Concept

Consensus forecast

Publications associées (51)

Challenges of COVID-19 Case Forecasting in the US, 2020-2021

Joseph Chadi Benoit Lemaitre, Pan Xu, Weitong Zhang, Wei Cao, Myungjin Kim, Shan Yu, Xinyi Li, Lei Gao

During the COVID-19 pandemic, forecasting COVID-19 trends to support planning and response was a priority for scientists and decision makers alike. In the United States, COVID-19 forecasting was coordinated by a large group of universities, companies, and ...

Public Library Science2024

Predictive performance of multi-model ensemble forecasts of COVID-19 across European nations

Ekaterina Krymova, Nicola Parolini, Andrea Kraus, David Kraus, Daniel Lopez, Markus Scholz, Tao Sun

Background:Short-term forecasts of infectious disease burden can contribute to situational awareness and aid capacity planning. Based on best practice in other fields and recent insights in infectious disease epidemiology, one can maximise the predictive p ...

eLIFE SCIENCES PUBL LTD2023

Improved extended-range prediction of persistent stratospheric perturbations using machine learning

On average every 2 years, the stratospheric polar vortex exhibits extreme perturbations known as sudden stratospheric warmings (SSWs). The impact of these events is not limited to the stratosphere: but they can also influence the weather at the surface of ...

Gottingen2023

National and subnational short-term forecasting of COVID-19 in Germany and Poland during early 2021

Ekaterina Krymova, Markus Scholz

Background During the COVID-19 pandemic there has been a strong interest in forecasts of the short-term development of epidemiological indicators to inform decision makers. In this study we evaluate probabilistic real-time predictions of confirmed cases an ...

London2022

A Dynamic Multi-Region MFD Model for Ride-Sourcing Systems with Ridesplitting

Nikolaos Geroliminis, Caio Vitor Beojone

Dynamic network-level models directly addressing ride-sourcing services can be useful for the development of efficient traffic management strategies both for city and company operators. Recent developments presented models under equilibrium situations for ...

2022

Building Electricity Demand Forecasting with Temporal Hierarchies

Lorenzo Donadio

Accurate building electricity load forecasts play a major role in the energy transition, as they facilitate flexibility deployment, grid stability and overall reduce costs and CO2 emissions. This research leaverages forecasting and reconciliation of tempor ...

2021

Activity-based modeling and simulation of epidemics

Michel Bierlaire, Cloe Cortes Balcells, Rico Krüger

The SARS-CoV-2 outburst in March 2020 has led to the lockdown of several countries across the world. Mobility restrictions have been constantly put into action and reversed to find the trade-off between minimizing the number of infections and death and mit ...

2021

RRT*-Based Algorithm for Trajectory Planning Considering Probabilistic Weather Forecasts

Maryam Kamgarpour, Eduardo Andres Enderiz

Convective weather and its inherent uncertainty constitute one of the major challenges in the air traffic management (ATM) system, entailing both safety hazards and economic losses. In the present work, we propose a stochastic algorithm for trajectory plan ...

2021

Characterizing the Reserve Provision Capability Area of Active Distribution Networks: A Linear Robust Optimization Method

Fabrizio Sossan, Mokhtar Bozorg, Mohsen Kalantar Neyestanaki

Distributed energy resources (DERs) installed in active distribution networks (ADNs) can be exploited to provide both active and reactive power reserves to the upper-layer grid (i.e., sub-transmission and transmission systems) at their connection point. Th ...

2021

Generating daily activity schedules using machine learning Master Thesis Sergej Gasparovich June 19, 2020 Prof. M. Bierlaire, J. Pougala, T. Hillel Transport and Mobility Laboratory, EPFL Y. Liu Visual Intelligence

Sergej Gasparovich

The study of how people schedule their daily activities is of interest in the context of transport demand forecasting using activity based-models, where activity schedules are generated in order to estimate the trip demand they produce. Machine learning ha ...

2020

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