Publications related to Technology forecasting

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

Towards Motion Forecasting with Real-World Perception Inputs: Are End-to-End Approaches Competitive?

Alexandre Massoud Alahi

Motion forecasting is crucial in enabling autonomous vehicles to anticipate the future trajectories of surrounding agents. To do so, it requires solving mapping, detection, tracking, and then forecasting problems, in a multi-step pipeline. In this complex ...

2024

Fast and Future: Towards Efficient Forecasting in Video Semantic Segmentation

Evann Pierre Guy Courdier

Deep learning has revolutionized the field of computer vision, a success largely attributable to the growing size of models, datasets, and computational power. Simultaneously, a critical pain point arises as several computer vision applications are deploye ...

EPFL2024

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

Benjamin Bejar Haro, Ekaterina Krymova, Ntorina Thanou, Nicola Parolini, Daniel Rodrigo Lopez, Andrea Kraus, Daniel Lopez, David Kraus, 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

Optimizing Sales Forecasting, Inventory, Pricing and Sourcing Decisions

Yara Kayyali El Alem

In this thesis we address various factors that contribute both theoretically and practically to mitigating supply demand mismatches. The thesis is composed of three chapters, where each chapter is an independent scientific paper. In the first paper, we dev ...

EPFL2023

Validating model-based data interpretation methods for quantification of reserve capacity

Ian Smith, Sai Ganesh Sarvotham Pai

Optimal performance of civil infrastructure is an important aspect of liveable cities. A judicious combination of physics-based models with monitoring data in a validated methodology that accounts for uncertainties is explored in this paper. This methodolo ...

2021

Waste collection inventory routing with non-stationary stochastic demands

Michel Bierlaire, Sacha Varone, Mohammad Yousef Maknoon, Iliya Dimitrov Markov

We solve a rich routing problem inspired from practice, in which a heterogeneous fixed fleet is used for collecting recyclable waste from large containers over a finite planning horizon. Each container is equipped with a sensor that communicates its level ...

PERGAMON-ELSEVIER SCIENCE LTD2020

Model based forecasting for demand response strategies

Lorenzo Nespoli

The incremental deployment of decentralized renewable energy sources in the distribution grid is triggering a paradigm change for the power sector. This shift from a centralized structure with big power plants to a decentralized scenario of distributed ene ...

EPFL2019

Information-gap decision theory (IGDT): a new promising method for management of uncertainties in hydropower projects: case study

Anton Schleiss, Felix Oberrauch

The design of hydropower is determined by estimates and long-term forecasts. These forecasts and estimates are highly uncertain and make performance evaluation and design choices challenging. Effective values deviating from the estimates can have a signifi ...

Hydropower&Dams, Aqua Media International2018

Hydropower Design under Uncertainties

Felix Oberrauch

The design of hydropower is determined by estimates and long-term forecasts. These forecasts and estimates are highly uncertain and make performance evaluation and design choices challenging. Performance evaluation of hydropower projects is affected by a n ...

EPFL2017

Implementation and Performance Analysis of the Lightning Potential Index as a Forecasting Tool

Farhad Rachidi-Haeri, Aleksandr Smorgonskii, Martina Lagasio

Severe weather events are responsible for hundreds of fatalities and millions of euros of damage every year in the Mediterranean basin. Lightning activity is a characteristic phenomenon of severe weather and often accompanies torrential rainfall, which und ...

Ieee2016

Model-free computation of ultra-short-term prediction intervals of solar irradiance

Jean-Yves Le Boudec, Mario Paolone, Dimitri Torregrossa

We propose an ultra-short-term dynamic interval predictor (DIP) of solar irradiance. Our DIP relies on experimentally observed correlations between the derivative of the solar irradiance and the forecast error in the next time-step. The main originalities ...

Pergamon-Elsevier Science Ltd2016

Excessive Volatility is Also a Feature of Individual Level Forecasts

Anjali Devi Vanapilli Nursimulu

The excessive volatility of prices in financial markets is one of the most pressing puzzles in social science. It has led many to question economic theory, which attributes beneficial effects to markets in the allocation of risks and the aggregation of inf ...

Routledge Journals, Taylor & Francis Ltd2014

A pre-estimation filtering process of bad data for linear power systems state estimators using PMUs

Jean-Yves Le Boudec, Mario Paolone, Marco Pignati, Lorenzo Zanni, Sidi-Rachid Cherkaoui, Styliani Sarri

The paper proposes a specific algorithm for the pre-estimation filtering of bad data (BD) in PMU-based power systems linear State Estimators (SEs). The approach is framed in the context of the so-called real-time SEs that take advantage of the high measure ...

Wroclaw University of Technology2014

Forecasting the demand for electric vehicles: accounting for attitudes and perceptions

Michel Bierlaire, Michaël Clément Louis Ghislain Thémans, Aurélie Glerum, Lidija Stankovikj

In the context of the arrival of electric vehicles on the car market, new mathematical models are needed to understand and predict the impact on the market shares. This research provides a comprehensive methodology to forecast the demand of a technology wh ...

Informs2014

Coupling satellite rainfall estimates and machine learning techniques for flow forecast: application to a large catchment in Southern Africa

Anton Schleiss, Théodora Cohen Liechti, José Pedro Gamito de Saldanha Calado Matos

Accurate river flow forecasting is an important asset for stream and reservoir management, being often translated into substantial social, economic and ecological gains. This contribution aims at coupling satellite rainfall estimates and machine learning t ...

TPU2013

Railway demand forecasting in revenue management using neural networks

Shadi Sharif Azadeh

This study analyses the use of neural networks to produce accurate forecasts of total bookings and cancellations before departure, of a major European rail operator. Effective forecasting models, can improve revenue performance of transportation companies ...

2013

A rewarding-punishing coordination mechanism based on Trust in a divergent supply chain

Naoufel Cheikhrouhou

Coordination of decentralized supply chains using contract design is a problem that has been widely addressed in the literature. We consider a divergent supply chain including a supplier and several retailers producing fashion products with short sale seas ...

Elsevier Science Bv2013

Electricity Load Forecasting for Residential Customers: Exploiting Aggregation and Correlation between Households

Karl Aberer, Tri Kurniawan Wijaya, Matteo Vasirani, Samuel François Roger Joseph Humeau

The recent development of smart meters has allowed the analysis of household electricity consumption in real time. Predicting electricity consumption at such very low scales should help to increase the efficiency of distribution networks and energy pricing ...

IEEE2013

Modeling the behavior of investors

Michel Bierlaire, Thomas Robin

We introduce a hybrid discrete choice framework to model the decisions of investors in stock markets. More specifically, we model the decision to buy or sell stocks using a binary logit model with latent classes, characterizing the perception of risk. The ...

2012