Improve Load Forecasting in Energy Communities through Transfer Learning using Open-Access Synthetic Profiles

2024/07/11 by Lukas Moosbrugger, Valentin Seiler, Moosbrugger, Lukas +5
Engineering · #Energy Load and Power Forecasting #FOS: Computer and information sciences #Machine Learning (cs.LG) #Power Systems and Technologies #Traffic Prediction and Management Techniques

paper · pdf · doi:10.48550/arxiv.2407.08434

openalex publication_date 2024/07/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

According to a conservative estimate, a 1% reduction in forecast error for a 10 GW energy utility can save up to 1.6 million annually. In our context, achieving precise forecasts of future power consumption is crucial for operating flexible energy assets using model predictive control approaches. Specifically, this work focuses on the load profile forecast of a first-year energy community with the common practical challenge of limited historical data availability. We propose to pre-train the load prediction models with open-access synthetic load profiles using transfer learning techniques to tackle this challenge. Results show that this approach improves both, the training stability and prediction error. In a test case with 74 households, the prediction mean squared error (MSE) decreased from 0.34 to 0.13, showing transfer learning based on synthetic load profiles to be a viable approach to compensate for a lack of historic data.

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