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Zhou X, Feng J, Wang J, Pan J. Privacy-preserving household load forecasting based on non-intrusive load monitoring: A federated deep learning approach. PeerJ Comput Sci 2022; 8:e1049. [PMID: 36092014 PMCID: PMC9455055 DOI: 10.7717/peerj-cs.1049] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/20/2022] [Accepted: 06/29/2022] [Indexed: 05/06/2023]
Abstract
Load forecasting is very essential in the analysis and grid planning of power systems. For this reason, we first propose a household load forecasting method based on federated deep learning and non-intrusive load monitoring (NILM). As far as we know, this is the first research on federated learning (FL) in household load forecasting based on NILM. In this method, the integrated power is decomposed into individual device power by non-intrusive load monitoring, and the power of individual appliances is predicted separately using a federated deep learning model. Finally, the predicted power values of individual appliances are aggregated to form the total power prediction. Specifically, by separately predicting the electrical equipment to obtain the predicted power, it avoids the error caused by the strong time dependence in the power signal of a single device. In the federated deep learning prediction model, the household owners with the power data share the parameters of the local model instead of the local power data, guaranteeing the privacy of the household user data. The case results demonstrate that the proposed approach provides a better prediction effect than the traditional methodology that directly predicts the aggregated signal as a whole. In addition, experiments in various federated learning environments are designed and implemented to validate the validity of this methodology.
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Affiliation(s)
- Xinxin Zhou
- School of Computer Science, Northeast Electric Power University, jilin, China
| | - Jingru Feng
- School of Computer Science, Northeast Electric Power University, jilin, China
| | - Jian Wang
- School of Computer Science, Northeast Electric Power University, jilin, China
| | - Jianhong Pan
- State Grid Jilin Electric Power Company Limited, Changchun, China
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Chen CW, Lin MH, Liao CC, Chang HP, Chu YW. iStable 2.0: Predicting protein thermal stability changes by integrating various characteristic modules. Comput Struct Biotechnol J 2020; 18:622-630. [PMID: 32226595 PMCID: PMC7090336 DOI: 10.1016/j.csbj.2020.02.021] [Citation(s) in RCA: 46] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/15/2019] [Revised: 02/25/2020] [Accepted: 02/27/2020] [Indexed: 11/15/2022] Open
Abstract
Protein mutations can lead to structural changes that affect protein function and result in disease occurrence. In protein engineering, drug design or and optimization industries, mutations are often used to improve protein stability or to change protein properties while maintaining stability. To provide possible candidates for novel protein design, several computational tools for predicting protein stability changes have been developed. Although many prediction tools are available, each tool employs different algorithms and features. This can produce conflicting prediction results that make it difficult for users to decide upon the correct protein design. Therefore, this study proposes an integrated prediction tool, iStable 2.0, which integrates 11 sequence-based and structure-based prediction tools by machine learning and adds protein sequence information as features. Three coding modules are designed for the system, an Online Server Module, a Stand-alone Module and a Sequence Coding Module, to improve the prediction performance of the previous version of the system. The final integrated structure-based classification model has a higher Matthews correlation coefficient than that of the single prediction tool (0.708 vs 0.547, respectively), and the Pearson correlation coefficient of the regression model likewise improves from 0.669 to 0.714. The sequence-based model not only successfully integrates off-the-shelf predictors but also improves the Matthews correlation coefficient of the best single prediction tool by at least 0.161, which is better than the individual structure-based prediction tools. In addition, both the Sequence Coding Module and the Stand-alone Module maintain performance with only a 5% decrease of the Matthews correlation coefficient when the integrated online tools are unavailable. iStable 2.0 is available at http://ncblab.nchu.edu.tw/iStable2.
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Affiliation(s)
- Chi-Wei Chen
- Department of Computer Science and Engineering, National Chung-Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
- Institute of Genomics and Bioinformatics, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
| | - Meng-Han Lin
- Institute of Genomics and Bioinformatics, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
| | - Chi-Chou Liao
- Institute of Genomics and Bioinformatics, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
- Institute of Molecular Biology, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
| | - Hsung-Pin Chang
- Department of Computer Science and Engineering, National Chung-Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
| | - Yen-Wei Chu
- Institute of Genomics and Bioinformatics, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
- Institute of Molecular Biology, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
- Agricultural Biotechnology Center, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
- Biotechnology Center, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
- Ph.D. Program in Translational Medicine, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
- Rong Hsing Research Center for Translational Medicine, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan
- Corresponding author at: Institute of Genomics and Bioinformatics, National Chung Hsing University, 145 Xingda Rd., South Dist., Taichung City 402, Taiwan.
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