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Short-term load forecasting (STLF) in buildings differs from its broader counterpart in that the load to be predicted does not seem to be stationary, seasonal and regular but, on the contrary, it may be subject to sudden changes and variations on its consumption behaviour. Classical STLF methods do not react fast enough to these perturbations (i.e., they are not robust) and the literature on building STLF has not yet explored this area. Hereby, we evaluate a well-known post-processing method (Learning Window Reinitialization) applied to two broadly-used STLF algorithms (Autoregressive Model and Support Vector Machines) in buildings to check their adaptability and robustness. We have tested the proposed method with real-world data and our results state that this methodology is especially suited for buildings with non-regular consumption profiles, as classical STLF methods are enough to model regular-profiled ones.
Cruz E. Borges; Yoseba K. Penya; Iván Fernandez; Juan Prieto; Oscar Bretos. Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings. Energies 2013, 6, 2110 -2129.
AMA StyleCruz E. Borges, Yoseba K. Penya, Iván Fernandez, Juan Prieto, Oscar Bretos. Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings. Energies. 2013; 6 (4):2110-2129.
Chicago/Turabian StyleCruz E. Borges; Yoseba K. Penya; Iván Fernandez; Juan Prieto; Oscar Bretos. 2013. "Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings." Energies 6, no. 4: 2110-2129.
The smart grid revolution demands a huge effort in redesigning and enhancing current power networks, as well as integrating emerging scenarios such as distributed generation, renewable energies or the electric vehicle. This novel situation will cause a huge flood of data that can only be handled, processed and exploited in real-time with the help of cutting-edge ICT (Information and Communication Technologies). We present here a new architecture that, contrary to the previous centralised and static model, distributes the intelligence all over the grid by means of individual intelligent nodes controlling a number of electric assets. The nodes own a profile of the standard smart grid ontology stored in the knowledge base with the inferred information about their environment in RDF triples. Since the system does not have a central registry or a service directory, the connectivity emerges from the view of the world semantically encoded by each individual intelligent node (i.e., profile + inferred information). We have described a use-case both with and without real-time requirements to illustrate and validate this novel approach.
Yoseba K. Penya; Juan Carlos Nieves; Angelina Espinoza; Cruz E. Borges; Aitor Peña; Mariano Ortega. Distributed Semantic Architecture for Smart Grids. Energies 2012, 5, 4824 -4843.
AMA StyleYoseba K. Penya, Juan Carlos Nieves, Angelina Espinoza, Cruz E. Borges, Aitor Peña, Mariano Ortega. Distributed Semantic Architecture for Smart Grids. Energies. 2012; 5 (11):4824-4843.
Chicago/Turabian StyleYoseba K. Penya; Juan Carlos Nieves; Angelina Espinoza; Cruz E. Borges; Aitor Peña; Mariano Ortega. 2012. "Distributed Semantic Architecture for Smart Grids." Energies 5, no. 11: 4824-4843.
We present here a combined aggregative short-term load forecasting method for smart grids, a novel methodology that allows us to obtain a global prognosis by summing up the forecasts on the compounding individual loads. More accurately, we detail here three new approaches, namely bottom-up aggregation (with and without bias correction), top-down aggregation (with and without bias correction), and regressive aggregation. Further, we have devised an experiment to compare their results, evaluating them with two datasets of real data and showing the feasibility of aggregative forecast combinations for smart grids.
Cruz Enrique Borges; Yoseba K. Penya; Ivan Fernandez. Evaluating Combined Load Forecasting in Large Power Systems and Smart Grids. IEEE Transactions on Industrial Informatics 2012, 9, 1570 -1577.
AMA StyleCruz Enrique Borges, Yoseba K. Penya, Ivan Fernandez. Evaluating Combined Load Forecasting in Large Power Systems and Smart Grids. IEEE Transactions on Industrial Informatics. 2012; 9 (3):1570-1577.
Chicago/Turabian StyleCruz Enrique Borges; Yoseba K. Penya; Ivan Fernandez. 2012. "Evaluating Combined Load Forecasting in Large Power Systems and Smart Grids." IEEE Transactions on Industrial Informatics 9, no. 3: 1570-1577.
The deployment of Next-Generation Networks (NGN) is a challenge that requires integrating heterogeneous services into a global system of All-IP telecommunications. These networks carry voice, data, and multimedia traffic over the Internet, providing users with the information they want in any format, amount, device, place or moment. Still, there are certain issues, such as the emerging security risks or the billing paradigms of the services offered, which demand deeper research in order to guarantee the stability and the revenue of such systems. Against this background, we analyse the security requirements of NGN and introduce a fraud management system based on misuse detection for Voice over IP services. Specifically, we address a fraud detection framework consisting of a rule engine built over a knowledge base. We detail the architecture of our model and describe a case study illustrating a possible fraud and how our system detects it, proving in this way, its feasibility in this task.
Igor Ruiz-Agundez; Yoseba K. Penya; Pablo Garcia Bringas. Fraud Detection for Voice over IP Services on Next-Generation Networks. Transactions on Petri Nets and Other Models of Concurrency XV 2010, 6033, 199 -212.
AMA StyleIgor Ruiz-Agundez, Yoseba K. Penya, Pablo Garcia Bringas. Fraud Detection for Voice over IP Services on Next-Generation Networks. Transactions on Petri Nets and Other Models of Concurrency XV. 2010; 6033 ():199-212.
Chicago/Turabian StyleIgor Ruiz-Agundez; Yoseba K. Penya; Pablo Garcia Bringas. 2010. "Fraud Detection for Voice over IP Services on Next-Generation Networks." Transactions on Petri Nets and Other Models of Concurrency XV 6033, no. : 199-212.
Mechanical properties are the attributes that measure the faculty of a metal to withstand several loads and tensions. Specifically, ultimate tensile strength is the force a material can resist until it breaks and, thus, it is one of the variables to control in the foundry process. The only way to examine this feature is the use of destructive inspections that renders the casting invalid with the subsequent cost increment. Nevertheless, the foundry process can be modelled as an expert knowledge cloud upon which we may apply several machine learnings techniques that allow foreseeing the probability for a certain value of a variable to happen. In this paper, we extend previous research on foundry production control by adapting and testing support vector machines and decision trees for the prediction in beforehand of the mechanical properties of castings. Finally, we compare the obtained results and show that decision trees are more suitable than the rest of the counterparts for the prediction of ultimate tensile strength.
Javier Nieves; Igor Santos; Yoseba K. Penya; Felix Brezo; Pablo García Bringas. Enhanced Foundry Production Control. Transactions on Petri Nets and Other Models of Concurrency XV 2010, 6261, 213 -220.
AMA StyleJavier Nieves, Igor Santos, Yoseba K. Penya, Felix Brezo, Pablo García Bringas. Enhanced Foundry Production Control. Transactions on Petri Nets and Other Models of Concurrency XV. 2010; 6261 ():213-220.
Chicago/Turabian StyleJavier Nieves; Igor Santos; Yoseba K. Penya; Felix Brezo; Pablo García Bringas. 2010. "Enhanced Foundry Production Control." Transactions on Petri Nets and Other Models of Concurrency XV 6261, no. : 213-220.
Jaime Devesa; Igor Santos; Xabier Cantero; Yoseba K. Penya; Pablo G. Bringas. AUTOMATIC BEHAVIOUR-BASED ANALYSIS AND CLASSIFICATION SYSTEM FOR MALWARE DETECTION. Proceedings of the 12th International Conference on Enterprise Information Systems 2010, 395 -399.
AMA StyleJaime Devesa, Igor Santos, Xabier Cantero, Yoseba K. Penya, Pablo G. Bringas. AUTOMATIC BEHAVIOUR-BASED ANALYSIS AND CLASSIFICATION SYSTEM FOR MALWARE DETECTION. Proceedings of the 12th International Conference on Enterprise Information Systems. 2010; ():395-399.
Chicago/Turabian StyleJaime Devesa; Igor Santos; Xabier Cantero; Yoseba K. Penya; Pablo G. Bringas. 2010. "AUTOMATIC BEHAVIOUR-BASED ANALYSIS AND CLASSIFICATION SYSTEM FOR MALWARE DETECTION." Proceedings of the 12th International Conference on Enterprise Information Systems , no. : 395-399.
Network Intrusion Detection Systems (NIDS) aim at preventing network attacks and unauthorised remote use of computers. More accurately, depending on the kind of attack it targets, an NIDS can be oriented to detect misuses (by defining all possible attacks) or anomalies (by modelling legitimate behaviour and detecting those that do not fit on that model). Still, since their problem knowledge is restricted to possible attacks, misuse detection fails to notice anomalies and vice versa. Against this, we present here ESIDE-Depian, the first unified misuse and anomaly prevention system based on Bayesian Networks to analyse completely network packets, and the strategy to create a consistent knowledge model that integrates misuse and anomaly-based knowledge. Finally, we evaluate ESIDE-Depian against well-known and new attacks showing how it outperforms a well-established industrial NIDS.
Pablo García Bringas; Yoseba K. Penya; Stefano Paraboschi; Paolo Salvaneschi. BAYESIAN-NETWORKS-BASED MISUSE AND ANOMALY PREVENTION SYSTEM. Proceedings of the Tenth International Conference on Enterprise Information Systems 2008, 62 -69.
AMA StylePablo García Bringas, Yoseba K. Penya, Stefano Paraboschi, Paolo Salvaneschi. BAYESIAN-NETWORKS-BASED MISUSE AND ANOMALY PREVENTION SYSTEM. Proceedings of the Tenth International Conference on Enterprise Information Systems. 2008; ():62-69.
Chicago/Turabian StylePablo García Bringas; Yoseba K. Penya; Stefano Paraboschi; Paolo Salvaneschi. 2008. "BAYESIAN-NETWORKS-BASED MISUSE AND ANOMALY PREVENTION SYSTEM." Proceedings of the Tenth International Conference on Enterprise Information Systems , no. : 62-69.