Transfer Learning for Electricity Demand Forecasting in Nordic Cities: A Case Study of Copenhagen and Reykjavik

In Nordic countries, demand response (DR) programs are critical for enhancing energy flexibility and reducing peak electricity consumption in buildings and industry. Accurate short-term demand forecasting is essential to support these programs, especially in the context of cities with varying levels of data availability. This paper compares three different forecasting approaches for hourly electricity demand prediction in the Nordic region: a baseline Long Short-Term Memory (LSTM) model, a transfer learning (TL)-based LSTM model, and a tree-based Extreme Gradient Boosting (XGBoost) model. We use Copenhagen, Denmark as a data-rich source city to pretrain an LSTM and fine-tune it on limited data from Reykjavik, Iceland simulating a low-data target environment. The results show that while XGBoost performs best with well-engineered features, TL offers a robust and scalable alternative in data-scarce scenarios, especially where traditional features may be limited or incomplete.