Jesus Lago

Jesus Lago
Amazon · International Technology

Doctor of Philosophy
Recommender and forecasting algorithms at Amazon

About

31
Publications
12,694
Reads
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1,021
Citations
Citations since 2016
31 Research Items
1019 Citations
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Introduction
I am an applied scientist at Amazon working on forecasting, ranking, and recommender algorithms to predict the success of deals on the Amazon marketplace and recommend them accordingly. In addition to my job at Amazon, I carry out research and I cosupervise a PhD student in the area of AI and the energy transition. I am the developer of optidef, a Latex library for defining optimization problems, and of the epftoolbox python library, a library for electricity price forecasting.
Additional affiliations
September 2016 - present
Flemish Institute for Technological Research
Position
  • PhD Student
Education
September 2016 - August 2020
Delft University of Technology
Field of study
  • Optimization, Machine Learning, Data Science, Renewable Sources, Energy Transition
September 2013 - March 2016
University of Freiburg
Field of study
  • Optimization, Modeling, Optimal Control

Publications

Publications (31)
Article
Full-text available
Motivated by the increasing integration among electricity markets, in this paper we propose two different methods to incorporate market integration in electricity price forecasting and to improve the predictive performance. First, we propose a deep neural network that considers features from connected markets to improve the predictive accuracy in a...
Article
Full-text available
In this paper, a novel modeling framework for forecasting electricity prices is proposed. While many predictive models have been already proposed to perform this task, the area of deep learning algorithms remains yet unexplored. To fill this scientific gap, we propose four different deep learning models for predicting electricity prices and we show...
Article
Full-text available
Due to the increasing integration of solar power into the electrical grid, forecasting short-term solar irradiance has become key for many applications, e.g. operational planning, power purchases, reserve activation, etc. In this context, as solar generators are geographically dispersed and ground measurements are not always easy to obtain, it is v...
Article
Full-text available
Fast online generation of feasible and optimal reference trajectories is crucial in tracking model predictive control, especially for stability and optimality in presence of a time varying parameter. In this paper, in order to circumvent the operational efforts of handling a discrete set of precomputed trajectories and switching between them, time...
Conference Paper
Full-text available
Generation of feasible and optimal reference trajectories is crucial in tracking Nonlinear Model Predictive Control. Especially, for stability and optimality in presence of a time varying parameter, adaptation of the tracking trajectory has to be implemented. General approaches are real-time generation of trajectories or switching between a discret...
Article
Electricity price forecasting (EPF) is a branch of forecasting on the interface of electrical engineering, statistics, computer science, and finance, which focuses on predicting prices in wholesale electricity markets for a whole spectrum of horizons. These range from a few minutes (real-time/intraday auctions and continuous trading), through days...
Preprint
Electricity price forecasting (EPF) is a branch of forecasting on the interface of electrical engineering, statistics, computer science, and finance, which focuses on predicting prices in wholesale electricity markets for a whole spectrum of horizons. These range from a few minutes (real-time/intraday auctions and continuous trading), through days...
Article
Electricity balancing is one of the main demanders of short-term flexibility. To improve its integration, the recent regulation of the European Union introduces a common standalone balancing energy market. It allows actors that have not participated or not been awarded in the preceding balancing capacity market to participate as voluntary bidders o...
Article
Full-text available
In this manuscript we explore European feature importance in Day Ahead Market (DAM) price forecasting models, and show that model performance can deteriorate when too many features are included due to over-fitting. We propose a greedy algorithm to search over candidate countries for European features to be used in a DAM price forecasting model, tha...
Article
Full-text available
While the field of electricity price forecasting has benefited from plenty of contributions in the last two decades, it arguably lacks a rigorous approach to evaluating new predictive algorithms. The latter are often compared using unique, not publicly available datasets and across too short and limited to one market test samples. The proposed new...
Article
State-of-the-art Model Predictive Control (MPC) applications for building heating adopt either a deterministic controller together with a nonlinear model or a linearized model with a stochastic MPC controller. However, deterministic MPC only considers one single realization of the disturbances and its performance strongly depends on the quality of...
Article
Full-text available
In this paper, we propose a data-driven methodology to identify the optimal placement of sensors in a multi-zone building. The proposed methodology is based on statistical tests that study the (in)dependence of measurements from various available sensors. The tests advice on a set of most dissimilar sensors to be retained, as they would convey the...
Article
To assess the impact of implementing energy efficiency and renewable energy measures, urban building energy models are emerging. In these models, due to the lack of data, the natural variability of the existing building stock is often highly underestimated and uncertainty on the simulated energy use arises. Therefore, this work proposes a probabili...
Preprint
Full-text available
State-of-the-art Model Predictive Control (MPC) applications for building heating adopt either a deterministic controller together with a nonlinear model or a linearized model with a stochastic MPC controller. However, deterministic MPC only considers one single realization of the disturbances and its performance strongly depends on the quality of...
Article
Full-text available
Power outages in electrical grids can have very negative economic and societal impacts rendering fault diagnosis paramount to their secure and reliable operation. In this paper, deep neural networks are proposed for fault detection and location in low-voltage smart distribution grids. Due to its key properties, the proposed method solves some of th...
Article
Full-text available
To correct grid imbalances and avoid grid failures, the transmission system operator (TSO) deploys balancing reserves and settles these imbalances by penalizing the market actors that caused them. In several countries, it is forbidden to influence the grid imbalances in order to let the TSO retain full control of grid regulation. In this paper, we...
Article
Full-text available
Seasonal thermal energy storage systems (STESSs) can shift the delivery of renewable energy sources and mitigate their uncertainty problems. However, to maximize the operational profit of STESSs and ensure their long-term profitability, control strategies that allow them to trade on wholesale electricity markets are required. While control strategi...
Preprint
Recent advancements in the fields of artificial intelligence and machine learning methods resulted in a significant increase of their popularity in the literature, including electricity price forecasting. Said methods cover a very broad spectrum, from decision trees, through random forests to various artificial neural network models and hybrid appr...
Preprint
While the field of electricity price forecasting has benefited from plenty of contributions in the last two decades, it arguably lacks a rigorous approach to evaluating new predictive algorithms. The latter are often compared using unique, not publicly available datasets and across too short and limited to one market test samples. The proposed new...
Article
Full-text available
Market-based procurement of balancing services in Europe is prone to strategic bidding due to the relatively small market size and a limited number of providers. In the European Union, balancing markets are undergoing substantial regulatory changes driven the efforts to harmonize the market design and better align it with the goals of the energy tr...
Article
Full-text available
In this paper, a gradient boosting tree model is proposed to detect, identify and localize single-phase-to-ground and three-phase faults in low voltage (LV) smart distribution grids. The proposed method is based on gradient boosting trees and considers branch-independent input features to be generalizable and applicable to different grid topologies...
Preprint
Due to the increasing integration of solar power into the electrical grid, forecasting short-term solar irradiance has become key for many applications, e.g.~operational planning, power purchases, reserve activation, etc. In this context, as solar generators are geographically dispersed and ground measurements are not always easy to obtain, it is v...
Article
Full-text available
To mitigate the effects of the intermittent generation of renewable energy sources, reliable and efficient energy storage is critical. Since nearly 80% of households energy consumption is destined to water and space heating, thermal energy storage is particularly important. In this context, we propose and validate a new model for one of the most ef...
Conference Paper
Full-text available
In the context of building heating systems control in office buildings, the current state-of-the-art applies either a deterministic Model Predictive Control (MPC) controller together with a nonlinear model, or a linearized model with a stochastic MPC controller. Deterministic MPC considers only one realization of the external disturbances, which ca...
Article
Full-text available
Due to the increasing integration of renewable sources in the electrical grid, electricity generation is expected to become more uncertain. In this context, seasonal thermal energy storage systems (STESSs) are key to shift the delivery of renewable energy sources and tackle their uncertainty problems. In this paper, we propose an optimal controller...
Conference Paper
Full-text available
Urban building energy models (UBEMs) are expected to play a key role in the integrated assessment of sustainability measures on both district and city level. However, due to limited availability of data sources, those models are often created through an archetype approach, which is a deterministic method to allocate building envelope characteristic...
Article
Full-text available
Recent research has seen several forecasting methods being applied for heat load forecasting of district heating networks. This paper presents two methods that gain significant improvements compared to the previous works. First, an automated way of handling non-linear dependencies in linear models is presented. In this context, the paper implements...
Article
Implementation of a liquid cooling transforms a refrigeration system into a combined cooling and heating system. Reclaimed heat can be used for building heating purposes or can be sold. Carbon dioxide based refrigeration systems are considered to have a particularly high potential for becoming efficient heat energy producers. In this paper, a CO2 s...
Thesis
Full-text available
Classical wind turbines suffer from a significant problem: while their power output scales with the square of the height, the mass does so cubically; as a result, material costs are high and the technology becomes non-competitive. Considering that the bulk of the power is generated by the outer parts of the rotor blades, AWE tries to extract wind p...

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Projects

Projects (2)
Project
INCITE is Marie Sklodowska-Curie European Training Network (ITN-ETN) funded by the HORIZON 2020 Programme that brings together experts on control and power systems, from academia and industry with the aim of training fourteen young researchers capable of providing innovative control solutions for the future electrical networks. New smart meters, distributed generation, renewable energy sources and the concern about the environment are redefining electrical networks. Now, both consumers and generators are active agents, capable of coordinating the power exchange in the electrical grids depending on multiple factors. To take full advantage of the new electrical networks, it is necessary a coordinated and harmonic interaction of the all actors in the network. Control algorithms are intended for this purpose; to act at several levels to conduct the electrical power exchange and improve efficiency, reliability and resilience of the network. INCITE seeks new control algorithms with an integral view of the future electrical networks, covering aspects like energy management, stability of electrical variables, monitoring and communication implementation, energy storage, among others. http://www.incite-itn.eu
Project
The production of renewable energy, based on solar panels and wind turbines, is intrinsically based on weather conditions. These must be forecasted. This makes it difficult to trade energy with long term contracts. As a result, renewable energy is almost exclusively traded on the spot markets, which become more important in most European countries. Forecast errors result in imbalance positions. The large correlation in forecast errors between different parties, results often in large imbalance costs. This could on the long term lower investments in renewable energy sources. Our objective is to safeguard the profitability of renewable energy sources.