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Optimizing Control Strategy Using Statistical Model Checking

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NASA Formal Methods (NFM 2013)

Part of the book series: Lecture Notes in Computer Science ((LNPSE,volume 7871))

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Abstract

This paper proposes a new efficient approach to optimize energy consumption for energy aware buildings. Our approach relies on stochastic hybrid automata for representing energy aware systems. The model is parameterized by several cost values that need to be optimized in order to minimize energy consumption. Our approach exploits a stochastic semantic together with simulation in order to estimate the best value for such parameters. Contrary to existing techniques that would estimate energy consumption for each value of the parameters, our approach relies on a new statistical engine that exploits ANOVA, a technique that can reduce the number of runs needed by the comparison algorithm to perform the estimates. Our approach has been implemented and our experiments show that we clearly outperform the naive approach.

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Author information

Authors and Affiliations

  1. Computer Science, Aalborg University, Denmark

    Alexandre David, Kim Guldstrand Larsen & Marius Mikučionis

  2. Laboratory of Trustworthy Computing, East China Normal University, Shanghai, China

    Dehui Du

  3. INRIA/IRISA, Rennes Cedex, France

    Axel Legay

Authors
  1. Alexandre David
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  2. Dehui Du
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  3. Kim Guldstrand Larsen
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  4. Axel Legay
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  5. Marius Mikučionis
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Editor information

Editors and Affiliations

  1. NASA Ames Research Center, M/S 269-2, Moffett Field, 94035-0001, CA, USA

    Guillaume Brat

  2. Stinger Ghaffarian Technologies Inc., NASA Ames Research Center, M/S 269-2, Moffett Field, 94035-0001, CA, USA

    Neha Rungta

  3. NASA Ames Research Center, Carnegie Mellon University, M/S 269-2, Moffett Field, 94035-0001, CA, USA

    Arnaud Venet

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David, A., Du, D., Guldstrand Larsen, K., Legay, A., Mikučionis, M. (2013). Optimizing Control Strategy Using Statistical Model Checking. In: Brat, G., Rungta, N., Venet, A. (eds) NASA Formal Methods. NFM 2013. Lecture Notes in Computer Science, vol 7871. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-38088-4_24

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  • DOI: https://doi.org/10.1007/978-3-642-38088-4_24

  • Publisher Name: Springer, Berlin, Heidelberg

  • Print ISBN: 978-3-642-38087-7

  • Online ISBN: 978-3-642-38088-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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