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A bilevel framework for decisionmaking under uncertainty with contextual information
MoralesGonzalez, Juan Miguel; PinedaMorente, Salvador; Muñoz Díaz, Miguel Ángel (Elsevier, 20211122)In this paper, we propose a novel approach for datadriven decisionmaking under uncertainty in the presence of contextual information. Given a finite collection of observations of the uncertain parameters and potential ... 
A high dimensional functional time series approach to evolution outlier detection for grouped smart meters
Elias Fernandez, Antonio; MoralesGonzalez, Juan Miguel; PinedaMorente, Salvador (Taylor and Francis, 20220101)Smart metering infrastructures collect data almost continuously in the form of finegrained long time series. These massive data series often have common daily patterns that are repeated between similar days or seasons and ... 
An exact dynamic programming approach to segmented isotonic regression
Bucarey, Víctor; Labbé, Martine; MoralesGonzalez, Juan Miguel; PinedaMorente, Salvador (Elsevier, 2021)This paper proposes a polynomialtime 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 ... 
Chronological timeperiod clustering for optimal capacity expansion planning
MoralesGonzalez, Juan Miguel; PinedaMorente, Salvador (20180712)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 TimePeriod Clustering for Optimal Capacity Expansion Planning
MoralesGonzalez, Juan Miguel; PinedaMorente, Salvador (20180706)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. ... 
Costdriven screening of network constraints for the unit commitment problem
Porras, Álvaro; PinedaMorente, Salvador; MoralesGonzalez, Juan Miguel; JimenezCordero, Maria Asuncion (The Institute of Electrical and Electronics Engineers (IEEE), 20220316)In an attempt to speed up the solution of the unit commitment (UC) problem, both machinelearning and optimizationbased methods have been proposed to lighten the full UC formulation by removing as many superfluous lineflow ... 
Electricity CostSharing in Energy Communities Under Dynamic Pricing and Uncertainty
Gržanić, Mirna; MoralesGonzalez, Juan Miguel; PinedaMorente, Salvador; Capuder, Tomislav (IEEE, 20210215)Most of the prosumers nowadays are constrained to trade only with the supplier under a flat tariff or dynamic timeofuse price signals. This paper models and discusses the costsaving benefits of flexible prosumers as ... 
Featuredriven improvement of renewable energy forecasting and trading
Inspired from recent insights into the common ground of machine learning, optimization and decisionmaking, this paper proposes an easytoimplement, but effective procedure to enhance both the quality of renewable energy ... 
Individualized exercises for continuous assessment in engineering
PinedaMorente, Salvador; Alguacil Conde, Natalia; PerezRuiz, Juan; MartinRivas, Sebastian; RuizGonzalez, Antonio Francisco (20190527)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
FernándezBlanco, Ricardo; MoralesGonzalez, Juan Miguel; PinedaMorente, Salvador; Porras, Álvaro (Elsevier, 202110)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 datadriven inverse ... 
Is learning for the unit commitment problem a lowhanging fruit?
PinedaMorente, Salvador; MoralesGonzalez, Juan Miguel (Elsevier, 20220216)The blast wave of machine learning and artificial intelligence has also reached the power systems community, and amid the frenzy of methods and blackbox tools that have been left in its wake, it is sometimes difficult to ... 
Is learning for the unit commitment problem a lowhanging fruit?
PinedaMorente, Salvador; MoralesGonzalez, Juan Miguel (Elsevier, 202206)The blast wave of machine learning and artificial intelligence has also reached the power systems community, and amid the frenzy of methods and blackbox tools that have been left in its wake, it is sometimes difficult ... 
Learning the price response of active distribution networks for TSODSO coordination
PinedaMorente, Salvador; MoralesGonzalez, Juan Miguel; Dvorkin, Yury (IEEE, 2021)The increase in distributed energy resources and flexible electricity consumers has turned TSODSO coordination strategies into a challenging problem. Existing decomposition/decentralized methods apply divideandconquer ... 
Prescribing net demand for twostage electricity generation scheduling
MoralesGonzalez, Juan Miguel; Muñoz, Miguel Ángel; PinedaMorente, Salvador (Elsevier, 2023)We consider a twostage generation scheduling problem comprising a forward dispatch and a realtime redispatch. The former must be conducted facing an uncertain net demand that includes nondispatchable electricity ... 
Prescriptive Analytics in Electricity Markets
Muñoz Diaz, Miguel Angel (UMA Editorial, 20221103)Decision making is critical for any business to survive in a market environment. Examples of decision making tasks are inventory management, resource allocation or portfolio selection. Optimization, understood as the ... 
Warmstarting constraint generation for mixedinteger optimization: A Machine Learning approach
JimenezCordero, Maria Asuncion; MoralesGonzalez, Juan Miguel; PinedaMorente, Salvador (Elsevier, 20221011)Mixed Integer Linear Programs (MILP) are well known to be NPhard (Nondeterministic Polynomialtime hard) problems in general. Even though pure optimizationbased methods, such as constraint generation, are guaranteed to ... 
Warmstarting constraint generation for mixedinteger optimization: A Machine Learning approach
JimenezCordero, Maria Asuncion; MoralesGonzalez, Juan Miguel; PinedaMorente, Salvador (Elsevier, 20221011)Mixed Integer Linear Programs (MILP) are well known to be NPhard (Nondeterministic Polynomialtime hard) problems in general. Even though pure optimizationbased methods, such as constraint generation, are guaranteed to ...