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Fusing TensorFlow with building energy simulation for intelligent energy management in smart cities

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A deep learning method for the trajectory reconstruction of cosmic rays with the DAMPE mission

Jennifer Maria Frieden, Chiara Perrina, Xin Wu, Arshia Ruina, Andrii Kotenko

A deep learning method for the particle trajectory reconstruction with the DAMPE experiment is presented. The developed algorithms constitute the first fully machine-learned track reconstruction pipeline for space astroparticle missions. Significant perfor ...

ELSEVIER2023

Reinforcement Learning for the occupant-centric operation of building energy systems: Theoretical and experimental investigations

Amirreza Heidari

Occupant behavior, defined as the presence and energy-related actions of occupants, is today known as a key driver of building energy use. Closing the gap between what is provided by building energy systems and what is actually needed by occupants requires ...

EPFL2022

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Occupant behavior is a highly stochastic phenomenon, which is known as a key challenge for the optimal control of residential energy systems. With the increasing share of renewable energy in the building sector, the volatile nature of renewable energy is a ...

TU Delft2022

A systematic framework for the multi-time integration of industrial complexes and urban systems

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

The energy integration of industrial complexes and urban agglomerations has many benefits in terms of rational energy use and reduced environmental impact, especially during scenarios of supply chain uncertainty and volatile market prices. In fact, the div ...

2022

The Rebound Effects from Industrial Energy Efficiency Improvements

Michel Zimmermann

In energy policy, energy efficiency constitutes a central element in reducing domestic and, specifically, industrial en-ergy use. Unfortunately, the effectiveness of energy efficiency improvements in achieving its targets is known to be limited by rebound ...

EPFL2022

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Objective: Continuous monitoring of biosignals via wearable sensors has quickly expanded in the medical and wellness fields. At rest, automatic detection of vital parameters is generally accurate. However, in conditions such as high-intensity exercise, sud ...

2022

Real-time model calibration with deep reinforcement learning

Olga Fink

The real-time, and accurate inference of model parameters is of great importance in many scientific and engineering disciplines that use computational models (such as a digital twin) for the analysis and prediction of complex physical processes. However, f ...

2022

A prescriptive Dirichlet power allocation policy with deep reinforcement learning

Olga Fink

Prescribing optimal operation based on the condition of the system, and thereby potentially prolonging its remaining useful lifetime, has tremendous potential in terms of actively managing the availability, maintenance, and costs of complex systems. Reinfo ...

2022

Momentum-Based Policy Gradient with Second-Order Information

Patrick Thiran, Negar Kiyavash, Mohammadsadegh Khorasani, Saber Salehkaleybar

Variance-reduced gradient estimators for policy gradient methods have been one of the main focus of research in the reinforcement learning in recent years as they allow acceleration of the estimation process. We propose a variance-reduced policy-gradient m ...

2022

In this letter, we present a method for integrating central pattern generators (CPGs), i.e. systems of coupled oscillators, into the deep reinforcement learning (DRL) framework to produce robust and omnidirectional quadruped locomotion. The agent learns to ...

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC2022