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Data-driven decision support: Analyzing and visualizing energy model outputs
The transition to a sustainable energy future requires robust decision-support tools to identify suitable energy transition pathways based on local circumstances. This is particularly challenging in the context of local energy systems, where data is frequently lacking or of low quality. The RE3ASON energy system model performs an assessment of the local energy demand, renewable potential and existing infrastructure and determines an optimal energy transition pathway based on this information. However, it is only applicable to Germany. A new development based on this model using the open-source Spine toolbox and SpineOpt aims to enhance the flexibility and adaptability of the model while extending the model scope to Switzerland. This project focuses on the analysis and visualization of model results in this context, offering an opportunity to work with cutting-edge tools in energy system modeling.
Keywords: Data visualisation, decision-making support, open-source tools, sustainable energy
The tasks of the project are:
• Identifying key metrics and results from a SpineOpt toy model that are critical for decision-making.
• Developing visualization techniques that effectively communicate these results to stakeholders.
• Implementing these techniques within the Spine toolbox or externally using Python.
• Testing and validating the visualization methods using a toy model.
• Documenting visualization methods and providing recommendations for further development.
The tasks of the project are: • Identifying key metrics and results from a SpineOpt toy model that are critical for decision-making. • Developing visualization techniques that effectively communicate these results to stakeholders. • Implementing these techniques within the Spine toolbox or externally using Python. • Testing and validating the visualization methods using a toy model. • Documenting visualization methods and providing recommendations for further development.
The primary objective of this project is to develop methods to analyze and visualize energy model outputs for better decision support. Energy system models typically provide results on costs, emissions, installed technologies, and energy flows. This project emphasizes identifying relevant results and the creation of
visualizations that provide clear insights across multiple scenarios. The resulting code should be modular and adaptable to differing needs based on the specific case study that is analyzed.
The primary objective of this project is to develop methods to analyze and visualize energy model outputs for better decision support. Energy system models typically provide results on costs, emissions, installed technologies, and energy flows. This project emphasizes identifying relevant results and the creation of visualizations that provide clear insights across multiple scenarios. The resulting code should be modular and adaptable to differing needs based on the specific case study that is analyzed.
If you are interested in the project, please forward your CV and transcript by e-mail to Febin Kachirayil fkachirayil@ethz.ch or Jiangyi Huang jiahuang@ethz.ch and cc Dr. Alena Lohrmann alohrmann@ethz.ch
This project could be carried out as a group project, together with the following projects: Input data management for advanced energy system models and Advanced optimization model for municipal energy transitions in Switzerland
If you are interested in the project, please forward your CV and transcript by e-mail to Febin Kachirayil fkachirayil@ethz.ch or Jiangyi Huang jiahuang@ethz.ch and cc Dr. Alena Lohrmann alohrmann@ethz.ch
This project could be carried out as a group project, together with the following projects: Input data management for advanced energy system models and Advanced optimization model for municipal energy transitions in Switzerland
Student project_output_workflow.pdf
