Enhancing energy consumption prediction in smart homes: a convergence-aware federated transfer learning approach
Achieving accurate energy consumption prediction can be challenging, particularly in residential buildings, which experience highly variable consumption behavior due to changes in occupants and the construction of new buildings. This variability, combined with the potential for privacy breaches thro...
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EDP Sciences
2024-01-01
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| Series: | Science and Technology for Energy Transition |
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| Online Access: | https://www.stet-review.org/articles/stet/full_html/2024/01/stet20240177/stet20240177.html |
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| author | Rizwan Atif Khan Anam Nawaz Ahmad Rashid Hassan Hassan Zohair Atteia Ghada Alkanhel Reem Samee Nagwan Abdel |
| author_facet | Rizwan Atif Khan Anam Nawaz Ahmad Rashid Hassan Hassan Zohair Atteia Ghada Alkanhel Reem Samee Nagwan Abdel |
| author_sort | Rizwan Atif |
| collection | DOAJ |
| description | Achieving accurate energy consumption prediction can be challenging, particularly in residential buildings, which experience highly variable consumption behavior due to changes in occupants and the construction of new buildings. This variability, combined with the potential for privacy breaches through conventional data collection methods, underscores the need for novel approaches to energy consumption forecasting. The proposed study suggests a new approach to predict energy consumption, utilizing Federated Learning (FL) to train a global model while ensuring local data privacy and transferring knowledge from information-rich to information-poor buildings. The proposed method learns the transferable knowledge from the source building without any privacy leakage and utilizes it for target buildings. Since the performance of the global model could be negatively affected by some participating nodes with poor performance due to noisy or limited data, we propose a client selection strategy on the server based on the normal distribution for choosing the best possible participants for the global model. Our method enables clients to participate selectively in the aggregation procedure to avoid model divergence due to poor performance. The proposed model is evaluated and conducts in-depth analyses of energy consumption patterns. We validate the performance by comparing its Mean Absolute Error (MAE), Mean Square Error (MSE), and R2 values to those of existing traditional and ensemble models. Our findings indicate that the proposed FL-based model with selective client participation outperforms its counterpart methods regarding predictive accuracy and robustness. The source code is available on GitHub (https://github.com/atifrizwan1/TFL-PP). |
| format | Article |
| id | doaj-art-16f1db55ee8143479730a1118aa73328 |
| institution | Kabale University |
| issn | 2804-7699 |
| language | English |
| publishDate | 2024-01-01 |
| publisher | EDP Sciences |
| record_format | Article |
| series | Science and Technology for Energy Transition |
| spelling | doaj-art-16f1db55ee8143479730a1118aa733282024-11-08T09:35:40ZengEDP SciencesScience and Technology for Energy Transition2804-76992024-01-01798510.2516/stet/2024060stet20240177Enhancing energy consumption prediction in smart homes: a convergence-aware federated transfer learning approachRizwan Atif0https://orcid.org/0000-0001-6669-8147Khan Anam Nawaz1Ahmad Rashid2https://orcid.org/0000-0001-6922-7412Hassan Hassan Zohair3Atteia Ghada4https://orcid.org/0000-0002-5462-595XAlkanhel Reem5Samee Nagwan Abdel6Department of Electronic Engineering, Kyung Hee UniversityDepartment of Computer Engineering, Jeju National UniversityFaculty of Computing and IT, Sohar UniversityDepartment of Mechanical Engineering, College of Engineering, Alfaisal UniversityDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityDepartment of Information Technology, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman UniversityAchieving accurate energy consumption prediction can be challenging, particularly in residential buildings, which experience highly variable consumption behavior due to changes in occupants and the construction of new buildings. This variability, combined with the potential for privacy breaches through conventional data collection methods, underscores the need for novel approaches to energy consumption forecasting. The proposed study suggests a new approach to predict energy consumption, utilizing Federated Learning (FL) to train a global model while ensuring local data privacy and transferring knowledge from information-rich to information-poor buildings. The proposed method learns the transferable knowledge from the source building without any privacy leakage and utilizes it for target buildings. Since the performance of the global model could be negatively affected by some participating nodes with poor performance due to noisy or limited data, we propose a client selection strategy on the server based on the normal distribution for choosing the best possible participants for the global model. Our method enables clients to participate selectively in the aggregation procedure to avoid model divergence due to poor performance. The proposed model is evaluated and conducts in-depth analyses of energy consumption patterns. We validate the performance by comparing its Mean Absolute Error (MAE), Mean Square Error (MSE), and R2 values to those of existing traditional and ensemble models. Our findings indicate that the proposed FL-based model with selective client participation outperforms its counterpart methods regarding predictive accuracy and robustness. The source code is available on GitHub (https://github.com/atifrizwan1/TFL-PP).https://www.stet-review.org/articles/stet/full_html/2024/01/stet20240177/stet20240177.htmlfederated learningenergy consumption forecastingenergy managementsmart buildingspartial client participation |
| spellingShingle | Rizwan Atif Khan Anam Nawaz Ahmad Rashid Hassan Hassan Zohair Atteia Ghada Alkanhel Reem Samee Nagwan Abdel Enhancing energy consumption prediction in smart homes: a convergence-aware federated transfer learning approach Science and Technology for Energy Transition federated learning energy consumption forecasting energy management smart buildings partial client participation |
| title | Enhancing energy consumption prediction in smart homes: a convergence-aware federated transfer learning approach |
| title_full | Enhancing energy consumption prediction in smart homes: a convergence-aware federated transfer learning approach |
| title_fullStr | Enhancing energy consumption prediction in smart homes: a convergence-aware federated transfer learning approach |
| title_full_unstemmed | Enhancing energy consumption prediction in smart homes: a convergence-aware federated transfer learning approach |
| title_short | Enhancing energy consumption prediction in smart homes: a convergence-aware federated transfer learning approach |
| title_sort | enhancing energy consumption prediction in smart homes a convergence aware federated transfer learning approach |
| topic | federated learning energy consumption forecasting energy management smart buildings partial client participation |
| url | https://www.stet-review.org/articles/stet/full_html/2024/01/stet20240177/stet20240177.html |
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