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Data-driven comparison of federated learning and model personalization for electric load forecasting

Energy and AI 2023年第4期14卷 3-16页

作者： Fabian Widmer Severin Nowak Benjamin Bowler Patrick Huber antonios papaemmanouil Lucerne University of Applied Science and Arts Institute of Electrical EngineeringCompetence Center Digital Energy and Electric PowerTechnikumsstrasse 216048HorwLucerneSwitzerland

Residential short-term electric load forecasting is essential in modern decentralized power *** forecasting methods mostly rely on neural networks and require access to private and sensitive electric load data for model *** neural network training aggregates all data on a centralized server to train one global ***,the aggregation of user data introduces security and data privacy *** contrast,this study investigates the modern neural network training methods of federated learning and model personalization as potential solutions to security and data privacy *** an extensive simulation approach,the investigated methods are compared to the conventional centralized method and a pre-trained baseline predictor to compare their respective *** study identifies that the underlying data structure of electric load data has a significant influence on the loss of a *** therefore conclude that a comparison of loss distributions will in fact be considered a comparison of data structures,rather than a comparison of the model *** an alternative method of comparison of loss values,this study develops the"differential comparison".The method allows for the isolated comparison of model loss differences by only comparing the losses of two models generated by the same data sample to build a distribution of *** differential comparison method was then used to identify model personalization as the best performing model training method for load forecasting among all analyzed methods,with a superior performance in 59.1%of all cases.

关键词： Federated learning Machine learning Model personalization Temporal convolutional network Electric load forecast Differential comparison

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