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

Lukas Moosbrugger, Valentin Seiler, Gerhard Huber, Peter Kepplinger

TL;DR

The paper tackles the problem of short-term load forecasting in energy communities where historical data are scarce. It introduces a transfer-learning approach that pre-trains an end-to-end bidirectional LSTM on open-access synthetic load profiles and then fine-tunes with real community data, incorporating calendar, weather, and lagged-load features. The key finding is that synthetic pre-training substantially improves both training stability and forecast accuracy, reducing the mean squared error from $0.34$ to $0.13$ in a 74-household test case, and enabling continuous learning as data accumulate. This approach offers a practical, data-efficient pathway for deploying model predictive control in energy communities and can be extended to smaller communities and alternative architectures.

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.

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

TL;DR

The paper tackles the problem of short-term load forecasting in energy communities where historical data are scarce. It introduces a transfer-learning approach that pre-trains an end-to-end bidirectional LSTM on open-access synthetic load profiles and then fine-tunes with real community data, incorporating calendar, weather, and lagged-load features. The key finding is that synthetic pre-training substantially improves both training stability and forecast accuracy, reducing the mean squared error from to in a 74-household test case, and enabling continuous learning as data accumulate. This approach offers a practical, data-efficient pathway for deploying model predictive control in energy communities and can be extended to smaller communities and alternative architectures.

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.
Paper Structure (8 sections, 7 figures)

This paper contains 8 sections, 7 figures.

Table of Contents

  1. Introduction
  2. Methods
  3. Datasets
  4. Model
  5. Features
  6. Application to energy communities
  7. Results
  8. Conclusion and Future Work

Figures (7)

  • Figure 1: Model architecture. The model processes an input sequence consisting of 48 hourly time steps and produces an output sequence of 24 hourly time steps, representing the predicted load profile for the following day.
  • Figure 2: Main concept, key ideas are highlighted in orange. The model is pre-trained with synthetic load profiles. As no weather information is known from the synthetic load profiles, a new feature will be added during finetuning. The model can start predicting from scratch at the very first deployment and is further trained with every new measurement available, improving steadily.
  • Figure 3: The prediction error (MSE) of the continuously trained model over the year 2010.
  • Figure 4: Example day with a decent prediction error.
  • Figure 5: December 24th (Christmas Eve) shows the highest prediction error of the whole year.
  • ...and 2 more figures