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DOE OSTI · 2558978

Transfer Learning Trained LSTM Models for Household Load Profile Forecasting

Klein-Seetharaman, Roshan·Zhu, Xiangqi·Mather, Barry
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Abstract

Grid edge renewable energy resources, such as rooftop solar photovoltaics, closely interact with consumer load profiles. Therefore, forecasting future electricity demand, ideally at the individual household level, is indispensable. In this paper, we present a transfer learning enhanced household load profile forecasting method. First, we tune a long short-term memory forecasting model to perform day-ahead prediction of household electricity load profiles. Then we improve these individualized models using transfer learning, and we use k-means clustering to create optimal source data sets. We find average improvements of 4.38% (largest improvement of 10.71%) when the entire data set was used to train the source model and 2.45% (largest improvement of 11.57%) in the mean absolute error when households were first clustered and used to train separate source models for each cluster. We find that transfer learning with clustered data can effectively boost the forecasting performance of the LSTM models. We use realistic household power measurements for 148 real residential households in Austin, Texas.

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deep learningload profile forecastingLSTMMATHEMATICS AND COMPUTING,POWER TRANSMISSION AND DISTRIBUTIONtransfer learning

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Klein-Seetharaman, Roshan, Zhu, Xiangqi, Mather, Barry. 2025-02-21. Transfer Learning Trained LSTM Models for Household Load Profile Forecasting. https://doi.org/10.1109/gridedge61154.2025.10887407

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