Thesis subject

Data-driven solar photovoltaic integration in Wageningen University campus

Level: MSc

Research area/discipline: Data science / Software engineering for Sustainability

Prerequisites: Programming in Python (INF-22306) and/or Software Engineering (INF-32306)

Short description:

Renewable energy generation and energy efficiency are key drivers to reduce CO2 emissions in the built environment1. Buildings are the largest energy consumers in the EU; they account for 40% of the total energy consumption and 36% of the CO2 emissions2.

In the Netherlands, an increasing deployment of solar Photovoltaic (PV) energy systems (e.g., on land and on buildings’ rooftops) has been noticed over the past few years. Wageningen university has also been paying an increasing attention to sustainable energy transition and renewable energy integration in buildings and farming applications; the WUR facilities in Lelystad are an example. This thesis will look at the technical potential of local solar PV energy production at Wageningen university campus using data science, GIS maps, modeling and simulation approaches. The final aim is to develop a data-driven framework to assess:

  1. the potential of solar PV energy production at different buildings located in Wageningen university campus
  2. the self-consumption and self-sufficiency ratios of solar energy in those buildings.

Further, the thesis will identify the opportunities and barriers to PV adoption at WU campus and provide recommendations to decision makers.


The work in this master thesis entails:

  1. To collect data, analyze and create an overview of the available and potential for solar PV panels installation in different buildings’ rooftops (or other suitable locations) in Wageningen university campus.
  2. To develop an algorithm that:

    • assesses energy generation potential of those PV systems using a data-driven approach.
    • evaluates the self-consumption and self-sufficiency of those buildings (i.e., electricity demand that can be fulfilled by integrated PV systems).
  3. To identify the opportunities and barriers to PV adoption at Wageningen university campus and provide recommendations to decision makers.

Required skills/knowledge:

Programming in Python, basic data analytics and GIS maps, interest about sustainability and energy transition.

Relevant literature:

  • Luthander, Rasmus, et al. "Photovoltaic self-consumption in buildings: A review." Applied energy 142 (2015): 80-94
  • Kucuksari, Sadik, et al. "An Integrated GIS, optimization and simulation framework for optimal PV size and location in campus area environments." Applied Energy 113 (2014): 1601-1613
  • Litjens, G. B. M. A., et al. "A spatio-temporal city-scale assessment of residential photovoltaic power integration scenarios." Solar Energy 174 (2018): 1185-1197

For more information:

For making an appointment to discuss the thesis topic, interested students can make contact:

Dr.Ir. Tarek Alskaif | Assistant Professor | Information Technology group (INF) | Wageningen University & Research (WUR) | Building No. 201 (Leeuwenborch), Hollandseweg 1, 6706 KN, Wageningen, The Netherlands | Room 6018 | T: +31 (0)317 487889 | WUR Profile |


1) European Parliament, Directive on energy efficiency 2012 EU, (2012) 1–56.

2) European Commission, Energy Performance of Buildings, (2014).