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References

    1. 1)
      • 1. ‘Buildings Energy Data Book’, Available at http://buildingsdatabook.eren.doe.gov.
        .
    2. 2)
    3. 3)
    4. 4)
    5. 5)
    6. 6)
    7. 7)
    8. 8)
    9. 9)
      • H. Zhao , D. Quach , S. Wang .
        9. Zhao, H., Quach, D., Wang, S., et al: ‘Learning based compact thermal modeling for energy-efficient smart building management’, IEEE/ACM International Conference Computer-Aided Design (ICCAD), 2–6 Nov2015, pp. 450456.
        . , 450 - 456
    10. 10)
      • W. Liu , H. Wang , H. Zhao .
        10. Liu, W., Wang, H., Zhao, H., et al: ‘Thermal modeling for energy-efficient smart building with advanced overfitting mitigation technique’, 2016 21st Asia and South Pacific Design Automation Conference (ASP-DAC), 25–28 Jan2016, pp. 417422.
        . , 417 - 422
    11. 11)
    12. 12)
      • X. Chen , X. Li , S.X.-D. Tan .
        12. Chen, X., Li, X., Tan, S.X.-D.: ‘From robust chip to smart building: CAD algorithms and methodologies for uncertainty analysis of building performance’, IEEE/ACM International Conference Computer-Aided Design (ICCAD), 2–6 Nov2015, pp. 457464.
        . , 457 - 464
    13. 13)
    14. 14)
    15. 15)
      • M.D. McKay .
        15. McKay, M.D.: ‘Latin hypercube sampling as a tool in uncertainty analysis of computer models’. Proc. of Winter Simulation Conf., 1992, pp. 557564.
        . Proc. of Winter Simulation Conf. , 557 - 564
    16. 16)
      • D.B. Crawley , L.K. Lawrie , F.C. Winkelmann .
        16. Crawley, D.B., Lawrie, L.K., Winkelmann, F.C., et al: ‘EnergyPlus: creating a new-generalization building energy simulation program’, 2001, 33, pp. 319331.
        . , 319 - 331
    17. 17)
    18. 18)
      • T. Hastie , R. Tibshirani , M. Wainwright . (2015)
        18. Hastie, T., Tibshirani, R., Wainwright, M.: ‘Statistical learning with sparsity: the Lasso and generalization’ (CRC press, 2015).
        .
    19. 19)
      • R. Tibshirani .
        19. Tibshirani, R.: ‘Regression shrinkage and selection via the Lasso’, Statistical Methodology, 1996, 58, pp. 267288.
        . Statistical Methodology , 267 - 288
    20. 20)
      • J. Huang , S. Ma , C.-H. Zhang .
        20. Huang, J., Ma, S., Zhang, C.-H.: ‘Adaptive Lasso for sparse high-dimensional regression models’, Statistica Sinica, 2008, 18, pp. 16031618.
        . Statistica Sinica , 1603 - 1618
    21. 21)
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