Jesus LagoOptimization, Control & ComputerScientist

B j.lagogarcia@tudelft.nlÍ www.jesuslago.com

jesus-lago-garcia-63639053

Research interestsOptimization, Machine Learning, Deep Learning, Forecasting, Optimal Control, Smart Grids.

EducationSep, 2016

TodayPhD in Systems and Control: Intrusive and non-intrusive control algorithms for theenergy market, Delft University of Technology.Development of algorithms and methods to improve the trading strategies used in the energy spot marketin order to safeguard the profitability of renewable energies. The PhD is founded by the INCITE network,a Marie Skłodowska-Curie European Training Network within the HORIZON 2020 Programe.

Oct,2013

Jul,2016MSc. Microsystems Engineering, University of Freiburg, Major in Optimal Control andNumerical Optimization, Final grade: 100% (1.0 in German grading system).

20072013

5-year Degree on Electronics, Automation and Robotics, University of Vigo & Universityof Freiburg, (equiv. to Master’s degree), Final grade: 80%, Graduated with honours.

Selected PublicationsOct, 2018 J. Lago, K. De Brabandere, F. De Ridder, B. De Schutter. Short-term forecasting of solar

irradiance without local telemetry: A generalized model using satellite data. Solar Energy 173(2018) 566–577.

Sep, 2018 J. Lago, M. Erhard, M. Diehl. Warping model predictive control for application in control of areal airborne wind energy system. Control Engineering Practice 78 (2018) 65–78.

April, 2018 J. Lago, F. De Ridder, B. De Schutter. Forecasting spot electricity prices: deep learningapproaches and empirical comparison of traditional algorithms. Applied Energy 221 (2018)386–405.

Feb, 2018 J. Lago, F. De Ridder, P. Vrancx, B. De Schutter. Forecasting day-ahead electricity prices inEurope: the importance of considering market integration. Applied Energy 211 (2018) 890-903.

ExperienceSep, 2016

TodayComputer Science Researcher, Algorithms, Modeling and Optimization Depart-ment, Vito-Energyville.My PhD is in collaboration between TU Delft and the Flamish research institute VITO. My main researchactivities are done at Energyville, a Flamish energy research center that VITO is part of.

May, 2015

Aug, 2016Research assistant, Control & Optimization Lab, University of Freiburg.{ Computation of periodic optimal trajectories of kite pumping cycles for energy maximization.{ Verification of the dynamic model parameters used on the airborne wind energy kite system.

Oct, 2015

Mar, 2016Lecturer assistant, Control & Optimization Lab, University of Freiburg.Lecturer assistant in the Master course Modelling and System Identification.

April, 2014

Aug, 2016Lecturer assistant, Design of MEMS Lab, University of Freiburg.Lecturer assistant in the Master course Micro-mechanics.

June, 2014

January, 2015Research assistant, Design of MEMS Lab, University of Freiburg.Development of a high frequency readout system for a biomedical sensor used for blood measurements.

Projectsoptidef Developer of the Latex library optidef that is used for defining optimization problems (https:

//github.com/jeslago/optidef).

PhD’s ThesisTitle Intrusive and non-intrusive control algorithms for the energy market

Supervisor Professor Bart De Schutter & Dr. Fjo De RidderDescription Unlike traditional energy sources, production of renewable energies is weather dependent and

up to some extent unpredictable. As a result, as the share of renewable energy sources inthe electrical grid increases, electricity prices become more volatil because the total energyproduction becomes more uncertain. The PhD looks into control algorithms that can be deployedto reduce price volatility and turn renewable sources into more profitable options. The researchinvolves, among others, developing electricity price forecasting techniques, as well as controlalgorithms for specific energy system that help stabilize the electricity market.

Master’s ThesisTitle Periodic Optimal Control and Model Predictive Control of a Tethered Kite for Airborne Wind

EnergySupervisor Professor Moritz Diehl & Dr. Michael ErhardDescription Model predictive control of a flying airborne wind energy kite to track periodic optimal trajectories

to maximize the extracted wind power by means of crosswind kite power. The kite control wasdone in real time and the periodic optimal trajectories were optimized offline.

SkillsScientific Machine Learning, Numerical Optimization, Optimal Control, Forecasting, Deep Learning,

System Modelling and Identification, System Theory and Control, Time Series Analysis.Computer python, R, MATLAB, LATEX, Linux.Technical Data Analysis, Electronics Design, Embedded Systems Design, Signal Processing, Microsystems

Engineer.

LanguagesNative Spanish, Galician

Advanced EnglishIntermediate German (B1), Portuguese

Basic Turkish