Forecasting Daily Electricity Load in Denmark: A Comparative Analysis with Renewable Energy Integration

This report outlines my university project for the Predictive Analytics course at Copenhagen Business School. The study aimed to forecast daily electricity load in Denmark by comparing traditional time series approaches with models that integrate renewable energy generation data. Using a five-year dataset (2016–2020) from the Open Power System Data platform, the project followed a rigorous pipeline that included exploratory data analysis to identify strong weekly seasonality and structural breaks, followed by stationarity testing that necessitated first-order differencing. The core analysis involved training and evaluating three primary modeling techniques: a Seasonal Naive baseline, standard and seasonal ARIMA models, and a Dynamic Regression model incorporating wind and solar generation variables. These models were rigorously validated using Ljung-Box diagnostic tests and compared across different forecast horizons, ultimately revealing that while Auto-ARIMA performed best for short-term predictions, the Dynamic Regression model offered superior accuracy for longer 30-day forecasts by effectively capturing weather-driven demand fluctuations.