Urban energy system planning: development of decision support methods

Type: Semester project (10 credits)
Period: Spring 2016 

 

Context

Urban energy systems are highly complex, span over multiple physical and temporal scales, and involve a multitude of actors. To tackle this complexity, modeling and optimization can help decision makers in the planning stages of such systems. Today, urban planners are foreseen as key actors in the process of developing low-carbon cities, and new decision support methodologies are required to assist them in this task.

The proposed project aims to gain a better understanding of two fields, namely urban planning and energy systems, in order to develop appropriate decision support methods (Strasser 2015). Working with existing multi-objective optimization results, several clustering algorithms shall be reviewed, applied and compared, to guide decision makers in the process of selecting among several Pareto-optimal alternatives (Aguirre and Taboada, 2011).

 

Tasks

  1. Literature review: familiarization with urban energy system planning, optimization methods and Post-Pareto analysis, including clustering methods
  2. Application of clustering analysis to a multi-objective optimization problem in the field of energy systems for decision support
  3. Critical analysis of results and elaboration of a systematic methodology
  4. Scientific report and presentation

 

Requirements

  • Programming skills in Matlab, R
  • Knowledge in statistical analysis
  • Background and interest in urban planning frameworks, and/or urban energy systems

 

How to apply

Please send a mail to sebastien.cajot@epfl.ch attaching your transcript of records and CV.
 

Compensation

As the IPESE research group is located at the EPFL campus in Sion, students will need to commute some days. A travel expense compensation of 1600 CHF (Master project) or 600 CHF (Semester project) will be given to students. The frequency of travel to Sion will be agreed with the project supervisor.

 

References

Aguirre, O., Taboada, H., 2011. A Clustering Method Based on Dynamic Self Organizing Trees for Post-Pareto Optimality Analysis. Procedia Computer Science, Complex adaptive sysytems 6, 195–200. doi:10.1016/j.procs.2011.08.037

Strasser, H., 2015. Implementation of Energy Strategies in Communities–From Pilot Project in Salzburg, Austria, to Urban Strategy, in: ASHRAE Transactions. Presented at the 2015 ASHRAE Winter Conference, Amer. Soc. Heating, Ref. Air-Conditoning Eng. Inc., Chicago, pp. 176–184.