Forecasting Daily Electricity Load in Denmark: A Comparative Analysis with Renewable Energy Integration
As part of my MSc in Data Science at Copenhagen Business School, I recently completed a project in Predictive Analytics focused on forecasting daily electricity load in Denmark, a country with one of the highest levels of renewable energy integration. The study applied time series approaches like ARIMA and seasonal ARIMA, as well as dynamic regression models that included solar and wind generation data.
The goal was to explore how these methods perform when renewable energy is part of the system, and to address key questions: How do different forecasting techniques compare? Does including renewable generation improve accuracy? And how does forecast performance change across different time horizons and approaches?
This type of research is becoming increasingly important as energy systems worldwide shift toward renewables. Reliable forecasting helps grid operators balance supply and demand, plan for variability, and ensure stable access to electricity as countries move closer to decarbonization targets.
Related Project
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.