Prof. Dr. Antonio Gabaldon

Department of Electrical Engineering, Universidad Politecnica de Cartagena, Cartagena, Spain

Prof. Dr. Antonio Gabaldon

Department of Electrical Engineering, Universidad Politecnica de Cartagena, Cartagena, Spain

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Academic Career Length

35Years (1992-Present)

(Calculated once Scilit publication data is synced: current year − year of first publication + 1)

Representative Publications

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30 October 2006 in IEEE Transactions on Power Systems

Journal Article

Classification, Filtering, and Identification of Electrical Customer Load Patterns Through the Use of Self-Organizing Maps

S.V. Verdu

M.O. Garcia

C. Senabre

Antonio Gabaldon

F.J.G. Franco

Different methodologies are available for clustering purposes. The objective of this paper is to review the capacity of some of them and specifically to test the ability of self-organizing maps (SOMs) to filter, classify, and extract patterns from distributor, commercializer, or customer electrical demand databases. These market participants can achieve an interesting benefit through the knowledge of these patterns, for example, to evaluate the potential for distributed generation, energy efficiency, and demand-side response policies (market analysis). For simplicity, customer classification techniques usually used the historic load curves of each user. The first step in the methodology presented in this paper is anomalous data filtering: holidays, maintenance, and wrong measurements must be removed from the database. Subsequently, two different treatments (frequency and time domain) of demand data were tested to feed SOM maps and evaluate the advantages of each approach. Finally, the ability of SOM to classify new customers in different clusters is also examined. Both steps have been performed through a well-known technique: SOM maps. The results clearly show the suitability of this approach to improve data management and to easily find coherent clusters between electrical users, accounting for relevant information about weekend demand patterns

1 January 2007 in IET Generation, Transmission & Distribution

Journal Article

Methods for customer and demand response policies selection in new electricity markets

S. Valero

Mario Ortiz

C. Senabre

C. Alvarez

F.J.G. Franco

Antonio Gabaldon

Different methodologies are available for clustering and classification purposes. The objective of the research is to prove the capability of self-organising maps (SOMs) to classify customers and their response potential from distributor, commercialiser, or customer electrical demand databases, with the help of load response modelling methodologies as support tools. The search for customer suitability is restricted to day-ahead and real-time products, in which interest is growing in developed countries. Therefore customer demand and response (demand and distributed generation policies) have been tested and compared with price curves. Both steps have been performed through SOMs. The results clearly show the capability of this approach to improve data management and easily to find coherent policies to accomplish cleared-demand offers in different prices scenarios.

1 January 2003 in IEE Proceedings - Generation, Transmission and Distribution

Journal Article

Implementation and assessment of physically based electrical load models: application to direct load control residential programmes

Angel Molina-Garcia

Antonio Gabaldon

J.A. Fuentes

C. Alvarez

The purpose of the paper is to describe physically based electrical models of heating, ventillation and air conditioning residential loads. These models are based on energy balances between the internal air, the dwelling constructive elements, the conditioner appliances and the external environment through a discrete state-space equation system. The main objective of these dynamic models is to evaluate load management programmes, above all direct load control actions. The models have been implemented and tested to assess their accuracy and suitability in this specific kind of applications. In this way, simulated results have been compared with data collected over a period of one year in different cities and for different load performances—steady state as well as response to remote control actions. The advantages of the models proposed here are also compared with models previously developed and described in the literature.

1 October 2012 in Electric Power Systems Research

Journal Article

Application of SOM neural networks to short-term load forecasting: The Spanish electricity market case study

Miguel Lopez

S. Valero

C. Senabre

Juan Aparicio

Antonio Gabaldon

4 May 2004 in IEEE Transactions on Power Systems

Journal Article

Assessment and Simulation of the Responsive Demand Potential in End-User Facilities: Application to a University Customer

C. Alvarez

Antonio Gabaldon

Angel Molina-Garcia

Many problems have appeared with the practical implementation of restructured electrical business in the U.S. and European Union such as lack of generation, network constraints, etc. A good example of these problems is the scarce participation of the demand in the electricity markets - energy, reserve, and other ancillary services - problems that could be solved through new demand responsive programs, aimed to replace the traditional demand side management programs into voluntary demand participation programs. A methodology for the generation of demand side bids and offers in large customer facilities and a real application to a university customer is presented in this paper. The methodology is based on the knowledge of the physical processes involved in the electricity consumption and on the flexibility of the required supply. The result of the methodology proposed is a set of demand packages that can be used to participate in different electricity markets, whose possibilities - in the market arena - will be explored in a consequent paper.

6 August 2018 in Energies

Journal Article

Load Forecasting for a Campus University Using Ensemble Methods Based on Regression Trees

María Del Carmen Ruiz Abellón

Antonio Gabaldon

Antonio Guillamón Frutos

Load forecasting models are of great importance in Electricity Markets and a wide range of techniques have been developed according to the objective being pursued. The increase of smart meters in different sectors (residential, commercial, universities, etc.) allows accessing the electricity consumption nearly in real time and provides those customers with large datasets that contain valuable information. In this context, supervised machine learning methods play an essential role. The purpose of the present study is to evaluate the effectiveness of using ensemble methods based on regression trees in short-term load forecasting. To illustrate this task, four methods (bagging, random forest, conditional forest, and boosting) are applied to historical load data of a campus university in Cartagena (Spain). In addition to temperature, calendar variables as well as different types of special days are considered as predictors to improve the predictions. Finally, a real application to the Spanish Electricity Market is developed: 48-h-ahead predictions are used to evaluate the economical savings that the consumer (the campus university) can obtain through the participation as a direct market consumer instead of purchasing the electricity from a retailer.

About Me

A. Gabaldón received his Ph.D. (1991) from the U. P. de Valencia (Spain). In 1989, he made a pre-doctoral visit at the University of Montréal. He has been a professor at the University of Murcia (1990–1999) and Spanish Air Force Academy (1993–2008). Since 2003, he has been a professor at the University of Cartagena (UPCT). His research is focused on distribution systems, markets, demand response, energy efficiency, railways, and non-invasive monitoring techniques. In the last decade, he has been the main investigator in five R&D projects granted by the Spanish government. He was a member of the network REDYD-2050. He participated in R&D contracts with utilities and public entities. Part of the results of his research activity are around 30 papers in scientific journals (IEEE, IET, Elsevier, Springer, MDPI, etc.). He is a reviewer of Spanish assessment agencies and journals listed in the JCR-ISI, such as Appl. Energy and En. Efficiency (Springer); Modern P. Systems and Clean Energy (Springer); and Energies, Sustainability and Appl. Sciences (MDPI), IEEE T. on Vehicular Tech. He has been guest editor of three issues on “Short Term Load Forecasting” for MDPI. He has held academic positions as Dean (1999–2008) and Vice Rector for Academic Affairs and Doctorate (2008–2010) at the UPCT. He has published books in engineering (magnetism and circuit theory), a YouTube channel for students, and technical works with professional organizations, such as CIGRE and the “ETP SmartGrids.”

Prof. Dr. Antonio Gabaldon

Prof. Dr. Antonio Gabaldon

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