An IHPO-WNN-Based Federated Learning System for Area-Wide Power Load Forecasting Considering Data Security Protection
With the rapid growth of power demand and the advancement of new power system intelligence, smart energy measurement system data quality and security are also facing the influence of diversified factors. To solve the series of problems such as low data prediction efficiency, poor security perception...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2023-10-01
|
| Series: | Energies |
| Subjects: | |
| Online Access: | https://www.mdpi.com/1996-1073/16/19/6921 |
| _version_ | 1797575923601506304 |
|---|---|
| author | Bujin Shi Xinbo Zhou Peilin Li Wenyu Ma Nan Pan |
| author_facet | Bujin Shi Xinbo Zhou Peilin Li Wenyu Ma Nan Pan |
| author_sort | Bujin Shi |
| collection | DOAJ |
| description | With the rapid growth of power demand and the advancement of new power system intelligence, smart energy measurement system data quality and security are also facing the influence of diversified factors. To solve the series of problems such as low data prediction efficiency, poor security perception, and “data islands” of the new power system, this paper proposes a federated learning system based on the Improved Hunter–Prey Optimizer Optimized Wavelet Neural Network (IHPO-WNN) for the whole-domain power load prediction. An improved HPO algorithm based on Sine chaotic mapping, dynamic boundaries, and a parallel search mechanism is first proposed to improve the prediction and generalization ability of wavelet neural network models. Further considering the data privacy in each station area and the potential threat of cyber-attacks, a localized differential privacy-based federated learning architecture for load prediction is designed by using the above IHPO-WNN as a base model. In this paper, the actual dataset of a smart energy measurement master station is selected, and simulation experiments are carried out through MATLAB software to test and examine the performance of IHPO-WNN and the federal learning system, respectively, and the results show that the method proposed in this paper has high prediction accuracy and excellent practical performance. |
| first_indexed | 2024-03-10T21:46:10Z |
| format | Article |
| id | doaj.art-e382cac50ce140afb421c06873d40fde |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-10T21:46:10Z |
| publishDate | 2023-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-e382cac50ce140afb421c06873d40fde2023-11-19T14:20:44ZengMDPI AGEnergies1996-10732023-10-011619692110.3390/en16196921An IHPO-WNN-Based Federated Learning System for Area-Wide Power Load Forecasting Considering Data Security ProtectionBujin Shi0Xinbo Zhou1Peilin Li2Wenyu Ma3Nan Pan4Kunming Power Supply Bureau, Yunnan Power Grid Co., Ltd., Kunming 650011, ChinaFaculty of Information Engineering and Automation, Kunming University of Science and Technology, Kunming 650500, ChinaKunming Power Supply Bureau, Yunnan Power Grid Co., Ltd., Kunming 650011, ChinaFaculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, ChinaFaculty of Civil Aviation and Aeronautics, Kunming University of Science and Technology, Kunming 650500, ChinaWith the rapid growth of power demand and the advancement of new power system intelligence, smart energy measurement system data quality and security are also facing the influence of diversified factors. To solve the series of problems such as low data prediction efficiency, poor security perception, and “data islands” of the new power system, this paper proposes a federated learning system based on the Improved Hunter–Prey Optimizer Optimized Wavelet Neural Network (IHPO-WNN) for the whole-domain power load prediction. An improved HPO algorithm based on Sine chaotic mapping, dynamic boundaries, and a parallel search mechanism is first proposed to improve the prediction and generalization ability of wavelet neural network models. Further considering the data privacy in each station area and the potential threat of cyber-attacks, a localized differential privacy-based federated learning architecture for load prediction is designed by using the above IHPO-WNN as a base model. In this paper, the actual dataset of a smart energy measurement master station is selected, and simulation experiments are carried out through MATLAB software to test and examine the performance of IHPO-WNN and the federal learning system, respectively, and the results show that the method proposed in this paper has high prediction accuracy and excellent practical performance.https://www.mdpi.com/1996-1073/16/19/6921electricity load forecastingimproved hunter–prey optimizerWNNfederated learningdifferential privacy |
| spellingShingle | Bujin Shi Xinbo Zhou Peilin Li Wenyu Ma Nan Pan An IHPO-WNN-Based Federated Learning System for Area-Wide Power Load Forecasting Considering Data Security Protection Energies electricity load forecasting improved hunter–prey optimizer WNN federated learning differential privacy |
| title | An IHPO-WNN-Based Federated Learning System for Area-Wide Power Load Forecasting Considering Data Security Protection |
| title_full | An IHPO-WNN-Based Federated Learning System for Area-Wide Power Load Forecasting Considering Data Security Protection |
| title_fullStr | An IHPO-WNN-Based Federated Learning System for Area-Wide Power Load Forecasting Considering Data Security Protection |
| title_full_unstemmed | An IHPO-WNN-Based Federated Learning System for Area-Wide Power Load Forecasting Considering Data Security Protection |
| title_short | An IHPO-WNN-Based Federated Learning System for Area-Wide Power Load Forecasting Considering Data Security Protection |
| title_sort | ihpo wnn based federated learning system for area wide power load forecasting considering data security protection |
| topic | electricity load forecasting improved hunter–prey optimizer WNN federated learning differential privacy |
| url | https://www.mdpi.com/1996-1073/16/19/6921 |
| work_keys_str_mv | AT bujinshi anihpownnbasedfederatedlearningsystemforareawidepowerloadforecastingconsideringdatasecurityprotection AT xinbozhou anihpownnbasedfederatedlearningsystemforareawidepowerloadforecastingconsideringdatasecurityprotection AT peilinli anihpownnbasedfederatedlearningsystemforareawidepowerloadforecastingconsideringdatasecurityprotection AT wenyuma anihpownnbasedfederatedlearningsystemforareawidepowerloadforecastingconsideringdatasecurityprotection AT nanpan anihpownnbasedfederatedlearningsystemforareawidepowerloadforecastingconsideringdatasecurityprotection AT bujinshi ihpownnbasedfederatedlearningsystemforareawidepowerloadforecastingconsideringdatasecurityprotection AT xinbozhou ihpownnbasedfederatedlearningsystemforareawidepowerloadforecastingconsideringdatasecurityprotection AT peilinli ihpownnbasedfederatedlearningsystemforareawidepowerloadforecastingconsideringdatasecurityprotection AT wenyuma ihpownnbasedfederatedlearningsystemforareawidepowerloadforecastingconsideringdatasecurityprotection AT nanpan ihpownnbasedfederatedlearningsystemforareawidepowerloadforecastingconsideringdatasecurityprotection |