Urban Energy SystemsOpen OpportunitiesIn the era of climate change and growing global energy demand, smart energy systems have become pivotal in ensuring sustainable, efficient, and reliable energy delivery. These systems, characterized by the integration of advanced metering infrastructure, renewable energy sources, and innovative demand response technologies, form the backbone of modern energy strategies aimed at reducing carbon footprints and enhancing energy security. The Swiss Confederation, cognizant of these imperatives, advocates for a robust transition towards intelligent energy networks, setting the ambitious goal of a net-zero carbon economy by 2050. As we push the boundaries of energy system innovation, the imperative of resilience cannot be overstated. Resilience in this context refers to the smart energy system's capacity to anticipate, withstand, and recover from various forms of disruption like environmental phenomena, technical failures, or human-induced events. This project acknowledges the complexity and interdependence of the smart energy ecosystem, encompassing residential buildings equipped with the latest in energy-efficient technologies, user interfaces that allow for dynamic interaction with the energy grid, and decentralized renewable energy generation units that contribute to a sustainable energy mix.
Electric vehicles (EVs), Heating, Ventilation, and Air Conditioning (HVAC) systems, and domestic appliances represent significant loads within the residential sector that can be managed to foster resilience. The bi-directional flow of energy in smart grids, facilitated by smart meters, allows for sophisticated energy management strategies that not only respond to system demands but also to user behaviors and preferences. The resilience of such an interconnected system hinges on its ability to maintain stability and operation despite unpredictable renewable energy generation patterns, potential cyber-physical threats, fluctuations in the energy market due to instability in the neighboring countries, and changes in user behavior. The Swiss energy paradigm provides an exemplary context for studying and enhancing the resilience of smart energy systems. By developing a conceptual framework for resilience assessment tailored to this context, this thesis aims to contribute to the body of knowledge that will empower stakeholders to design, implement, and maintain robust energy systems. - Building, Conceptual Modelling, Systems Theory and Control
- Master Thesis, Semester Project
| Switzerland is committed to transitioning to a renewable energy system. The Swiss government has set a target of achieving net-zero carbon emissions by 2050. This will require a significant increase in the use of renewable energy sources. The Swiss power grid is also vulnerable to imbalances be-tween supply and demand. Demand flexibility can help to mitigate this risk and ensure the reliable operation of the power grid. Demand flexibility is the ability to shift or reduce energy use in response to changes in sup-ply or price. This is becoming increasingly important as the power grid transitions to renewable energy sources, such as solar and wind power, which are intermittent and less predictable. Demand flexibility can help to balance the grid and reduce the need for expensive and polluting backup power plants. Non-Intrusive Load Monitoring (NILM) and customer segmentation modeling are powerful tools that can be used to develop demand flexibility programs. NILM can be used to identify high-energy-consuming appliances and to track their energy usage over time. Customer segmentation modeling can be used to identify different groups of customers based on their energy consumption patterns. This information can then be used to develop targeted demand flexibility programs that are more likely to be effective for each group of customers. - Building not elsewhere classified, Building Science and Techniques, Neural Networks, Genetic Alogrithms and Fuzzy Logic, Signal Processing, Simulation and Modelling
- Master Thesis
| Buildings appear as significant energy consumers, especially due to the management of heating, ventilation, and air-conditioning (HVAC). Each building has unique characteristics such as varied geometries, floor layouts, construction properties, age, climatic regions, orientation, and service systems. Better control of indoor temperature in buildings seems to be a means of energy savings. Traditional approaches rely on building modeling for this purpose.
While physics-based models may be precise and aligned with expected physical behaviors, their complex design can limit their application and scalability.
An alternative modeling approach based solely on sensor data (temperature, solar irradiance, etc.) aims to be more flexible and is generating increasing interest. However, these approaches require diverse data in sufficient quantity to train the model parameters and might demand more computing power than what buildings can accommodate.
The complexity of models, their instability, or the lack of data poses obstacles when attempting to model a new building.
The primary goal of this project is to leverage the flexibility of data-driven methods to model the thermal behavior of buildings, emphasizing the development of a transferable model.
This approach aims to streamline the modeling process by enabling the initial learning of a model for one building and its subsequent adaptation to other buildings. - Artificial Intelligence and Signal and Image Processing, Engineering and Technology, Systems Theory and Control
- Master Thesis, Semester Project
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