Title
Evaluating Energy Consumption for Cyber-Physical Energy System: An Environment Ontology-Based Approach
Abstract
Energy consumption evaluation is one of the most important steps in Cyber-Physical Energy System (CPES) development. However, due to the lack of accurate and effective modeling and evaluation approaches considering the uncertainty of environment, it is hard to conduct the quantitative analysis for the energy consumption of CPESs. To address the above issue, this paper proposes an environment-aware energy consumption evaluation framework based on the Statistical Model Checking (SMC). In our framework, the environment uncertainty of CPESs is modeled using the Stochastic Hybrid Automata (SHA). In order to describe various environment modeling patterns, we create a collection of parameterized SHA models and save them to a domain specific environment ontology. Based on the domain environment ontology and user designs in the form of UML sequence diagrams and activity diagrams, our framework can automatically guide the construction of CPES models using networks of SHA and conduct the corresponding energy consumption evaluation. A case study based on an energy-aware building design demonstrates that our approach can not only support the accurate environment modeling with various uncertain factors, but also can be used to reason the relations between the energy consumption and environment uncertainties of CPES designs.
Year
DOI
Venue
2015
10.1109/COMPSAC.2015.114
2015 IEEE 39th Annual Computer Software and Applications Conference
Keywords
Field
DocType
Cyber-Physical Energy Systems,Statistical Model Checking,Stochastic Hybrid Automata (SHA),Environment Ontology,Uncertainty of Environment
Ontology (information science),Ontology,Sequence diagram,Unified Modeling Language,Computer science,Automaton,Real-time computing,Activity diagram,Cyber-physical system,Energy consumption
Conference
Volume
ISSN
Citations 
2
0730-3157
0
PageRank 
References 
Authors
0.34
16
6
Name
Order
Citations
PageRank
Xiaohong Chen162.23
Fan Gu200.68
Mingsong Chen327925.10
Dehui Du414716.40
Jing Liu525027.30
Haiying Sun655.17