Differentially Private Clustered Federated Load Prediction Based on the Louvain Algorithm
Load forecasting plays a fundamental role in the new type of power system. To address the data heterogeneity and security issues encountered in load forecasting for smart grids, this paper proposes a load-forecasting framework suitable for residential energy users, which allows users to train person...
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Algorithms |
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| Online Access: | https://www.mdpi.com/1999-4893/18/1/32 |
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| author | Tingzhe Pan Jue Hou Xin Jin Chao Li Xinlei Cai Xiaodong Zhou |
| author_facet | Tingzhe Pan Jue Hou Xin Jin Chao Li Xinlei Cai Xiaodong Zhou |
| author_sort | Tingzhe Pan |
| collection | DOAJ |
| description | Load forecasting plays a fundamental role in the new type of power system. To address the data heterogeneity and security issues encountered in load forecasting for smart grids, this paper proposes a load-forecasting framework suitable for residential energy users, which allows users to train personalized forecasting models without sharing load data. First, the similarity of user load patterns is calculated under privacy protection. Second, a complex network is constructed, and a federated user clustering method is developed based on the Louvain algorithm, which divides users into multiple clusters based on load pattern similarity. Finally, a personalized and adaptive differentially private federated learning Long Short-Term Memory (LSTM) model for load forecasting is developed. A case study analysis shows that the proposed method can effectively protect user privacy and improve model prediction accuracy when dealing with heterogeneous data. The framework can train load-forecasting models with a fast convergence rate and better prediction performance than current mainstream federated learning algorithms. |
| format | Article |
| id | doaj-art-be6c7091ed974b45863dc24e0588bdb6 |
| institution | Kabale University |
| issn | 1999-4893 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Algorithms |
| spelling | doaj-art-be6c7091ed974b45863dc24e0588bdb62025-01-24T13:17:33ZengMDPI AGAlgorithms1999-48932025-01-011813210.3390/a18010032Differentially Private Clustered Federated Load Prediction Based on the Louvain AlgorithmTingzhe Pan0Jue Hou1Xin Jin2Chao Li3Xinlei Cai4Xiaodong Zhou5CSG Science Research Institute Co., Ltd., Guangzhou 510640, ChinaPower Dispatching Control Center of Guangdong Power Grid Co., Ltd., Guangzhou 510060, ChinaCSG Science Research Institute Co., Ltd., Guangzhou 510640, ChinaPower Dispatching Control Center of Guangdong Power Grid Co., Ltd., Guangzhou 510060, ChinaPower Dispatching Control Center of Guangdong Power Grid Co., Ltd., Guangzhou 510060, ChinaCSG Science Research Institute Co., Ltd., Guangzhou 510640, ChinaLoad forecasting plays a fundamental role in the new type of power system. To address the data heterogeneity and security issues encountered in load forecasting for smart grids, this paper proposes a load-forecasting framework suitable for residential energy users, which allows users to train personalized forecasting models without sharing load data. First, the similarity of user load patterns is calculated under privacy protection. Second, a complex network is constructed, and a federated user clustering method is developed based on the Louvain algorithm, which divides users into multiple clusters based on load pattern similarity. Finally, a personalized and adaptive differentially private federated learning Long Short-Term Memory (LSTM) model for load forecasting is developed. A case study analysis shows that the proposed method can effectively protect user privacy and improve model prediction accuracy when dealing with heterogeneous data. The framework can train load-forecasting models with a fast convergence rate and better prediction performance than current mainstream federated learning algorithms.https://www.mdpi.com/1999-4893/18/1/32federated learningload forecastingadaptive differential privacyLouvain algorithmclustered |
| spellingShingle | Tingzhe Pan Jue Hou Xin Jin Chao Li Xinlei Cai Xiaodong Zhou Differentially Private Clustered Federated Load Prediction Based on the Louvain Algorithm Algorithms federated learning load forecasting adaptive differential privacy Louvain algorithm clustered |
| title | Differentially Private Clustered Federated Load Prediction Based on the Louvain Algorithm |
| title_full | Differentially Private Clustered Federated Load Prediction Based on the Louvain Algorithm |
| title_fullStr | Differentially Private Clustered Federated Load Prediction Based on the Louvain Algorithm |
| title_full_unstemmed | Differentially Private Clustered Federated Load Prediction Based on the Louvain Algorithm |
| title_short | Differentially Private Clustered Federated Load Prediction Based on the Louvain Algorithm |
| title_sort | differentially private clustered federated load prediction based on the louvain algorithm |
| topic | federated learning load forecasting adaptive differential privacy Louvain algorithm clustered |
| url | https://www.mdpi.com/1999-4893/18/1/32 |
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