Distributed Semantic Architecture for Smart Grids

Penya, Yoseba K.; Nieves, Juan Carlos; Espinoza, Angelina; Borges, Cruz E.; Pena, Aitor; Ortega, Mariano

doi:10.3390/en5114824

Cited by 18 publications

(9 citation statements)

References 25 publications

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“…Sin embargo, la inclusión de estas fuentes, también significa que cantidades innumerables de datos necesitarían ser manejados y procesados. Por ejemplo, la investigación desarrollada en Penya et al (2012) presenta una arquitectura de nodos inteligentes individuales que controlan una serie de activos eléctricos a partir del procesamiento de datos registrados por las redes de sensores inteligentes.…”

Section: Implementación E Integración De Recursos Distribuidos Y Geneunclassified

Visión General, Características y Funcionalidades de la Red Eléctrica Inteligente (Smart Grid)

2018

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Este artículo presenta una visión general de la red eléctrica inteligente (Smart Grid) con sus características y funcionalidades y se identifica las actividades de investigación, tendencias, problemas y desafíos de la Smart Grid, Para ello se realizó una revisión de la literatura donde se visualiza como la integración de los recursos energéticos distribuidos (generación distribuida y almacenamiento energético), así como los avances de las tecnologías de la información y las comunicaciones, han desarrollado el concepto de la moderna red eléctrica. En esta área, la implementación de políticas y la rápida integración de sistemas de gestión, están teniendo como resultado una gran revolución en la generación, la trasmisión, la distribución, la operación y el consumo energético.

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Section: Implementación E Integración De Recursos Distribuidos Y Geneunclassified

Visión General, Características y Funcionalidades de la Red Eléctrica Inteligente (Smart Grid)

2018

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“…Please note that the meter is virtually connected to a real-time processing network as described in [29], to which we will refer from now on as the Platform. …”

Section: Overall Methodologymentioning

confidence: 99%

Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings

et al. 2013

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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.

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“…logic-based). This was the approach followed in [47] and implemented in the EN-ERGOS project [45], both works preceding this current research. The experimental validation carried out in those previous work brought into light that the use of these types of languages were not appropriate for the real-time information processing required in Smart Grid applications.…”

Section: Introductionmentioning

confidence: 99%

A comprehensive common-sense-based architecture for understanding voltage-sag events in electrical grids

Santofimia

García

Villanueva

et al. 2018

ICA

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This research is intended to evaluate the suitability of a common-sense-based approach for providing causal explanations to power quality disturbances and, more specifically, to voltage-sage events, with the aim of improving the security and reliability of the electrical grid. Since voltage sags are one of the most common power-quality disturbance and may have a severe impact on sensitive loads and users, the case study presented is dedicated to find their causes as a prerequisite to prevent them. The main contribution presented in this work is in the knowledge management domain. However, the proposed architecture adopts a multi-layer approach that comprehensively faces all the necessary stages, comprising: measurement and data collection, filtering, distribution, homogenization, and integration up to the process of inference of the possible cause of the event. The architecture of the proposed system comprises three layers, namely: the information gathering, the information modeling and the information understanding layers. The proposed system has been evaluated using a synthetic dataset of simulated voltages sags. Information about weather conditions and different location features and environmental circumstances is aggregated to the electrical features of the voltage sag, in order to improve the inference of the external cause of the event. Even though a real-world dataset would be required to fully validate the proposed system and assess the benefits in a real scenario, the results obtained with the synthetic dataset are satisfactory and an overall accuracy above 90% of the cause identification is achieved.

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Distributed Semantic Architecture for Smart Grids

Cited by 18 publications

References 25 publications

Visión General, Características y Funcionalidades de la Red Eléctrica Inteligente (Smart Grid)

Visión General, Características y Funcionalidades de la Red Eléctrica Inteligente (Smart Grid)

Assessing Tolerance-Based Robust Short-Term Load Forecasting in Buildings

A comprehensive common-sense-based architecture for understanding voltage-sag events in electrical grids

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