In this thesis we aimed to find the best methods for short-term load forecasting in the Norwegian electricity market during times of unprecedented price movements. We answered three questions related to this aim. The first was which model achieved the most accurate forecast. The second was whether our proposed models outperform the official forecasts published on the Entso-E platform. The third question asked was if the price movements had any effect on the accuracy of the load forecast.
We constructed two SARIMAX models, a Gradient boosted decision tree, a Random Forest, and a Multilayer perceptron model. Our findings show the two SARIMAX models to be most accurate. These models outperformed the forecasts published on the Entso-E platform in four out of the five Norwegian bidding zones, measured in MAPE and RMSE. Finally, we have shown that forecasting load with and without price information did not result in significant differences in accuracy. Our findings did not indicate an increase in difficulty of forecasting 2021 compared to 2019, neither for the three southern bidding zones with higher price increase nor the northern two zones
