ETHOS Student Projects

This project is offered in collaboration with the Urban Energy Systems group at EMPA.

The proposed project will develop a common database of metering and sub-metering data from Swiss households. This data will be used to develop non-intrusive load monitoring (NILM) algorithms and customer segmentation models. The project will also evaluate the demand flexibility potential of Swiss households and develop targeted demand flexibility programs. The project will develop new tools and knowledge that can be used to design and implement effective demand flexibility programs in Switzerland. The project will also contribute to the development of a more reliable and sustainable power system.

The key steps envisioned for the completion of the project are as follows:

• Literature review
• Data collection and preparation
• NILM algorithm development and validation
• Customer segmentation modeling
• Development of household energy model
• Demand flexibility analysis
• Documentation

Supervisor: Andrew Sonta ([email protected])

Household energy consumption patterns, accounting for ~25% of European electricity demand, play a pivotal role in demand flexibility to support the grids under increasing intermittent renewable generations. The specific patterns of household appliance usage and time preferences can be the complex consequence of asset and facility conditions, household economic status, resident occupational and recreational lifestyles, and local social-organizational context. We have also been working towards integrating household energy models with socio-economic survey data to emulate these complex and heterogeneous patterns. Agent-based modeling (ABM) via large language models (LLMs) is a promising approach to reflect individual household properties and simulate complex human-like reasoning, behavioral adaption, and interactions in this process via LLM.  In this project, we will leverage existing LLM-agent frameworks to simulate Swiss households’ energy behavior using our collected demographic and time-use survey data, and gain understanding of  populational behavioural shifts & individual reactions to different demand response policy and extreme weather scenarios.

Supervisors: Vasantha Ramani ([email protected]) and Yufei Zhang ([email protected])

With the increasing availability of low-cost sensing technologies, indoor sensing can move beyond measuring environmental conditions and occupant presence toward understanding how people interact within shared spaces. This project will investigate how multimodal, privacy-preserving sensors can be used to characterize social dynamics in office environments. By combining distributed acoustic and environmental measurements, the project will examine when and where social interactions occur, how interaction patterns vary across space and time, and how they relate to different workspace conditions. The project will involve sensor deployment, data collection and synchronization, feature extraction, and analysis of multimodal sensing data, with the broader goal of developing non-invasive approaches for understanding human–environment interactions in shared workplaces.

Supervisors: Xinting Gao ([email protected]) and Shiyu Liu ([email protected])

With the amount of data about urban environments steadily rising, we have new possibilities to understand the structure of cities. Data about urban form—such as characteristics of buildings, pedestrian and vehicle street networks, public space, and more—can tell us a great deal about how cities are planned and designed. As different cities, or different parts of the same city, would be expected to have different characteristics of design, we would also expect those cities to create different experiences for their inhabitants. In this project, we will use machine learning clustering tools to identify different “natural” city forms, and we will attempt to interpret the results of the clustering algorithm using surveys that capture residents’ experiences of the different urban forms as pedestrians and cyclists. The results of this investigation will enable new research that allows comparison of different forms vis-a-vis human experiences and behaviors in the urban built environment.

Supervisor: Andrew Sonta ([email protected]) 

Public spaces are not used uniformly over time. Each place can exhibit a distinctive rhythm shaped by daily routines, weekdays and weekends, local context, and its role within the city. This project will investigate how privacy-preserving spatiotemporal observations can be used to identify and compare patterns of activity across different urban contexts. The student will develop methods to aggregate and visualize activity patterns, identify recurring temporal signatures, and group public spaces according to how their use changes over time. The project will also examine which rhythms are shared across cities and which are locally specific. The broader goal is to develop a place-centered approach to understanding urban activity without relying on personal or demographic information.

Supervisor: Xinting Gao ([email protected])

Household energy consumption comes from the operation of various electrical appliances, space and water heating, space cooling, lighting and other end uses.  The distribution of energy for various end uses may vary depending on the type of dwelling, household size, income level, types of appliances, weather conditions and several other factors. This study is aimed at understanding the differences and similarities in occupancy and occupant behavior globally based on smart meter data and end use energy distributions in residential units.

Supervisor: Vasantha Ramani ([email protected])

Demand side management involves changes in energy consumption by the customers in order to reduce, remove or shift demand. This study is aimed at developing household-specific demand management strategies based on their domestic appliance usage. The main energy meter data is cumulative of the energy load from all the appliances, which can be disaggregated to identify various appliance usage using non-intrusive load monitoring (NILM) techniques. The extracted appliance signature is indicative of time and duration of appliance usage, which can then be used to identify deferrable and non-deferrable appliances in the household. This study is aimed at identifying different user types based on their interaction with domestic appliances and developing strategies for demand response.

Supervisor: Vasantha Ramani ([email protected])

Summary: This project aims to develop a composite index of walkability and cyclability, integrating physical (infrastructure), social (mobility practices), and environmental (air pollution, noise) dimensions, across multiple spatial scales (street segment, neighborhood, municipality). The framework will be piloted in the Canton of Vaud.

Data: TLM networks (sidewalks, cycling paths), Open data (OSM / Ouverture).

Prerequisites: Advanced Python + advanced statistics, Spatial Data Science, GIS; interest in active mobility, sustainable urbanism, and accessibility.

Contact: Andrew Sonta ([email protected]) 

Partner: Bureau Action Située

Summary: This project explores the relationship between the shape and extent of individuals’ activity spaces (covered areas, distances traveled, spatial dispersion) and their daily carbon footprint. The aim is to identify low-carbon mobility profiles while maintaining accessibility to essential activities, location innovation rate, spatial consumption, etc.

Data: Panel Lémanique 2023 (GPS data and questionnaires), TLM networks, emission factors (OFEV / MOBITOOL), GTFS datasets, and Open Data on transport networks.

Prerequisites: Advanced Python + advanced statistics, Spatial Data Science, GIS; strong interest in low-carbon lifestyles and sustainable mobility.

Contact: Andrew Sonta ([email protected]) 

Partner: Bureau Action Située

Summary: This project investigates how transport nodes—such as train stations, bus stops, and multimodal hubs—can be redesigned as inclusive centralities that better accommodate care-related mobility (e.g. childcare, elderly care, dependent relatives). The goal is to characterize care-related mobility chains and develop multidimensional accessibility indicators to evaluate and diagnose caregiving-friendly hubs.

Data: Panel Lémanique 2023 (GPS data and questionnaires), TLM networks, GTFS datasets, Open Data on local services and facilities.

Prerequisites: Advanced Python + advanced statistics, Spatial Data Science, GIS; interest in daily mobility patterns and social inequalities.

Contact: Andrew Sonta ([email protected]) 

Partner: Bureau Action Située