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])

Building energy demand flexibility helps with balancing the grid and increasing uptake of local renewable generations. In office buildings, the majority of the energy consumption, such as HVAC, lighting, and plug loads, are usually dominated by occupants’ behavior. Thus, the recent trend of hybrid working and other types of flexible work arrangement (FWA) might unlock much further energy flexibility potential. However, how FWA actions influence the building occupancy level and therefore the consequent energy consumption are very case-specific and remains unclear in general. In this project, students will conduct simulation study on a joint-energy-occupancy simulation testbed to discover these underlying relationships, and will also be involved in development of meta-models for fast evaluation of the impacts from FWA on energy energy flexibility.

Supervisor: Yufei Zhang ([email protected])

The importance of the design of the built environment on human behavior and experiences has been studied, but largely from a theoretical perspective and lacking empirical evidence. This project aims to understand where data-driven analysis is needed and to conduct data analysis to better characterize the human and built environment relationships. Some components of this topic include investigating how physical features of the existing built environment influence human outcomes and experiences (e.g., social cohesion and well-being) and how human behaviors are interacting with the built environment (e.g. active mobility such as walking). The detailed focus of this project will be shaped based on students’ interests and background. 

Supervisor: Kanaha Shoji ([email protected])

In most industrialized nations, the building sector accounts for about 40% of the total energy consumption. A significant share of this energy is used to thermal control buildings and provide thermally comfortable indoor environments. However, technical building systems are typically designed and operated considering fixed set-point temperatures based on the ‘one-size-fits-all’ principle assuming universal thermal comfort requirements. Furthermore, the indoor environment frequently changes abruptly across buildings or between various parts within a single building, and the steady-state temperature settings are the exception rather than the rule. Building temperature ranges should instead be based on real-time empirical evidence regarding the needs of its occupants. This project will develop a new modeling strategy that considers the occupant and his/her needs, linking perception to an action (behavior). Moreover, this project will also investigate the application of ‘personalized environmental control systems’ (PECS) as a means to improve personal thermal comfort while potentially reducing energy consumption.

Supervisor: Matteo Favero ([email protected]) 

Comfort and satisfaction of occupants are key objectives of building management and lead to complex trade-offs with energy and emission targets. Recent advances in sensing and machine-learning have catalyzed a new paradigm of personalized comfort models that can predict and analyze individuals’ dynamic comfort statuses based on streamed localized/portable/wearable sensors alongside comfort perception feedback from mobile apps. This study aims to progress from the previously-collected personalized comfort datasets and recently developed models in our lab, to address the remaining issues in modeling and analysis strategies. We may together address one or several of the following subtasks, depending on the duration of the project: 1). data-mining bodily and ambient measurements: critical change in ambient and occupant’s bodily condition may be associated with potential comfort status change. We want to apply widely-used change point detection & data-drift detection algorithms to spot those changes. 2). Heterogeneity analysis of individual comfort perception: occupants’ perception of comfort status can be rather heterogeneous by nature, and it is necessary to propose metrics that effectively cluster representative types of comfort preference and sensitivity.  3). Feature analysis for interpretable personalized comfort prediction: we will further explore recently-developed machine-learning comfort status prediction and feature analysis pipeline to draw critical guidelines for successful real-world deployment of personalized comfort models, such as minimally-required features set that are most relevant, and representative scenarios of comfort status transition under interactions of multiple features. 

Supervisor: Yufei Zhang and Matteo Favero ([email protected])

With the advent of low-cost sensing devices, it is becoming easier to measure the qualities of the indoor environment, such as air quality, occupant presence, noise levels, and more. Doing so can improve our understanding of the performance of the building and also gather non-invasive information about the occupant’s experience. This project will develop an indoor environment sensing strategy and gather initial data. The aim of the sensing strategy will be to focus on human-building interactions, such as occupant presence and occupant social interactions in the office. The data will be analyzed to determine what occupant interactions can be inferred through the non-invasive, privacy-preserving data collection.

Supervisor: Andrew Sonta and Vasantha Ramani ([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])

With the amount of data about urban environmentals 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. 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])