Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis
Accurate cooling consumption forecasts are crucial for optimizing energy management, storage, and overall efficiency in interconnected HVAC systems. Weather conditions, building characteristics, and operational parameters significantly impact prediction accuracy. Since meteorological conditions high...
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| Language: | English |
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2024-12-01
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| Series: | Emerging Science Journal |
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| Online Access: | https://ijournalse.org/index.php/ESJ/article/view/2563 |
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| author | Fernando Pedro Silva Almeida Mauro Castelli Nadine Côrte-Real |
| author_facet | Fernando Pedro Silva Almeida Mauro Castelli Nadine Côrte-Real |
| author_sort | Fernando Pedro Silva Almeida |
| collection | DOAJ |
| description | Accurate cooling consumption forecasts are crucial for optimizing energy management, storage, and overall efficiency in interconnected HVAC systems. Weather conditions, building characteristics, and operational parameters significantly impact prediction accuracy. Since meteorological conditions highly influence cooling demand, leveraging external air data and user metrics offers a promising approach to estimate a building's hourly cooling energy usage. This study addresses the gap in existing research by comprehensively analyzing the performance of various machine learning algorithms, including ensemble learning and deep learning models, to improve prediction accuracy. By leveraging weather conditions, building characteristics, and operational parameters, we aim to predict cooling consumption across multiple systems (Cooling Ceiling, Ventilation, Free Cooling, and Total Cooling). Data from four weather stations, encompassing diverse features relevant to the European Central Bank (ECB) building's cooling consumption in Frankfurt, were employed. Our methodology includes the use of K-Nearest Neighbor, Decision Tree, Support Vector Regression, Linear Regression, Random Forest, Gradient Boosting, XGBoost, Adaboost, Long-Short-Term Memory, and Gated Recurrent Unit. Models. The results consistently demonstrate the superiority of the Random Forest model across different weather stations and feature sets. This model achieved a Mean Squared Error of approximately 0.002-0.003, Mean Absolute Error of around 0.031-0.034, and Root Mean Squared Error of about 0.052-0.069. These findings contribute to improved building cooling load management, promoting insights into optimal energy utilization and sustainable building practices.
Doi: 10.28991/ESJ-2024-08-06-01
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| format | Article |
| id | doaj-art-091220bf848345dcaa8f6b60aefde7a7 |
| institution | Kabale University |
| issn | 2610-9182 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Ital Publication |
| record_format | Article |
| series | Emerging Science Journal |
| spelling | doaj-art-091220bf848345dcaa8f6b60aefde7a72024-12-07T14:11:58ZengItal PublicationEmerging Science Journal2610-91822024-12-01862120214310.28991/ESJ-2024-08-06-01732Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative AnalysisFernando Pedro Silva Almeida0Mauro Castelli1Nadine Côrte-Real2NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide. 1070-312, Lisboa,NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide. 1070-312, Lisboa,NOVA Information Management School (NOVA IMS), Universidade NOVA de Lisboa, Campus de Campolide. 1070-312, Lisboa,Accurate cooling consumption forecasts are crucial for optimizing energy management, storage, and overall efficiency in interconnected HVAC systems. Weather conditions, building characteristics, and operational parameters significantly impact prediction accuracy. Since meteorological conditions highly influence cooling demand, leveraging external air data and user metrics offers a promising approach to estimate a building's hourly cooling energy usage. This study addresses the gap in existing research by comprehensively analyzing the performance of various machine learning algorithms, including ensemble learning and deep learning models, to improve prediction accuracy. By leveraging weather conditions, building characteristics, and operational parameters, we aim to predict cooling consumption across multiple systems (Cooling Ceiling, Ventilation, Free Cooling, and Total Cooling). Data from four weather stations, encompassing diverse features relevant to the European Central Bank (ECB) building's cooling consumption in Frankfurt, were employed. Our methodology includes the use of K-Nearest Neighbor, Decision Tree, Support Vector Regression, Linear Regression, Random Forest, Gradient Boosting, XGBoost, Adaboost, Long-Short-Term Memory, and Gated Recurrent Unit. Models. The results consistently demonstrate the superiority of the Random Forest model across different weather stations and feature sets. This model achieved a Mean Squared Error of approximately 0.002-0.003, Mean Absolute Error of around 0.031-0.034, and Root Mean Squared Error of about 0.052-0.069. These findings contribute to improved building cooling load management, promoting insights into optimal energy utilization and sustainable building practices. Doi: 10.28991/ESJ-2024-08-06-01 Full Text: PDFhttps://ijournalse.org/index.php/ESJ/article/view/2563cooling loadsmachine learningdeep learningensemble learninghvac systems. |
| spellingShingle | Fernando Pedro Silva Almeida Mauro Castelli Nadine Côrte-Real Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis Emerging Science Journal cooling loads machine learning deep learning ensemble learning hvac systems. |
| title | Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis |
| title_full | Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis |
| title_fullStr | Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis |
| title_full_unstemmed | Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis |
| title_short | Leveraging Feature Sets and Machine Learning for Enhanced Energy Load Prediction: A Comparative Analysis |
| title_sort | leveraging feature sets and machine learning for enhanced energy load prediction a comparative analysis |
| topic | cooling loads machine learning deep learning ensemble learning hvac systems. |
| url | https://ijournalse.org/index.php/ESJ/article/view/2563 |
| work_keys_str_mv | AT fernandopedrosilvaalmeida leveragingfeaturesetsandmachinelearningforenhancedenergyloadpredictionacomparativeanalysis AT maurocastelli leveragingfeaturesetsandmachinelearningforenhancedenergyloadpredictionacomparativeanalysis AT nadinecortereal leveragingfeaturesetsandmachinelearningforenhancedenergyloadpredictionacomparativeanalysis |