Improving accuracy and convergence of federated learning edge computing methods for generalized DER forecasting applications in power grid
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Abstract (arXiv)
This proposal aims to develop more accurate federated learning (FL) methods with faster convergence properties and lower communication requirements, specifically for forecasting distributed energy resources (DER) such as renewables, energy storage, and loads in modern, low-carbon power grids. This will be achieved by (i) leveraging recently developed extensions of FL such as hierarchical and iterative clustering to improve performance with non-IID data, (ii) experimenting with different types of FL global models well-suited to time-series data, and (iii) incorporating domain-specific knowledge from power systems to build more general FL frameworks and architectures that can be applied to diverse types of DERs beyond just load forecasting, and with heterogeneous clients.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 9 of its 14 references that are in the map. First posted on arXiv 13 Oct 2024.
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References · 9
- 2016An overview of gradient descent optimization algorithmscited by 1,021
- 2021Federated Learning Based on Dynamic Regularizationcited by 555
- 2020An Efficient Framework for Clustered Federated Learningcited by 505
- 2020HeteroFL: Computation and Communication Efficient Federated Learning for Heterogeneous Clientscited by 334
- 2020Federated learning with hierarchical clustering of local updates to improve training on non-IID datacited by 222
- 2021Federated Learning with Hyperparameter-based Clustering for Electrical Load Forecastingcited by 13
- 2021DER Forecast using Privacy Preserving Federated Learningcited by 10
- 2019A Time-Series Distribution Test System Based on Real Utility Datacited by 9
- 2022Federated Learning based Energy Demand Prediction with Clustered Aggregationcited by 9
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