Listar por autor "Pineda-Morente, Salvador"
Mostrando ítems 1-20 de 30
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A bilevel framework for decision-making under uncertainty with contextual information
Morales-González, Juan Miguel
; Pineda-Morente, Salvador; Muñoz Díaz, Miguel Ángel (Elsevier, 2021-11-22)
In this paper, we propose a novel approach for data-driven decision-making under uncertainty in the presence of contextual information. Given a finite collection of observations of the uncertain parameters and potential ... -
A data-based approach for solving the Rank Pricing Problem.
The Rank Pricing Problem is a challenging mixed-integer optimization problem. It aims to determine the optimal pricing strategies of a set of products ranked by customer preferences. Given its NP-hard nature, existing ... -
A high dimensional functional time series approach to evolution outlier detection for grouped smart meters
Elías Fernández, Antonio; Morales-González, Juan Miguel; Pineda-Morente, Salvador (Taylor and Francis, 2022-01-01)
Smart metering infrastructures collect data almost continuously in the form of fine-grained long time series. These massive data series often have common daily patterns that are repeated between similar days or seasons and ... -
A novel embedded min-max approach for feature selection in nonlinear Support Vector Machine classification
Jiménez-Cordero, María Asunción; Morales-González, Juan Miguel; Pineda-Morente, Salvador (Elsevier, 2021)
In recent years, feature selection has become a challenging problem in several machine learning fields, such as classification problems. Support Vector Machine (SVM) is a well-known technique applied in classification ... -
An enhanced heuristic framework for solving the Rank Pricing Problem
Jiménez-Cordero, María Asunción; Pineda-Morente, Salvador; Morales-González, Juan Miguel (Elsevier, 2025-03-12)
The Rank Pricing Problem (RPP) is a challenging bilevel optimization problem with binary variables whose objective is to determine the optimal pricing strategy for a set of products to maximize the total benefit, given ... -
An exact dynamic programming approach to segmented isotonic regression
Bucarey, Víctor; Labbé, Martine; Morales-González, Juan Miguel; Pineda-Morente, Salvador (Elsevier, 2021)
This paper proposes a polynomial-time algorithm to construct the monotone stepwise curve that minimizes the sum of squared errors with respect to a given cloud of data points. The fitted curve is also constrained on the ... -
Aproximación lineal basada en datos del flujo de cargas óptimo en sistemas eléctricos.
(2024-02)El Flujo de Cargas Optimo (OPF) es un problema de optimización cuyo objetivo es determinar el despacho de potencia de las centrales generadoras de una red eléctrica para satisfacer la demanda al mínimo coste, cumpliendo ... -
Chronological time-period clustering for optimal capacity expansion planning
Morales-González, Juan Miguel; Pineda-Morente, Salvador (2018-07-12)
To reduce the computational burden of capacity expansion models, power system operations are commonly accounted for in these models using representative time periods of the planning horizon such as hours, days or weeks. ... -
Chronological Time-Period Clustering for Optimal Capacity Expansion Planning
Morales-González, Juan Miguel; Pineda-Morente, Salvador (2018-07-06)
To reduce the computational burden of capacity expansion models, power system operations are commonly accounted for in these models using representative time periods of the planning horizon such as hours, days or weeks. ... -
Cost-driven screening of network constraints for the unit commitment problem
Porras, Álvaro; Pineda-Morente, Salvador; Morales-González, Juan Miguel; Jiménez-Cordero, María Asunción (The Institute of Electrical and Electronics Engineers (IEEE), 2022-03-16)
In an attempt to speed up the solution of the unit commitment (UC) problem, both machine-learning and optimization-based methods have been proposed to lighten the full UC formulation by removing as many superfluous line-flow ... -
Data-Driven Screening of Network Constraints for Unit Commitment
Pineda-Morente, Salvador; Morales-González, Juan Miguel; Jiménez-Cordero, María Asunción (IEEE Xplore, 2020)
The transmission-constrained unit commitment (TC-UC) problem is one of the most relevant problems solved by independent system operators for the daily operation of power systems. Given its computational complexity, this ... -
Electricity Cost-Sharing in Energy Communities Under Dynamic Pricing and Uncertainty
Gržanić, Mirna; Morales-González, Juan Miguel; Pineda-Morente, Salvador; Capuder, Tomislav (IEEE, 2021-02-15)
Most of the prosumers nowadays are constrained to trade only with the supplier under a flat tariff or dynamic time-of-use price signals. This paper models and discusses the cost-saving benefits of flexible prosumers as ... -
Feature-driven improvement of renewable energy forecasting and trading
Inspired from recent insights into the common ground of machine learning, optimization and decision-making, this paper proposes an easy-to-implement, but effective procedure to enhance both the quality of renewable energy ... -
Individualized exercises for continuous assessment in engineering
Pineda-Morente, Salvador; Alguacil Conde, Natalia; Pérez-Ruiz, Juan; Martín-Rivas, Sebastián; Ruiz-González, Antonio Francisco (2019-05-27)
This project focuses on the development of a web application that automatically grades the solution to engineering exercises. The input data of each exercise is different for each student in order to reduce plagiarism and ... -
Inverse optimization with kernel regression: Application to the power forecasting and bidding of a fleet of electric vehicles
Fernández-Blanco, Ricardo; Morales-González, Juan Miguel; Pineda-Morente, Salvador; Porras, Álvaro (Elsevier, 2021-10)
This paper considers an aggregator of Electric Vehicles (EVs) who aims to learn the aggregate power of his/her fleet while also participating in the electricity market. The proposed approach is based on a data-driven inverse ... -
Is learning for the unit commitment problem a low-hanging fruit?
Pineda-Morente, Salvador; Morales-González, Juan Miguel (Elsevier, 2022-06)
The blast wave of machine learning and artificial intelligence has also reached the power systems community, and amid the frenzy of methods and black-box tools that have been left in its wake, it is sometimes difficult ... -
Is learning for the unit commitment problem a low-hanging fruit?
Pineda-Morente, Salvador; Morales-González, Juan Miguel (Elsevier, 2022-02-16)
The blast wave of machine learning and artificial intelligence has also reached the power systems community, and amid the frenzy of methods and black-box tools that have been left in its wake, it is sometimes difficult to ... -
Learning the price response of active distribution networks for TSO-DSO coordination
Pineda-Morente, Salvador; Morales-González, Juan Miguel; Dvorkin, Yury (IEEE, 2021)
The increase in distributed energy resources and flexible electricity consumers has turned TSO-DSO coordination strategies into a challenging problem. Existing decomposition/decentralized methods apply divide-and-conquer ... -
Learning-based state estimation in distribution systems with limited real-time measurements
Gómez de la Varga, J.; Pineda-Morente, Salvador; Morales-González, Juan Miguel; Porras, A. (Elsevier, 2025-04)
The task of state estimation in active distribution systems faces a major challenge due to the integration of different measurements with multiple reporting rates. As a result, distribution systems are essentially unobservable ... -
Learning-based State Estimation in Distribution Systems with Limited Real-Time Measurements.
Gómez de la Varga, J.; Pineda-Morente, Salvador; Morales-González, Juan Miguel; Porras, Álvaro (2024-01)
The task of state estimation in active distribution systems faces a major challenge due to the integration of different measurements with multiple reporting rates. As a result, distribution systems are essentially unobservable ...