Identification and statistical evaluation of key parameters for decentralized sustainable energy systems planning

Semester project Fall 2024

Context

In recent years, there has been a growing emphasis on decentralized sustainable energy systems to address the challenges of climate change and resource depletion. However, effective planning requires a thorough understanding of key parameters and indicators to optimize resource allocation and decision-making processes. This project aims to leverage available data and statistical methods to identify, evaluate, and spatially characterize districts for the development of decentralized energy systems.

Realisation

1. Literature Review and Parameter Identification:

  • Conduct a comprehensive literature review to identify relevant parameters and indicators essential to spatially characterize the districts.
  • Explore existing methodologies and frameworks used in similar projects.

2. Data exploratioin:

  • Explore available data sources and methodologies for data analysis.

3. Scale of Analysis Determination:

  • Determine the appropriate scale of analysis for spatial characterization of districts, considering factors such as geographical size, population density, and energy consumption patterns.

4. Statistical Mapping and Analysis:

  • Utilize principal component analysis (PCA) and other statistical techniques to identify key parameters and their relationships.
  • Create statistical maps and tables to visualize the spatial distribution of parameters such as district heating potential, refurbishment potential, solar potential, energy demand density, etc.

5. Policy Analysis:

  • Investigate how political measures and policies can influence the transition to decentralized sustainable energy systems.
  • Analyze the timing of investments and interventions based on identified opportunities and constraints.
  • Consider bottom-up approaches to involve local communities and stakeholders in the planning process.

We are looking for higly motivated students with good computational skills (knowledge of Python is recommended)

If your are interested in working in this topic, send us your CV with a short motivation letter.

Contacts: [mailto:catarina.braz@epfl.ch] and [mailto:luc.girardin.epfl.ch]