Publications associées à Quantifying the impacts of climate change and extreme climate events on energy systems

Advanced control strategies to exploit the hydropower potential enhancing ancillary services provision to the power system

The European Union's Green Deal aims for a 55% reduction in greenhouse gas emissions by 2030. To reach this goal, a massive integration of Renewable Energy Sources (RES) into the power grid is necessary. As RES become a large part of the electricity genera ...

EPFL2024

Renewable energy integration and waste heat valorization in aluminum remelting for co-producing kerosene and methanol

François Maréchal, Daniel Alexander Florez Orrego, Meire Ellen Gorete Ribeiro Domingos, Réginald Germanier

The aluminium sector relies on natural gas for the conversion of recycled scrap into new feedstock, which results in substantial atmospheric emissions. Hydric resources are also impacted, as they serve as heat sinks for the waste heat generated during the ...

Associazione Italiana Di Ingegneria Chimica2024

Quantifying the Unknown: Data-Driven Approaches and Applications in Energy Systems

Paul Scharnhorst

In light of the challenges posed by climate change and the goals of the Paris Agreement, electricity generation is shifting to a more renewable and decentralized pattern, while the operation of systems like buildings is increasingly electrified. This calls ...

EPFL2024

Absolute energy levels of liquid water from many-body perturbation theory with effective vertex corrections

Alfredo Pasquarello, Thomas Bischoff, Aleksei Tal

We demonstrate the importance of addressing the F vertex and thus going beyond the GW approximation for achieving the energy levels of liquid water in manybody perturbation theory. In particular, we consider an effective vertex function in both the polariz ...

National Academy of Sciences2024

Climate risk assessment of buildings: An analysis of operating emissions of commercial offices in Australia

Arianna Brambilla

Building climate risk assessment involves benchmarking a building's energy use intensity against decarbonisation pathways to mitigate the impacts on climate change. Various climate risk assessment tools and frameworks are used for commercial buildings in d ...

Elsevier Science Sa2024

Beyond the average consumer: Mapping the potential of demand-side management among patterns of appliance usage

Claudia Rebeca Binder Signer, Matteo Barsanti, Selin Yilmaz

To support the decarbonisation of the power sector and offset the volatility of a system with high levels of renewables, there is growing interest in residential Demand-Side Management (DSM) solutions. Traditional DSM strategies require consumers to active ...

2024

Fusing Pre-existing Knowledge and Machine Learning for Enhanced Building Thermal Modeling and Control

Loris Di Natale

Buildings play a pivotal role in the ongoing worldwide energy transition, accounting for 30% of the global energy consumption. With traditional engineering solutions reaching their limits to tackle such large-scale problems, data-driven methods and Machine ...

EPFL2024

Network time series forecasting in photovoltaics power production

Jelena Simeunovic

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 ...

EPFL2024

Green hydrogen production using Shewanella oneidensis MR-1 bioanode and cuprous oxide-based photocathode

Michele Morgante

In recent years, many efforts have been made to find alternative renewable energy sources that can ideally replace the use of fossil fuels in all aspects. One of the new emerging energy technologies is the bioelectrochemical system, of which two types are ...

EPFL2024

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

Giovanni Ansaloni, Alexandre Sébastien Julien Levisse, Pengbo Yu, Flavio Ponzina

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 ...

IEEE2024