The huge amount of data collected everyday for different purposes by a multitude of smart devices enables the delivery of added-value services to end-users by means of smart applications. Among them, the applications devoted to optimizing the energy consumption through smart power grids are gaining more and more attention due to their impact on both the environment and the costs for the users. The design and evaluation of Smart Energy systems is very complex due to the heterogeneity of involved devices and technologies, and to the high variability of energy production and consumption profiles. In this regard, in this paper we propose a model-based methodology for the evaluation of Smart Energy systems, which merges system modeling and cognitive computing techniques to obtain a representation of the systems' behavior that takes into account a data-driven characterization of the workload and of the overall context. Such a representation allows to estimate properties of interest in different operative conditions, and can be profitably used to make design choices and to tune the application behavior during operation based on collected data. In order to demonstrate the effectiveness of our proposal, we present an example Smart Energy system modeled by means of the Stochastic Activity Network (SAN) formalism, and we show how it is possible to perform several analyses on the system configuration by means of model simulations.

A model-Based evaluation methodology for smart energy systems

Venticinque, Salvatore
2018

Abstract

The huge amount of data collected everyday for different purposes by a multitude of smart devices enables the delivery of added-value services to end-users by means of smart applications. Among them, the applications devoted to optimizing the energy consumption through smart power grids are gaining more and more attention due to their impact on both the environment and the costs for the users. The design and evaluation of Smart Energy systems is very complex due to the heterogeneity of involved devices and technologies, and to the high variability of energy production and consumption profiles. In this regard, in this paper we propose a model-based methodology for the evaluation of Smart Energy systems, which merges system modeling and cognitive computing techniques to obtain a representation of the systems' behavior that takes into account a data-driven characterization of the workload and of the overall context. Such a representation allows to estimate properties of interest in different operative conditions, and can be profitably used to make design choices and to tune the application behavior during operation based on collected data. In order to demonstrate the effectiveness of our proposal, we present an example Smart Energy system modeled by means of the Stochastic Activity Network (SAN) formalism, and we show how it is possible to perform several analyses on the system configuration by means of model simulations.
9781538647059
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11591/402734
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