The thesis written by Qianyun Wen, PhD student at the Department of Management and Engineering, investigates how large datasets from smart grids and digital technologies can be used to develop models and tools to support various stages of the energy transition.
"Data-driven tools can not only improve decision-making but also make it more accessible and user-friendly. For these tools to have a real impact, it is crucial that they provide reliable results and are adapted to the users' needs," says Qianyun Wen, a PhD candidate at the division of Environmental Technology and Management at Link枚ping University.
The study analyses three key areas: choosing heating technologies for buildings, forecasting energy usage and generation, and establishing energy-sharing communities. The results show that a combination of techniques, such as machine learning and optimization models, can help decision-makers find more sustainable and efficient solutions.
Qianyun Wen emphasizes that transparency and user-friendliness are key.
To create a sustainable energy transition, we need tools that are not only technologically advanced but also practical and easy to use for various stakeholders.
The thesis provides guidelines on how data-driven decision techniques can be implemented and adapted to different challenges. The research is an important step in helping decision-makers manage the complexities of the energy transition in a more informed and sustainable way.
