Presenter: Leif Andersson SINTEF
The ability to generate long-term energy-demand forecasts, as well as decomposing the sinks, is thus key to accelerate the decarbonization of energy supply on the NCS. Unfortunately, such forecasts are not publicly available or reported by operators.
Here, we explore methodologies for estimating long-term energy demand on installations on the NCS based on a combination of statistical machine-learning type methods using public reported data in combination with first-principle modelling and engineering principles. We report on preliminary results, our methodical approach, and challenges of generating such energy-demand forecasts