roberto rocchetta

Scopus Publications

Risk-informed integration of renewable energy systems and storage in electric power grids: Assessing safety and economic viability via an efficient approach
Ozcel Cangul, Roberto Rocchetta, Edoardo Patelli
Energy Reports, 2026
Analysis of RUL dynamics and uncertainty via time transformation
Pierre Dersin, Roberto Rocchetta
Reliability Engineering and System Safety, 2026
This work introduces a novel analytical method to analyze the dynamics of remaining useful life (RUL) and quantify uncertainty in its estimation. The approach employs a time transformation that makes the mean residual life (MRL) a linear function of transformed time, enabling the derivation of explicit RUL confidence bounds. Once mapped back to physical space, the bounds quantify aleatoric (stochastic) uncertainty in RUL and yield asymmetrical confidence intervals for both parametric and non-parametric lifetime distributions. The approach leverages a key feature of reliability distributions: the average RUL loss rate, k , in transformed time, facilitating a direct derivation of confidence bounds. In parametric cases, k is uniquely defined by the reliability distribution parameters, while for non-parametric distributions, it is derived from data by estimating the coefficient of variation. Higher slopes indicate faster degradation, leading to narrower confidence intervals and lower RUL variance. The method’s applicability to stochastic processes and robustness under different data volumes are also investigated and discussed. The novel approach reveals heretofore unknown insights into classical reliability distributions. It is demonstrated through real-world applications, including LED reliability assessment, parallel system RUL estimation, and turbofan lifespan prediction using NASA N-CMAPSS data, offering a new perspective on the evolving dynamics of mean residual life and remaining useful life.
Nonlinear reconciliation: Error reduction theorems
Transactions on Machine Learning Research, 2026
Optimization of mobility incentives in electric vehicle car sharing systems: A reinforcement learning framework
Roberto Rocchetta, Lorenzo Nespoli, Vasco Medici, Aviva Shemesh, Yael Parag, Jalomi Maayan Tardif
Sustainable Cities and Society, 2025
Influence Learning in Complex Systems
Transactions on Machine Learning Research, 2025
A survey on LED Prognostics and Health Management and uncertainty reduction
Roberto Rocchetta, Elisa Perrone, Alexander Herzog, Pierre Dersin, Alessandro Di Bucchianico
Microelectronics Reliability, 2024
Hybrid Prognostics and Health Management (PHM) frameworks for light-emitting diodes (LEDs) seek accurate remaining useful life (RUL) predictions by merging information from physics-of-failure laws with data-driven models and tools for online monitoring and data collection. Uncertainty quantification (UQ) and uncertainty reduction are essential to achieve accurate predictions and assess the effect of heterogeneous operational-environmental conditions, lack of data, and noises on LED durability. Aleatory uncertainty is considered in hybrid frameworks, and probabilistic models and predictions are applied to account for inherent variability and randomness in the LED lifetime. On the other hand, hybrid frameworks often neglect epistemic uncertainty, lacking formal characterization and reduction methods. In this survey, we propose an overview of accelerated data collection methods and modeling options for LEDs. In contrast to other works, this review focuses on uncertainty quantification and the fusion of hybrid PHM models with optimal design of experiment methods for epistemic uncertainty reduction. In particular, optimizing the data collection with a combination of statistical optimality criteria and accelerated degradation test schemes can substantially reduce the epistemic uncertainty and enhance the performance of hybrid prognostic models.
Uncertainty analysis and interval prediction of LEDs lifetimes
Roberto Rocchetta, Zhouzhao Zhan, Willem Dirk van Driel, Alessandro Di Bucchianico
Reliability Engineering and System Safety, 2024
A Survey on Scenario Theory, Complexity, and Compression-Based Learning and Generalization
Roberto Rocchetta, Alexander Mey, Frans A. Oliehoek
IEEE Transactions on Neural Networks and Learning Systems, 2024
This work investigates formal generalization error bounds that apply to support vector machines (SVMs) in realizable and agnostic learning problems. We focus on recently observed parallels between probably approximately correct (PAC)-learning bounds, such as compression and complexity-based bounds, and novel error guarantees derived within scenario theory. Scenario theory provides nonasymptotic and distributional-free error bounds for models trained by solving data-driven decision-making problems. Relevant theorems and assumptions are reviewed and discussed. We propose a numerical comparison of the tightness and effectiveness of theoretical error bounds for support vector classifiers trained on several randomized experiments from 13 real-life problems. This analysis allows for a fair comparison of different approaches from both conceptual and experimental standpoints. Based on the numerical results, we argue that the error guarantees derived from scenario theory are often tighter for realizable problems and always yield informative results, i.e., probability bounds tighter than a vacuous [0, 1] interval. This work promotes scenario theory as an alternative tool for model selection, structural-risk minimization, and generalization error analysis of SVMs. In this way, we hope to bring the communities of scenario and statistical learning theory closer, so that they can benefit from each other's insights.
A BUSINESS MODEL OR A TARIFFING ISSUE? PEAK SHAVING IN LOCAL DISTRIBUTION GRIDS THROUGH ELECTRIC VEHICLE FLEETS
Guntram Preßmair, Jakob Papouschek, Roberto Rocchetta
Iet Conference Proceedings, 2024
This paper addresses the question how shared electric vehicle fleets, i.e. car-sharing or company fleets, can offer flexibility services for the electricity grid through smart and bidirectional charging. Specifically, the focus is on the perspective of DSOs, analysing the peak shaving potential at a common grid node. The paper provides initial insights from one of the case studies of the research project GAMES and demonstrates the economic potential under current and also possible future market designs for a station-based car sharing scheme in Zurich, Switzerland. The model deployed in this work is a linear economic dispatch model, providing optimal charging and discharging schedules for the electric vehicles of the fleet. The results suggest, that technically there could be the potential to reduce the annual peak load of the Zurich grid area by up to 4%. However, significant changes in the tariffing structure for charging peak loads need to be implemented to make business models for such use cases economically feasible.
Optimal Allocation and Sizing of Decentralized Solar Photovoltaic Generators Using Unit Financial Impact Indicator
Ozcel Cangul, Roberto Rocchetta, Murat Fahrioglu, Edoardo Patelli
Sustainability Switzerland, 2023
A novel financial metric denominated unit financial impact indicator (UFII) is proposed to minimize the payback period for solar photovoltaic (PV) systems investments and quantify the financial efficiency of allocation and sizing strategies. However, uncontrollable environmental conditions and operational uncertainties, such as variable power demands, component failures, and weather conditions, can threaten the robustness of the investment, and their effect needs to be accounted for. Therefore, a new probabilistic framework is proposed for the robust and optimal positioning and sizing of utility-scale PV systems in a transmission network. The probabilistic framework includes a new cloud intensity simulator to model solar photovoltaic power production based on historical data and quantified using an efficient Monte Carlo method. The optimized solution obtained using weighted sums of expected UFII and its variance is compared against those obtained by using well-established economic metrics from literature. The efficiency and usefulness of the proposed approach are tested on the 14-bus IEEE power grid case study. The results prove the applicability and efficacy of the new probabilistic metric to quantify the financial effectiveness of solar photovoltaic investments on different nodes and geographical regions in a power grid, considering the unavoidable conditional and operational uncertainty.
A robust model selection framework for fault detection and system health monitoring with limited failure examples: Heterogeneous data fusion and formal sensitivity bounds
Roberto Rocchetta, Qi Gao, Dimitrios Mavroeidis, Milan Petkovic
Engineering Applications of Artificial Intelligence, 2022
Enriching stochastic model updating metrics: An efficient Bayesian approach using Bray-Curtis distance and an adaptive binning algorithm
Wenhua Zhao, Lechang Yang, Chao Dang, Roberto Rocchetta, Marcos Valdebenito, David Moens
Mechanical Systems and Signal Processing, 2022
Enhancing the resilience of critical infrastructures: Statistical analysis of power grid spectral clustering and post-contingency vulnerability metrics
Roberto Rocchetta
Renewable and Sustainable Energy Reviews, 2022
From inference to design: A comprehensive framework for uncertainty quantification in engineering with limited information
A. Gray, A. Wimbush, M. de Angelis, P.O. Hristov, D. Calleja, E. Miralles-Dolz, R. Rocchetta
Mechanical Systems and Signal Processing, 2022
Prediction of the Luminous Flux Degradation of Light Emitting Diodes with an Interval Regressions Model
Roberto Rocchetta, Zhuozhao Zhan, Alessandro Di Bucchianico .
Proceedings of the 32nd European Safety and Reliability Conference Esrel 2022 Understanding and Managing Risk and Reliability for A Sustainable Future, 2022
A scenario optimization approach to reliability-based and risk-based design: Soft-constrained modulation of failure probability bounds
Roberto Rocchetta, Luis G. Crespo
Reliability Engineering and System Safety, 2021
Soft-constrained interval predictor models and epistemic reliability intervals: A new tool for uncertainty quantification with limited experimental data
Roberto Rocchetta, Qi Gao, Milan Petkovic
Mechanical Systems and Signal Processing, 2021
New probabilistic guarantees on the accuracy of Extreme Learning Machines: an application to decision-making in a reliability context
Roberto Rocchetta
Proceedings of the 31st European Safety and Reliability Conference Esrel 2021, 2021
Bayesian Information Fusion for Imprecise Probabilistic Models with Different Types of Information
Chenxing Wang, Lechang Yang, R. Rocchetta
Proceedings 2021 3rd International Conference on System Reliability and Safety Engineering Srse 2021, 2021
Constructing Consonant Predictive Beliefs from Data with Scenario Theory
Proceedings of Machine Learning Research, 2021
A post-contingency power flow emulator for generalized probabilistic risks assessment of power grids
Roberto Rocchetta, Edoardo Patelli
Reliability Engineering and System Safety, 2020
A scenario optimization approach to reliability-based design
Roberto Rocchetta, Luis G. Crespo, Sean P. Kenny
Reliability Engineering and System Safety, 2020
An empirical approach to reliability-based design using scenario optimization
30th European Safety and Reliability Conference Esrel 2020 and 15th Probabilistic Safety Assessment and Management Conference Psam 2020, 2020
An empirical approach to reliability-based design using scenario optimization
Roberto Rocchetta, Luis G. Crespo
Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference, 2020
Scenario-based generalization bound for anomaly detection support vector machine ensembles
30th European Safety and Reliability Conference Esrel 2020 and 15th Probabilistic Safety Assessment and Management Conference Psam 2020, 2020
Scenario-based generalization bound for anomaly detection support vector machine ensembles
Roberto Rocchetta, Milan Petkovic, Qi Gao
Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference, 2020
Bayesian calibration and probability bounds analysis solution to the Nasa 2020 UQ challenge on optimization under uncertainty
30th European Safety and Reliability Conference Esrel 2020 and 15th Probabilistic Safety Assessment and Management Conference Psam 2020, 2020
Bayesian calibration and probability bounds analysis solution to the nasa 2020 uq challenge on optimization under uncertainty
A. Gray, A. Wimbush, M. De Angelis, P.O. Hristov, E. Miralles-Dolz, D. Calleja, R. Rocchetta
Proceedings of the 30th European Safety and Reliability Conference and the 15th Probabilistic Safety Assessment and Management Conference, 2020
A reinforcement learning framework for optimal operation and maintenance of power grids
R. Rocchetta, L. Bellani, M. Compare, E. Zio, E. Patelli
Applied Energy, 2019
Solution of the benchmark control problem by scenario optimization
Roberto Rocchetta, Luis G. Crespo, Sean P. Kenny
ASME 2019 Dynamic Systems and Control Conference Dscc 2019, 2019
Financially optimal solar power sizing and positioning in a power grid
Ozcel Cangul, Roberto Rocchetta, Edoardo Patelli, Murat Fahrioglu
2018 IEEE International Energy Conference Energycon 2018, 2018
Assessment of power grid vulnerabilities accounting for stochastic loads and model imprecision
Roberto Rocchetta, Edoardo Patelli
International Journal of Electrical Power and Energy Systems, 2018
On-line Bayesian model updating for structural health monitoring
Roberto Rocchetta, Matteo Broggi, Quentin Huchet, Edoardo Patelli
Mechanical Systems and Signal Processing, 2018
Do we have enough data? Robust reliability via uncertainty quantification
Roberto Rocchetta, Matteo Broggi, Edoardo Patelli
Applied Mathematical Modelling, 2018
A power-flow emulator approach for resilience assessment of repairable power grids subject to weather-induced failures and data deficiency
Roberto Rocchetta, Enrico Zio, Edoardo Patelli
Applied Energy, 2018
Effect of load-generation variability on power grid cascading failures
R. Rocchetta, E. Patelli, L. Bing, G. Sansavini
Safety and Reliability Safe Societies in A Changing World Proceedings of the 28th International European Safety and Reliability Conference Esrel 2018, 2018
Stochastic analysis and reliability-cost optimization of distributed generators and air source heat pumps
Roberto Rocchetta, Edoardo Patelli
2017 2nd International Conference on System Reliability and Safety Icsrs 2017, 2017
An efficient framework for reliability assessment of power networks installing renewable generators and subject to parametric P-box uncertainty
Roberto Rocchetta, Edoardo Patelli
Safety and Reliability Theory and Applications Proceedings of the 27th European Safety and Reliability Conference Esrel 2017, 2017
Imprecise probabilistic framework for power grids risk assessment and sensitivity analysis
Risk Reliability and Safety Innovating Theory and Practice Proceedings of the 26th European Safety and Reliability Conference Esrel 2016, 2017
Power grid robustness to sere failures: Topological and flow based metrics comparison
Roberto Rocchetta, Edoardo Patelli
Eccomas Congress 2016 Proceedings of the 7th European Congress on Computational Methods in Applied Sciences and Engineering, 2016
Risk assessment and risk-cost optimization of distributed power generation systems considering extreme weather conditions
R. Rocchetta, Y.F. Li, E. Zio
Reliability Engineering and System Safety, 2015
On Bayesian approaches for real-time crack detection
R Rocchetta, M Broggi, E Patelli, Quentin Huchet
Safety and Reliability of Complex Engineered Systems Proceedings of the 25th European Safety and Reliability Conference Esrel 2015, 2015

RECENT SCHOLAR PUBLICATIONS

Risk-informed integration of renewable energy systems and storage in electric power grids: assessing safety and economic viability via an efficient approach
O Cangul, R Rocchetta, E Patelli
Energy Reports 15, 109338 , 2026
2026
Analysis of RUL dynamics and uncertainty via time transformation
P Dersin, R Rocchetta
Reliability Engineering & System Safety, 111730 , 2025
2025
Citations: 2
Nonlinear reconciliation: Error reduction theorems
L Nespoli, A Biswas, R Rocchetta, V Medici
arXiv preprint arXiv:2507.22500 , 2025
2025
Citations: 1
Towards Risk-Informed Transmission Grid Outage Planning
GB Roberto Rocchetta, Andrea Bellè
Proceedings of the 35th European Safety and Reliability Conference … , 2025
2025
Citations: 2
Optimization of mobility incentives in electric vehicle car sharing systems: A reinforcement learning framework
R Rocchetta, L Nespoli, V Medici, A Shemesh, Y Parag, JM Tardif
Sustainable Cities and Society 120, 106107 , 2025
2025
Citations: 9
An integrated uncertainty quantification and optimization for solving the 2025 NASA-DNV challenge
R Rocchetta, L Nespoli, V Medici, Y Chen, M Angelis, D Ochnio, E Patelli, ...
35th European Safety and Reliability Conference (ESREL 2025) and the 33rd … , 2025
2025
Citations: 1
Influence Learning in Complex Systems
E Congeduti, R Rocchetta, FA Oliehoek
Transactions on Machine Learning Research , 2025
2025
REPORT ON DIGITAL CROSS-SECTOR PLATFORM PROOF-OF-CONCEPT
R Rocchetta, V Medici, JM Tardif
2024
A business model or a tariffing issue? Peak shaving in local distribution grids through electric vehicle fleets
G Preßmair, J Papouschek, R Rocchetta
IET Conference Proceedings CP876 2024 (5), 251-254 , 2024
2024
Citations: 1
A survey on LED Prognostics and Health Management and uncertainty reduction
R Rocchetta, E Perrone, A Herzog, P Dersin, A Di Bucchianico
Microelectronics Reliability 157, 115399 , 2024
2024
Citations: 8
Uncertainty analysis and interval prediction of LEDs lifetimes
R Rocchetta, Z Zhan, WD van Driel, A Di Bucchianico
Reliability Engineering & System Safety 242, 109715 , 2024
2024
Citations: 21
Risk Informed Operational Planning Of Power Transmission Grids: Overview Of Recent Developments
R Rocchetta, L Nespoli, V Medici, D Raoofsheibani, B Gjorgiev, ...
34-th European Safety and Reliability Conference (ESREL), 147-155 , 2024
2024
Citations: 1
A survey on scenario theory, complexity, and compression-based learning and generalization
R Rocchetta, A Mey, FA Oliehoek
IEEE Transactions on Neural Networks and Learning Systems , 2023
2023
Citations: 14
Personalised Health Monitoring for Early Disease Detection
K Proksch, A Di Bucchianico, S Keizer, M de Jongh, M Regis, R Rocchetta, ...
2023
Citations: 1
Optimal allocation and sizing of decentralized solar photovoltaic generators using unit financial impact indicator
O Cangul, R Rocchetta, M Fahrioglu, E Patelli
Sustainability 15 (15), 11715 , 2023
2023
Citations: 3
Confidence Intervals for RUL: A New Approach based on Time Transformation and Reliability Theory
R Rocchetta
2023
Rule-based deep reinforcement learning for optimal control of electrical batteries in an energy community
R Rocchetta, L Nespoli, V Medici, S Basso, M Derboni, M Salani
Proceedings of the Proceedings of the 33rd European Safety and Reliability … , 2023
2023
Citations: 2
Exploring the link between scenario theory, complexity-and compression-based learning
R Rocchetta, M Alexander, FA Oliehoek
TechRxiv 2022 (1220) , 2022
2022
Enriching stochastic model updating metrics: An efficient Bayesian approach using Bray-Curtis distance and an adaptive binning algorithm
W Zhao, L Yang, C Dang, R Rocchetta, M Valdebenito, D Moens
Mechanical Systems and Signal Processing 171, 108889 , 2022
2022
Citations: 23
Enhancing the resilience of critical infrastructures: Statistical analysis of power grid spectral clustering and post-contingency vulnerability metrics
R Rocchetta
Renewable and Sustainable Energy Reviews 159, 112185 , 2022
2022
Citations: 67

MOST CITED SCHOLAR PUBLICATIONS

A reinforcement learning framework for optimal operation and maintenance of power grids
R Rocchetta, L Bellani, M Compare, E Zio, E Patelli
Applied energy 241, 291-301 , 2019
2019
Citations: 299
On-line Bayesian model updating for structural health monitoring
R Rocchetta, M Broggi, Q Huchet, E Patelli
Mechanical Systems and Signal Processing 103, 174-195 , 2018
2018
Citations: 166
Risk assessment and risk-cost optimization of distributed power generation systems considering extreme weather conditions
R Rocchetta, Y Li, E Zio
Reliability Engineering & System Safety 136, 47-61 , 2015
2015
Citations: 146
A power-flow emulator approach for resilience assessment of repairable power grids subject to weather-induced failures and data deficiency
R Rocchetta, E Zio, E Patelli
Applied energy 210, 339-350 , 2018
2018
Citations: 114
Assessment of power grid vulnerabilities accounting for stochastic loads and model imprecision
R Rocchetta, E Patelli
International Journal of Electrical Power & Energy Systems 98, 219-232 , 2018
2018
Citations: 79
Enhancing the resilience of critical infrastructures: Statistical analysis of power grid spectral clustering and post-contingency vulnerability metrics
R Rocchetta
Renewable and Sustainable Energy Reviews 159, 112185 , 2022
2022
Citations: 67
From inference to design: A comprehensive framework for uncertainty quantification in engineering with limited information
A Gray, A Wimbush, M de Angelis, PO Hristov, D Calleja, E Miralles-Dolz, ...
Mechanical Systems and Signal Processing 165, 108210 , 2022
2022
Citations: 61
Do we have enough data? Robust reliability via uncertainty quantification
R Rocchetta, M Broggi, E Patelli
Applied Mathematical Modelling 54, 710-721 , 2018
2018
Citations: 52
A scenario optimization approach to reliability-based and risk-based design: Soft-constrained modulation of failure probability bounds
R Rocchetta, LG Crespo
Reliability Engineering & System Safety 216, 107900 , 2021
2021
Citations: 43
Opencossan 2.0: an efficient computational toolbox for risk, reliability and resilience analysis
E Patelli, S Tolo, H George-Williams, J Sadeghi, R Rocchetta, ...
2018
Citations: 41
A scenario optimization approach to reliability-based design
R Rocchetta, LG Crespo, SP Kenny
Reliability Engineering & System Safety 196, 106755 , 2020
2020
Citations: 32
A post-contingency power flow emulator for generalized probabilistic risks assessment of power grids
R Rocchetta, E Patelli
Reliability Engineering & System Safety 197, 106817 , 2020
2020
Citations: 28
Enriching stochastic model updating metrics: An efficient Bayesian approach using Bray-Curtis distance and an adaptive binning algorithm
W Zhao, L Yang, C Dang, R Rocchetta, M Valdebenito, D Moens
Mechanical Systems and Signal Processing 171, 108889 , 2022
2022
Citations: 23
A robust model selection framework for fault detection and system health monitoring with limited failure examples: Heterogeneous data fusion and formal sensitivity bounds
R Rocchetta, Q Gao, D Mavroeidis, M Petkovic
Engineering Applications of Artificial Intelligence 114 (105140) , 2022
2022
Citations: 22
Uncertainty analysis and interval prediction of LEDs lifetimes
R Rocchetta, Z Zhan, WD van Driel, A Di Bucchianico
Reliability Engineering & System Safety 242, 109715 , 2024
2024
Citations: 21
Soft-constrained interval predictor models and epistemic reliability intervals: A new tool for uncertainty quantification with limited experimental data
R Rocchetta, Q Gao, M Petkovic
Mechanical Systems and Signal Processing 161, 107973 , 2021
2021
Citations: 19
A survey on scenario theory, complexity, and compression-based learning and generalization
R Rocchetta, A Mey, FA Oliehoek
IEEE Transactions on Neural Networks and Learning Systems , 2023
2023
Citations: 14
Stochastic analysis and reliability-cost optimization of distributed generators and air source heat pumps
R Rocchetta, E Patelli
2017 2nd International Conference on System Reliability and Safety (ICSRS … , 2017
2017
Citations: 14
Optimization of mobility incentives in electric vehicle car sharing systems: A reinforcement learning framework
R Rocchetta, L Nespoli, V Medici, A Shemesh, Y Parag, JM Tardif
Sustainable Cities and Society 120, 106107 , 2025
2025
Citations: 9
A survey on LED Prognostics and Health Management and uncertainty reduction
R Rocchetta, E Perrone, A Herzog, P Dersin, A Di Bucchianico
Microelectronics Reliability 157, 115399 , 2024
2024
Citations: 8

roberto rocchetta

RESEARCH, TEACHING, or OTHER INTERESTS

Scopus Publications

RECENT SCHOLAR PUBLICATIONS

MOST CITED SCHOLAR PUBLICATIONS