Using Dynamic Double Machine Learning for Guided District Heating Forecasting and Physical Parameter Extraction
This thesis’ main goals were to provide accurate forecasts and informative physical parameter estimates for energy use of the district heating in the Tingbjerg neighborhood, Copenhagen. Our work is aimed as a contribution for future work towards efficient on-demand energy production. We applied Dynamic Double Machine Learning to estimate the causal effects of weather observations on energy use. Th