Adaptively coping with concept drifts in energy time series forecasting using profiles

Abstract

Accurate electrical load forecasts are necessary to stabilize the electricity grid, e.g., by optimally operating energy storage systems or using demand-side management. However, an implicit assumption of most load forecasting methods is that future data looks similar to past data. Unfortunately, this assumption often does not hold; for example, recruiting new staff or a pandemic can lead to demand changes resulting in so-called concept drifts in the underlying data. Most methods for coping with such concept drifts rely on computationally expensive retraining. We propose a new method for coping with concept drifts based on profiles and a linear regression model that avoids expensive retraining. Compared to a simple baseline and five state-of-The-Art benchmark models on two different data sets, our method has lower computational costs and higher forecast accuracy, making it especially interesting for smart grid applications.