Deep learning in the built environment: automatic detection of rooftop solar panels using Convolutional Neural Networks

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Accurate forecasting of photovoltaic (PV) power production is crucial for the integration of more renewable energy sources into the power grid. PV power production is highly intermittent, due to the stochastic cloud behaviour and cloud dynamics. Previous w ...

Like many other countries, Switzerland offers various incentives to promote residential solar PV, but not all households have equal access to them. Using a microsimulation approach based on merged data from the Swiss Household Budget Survey and Household E ...

Solar photovoltaics (PV) is one of the most competitive renewable energy technologies in order to meet the increasing global energy demand and decrease CO2 emissions by competing effectively with fossil fuels. One of the important applications of PV energy ...

Modern neuroscience research is generating increasingly large datasets, from recording thousands of neurons over long timescales to behavioral recordings of animals spanning weeks, months, or even years. Despite a great variety in recording setups and expe ...

Recent advancements in deep learning have revolutionized 3D computer vision, enabling the extraction of intricate 3D information from 2D images and video sequences. This thesis explores the application of deep learning in three crucial challenges of 3D com ...

The upcoming Square Kilometre Array Observatory will produce images of neutral hydrogen distribution during the epoch of reionization by observing the corresponding 21-cm signal. However, the 21-cm signal will be subject to instrumental limitations such as ...

Monitoring forests, in particular their response to climate and land use change, requires studying long time scales. While efficient deep learning methods have been developed to process short time series of satellite imagery, leveraging long time series of ...

The electrification of the transport sector is a key element in decarbonizing our societies. However, energy systems will have to cope with additional electricity demand due to the charging needs of electric vehicles (EVs) together with the integration of ...

In the rapidly evolving landscape of machine learning research, neural networks stand out with their ever-expanding number of parameters and reliance on increasingly large datasets. The financial cost and computational resources required for the training p ...

Lightweight modules are essential for next-generation vehicle-integrated photovoltaic (VIPV) applications, such as solar-powered cars, allowing integration of solar cells beyond the roof, and on the hood, boot and body panels, and thereby extending the dri ...

This paper presents a comparison of machine learning (ML) methods used for three-dimensional localization of partial discharges (PD) in a power transformer tank. The study examines ML and deep learning (DL) methods, ranging from support vector machines (SV ...

Machine learning (ML) enables artificial intelligent (AI) agents to learn autonomously from data obtained from their environment to perform tasks. Modern ML systems have proven to be extremely effective, reaching or even exceeding human intelligence. Altho ...

The ability to reason, plan and solve highly abstract problems is a hallmark of human intelligence. Recent advancements in artificial intelligence, propelled by deep neural networks, have revolutionized disciplines like computer vision and natural language ...

Photo-electrochemical (PEC) devices allow for converting solar energy into chemical energy and for the production of energetically dense solar fuels. Light absorption, charge separation and transport, electrochemical reactions, and ionic transport are requ ...

In the past few years, Machine Learning (ML) techniques have ushered in a paradigm shift, allowing the harnessing of ever more abundant sources of data to automate complex tasks. The technical workhorse behind these important breakthroughs arguably lies in ...

Machine learning algorithms such as Convolutional Neural Networks (CNNs) are characterized by high robustness towards quantization, supporting small-bitwidth fixed-point arithmetic at inference time with little to no degradation in accuracy. In turn, small ...

Digital twins are virtual models of physical objects or systems that enable real-time monitoring and analysis. In the ﬁeld of stone masonry buildings, digital twins can be used to assess damage, predict maintenance needs, and opti- mize building performanc ...

In this thesis we explore the applications of projective geometry, a mathematical theory of the relation between 3D scenes and their 2D images, in modern learning-based computer vision systems. This is an interesting research question which contradicts the ...

Photometric stereo, a computer vision technique for estimating the 3D shape of objects through images captured under varying illumination conditions, has been a topic of research for nearly four decades. In its general formulation, photometric stereo is an ...

Perovskite solar cells (PSCs) offer an attractive solution to increase the efficiency of PV panels via perovskite/silicon tandems thanks to their potential cost-effectiveness and high efficiency. In inverted PSC architecture (p-i-n), commonly used hole con ...

Deep learning in the built environment: automatic detection of rooftop solar panels using Convolutional Neural Networks

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Network time series forecasting in photovoltaics power production

Distributional justice, effectiveness, and costs of current and alternative solar PV incentive schemes in Switzerland

Reliability and Long-term Performance of Building Integrated Photovoltaic (BIPV) Modules

Robust machine learning for neuroscientific inference

Advancing Self-Supervised Deep Learning for 3D Scene Understanding

Deep learning approach for identification of H II regions during reionization in 21-cm observations - II. Foreground contamination

Multi-temporal forest monitoring in the Swiss Alps with knowledge-guided deep learning

From PV to EV: Mapping the Potential for Electric Vehicle Charging with Solar Energy in Europe

Random matrix methods for high-dimensional machine learning models

Encapsulation strategies for mechanical impact and damp heat reliability improvement of lightweight photovoltaic modules towards vehicle-integrated applications

Partial discharge localization in power transformer tanks using machine learning methods

Few-shot Learning for Efficient and Effective Machine Learning Model Adaptation

Infusing structured knowledge priors in neural models for sample-efficient symbolic reasoning

Modeling, design and economics of PEC devices

Topics in statistical physics of high-dimensional machine learning

DBFS: Dynamic Bitwidth-Frequency Scaling for Efficient Software-defined SIMD

Digital Twins of Stone Masonry Buildings for Damage Assessment

Incorporating Projective Geometry into Deep Learning

Aggregating Spatial and Photometric Context for Photometric Stereo

Identification and Mitigation of Degradation Mechanisms Induced by Heat and Light in NiOx-based Perovskite Solar Cells