Matthias C. M. Troffaes
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View article: Under frequency load shedding aware unit commitment in island power systems
Under frequency load shedding aware unit commitment in island power systems Open
The transition from fossil fuels to renewable energy sources in power systems has resulted in lower system inertia and deteriorated frequency response characteristics. The challenge becomes even more pronounced in island power systems, whe…
View article: Robust Bayesian causal estimation for causal inference in medical diagnosis
Robust Bayesian causal estimation for causal inference in medical diagnosis Open
Causal effect estimation is a critical task in statistical learning that aims to find the causal effect on subjects by identifying causal links between a number of predictor (or, explanatory) variables and the outcome of a treatment. In a …
View article: Data-driven estimation of the amount of under frequency load shedding in small power systems
Data-driven estimation of the amount of under frequency load shedding in small power systems Open
This paper presents a data-driven methodology for estimating under frequency load shedding (UFLS) in small power systems. UFLS plays a vital role in maintaining system stability by shedding load when the frequency drops below a specified t…
View article: Elicitation for Decision Problems Under Severe Uncertainties
Elicitation for Decision Problems Under Severe Uncertainties Open
International audience
View article: Data-Driven Infrastructure Planning for Offshore Wind Farms
Data-Driven Infrastructure Planning for Offshore Wind Farms Open
Offshore wind farms are one of the major renewable energy resources that can help the UK to reach its net zero target. Under the 10 point plan of the green revolution, the UK is set to quadruple its wind energy production by increasing its…
View article: Regret-based budgeted decision rules under severe uncertainty
Regret-based budgeted decision rules under severe uncertainty Open
One way to make decisions under uncertainty is to select an optimal option\nfrom a possible range of options, by maximizing the expected utilities derived\nfrom a probability model. However, under severe uncertainty, identifying\nprecise p…
View article: Data-Driven Estimation of Under Frequency Load Shedding after Outages in Small Power Systems
Data-Driven Estimation of Under Frequency Load Shedding after Outages in Small Power Systems Open
View article: Data-driven Estimation of Under Frequency Load Shedding after Outages in Small Power Systems
Data-driven Estimation of Under Frequency Load Shedding after Outages in Small Power Systems Open
This paper presents a data-driven methodology for estimating Under Frequency Load Shedding (UFLS) in small power systems. UFLS plays a vital role in maintaining system stability by shedding load when the frequency drops below a specified t…
View article: A Robust Bayesian Approach for Causal Inference Problems
A Robust Bayesian Approach for Causal Inference Problems Open
Causal inference concerns finding the treatment effect on subjects along with causal links between the variables and the outcome. However, the underlying heterogeneity between subjects makes the problem practically unsolvable. Additionally…
View article: Inclusion of frequency nadir constraint in the unit commitment problem of small power systems using machine learning
Inclusion of frequency nadir constraint in the unit commitment problem of small power systems using machine learning Open
View article: Inclusion of Frequency Nadir Constraint in the Unit Commitment Problem of Small Power Systems Using Machine Learning
Inclusion of Frequency Nadir Constraint in the Unit Commitment Problem of Small Power Systems Using Machine Learning Open
View article: Inclusion of Frequency Nadir constraint in the Unit Commitment Problem of Small Power Systems Using Machine Learning
Inclusion of Frequency Nadir constraint in the Unit Commitment Problem of Small Power Systems Using Machine Learning Open
As the intention is to reduce the amount of thermal generation and to increase the share of clean energy, power systems are increasingly becoming susceptible to frequency instability after outages due to reduced levels of inertia. To addre…
View article: Iterative importance sampling with Markov chain Monte Carlo sampling in robust Bayesian analysis
Iterative importance sampling with Markov chain Monte Carlo sampling in robust Bayesian analysis Open
View article: A Robust Bayesian Analysis of Variable Selection under Prior Ignorance
A Robust Bayesian Analysis of Variable Selection under Prior Ignorance Open
View article: A robust Bayesian bias‐adjusted random effects model for consideration of uncertainty about bias terms in evidence synthesis
A robust Bayesian bias‐adjusted random effects model for consideration of uncertainty about bias terms in evidence synthesis Open
Meta‐analysis is a statistical method used in evidence synthesis for combining, analyzing and summarizing studies that have the same target endpoint and aims to derive a pooled quantitative estimate using fixed and random effects models or…
View article: Decision Making Under Severe Uncertainty on a Budget
Decision Making Under Severe Uncertainty on a Budget Open
View article: Foundations For Temporal Reasoning Using Lower Previsions Without A Possibility Space
Foundations For Temporal Reasoning Using Lower Previsions Without A Possibility Space Open
We introduce a new formal mathematical framework for probability theory,
\ntaking random quantities to be the fundamental objects of interest, without reference to
\na possibility space, in spirit of de Finetti’s treatment of probability, …
View article: Inclusion of frequency stability constraints in unit commitment using separable programming
Inclusion of frequency stability constraints in unit commitment using separable programming Open
View article: Robust Decision Analysis under Severe Uncertainty and Ambiguous Tradeoffs: An Invasive Species Case Study
Robust Decision Analysis under Severe Uncertainty and Ambiguous Tradeoffs: An Invasive Species Case Study Open
Bayesian decision analysis is a useful method for risk management decisions, but is limited in its ability to consider severe uncertainty in knowledge, and value ambiguity in management objectives. We study the use of robust Bayesian decis…
View article: Improving and benchmarking of algorithms for Γ-maximin, Γ-maximax and interval dominance
Improving and benchmarking of algorithms for Γ-maximin, Γ-maximax and interval dominance Open
View article: Bayesian Adaptive Selection Under Prior Ignorance
Bayesian Adaptive Selection Under Prior Ignorance Open
Bayesian variable selection is one of the popular topics in modern day statistics. It is an important tool for high dimensional statistics, where the number of model parameters is greater than the number of observations. Several Bayesian m…
View article: Modelling of modular battery systems under cell capacity variation and degradation
Modelling of modular battery systems under cell capacity variation and degradation Open
View article: Uncertainty Quantification in Lasso-Type Regularization Problems
Uncertainty Quantification in Lasso-Type Regularization Problems Open
Regularization techniques, which sit at the interface of statistical modeling and machine learning, are often used in the engineering or other applied sciences to tackle high dimensional regression (type) problems. While a number of regula…
View article: An economic model for offshore transmission asset planning under severe uncertainty
An economic model for offshore transmission asset planning under severe uncertainty Open
The inherent uncertainties associated with offshore wind are substantial, as are the investments. Therefore, investors are keen to identify and evaluate the risks. This paper presents a model to economically evaluate projects from an offsh…
View article: Binary Credal Classification Under Sparsity Constraints
Binary Credal Classification Under Sparsity Constraints Open
View article: A sensitivity analysis and error bounds for the adaptive lasso.
A sensitivity analysis and error bounds for the adaptive lasso. Open
Sparse regression is an efficient statistical modelling technique which
\nis of major relevance for high dimensional problems. There are several ways of
\nachieving sparse regression, the well-known lasso being one of them. However,
\nlass…
View article: A Review of Methods to Study Resilience of Complex Engineering and Engineered Systems
A Review of Methods to Study Resilience of Complex Engineering and Engineered Systems Open
Uncertainty and interconnectedness in complex engineering and engineered systems such as power-grids and telecommunication networks are sources of vulnerability compromising the resilience of these systems. Conditions of uncertainty and in…
View article: Improving and benchmarking of algorithms for decision making with lower previsions
Improving and benchmarking of algorithms for decision making with lower previsions Open
View article: Using interval dominance and Gamma-maximin for decision making in offshore power transmission.
Using interval dominance and Gamma-maximin for decision making in offshore power transmission. Open
View article: A Cantelli-Type Inequality for Constructing Non-Parametric P-Boxes Based on Exchangeability
A Cantelli-Type Inequality for Constructing Non-Parametric P-Boxes Based on Exchangeability Open
In this paper we prove a new probability inequality that
\ncan be used to construct p-boxes in a non-parametric
\nfashion, using the sample mean and sample standard
\ndeviation instead of the true mean and true standard deviation.
\nThe in…