Federated Learning Enabled Prediction of Energy Consumption in Transactive Energy Communities

Mendes, Nuno Serra Pacheco; Moura, Pedro; Mendes, Jérôme; Salles, Rodrigo; Mohammadi, Javad

doi:10.1109/isgt-europe54678.2022.9960350

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Cited by 9 publications

References 12 publications

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Federated learning framework for prediction of net energy demand in transactive energy communities

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Sustainable Energy, Grids and Networks

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Adaptive DFL‐based straggler mitigation mechanism for synchronous ring topology in digital twin networks

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Decentralised federated learning (DFL) transforms collaborative energy consumption prediction using distributed computation across a large network of edge nodes, ensuring data confidentiality by eliminating central data aggregation. Preserving individual privacy in energy forecasting is paramount, as it safeguards personal data from unauthorised examination. This highlights the importance of effectively handling local data to provide privacy protection. The authors proposed a DFL framework for residential energy forecasting, focusing on improving the performance and convergence of the collaborative model. The proposed framework enables local training of the long short‐term memory model with real‐time household energy data in a ring topology. Importantly, the framework addresses the issue of straggler nodes, nodes that lag in computation or communication, by proposing a heuristic straggler identification and mitigation mechanism to reduce their negative impact on overall system performance and communication efficiency. This approach improves collaborative energy prediction performance and ensures an overall reduction in waiting time, thus improving the convergence performance. Experimental results consistently demonstrate a low mean absolute error ranging from 3 to 3.2 across all edge nodes. The empirical findings unequivocally illustrate the efficiency of the proposed DFL architecture, highlighting its ability to improve communication efficiency and concurrently enhance performance.

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Federated Learning Enabled Prediction of Energy Consumption in Transactive Energy Communities

Cited by 9 publications

References 12 publications

Deep learning-enabled integration of renewable energy sources through photovoltaics in buildings

Deep learning-enabled integration of renewable energy sources through photovoltaics in buildings

Federated learning framework for prediction of net energy demand in transactive energy communities

Adaptive DFL‐based straggler mitigation mechanism for synchronous ring topology in digital twin networks

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