ABSTRACT
The safe and effective management and utilization of the load data of electricity has become one of the important issues for power supply and distribution departments as electricity is an important part of industry 4.0. Accurate forecasting of power load is of great significance for the safety and stability of power grid dispatching and economical operation. However, many of the current power data sets have serious problems of data island; furthermore, the centralized storage of large amounts of data may cause privacy leakage of the original data owners and faces regulations of security supervision. Therefore, federated learning is introduced to address these issues. Nevertheless, this approach is not sufficient to provide adequate data privacy protection. The present research proposes a federated learning model based on improved differential privacy algorithm. The model uses multi-scale Laplacian algorithm to analyze data distribution and generate noises in accordance with data patterns. Moreover, the parameters of the model are protected by attribute-based access control (ABAC). The simulation results show that the model proposed by the present research makes accurate forecasting and the improved differential privacy algorithm has less influence on the model's accuracy; the model also shows a good resistance to attacks, which ensures the security of data while having a high precision.
- Bedi G, Venayagamoorthy G K, Singh R. Development of an IoT-Driven Building Environment for Prediction of Electric Energy Consumption[J]. IEEE Internet of Things Journal, 2020, 7(6): 4912-4921.Google ScholarCross Ref
- Bedi G, Venayagamoorthy G K, Singh R, Review of Internet of Things (IoT) in electric power and energy systems[J]. IEEE Internet of Things Journal, 2018, 5(2): 847-870.Google ScholarCross Ref
- B. McMahan and D. Ramage, “Federated learning: Collaborative machine learning without centralized training data,” Google Research Blog, vol. 3, 2017.Google Scholar
- Lu Y, Huang X, Dai Y, Differentially private asynchronous federated learning for mobile edge computing in urban informatics[J]. IEEE Transactions on Industrial Informatics, 2019, 16(3): 2134-2143.Google ScholarCross Ref
- Triastcyn A, Faltings B. Federated learning with bayesian differential privacy[C]//2019 IEEE International Conference on Big Data (Big Data). IEEE, 2019: 2587-2596.Google Scholar
- Wu Y, Pan Z, Luo X, A hybrid forecasting method of electricity consumption based on trend extrapolation theory and LSSVM[C]//2016 IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC). IEEE, 2016: 2333-2337.Google Scholar
- Yildiz B, Bilbao J I, Sproul A B. A review and analysis of regression and machine learning models on commercial building electricity load forecasting[J]. Renewable and Sustainable Energy Reviews, 2017, 73: 1104-1122.Google ScholarCross Ref
- Cai M, Pipattanasomporn M, Rahman S. Day-ahead building-level load forecasts using deep learning vs. traditional time-series techniques[J]. Applied Energy, 2019, 236: 1078-1088.Google ScholarCross Ref
- Tan M, Yuan S, Li S, Ultra-short-term industrial power demand forecasting using LSTM based hybrid ensemble learning[J]. IEEE transactions on power systems, 2019, 35(4): 2937-2948.Google Scholar
- Kong X, Li C, Zheng F, Improved deep belief network for short-term load forecasting considering demand-side management[J]. IEEE Transactions on Power Systems, 2019, 35(2): 1531-1538.Google ScholarCross Ref
- Lyu L, Yu J, Nandakumar K, Towards fair and privacy-preserving federated deep models[J]. IEEE Transactions on Parallel and Distributed Systems, 2020, 31(11): 2524-2541.Google ScholarCross Ref
- Yin B, Yin H, Wu Y, FDC: A secure federated deep learning mechanism for data collaborations in the Internet of Things[J]. IEEE Internet of Things Journal, 2020, 7(7): 6348-6359.Google ScholarCross Ref
Recommendations
LSTM Short-term Residential Load Forecasting Based on Federated Learning
CMAAE 2021: 2021 International Conference on Mechanical, Aerospace and Automotive EngineeringThe ever-expanding smart grids allow a large number of household electricity load data to be collected and stored for further research. Deep learning models are trained to analyze and predict residents' load by using their electricity data, which is ...
A blockchain-based framework for federated learning with privacy preservation in power load forecasting
AbstractPower load forecasting plays an important role in the efficient operation of power systems. For traditional load forecasting methods, the reliance on centralized data raises privacy concerns. Recently, federated learning has emerged as a ...
Highlights- Improved global model aggregation in FL using public data sets and decentralized blockchain.
- Blockchain-based incentive mechanism for secure deep learning model training.
- Scheme achieves privacy, efficiency and comparable ...
Aggregate Model for Power Load Forecasting Based on Conditional Autoencoder
Intelligent Computing Theories and ApplicationAbstractLoad forecasting is an important machine learning problem in the field of power system, which is of great significance to power system source load balance, power supply planning and system maintenance. With the development of power technology, ...
Comments