Data Preprocessing and Data Quality Assessment for Energy System Optimization

The European energy system is undergoing a fundamental transition due to decarbonization efforts. Besides, in recent years, the energy system’s needs have been subject to significant changes due to disruptive events. Under these circumstances, decision-makers need more complex energy system optimization models to make effective decisions at all levels, from policy-making to energy transport. To make reliable decisions, these models require real-world data that is highly interconnected and consistent. However, obtaining such data with sufficient quality is challenging.
In this talk, we will focus on the challenges of data preprocessing and data quality assessment in energy system optimization, particularly in gas transport network optimization. We will discuss how to assess data quality and how to improve data through various data preprocessing strategies. We will explore mathematical optimization methods in this effort, including detecting and correcting complex data errors. We will illustrate these approaches with real-world examples using open gas network data.