The construction of a usable, formal, and extensible modeller and simulator for Smart Energy Grids is of a paramount importance in the industrial settings. Final users are interested in deploying effective smart-home configurations able to satisfy energy requests in the most economical way. Hence, a tool able to forecast both energy consumption and related costs of a smarthome configuration is needed. In this paper, the µGRIMOIRE (micro GRId MOdelling envIRonmEnt) toolset is presented. This tool is based on the well-known model-driven paradigm and its successful applications in the generation of formal/quantitative models for complex systems. By using a Domain Specific Modelling Language, a final user can define a smart-home system configuration and energy saving logics. Then, the tool offers the possibility of evaluating the desired user metrics by translating the model into a Fluid Stochastic Petri Net model representing both discrete and continuous variables. © Gentile et al.
µGRIMOIRE: A tool for smart micro grids modelling and energy profiling / Gentile, ; and Marrone, U.; and Mazzocca, S.; and Nardone, N.. - In: THE OPEN CYBERNETICS & SYSTEMICS JOURNAL. - ISSN 1874-110X. - 10:(2016), pp. 263-282. [10.2174/1874110X01610010263]
µGRIMOIRE: A tool for smart micro grids modelling and energy profiling
S. and Mazzocca;N. and Nardone
2016
Abstract
The construction of a usable, formal, and extensible modeller and simulator for Smart Energy Grids is of a paramount importance in the industrial settings. Final users are interested in deploying effective smart-home configurations able to satisfy energy requests in the most economical way. Hence, a tool able to forecast both energy consumption and related costs of a smarthome configuration is needed. In this paper, the µGRIMOIRE (micro GRId MOdelling envIRonmEnt) toolset is presented. This tool is based on the well-known model-driven paradigm and its successful applications in the generation of formal/quantitative models for complex systems. By using a Domain Specific Modelling Language, a final user can define a smart-home system configuration and energy saving logics. Then, the tool offers the possibility of evaluating the desired user metrics by translating the model into a Fluid Stochastic Petri Net model representing both discrete and continuous variables. © Gentile et al.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.


