Improve Load Forecasting in Energy Communities through Transfer Learning using Open-Access Synthetic Profiles
This addresses the challenge of accurate load forecasting for energy communities, which is crucial for cost savings and efficient operation, though it is incremental as it applies transfer learning to a specific domain.
The paper tackled the problem of load forecasting in energy communities with limited historical data by pre-training models on open-access synthetic load profiles using transfer learning, resulting in a reduction of prediction mean squared error from 0.34 to 0.13 for a test case with 74 households.
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.