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°Ç¹° ¿¡³ÊÁö ½Ã¹Ä·¹ÀÌ¼Ç ºÒÈ®½Ç¼º ºÐ¼®À» À§ÇÑ ¸óÅ×Ä«¸¦·Î »ùÇøµ ±â¹Ý ¸ÞŸ¸ðµ¨ °£ÀÇ ºñ±³ / Comparative Study of Monte-Carlo Sampling based Meta-models for Uncertainty Analysis of Building Energy Simulation / Ãß°è-05. °ÇÃàȯ°æ¹×¼³ºñ |
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´ëÇѰÇÃàÇÐȸ Çмú¹ßÇ¥´ëȸ ³í¹®Áý, Vol.35 No.2 (2015-10) |
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½ÃÀÛÆäÀÌÁö(219) ÃÑÆäÀÌÁö(2) |
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ºÒÈ®½Ç¼º ; ¸ÞŸ¸ðµ¨ ; ºôµù ½Ã¹Ä·¹ÀÌ¼Ç ; Uncertainty ; Meta-model ; Building simulation |
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Uncertainty analysis for whole-building simulation tools has been widely used to account for risks of predicted outputs for rational decision making. However, the stochastic approach requires significant computation time and efforts for uncertainty propagation compared to the deterministic approach. This paper addresses two meta-models (Gaussian Process Emulator [GPE] and Polynomial Chaos Expansion [PCE]) which can be regarded as a surrogate model of the dynamic whole-building simulators. In the paper, comparative results are presented in terms of prediction capability under different training dataset and inputs. |