP Strategy

Energies (Basel), 2023-05, Vol.16 (10), p.4110 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en16104110

Full text available

Citations Cited by
15
Knock-Prediction System for Kerosene Engines Using In-Cylinder Pressure Signal
Knock-Prediction System for Kerosene Engines Using In-Cylinder Pressure Signal
Material Type:
Article 		Add to My Research

Knock-Prediction System for Kerosene Engines Using In-Cylinder Pressure Signal

Energies (Basel), 2023-03, Vol.16 (6), p.2766 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en16062766

Full text available

Citations Cited by
16
Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study
Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study
Material Type:
Article 		Add to My Research

Efficient Wind Power Prediction Using Machine Learning Methods: A Comparative Study

Energies (Basel), 2022-04, Vol.15 (7), p.2327 [Peer Reviewed Journal]

2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en15072327

Full text available

Citations Cited by
17
Air Temperature Forecasting Using Machine Learning Techniques: A Review
Air Temperature Forecasting Using Machine Learning Techniques: A Review
Material Type:
Article 		Add to My Research

Air Temperature Forecasting Using Machine Learning Techniques: A Review

Energies (Basel), 2020-08, Vol.13 (16), p.4215 [Peer Reviewed Journal]

2020. This work is licensed under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en13164215

Full text available

Citations Cited by
18
Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms
Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms
Material Type:
Article 		Add to My Research

Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms

Energies (Basel), 2023-06, Vol.16 (11), p.4499 [Peer Reviewed Journal]

COPYRIGHT 2023 MDPI AG ;2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en16114499

Full text available

Citations Cited by
19
Statistical Safety Factor in Lightning Performance Analysis of Overhead Distribution Lines
Statistical Safety Factor in Lightning Performance Analysis of Overhead Distribution Lines
Material Type:
Article 		Add to My Research

Statistical Safety Factor in Lightning Performance Analysis of Overhead Distribution Lines

Energies (Basel), 2022-11, Vol.15 (21), p.8248 [Peer Reviewed Journal]

COPYRIGHT 2022 MDPI AG ;2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en15218248

Full text available

Citations Cited by
20
Machine Learning Methods in Damage Prediction of Masonry Development Exposed to the Industrial Environment of Mines
Machine Learning Methods in Damage Prediction of Masonry Development Exposed to the Industrial Environment of Mines
Material Type:
Article 		Add to My Research

Machine Learning Methods in Damage Prediction of Masonry Development Exposed to the Industrial Environment of Mines

Energies (Basel), 2022-06, Vol.15 (11), p.3958 [Peer Reviewed Journal]

2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en15113958

Full text available

Citations Cited by

Results 1 - 20 of 445  for All Library Resources

Sorted by: Sorted by: RelevanceOr hit Enter to replace sort method
Results 1 2 3 4 5 next page
INSPIRE LIBRARY - TON DUC THANG UNIVERSITY (84-028) 37 755 057 Feedback
19 Nguyen Huu Tho St. Dist.7, HCM thuvien@tdtu.edu.vn

Copyright © 2017 INSPIRE LIBRARY - TON DUC THANG UNIVERSITY

Searching Remote Databases, Please Wait