Impact of Environmental Conditions on Renewable Energy Prediction: An Investigation Through Tree-Based Community Learning
The real-time prediction of energy production is essential for effective energy management and planning. Forecasts are essential in various areas, including the efficient utilization of energy resources, the provision of energy flexibility services, decision-making amidst uncertainty, the balancing...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Applied Sciences |
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| Online Access: | https://www.mdpi.com/2076-3417/15/1/336 |
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| author | Ferdi Doğan Saadin Oyucu Derya Betul Unsal Ahmet Aksöz Majid Vafaeipour |
| author_facet | Ferdi Doğan Saadin Oyucu Derya Betul Unsal Ahmet Aksöz Majid Vafaeipour |
| author_sort | Ferdi Doğan |
| collection | DOAJ |
| description | The real-time prediction of energy production is essential for effective energy management and planning. Forecasts are essential in various areas, including the efficient utilization of energy resources, the provision of energy flexibility services, decision-making amidst uncertainty, the balancing of supply and demand, and the optimization of online energy systems. This study examines the use of tree-based ensemble learning models for renewable energy production prediction, focusing on environmental factors such as temperature, pressure, and humidity. The study’s primary contribution lies in demonstrating the effectiveness of the bagged trees model in reducing overfitting and achieving higher accuracy compared to other models, while maintaining computational efficiency. The results indicate that less sophisticated models are inadequate for accurately representing complex datasets. The results evaluate the effectiveness of machine learning methods in delivering valuable insights for energy sectors managing environmental conditions and predicting renewable energy sources |
| format | Article |
| id | doaj-art-a60f9423f5fa48f4beb10651be6cdd2e |
| institution | Kabale University |
| issn | 2076-3417 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Applied Sciences |
| spelling | doaj-art-a60f9423f5fa48f4beb10651be6cdd2e2025-01-10T13:15:12ZengMDPI AGApplied Sciences2076-34172025-01-0115133610.3390/app15010336Impact of Environmental Conditions on Renewable Energy Prediction: An Investigation Through Tree-Based Community LearningFerdi Doğan0Saadin Oyucu1Derya Betul Unsal2Ahmet Aksöz3Majid Vafaeipour4Department of Computer Engineering, Adıyaman University, Adiyaman 02040, TürkiyeDepartment of Computer Engineering, Adıyaman University, Adiyaman 02040, TürkiyeDepartment of Electrical and Electronics Engineering, Cumhuriyet University, Sivas 58140, TürkiyeMOBILERS Research Team, Sivas Cumhuriyet University, Sivas 58580, TürkiyeElectric Vehicle and Energy Research Group (EVERGI), Mobility, Logistics and Automotive Technology Research Centre (MOBI), Department of Electrical Engineering and Energy Technology, Vrije Universiteit Brussel, 1050 Brussels, BelgiumThe real-time prediction of energy production is essential for effective energy management and planning. Forecasts are essential in various areas, including the efficient utilization of energy resources, the provision of energy flexibility services, decision-making amidst uncertainty, the balancing of supply and demand, and the optimization of online energy systems. This study examines the use of tree-based ensemble learning models for renewable energy production prediction, focusing on environmental factors such as temperature, pressure, and humidity. The study’s primary contribution lies in demonstrating the effectiveness of the bagged trees model in reducing overfitting and achieving higher accuracy compared to other models, while maintaining computational efficiency. The results indicate that less sophisticated models are inadequate for accurately representing complex datasets. The results evaluate the effectiveness of machine learning methods in delivering valuable insights for energy sectors managing environmental conditions and predicting renewable energy sourceshttps://www.mdpi.com/2076-3417/15/1/336environmental factorsrenewable energy sourcesmachine learningELdecision tree |
| spellingShingle | Ferdi Doğan Saadin Oyucu Derya Betul Unsal Ahmet Aksöz Majid Vafaeipour Impact of Environmental Conditions on Renewable Energy Prediction: An Investigation Through Tree-Based Community Learning Applied Sciences environmental factors renewable energy sources machine learning EL decision tree |
| title | Impact of Environmental Conditions on Renewable Energy Prediction: An Investigation Through Tree-Based Community Learning |
| title_full | Impact of Environmental Conditions on Renewable Energy Prediction: An Investigation Through Tree-Based Community Learning |
| title_fullStr | Impact of Environmental Conditions on Renewable Energy Prediction: An Investigation Through Tree-Based Community Learning |
| title_full_unstemmed | Impact of Environmental Conditions on Renewable Energy Prediction: An Investigation Through Tree-Based Community Learning |
| title_short | Impact of Environmental Conditions on Renewable Energy Prediction: An Investigation Through Tree-Based Community Learning |
| title_sort | impact of environmental conditions on renewable energy prediction an investigation through tree based community learning |
| topic | environmental factors renewable energy sources machine learning EL decision tree |
| url | https://www.mdpi.com/2076-3417/15/1/336 |
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