Machine Learning and Geographic Information Systems for large-scale mapping of renewable energy potential

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Agrivoltaic systems are characterized by the co-existence of photovoltaic panels on agricultural land, allowing simultaneous solar energy and food production without need for further land. Agrivoltaic installations alter the local microclimatic conditions ...

The recent geopolitical conflicts in Europe have underscored the vulnerability of the current energy system to the volatility of energy carrier prices. In the prospect of defining robust energy systems ensuring sustainable energy supply in the future, the ...

Recent progress in computer vision has been driven by a simple yet transformative observation: the potential of 30-year-old neural networks can be unlocked by massively scaling their parameters and the amount of human-labeled training data. This breakthrou ...

Machine learning models (e.g., neural networks) achieve high accuracy in wind power forecasting, but they are usually regarded as black boxes that lack interpretability. To address this issue, the paper proposes a glass-box model that combines high accurac ...

Deep learning has shown remarkable potential for industrial applications, particularly in predictive maintenance and condition monitoring. A key challenge in this domain is prognostics, specifically predicting the Remaining Useful Life (RUL) of assets - a ...

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 ...

Interface engineering has proven to be an effective approach for passivating interfacial imperfections to mitigate non-radiative recombination, but the subpar interface quality between the perovskite and the charge transport layer has hindered advancements ...

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 ...

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 ...

This study presents a coupled techno-economic and environmental model of hybrid sensible-latent thermal energy storage (TES) systems, integrating phase change material (PCM) macro-capsules to enhance storage capacity and performance. The multi-scale model ...

The current machine learning paradigm relies on continuous representations and fixed neural network architectures to approximate environmental structures, leading to challenges with continual learning, internal structure design, and goal-directed behavior ...

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 ...

When arranged in a crossbar configuration, resistive memory devices can be used to execute Matrix-Vector Multiplications (MVMs), the most dominant operation of many Machine Learning (ML) algorithms, in constant time complexity. Nonetheless, when performing ...

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 ...

Photovoltaic (PV) technology is necessary for global decarbonization. However, one of the challenges of the technology is that its land use may conflict with other space demands. Building-integrated photovoltaic (BIPV) is a solution to efficiently use the ...

Proteins are the molecular machines of life, driving essential biological processes such as enzymatic catalysis, cellular signaling, and immune responses. The ability to design entirely new proteins with desired structures and functions has immense implica ...

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 ...

Cyclic peptides are attractive for drug discovery due to their excellent binding properties and the potential to cross cell membranes. However, by far not all cyclic peptides are cell permeable, and measuring or predicting their membrane permeability is no ...

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 ...

Decentralized learning (DL) is an emerging approach that enables nodes to collaboratively train a machine learning model without sharing raw data. In many application domains, such as healthcare, this approach faces challenges due to the high level of hete ...

Machine Learning and Geographic Information Systems for large-scale mapping of renewable energy potential

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Controls of Ecohydrological Grassland Dynamics in Agrivoltaic Systems

Identification of typical district configurations: A two-step global sensitivity analysis framework

Exploiting Representation Similarities in Self-Supervised Learning for Vision Tasks

Explainable modeling for wind power forecasting: A Glass-Box model with high accuracy

Overcoming Distribution Shifts and Imbalance Challenges in Representation Learning for Deep Regression Models

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

Localized Tunneling 1D Perovskitoid Passivated Contacts for Efficient and Stable Perovskite Solar Modules

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

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

Optimization of hybrid sensible-latent seasonal heat storage systems

Modelleyen: Continual Learning and Planning via Structured Modelling of Environment Dynamics

Network time series forecasting in photovoltaics power production

LIONHEART: A Layer-based Mapping Framework for Heterogeneous Systems with Analog In-Memory Computing Tiles

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

Advanced Manufacturing and Characterization of Building-Integrated Photovoltaic Modules

Computational design of complex protein folds and soluble analogs of membrane proteins

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

Bulk measurement of membrane permeability for random cyclic peptides in living cells to guide drug development

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

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