Data for a sustainable tomorrow A research project by Ezgi Köker Gökgöl

In her Master's thesis, she developed a decision support map that ranks homes by their energy efficiency, thus helping policymakers and homeowners to prioritise renovation projects, cut emissions and make better energy investments.

In this interview, Ezgi explains her reasons, gives details of her methods and shares her passion for sustainability, all of which helped her to develop a data-driven solution for a greener future.

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Ezgi Köker Gökgöl, a graduate of the MSc in Applied Information and Data Science programme at Lucerne University of Applied Sciences and Arts, developed a decision support map to identify buildings that most urgently need retrofitting.

First of all, please tell us something about yourself: What hashtags best describe you?

#AnalyticalThinking #PuzzleSolver #DataforFuture #KindtoNature

Please tell us more.

For me, every problem or task is like a puzzle in life, and my brain automatically creates algorithms that sometimes even take them to unpredictable advanced stages. As I get older, I realise that you can achieve the best results when you dream big and shape those dreams to see what steps you can take without losing sight of the actual circumstances. The most significant threat we currently face is global warming, and my biggest dream is to help sustain life for ourselves and future generations. I believe that the most productive way to live this dream is to use the available data to recognise the severity of the situation and find the most feasible solution.

About your job: What do you do?

I am not working at the moment, but I am looking for an opportunity to live my dream. 

What did you do before? 

Before coming to Switzerland, I had an academic career in Turkey. I focused on analysing water systems so as to improve water quality and reduce leaks – as a contribution to sustaining life. At the same time, I worked as a teaching and research assistant at the Middle East Technical University. After moving to Switzerland, I decided to learn more about data science and hone my technical skills. That's why I enrolled in the Applied Information and Data Science MSc programme at Lucerne University of Applied Sciences and Arts.

Please tell us about your research project.

Before explaining what we actually did, I would like to explain the reasons behind the study. The energy sector is the source of nearly three-quarters of the global greenhouse gas emissions, and buildings make up around one-third of them. The largest share of the energy consumed in buildings comes from their heating systems, which mostly run on fossil fuels. To meet the demands of increasing populations, we must switch to clean energy immediately.

Given the necessity of the renovation projects and the large investments they require, choosing the right buildings that will maximise the improvement is of the utmost importance for decision-makers. Moreover, knowing how energy efficient their buildings are will help homeowners decide how to retrofit them, and households will thus be able to save significantly through reduced energy consumption. 

With this in mind, my Master's thesis aims to develop a decision support map based on a rating system that classifies buildings within a municipality from "good" to "bad" depending on their energy use intensity, their heating system type, and their age. The specific heating energy requirement is the main indicator here, but only a few countries make this available nationwide. I therefore chose a small Swiss municipality of Wittenbach in the canton of St. Gallen for my case study. 

Based on the physical properties and heating energy demands of the buildings, I developed a ranking system that uses an analytic hierarchy process (AHP) and evaluates each building by considering its energy usage, heating system, and construction year. The subsequent rankings on the energy efficiency scale are then shown on the map of the municipality and serve as a decision support for finding the most suitable properties to retrofit.

What data and method did you use, and what insights did you gain or do you hope to gain?

For the case study, we chose the municipality of Wittenbach, a town near the city of St. Gallen. We learned about the characteristics of the buildings from the Federal Register of Buildings and Dwellings for the canton of St. Gallen, which is available from the Federal Statistical Office.

The second dataset required for the ranking system was the energy demands of the households in Wittenbach. These are available from the Swiss Federal Office of Energy as a heatmap of energy demands by residential and commercial buildings in Switzerland. The underlying data for the map is provided by the Swiss District Heating Association and is based on information from the Federal Register of Buildings and Dwellings.

Studying the physical properties and the heating energy demands of the buildings made it possible to develop a ranking system through an analytic hierarchy process, a widely used method for multicriteria analysis. This method helps to select the best option in a structured manner by applying several weighted criteria – similar to the way we select the best candidate for a job. Each household within the municipality is evaluated by considering its energy usage, existing heating system and age so as to produce a ranking based on the energy efficiency scale. Finally, these rankings are represented visually on the map of the municipality to help with the decision of finding the most suitable candidates to retrofit.

How can your insights help our society?

The multi-layered decision support map with interactive details developed in this study adds value for both homeowners and decision-makers when evaluating the potential of a particular renovation.
The map enables homeowners to assess the energy efficiency of their buildings easily and to compare them with those of others in the municipality. The findings clearly show that energy-efficient renovations not only have ecological benefits but can also positively influence household budgets, for example, through significantly lower energy consumption.

The map provides policymakers with an overview of buildings in particular need of renovation and of spatial clusters of low-efficiency values. This information then helps with planning investments more effectively, developing regional support programmes and designing municipal strategies based on efficiency classes and their distribution.

What are your goals for your project in future?

The biggest dream I had while working on this project was to be able to make the outcomes become part of a solution that can be applied in real life. For me, this would be the best indication that the work was worthwhile. In fact the results of my study were later used as the initial step of an energy replacement systems recommender tool by the Energy Demand Governance project (EDGE https://www.sweet-edge.ch/en/home) that my advisors are working on. That gave me the satisfaction of being a part of the solution. 

How did your studies in the Applied Information and Data Science programme influence the project?

The Applied Information and Data Science Master's programme at HSLU has a broad range of courses that cover various topics such as healthcare, energy systems or computational language technologies. This range helped me to see which field I would enjoy working on before I chose my thesis topic and how I could contribute more effectively to making our planet more sustainable. Moreover, the programming skills and new statistical tools I acquired through the programme helped me to maximise the outcomes of my thesis. Finally, and maybe not directly related to my project, I thought it was a nice way to learn about different fields, such as business administration or data ideation – especially for me because of my background as an engineer.

What advice would you give to others starting on similar projects?

I believe that all research projects are driven by curiosity and interest. The advice I would like to give to others would be to find the subject that catches their interest during their courses and to develop it in their thesis, which is more like a marathon than a 100-meter sprint. While writing the thesis, there will invariably be easy times as well as setbacks. During those tough moments, I think it's important to remind yourself that you love what you're working on and to believe that it will make a difference.

And finally: What new hashtag are you aiming for in future?

#SustainableFuture: For a future where people are more conscious and responsible towards the planet. 
#WorkWithLove: To find my place in life where I can contribute to this.

We want to thank Ezgi Köker Gökgöl for her dedication and for sharing these valuable insights.

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