About
46
Publications
9,830
Reads
How we measure 'reads'
A 'read' is counted each time someone views a publication summary (such as the title, abstract, and list of authors), clicks on a figure, or views or downloads the full-text. Learn more
587
Citations
Publications
Publications (46)
Background
Functional impairment is one of the most decisive prognostic factors in patients with complex chronic diseases. A more significant functional impairment indicates that the disease is progressing, which requires implementing diagnostic and therapeutic actions that stop the exacerbation of the disease.
Objective
This study aimed to predic...
The Linear Hinges Model (LHM) is an efficient approach to flexible and robust one-dimensional curve fitting under stringent high-noise conditions. However, it was initially designed to run in a single-core processor, accessing the whole input dataset. The surge in data volumes, coupled with the increase in parallel hardware architectures and specia...
Las Jornadas de Buenas Prácticas Docentes de la Universidad Pontificia Comillas, celebradas en junio de 2023, han experimentado un aumento notable en la participación. Esto refleja el creciente interés del profesorado por la innovación educativa. El monográfico presentado por la Oficina de Apoyo a la Innovación Docente muestra las contribuciones de...
Saliency maps have become one of the most widely used interpretability techniques for convolutional neural networks (CNN) due to their simplicity and the quality of the insights they provide. However, there are still some doubts about whether these insights are a trustworthy representation of what CNNs use to come up with their predictions. This pa...
This study suggests using wearable activity trackers to identify mobility patterns in chronic complex patients (CCPs) and investigate their relation with the Barthel index (BI) to assess functional decline. CCPs are individuals who suffer from multiple, chronic health conditions that often lead to a progressive decline in their functional capacity....
The power sector is a major contributor to anthropogenic global warming and is responsible for 38% of total energy-related carbon dioxide emissions and 66% of carbon dioxide emission growth in 2018. In OECD member countries, the residential sector consumes a significant amount of electrical energy, with household refrigerating appliances alone acco...
Governments worldwide have adopted different public health measures in order to slow down the spread of COVID-19. As a result, the electricity demand has been impacted by the changes in human activity. Many of the Latin America and the Caribbean (LAC) countries have adopted different approaches to control the COVID-19 pandemic, including severe shu...
BREACH is a side-channel attack to HTTPS that allows an attacker to obtain victims’ credentials under certain conditions. An attacker with a privileged position on the network can guess character by character a secret session key just by analyzing the size of the responses returned by the server over HTTPS and encrypted. Heal the Breach (HTB) is th...
In liberalized electricity markets, aggregated stepwise supply and demand curves are at the core of many relevant processes. Efficient and meaningful representations of the offer curves is an essential procedure for agents participating in those markets. However, there is not a formal framework that allows operating with those offer curves using ba...
En esta monografía, se presenta un análisis del impacto de la pandemia en términos de demanda eléctrica comparándolo con un modelo de un año habitual para cada uno de los países seleccionados. Esto permitió identificar primero cuánta electricidad se dejó de demandar y por ende de cobrar en la pandemia; cuáles fueron las alteraciones a este consumo;...
Background: The COVID-19 pandemic has had global effects; cases have been counted in the tens of millions, and there have been over two million deaths throughout the world. Health systems have been stressed in trying to provide a response to the increasing demand for hospital beds during the different waves. This paper analyzes the dynamic response...
Non-intrusive load monitoring (NILM) has become an important subject of study, since it provides benefits to both consumers and utility companies. The analysis of smart meter signals is useful for identifying consumption patterns and user behaviors, in order to make predictions and optimizations to anticipate the use of electrical appliances at hom...
The deployment of microgrids could be fostered by control systems that do not require very complex modelling, calibration, prediction and/or optimisation processes. This paper explores the application of Reinforcement Learning (RL) techniques for the operation of a microgrid. The implemented Deep Q-Network (DQN) can learn an optimal policy for the...
Temperature is widely known as one of the most important drivers to forecast electricity and gas variables, such as the load. Because of that reason, temperature forecasting is and has been for years of great interest for energy forecasters and several approaches and methods have been published. However, these methods usually do not consider temper...
Demand forecasting is and has been for years a topic of great interest in the electricity sector, being the temperature one of its major drivers. Indeed, one of the challenges when modelling the load is to choose the right weather station, or set of stations, for a given load time series. However, only a few research papers have been devoted to thi...
When facing any forecasting problem not only is accuracy on the predictions sought. Also, useful information about the underlying physics of the process and about the relevance of the forecasting variables is very much appreciated. In this paper, it is presented an automatic specification procedure for models that are based on additivity assumption...
In order to mitigate the excessive computational cost of atrium fire simulations, a novel methodology based on the use of the Fractional Factorial Design technique to obtain an experimental validated tool, in the form of a surface response model, capable to predict fire induced conditions is proposed. This methodology is supported by results from a...
Residual demand curves (RDCs) can be used to represent the strategic interaction of participants in electricity markets. RDCs relate the energy that an agent can buy or sell in one hour with the clearing market price that would be obtained in such hour, assuming the market is organized as simple bid independent auctions. Despite the fact that they...
Electricity markets are based on several sequential energy and reserve trading mechanisms to constantly maintain the balance between generation and demand. During the last years, reserve markets are getting much importance all around the world with the increasing social awareness of the renewable energy benefits. Additional reserve quantities and l...
With the increasing penetration of intermittent technologies, such as renewable energy sources, electricity secondary (spinning) reserve markets are getting much relevance all around the world to constantly maintain the balance between generation and demand. In this new context, generation utilities are increasingly demanding powerful tools to bett...
Flexibility of distributed energy resources is increasingly needed in the electricity markets and grids. But this dispersed flexibility can be used efficiently only if it is aggregated to wholesale market products. A set of software tools was developed for an actor or a company who aggregates flexible household demand for the electricity market and...
This paper studies an approach to identify representative operating and contingency (OC) scenarios for the design of underfrequency load-shedding (UFLS) schemes. In small isolated power systems, contingency scenarios are outages of generating units. Usually, only N-1 outages are considered. In this paper, simultaneous outages of several units are a...
In this paper, the three main forecasting topics that are currently getting the most attention in electric power systems are
addressed: load, wind power and electricity prices. Each of these time series exhibits its own stylized features and is therefore
forecasted in a very different manner. The complete set of forecasting models and techniques in...
This paper studies an approach to identify representative operating and contingency scenarios for the design of underfrequency load-shedding (UFLS) schemes. In small isolated power systems, contingency scenarios are outages of generating units. Not only N-1 outages are considered, but simultaneous outages of several units must be considered. Cluste...
Purpose
The purpose of this paper is to analyze medium‐term risks faced by electrical generation companies in competitive environments. Market risks faced by generation companies are caused by several variables subject to uncertainty. Hydro conditions, fuel (coal and natural gas) prices, system demand, and CO 2 emission price are the risk factors c...
This paper investigates the intraday price volatility process in four Australian wholesale electricity markets; namely New South Wales, Queensland, South Australia and Victoria. The data set consists of half-hourly electricity prices and demand volumes over the period January 1, 2002 to June 1, 2003. A range of processes including GARCH, RiskMetric...
Huge amounts of data are available in many disciplines of Science and
Industry. In order to extract useful information from these data, a
large number of apparently very different learning approaches have been
created during the last decades. Each domain uses its own terminology
(often incomprehensible to outsiders), even though all approaches
basi...
Forecasting industrial end-use natural gas consumption is an important prerequisite for efficient system operation and a basis for planning decisions. This paper presents a novel prediction model that provides forecasting in a medium-term horizon (1–3 years) with a very high resolution (days) based on a decomposition approach. The forecast is obtai...
This paper describes a procedure for medium-and long-term risk analysis by using decision trees. A market equilibrium model is presented in order to assess the impact of the different sources of uncertainty. Decision trees are defined and applied in a study case, showing the advantages of these techniques for medium-term operation and planning. The...
The available amount of data for the companies that operate in the Spanish electricity market is very high, and contains information which is potentially very valuable. The knowledge about the strategic behavior of a market agent competitors can be obtained from the supply curves, given a competitive advantage to the agent for its operation. In thi...
As a result of the deregulation processes, liberalized markets, where electricity futures and derivatives are traded, have arisen all over the world. Utilities, consumers, traders and, generally, market agents must do quantitative assessments of their positions. Basic analytical data are the forward and volatility curves of the traded products. Ide...
In deregulated electrical systems, production schedule for power plants is the result of an auction process. In the Spanish case, this schedule includes two main concepts: energy production (to be actually produced) and secondary reserve (to maintain available). The generation company faces the problem of converting energy schedule into a power sch...
In this paper, we present and compare two approaches to model
supply and demand curves of a sealed-bid auction market. Both the linear
hinges model and the sigmo model are able to extract the relevant
information from these bidding curves, without losing significant market
information. We discuss their main similarities and important
differences us...
This paper describes SGO, a management information system for
bidding in deregulated electricity markets, developed for the Spanish
case. SGO has a client-server architecture and consists of a set of
integrated cooperative and flexible software tools for assisting the
users during the whole bidding process: resources identification, bids
generation...
This paper proposes a novel methodology to identify congestion
problems under both "traditional" and "new" uncertainties such as
generation costs, location and size of new generators, retirement of old
ones, generation patterns, etc. The methodology allows not only
identifying the transmission paths and corridors which will have
congestion problems...
This paper presents a new approach based on Genetic Algorithms (GA) to find the optimal bidding strategies of a market participant. Uncertainty about competitors behavior is included in our model, which incorporates risk management. Two uncertainty levels have been considered. The first one is used to weight nominal competitors behaviors (e.g. aggr...
Due to the large size of electric power systems, there is a very high computational burden when obtaining the optimum network by using classical optimization techniques. Several authors have used heuristics and/or sensitivities in order to guide the search of optimal network investments. This paper proposes an automatic learning approach in order t...
The purpose of this paper is to present a price forecasting model for emerging electricity markets. The model combines statistical techniques (as linear regression or time series analysis) with a fundamental market model. According to this, a medium-term forecasting model (obtained through linear regression) is adjusted with short-term information...
In this paper it is proposed a new methodology for modeling daily outdoor air temperature based on the Linear Hinges Model (LHM). In particular, the proposed model tries to capture the seasonal evolution of temperature throughout the year and it consists of two terms: the deterministic component describing the expected temperature, and the stochast...