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.