Revealing the Effects of Data Heterogeneity in Federated Learning Regression Models for Short-Term Solar Power Forecasting

Revealing the Effects of Data Heterogeneity in Federated Learning Regression Models for Short-Term Solar Power Forecasting

Accurate short-term power forecasting is crucial for the successful commercialization of solar energy, helping to prevent financial losses in energy markets. Federated learning (FL) offers a promising approach for power forecasting with small databases, enabling marketers to collaboratively develop...

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Bibliographic Details
Main Authors: Robin Nachtigall, Marc Leon Haller, Andreas Wagner, Viktor Walter
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Federated machine learning
heterogeneity
regression
time series
solar power forecasting
Online Access:https://ieeexplore.ieee.org/document/10716358/
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https://ieeexplore.ieee.org/document/10716358/

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