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1
A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA
A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA
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A New Hybrid Prediction Method of Ultra-Short-Term Wind Power Forecasting Based on EEMD-PE and LSSVM Optimized by the GSA

Energies (Basel), 2018-04, Vol.11 (4), p.697 [Peer Reviewed Journal]

Copyright MDPI AG 2018 ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en11040697

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2
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
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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

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3
Building Energy Consumption Prediction: An Extreme Deep Learning Approach
Building Energy Consumption Prediction: An Extreme Deep Learning Approach
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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

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4
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
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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

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5
A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction
A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction
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A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction

Energies (Basel), 2019-01, Vol.12 (2), p.254 [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/en12020254

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6
Air Temperature Forecasting Using Machine Learning Techniques: A Review
Air Temperature Forecasting Using Machine Learning Techniques: A Review
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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

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7
Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting
Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting
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Big Data Mining of Energy Time Series for Behavioral Analytics and Energy Consumption Forecasting

Energies (Basel), 2018-02, Vol.11 (2), p.452 [Peer Reviewed Journal]

Copyright MDPI AG 2018 ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en11020452

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8
Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms
Forecasting Electricity Demand in Turkey Using Optimization and Machine Learning Algorithms
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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

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9
Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques
Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques
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Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques

Energies (Basel), 2019-04, Vol.12 (9), p.1621 [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/en12091621

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10
Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder
Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder
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Electric Energy Consumption Prediction by Deep Learning with State Explainable Autoencoder

Energies (Basel), 2019-02, Vol.12 (4), p.739 [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/en12040739

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11
Short-Term PV Power Forecasting Using a Regression-Based Ensemble Method
Short-Term PV Power Forecasting Using a Regression-Based Ensemble Method
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Short-Term PV Power Forecasting Using a Regression-Based Ensemble Method

Energies (Basel), 2022-06, Vol.15 (11), p.4171 [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/en15114171

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12
A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power
A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power
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A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power

Energies (Basel), 2020-12, Vol.13 (24), p.6623 [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/en13246623

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13
A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model
A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model
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A Novel Deep Learning Approach for Wind Power Forecasting Based on WD-LSTM Model

Energies (Basel), 2020-09, Vol.13 (18), p.4964 [Peer Reviewed Journal]

2020 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 (http://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/en13184964

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14
Deep Learning Models for Long-Term Solar Radiation Forecasting Considering Microgrid Installation: A Comparative Study
Deep Learning Models for Long-Term Solar Radiation Forecasting Considering Microgrid Installation: A Comparative Study
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Deep Learning Models for Long-Term Solar Radiation Forecasting Considering Microgrid Installation: A Comparative Study

Energies (Basel), 2020-01, Vol.13 (1), p.147 [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/en13010147

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15
Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information
Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information
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Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information

Energies (Basel), 2019-01, Vol.12 (2), p.215 [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/en12020215

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16
Day-Ahead Solar Irradiance Forecasting for Microgrids Using a Long Short-Term Memory Recurrent Neural Network: A Deep Learning Approach
Day-Ahead Solar Irradiance Forecasting for Microgrids Using a Long Short-Term Memory Recurrent Neural Network: A Deep Learning Approach
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Day-Ahead Solar Irradiance Forecasting for Microgrids Using a Long Short-Term Memory Recurrent Neural Network: A Deep Learning Approach

Energies (Basel), 2019-05, Vol.12 (10), p.1856 [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/en12101856

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17
Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting
Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting
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Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting

Energies (Basel), 2019-03, Vol.12 (6), p.1093 [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. ;ISSN: 1996-1073 ;EISSN: 1996-1073 ;DOI: 10.3390/en12061093

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18
A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption
A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption
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A Review of the Data-Driven Prediction Method of Vehicle Fuel Consumption

Energies (Basel), 2023-07, Vol.16 (14), p.5258 [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/en16145258

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19
Detection of Non-Technical Losses on a Smart Distribution Grid Based on Artificial Intelligence Models
Detection of Non-Technical Losses on a Smart Distribution Grid Based on Artificial Intelligence Models
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Detection of Non-Technical Losses on a Smart Distribution Grid Based on Artificial Intelligence Models

Energies (Basel), 2024-04, Vol.17 (7), p.1729 [Peer Reviewed Journal]

COPYRIGHT 2024 MDPI AG ;2024 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/en17071729

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20
A Novel Electricity Theft Detection Strategy Based on Dual-Time Feature Fusion and Deep Learning Methods
A Novel Electricity Theft Detection Strategy Based on Dual-Time Feature Fusion and Deep Learning Methods
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A Novel Electricity Theft Detection Strategy Based on Dual-Time Feature Fusion and Deep Learning Methods

Energies (Basel), 2024-01, Vol.17 (2), p.275 [Peer Reviewed Journal]

COPYRIGHT 2024 MDPI AG ;2024 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/en17020275

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