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Energy Prediction using Federated Learning
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In this work, we demonstrate the viability of using federated learning to successfully predict energy consumption as well as solar production for all households within a certain network using low-power and low-space consuming embedded devices. We also demonstrate our prediction performance improving over time without the need for sharing private consumer energy data. We simulate a system with four nodes using data for one year to show this.
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Cited by 1 Pith paper
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FedCCL: Federated Clustered Continual Learning Framework for Privacy-focused Energy Forecasting
A federated learning framework that clusters clients by static features before training reaches near-centralized photovoltaic forecasting accuracy with minimal degradation on new sites.
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