1. bookVolumen 8 (2023): Heft 1 (January 2023)
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
2444-8656
Erstveröffentlichung
01 Jan 2016
Erscheinungsweise
2 Hefte pro Jahr
Sprachen
Englisch
Uneingeschränkter Zugang

Sensitivity analysis of design parameters of envelope enclosure performance in the dry-hot and dry-cold areas

Online veröffentlicht: 18 Oct 2021
Volumen & Heft: Volumen 8 (2023) - Heft 1 (January 2023)
Seitenbereich: 195 - 208
Eingereicht: 07 Mar 2021
Akzeptiert: 15 May 2021
Zeitschriftendaten
License
Format
Zeitschrift
eISSN
2444-8656
Erstveröffentlichung
01 Jan 2016
Erscheinungsweise
2 Hefte pro Jahr
Sprachen
Englisch

Fig. 1

Technology roadmap of this research.
Technology roadmap of this research.

Fig. 2

Picture information collected on the site investigation.
Picture information collected on the site investigation.

Fig. 3

Original building energy consumption model.
Original building energy consumption model.

Fig. 4

Energy consumption model verification and error evaluation.
Energy consumption model verification and error evaluation.

Fig. 5

ANN modeling and drawing Loss curve.
ANN modeling and drawing Loss curve.

Fig. 6

Draw percentage stacked histograms for sensitivity analysis method verification.
Draw percentage stacked histograms for sensitivity analysis method verification.

Fig. 7

Percentage stacked histogram of sensitivity analysis results.
Percentage stacked histogram of sensitivity analysis results.

Fig. 8

The prediction curve (P-C) of the ANN model.
The prediction curve (P-C) of the ANN model.

Zhang, et al. (2015), China’s energy consumption in the building sector: A life cycle approach. Search in Google Scholar

Loo, L.D. and M. Mahdavinejad. (2018), Analysis of Design Indicators of Sustainable Buildings with an Emphasis on Efficiency of Energy Consumption (Energy Efficiency). Civil Engineering Journal-Tehran, 4(4): p. 897-905. Search in Google Scholar

Freitas, J.d.S., et al. (2020), Modeling and assessing BIPV envelopes using parametric Rhinoceros plugins Grasshopper and Ladybug. Renewable Energy, 160. Search in Google Scholar

Evola, G., et al. (2020), A novel comprehensive workflow for modelling outdoor thermal comfort and energy demand in urban canyons: Results and critical issues Energy & Buildings, 216. Search in Google Scholar

Lopez-Cabeza, V.P., et al. (2021), Modelling of surface and inner wall temperatures in the analysis of courtyard thermal performances in Mediterranean climates. Journal of Building Performance Simulation, 14(2): p. 181-202. Search in Google Scholar

Kim, B., et al. (2019), Urban building energy modeling considering the heterogeneity of HVAC system stock: A case study on Japanese office building stock. Energy and Buildings, 199: p. 547-561. Search in Google Scholar

Mun, J. and M. Krarti. (2015), Implementation of a new CTF method stability algorithm into EnergyPlus. Building Simulation, 8(6): p. 613-620. Search in Google Scholar

(2001), DOE releases EnergyPlus program. Ashrae Journal, 43(6): p. 6-6. Search in Google Scholar

Zuo, W.D., et al. (2014), Acceleration of the matrix multiplication of Radiance three phase daylighting simulations with parallel computing on heterogeneous hardware of personal computer. Journal of Building Performance Simulation, 7(2): p. 152-163. Search in Google Scholar

Reinhart, C.F. and O. Walkenhorst. (2001), Validation of dynamic RADIANCE-based daylight simulations for a test office with external blinds. Energy and Buildings, 33(7): p. 683-697. Search in Google Scholar

Pang, Z.H., et al. (2020), The role of sensitivity analysis in the building performance analysis: A critical review. Energy and Buildings, 209. Search in Google Scholar

Zhang, Y.F., H. Yokota, and Y.L. Zhu. (2019), Sensitivity Analyses on Chloride Ion Penetration into Undersea Tunnel Concrete. Journal of Advanced Concrete Technology, 17(10): p. 592-602. Search in Google Scholar

Velarde, J., C. Kramhoft, and J.D. Sorensen. (2019), Global sensitivity analysis of offshore wind turbine foundation fatigue loads. Renewable Energy, 140: p. 177-189. Search in Google Scholar

Rao, J. and F. Haghighat. (1993), A PROCEDURE FOR SENSITIVITY ANALYSIS OF AIR-FLOW IN MULTIZONE BUILDINGS. Building and Environment, 28(1): p. 53-62. Search in Google Scholar

Lomas, K.J. and H. Eppel. (1992), SENSITIVITY ANALYSIS TECHNIQUES FOR BUILDING THERMAL SIMULATION PROGRAMS. Energy and Buildings, 19(1): p. 21-44. Search in Google Scholar

Vytlacil, D. (2010), SENSITIVITY ANALYSIS OF BUILDING STOCK MANAGEMENT MODEL. Cesb 10: Central Europe Towards Sustainable Building - from Theory to Practice, ed. P. Hajek, et al. 735-738. Search in Google Scholar

Costamagna, A., et al.(2016), A model for the operations to render epidemic-free a hog farm infected by the Aujeszky disease %J Applied Mathematics and Nonlinear Sciences. 1(1): p. 207-228. Search in Google Scholar

Sanchez, D.G., et al. (2014), Application of sensitivity analysis in building energy simulations: Combining first- and second-order elementary effects methods. Energy and Buildings, 68: p. 741-750. Search in Google Scholar

Hughes, M., et al. (2015), Global sensitivity analysis of England’s housing energy model. Journal of Building Performance Simulation, 8(5): p. 283-294. Search in Google Scholar

Kulhanek, F. and K. Poyraz. (2015), SENSITIVITY ANALYSIS OF BUILDING STRUCTURES WITHIN THE SCOPE OF ENERGY, ENVIRONMENT AND INVESTMENT. Civil Engineering Journal-Stavebni Obzor, 3. Search in Google Scholar

Sun, Y.J. (2015), Sensitivity analysis of macro-parameters in the system design of net zero energy building. Energy and Buildings, 86: p. 464-477. Search in Google Scholar

Maltais, L.G. and L. Gosselin. (2017), Daylighting ’energy and comfort’ performance in office buildings: Sensitivity analysis, metamodel and pareto front. Journal of Building Engineering, 14: p. 61-72. Search in Google Scholar

Ostergard, T., R.L. Jensen, and S.E. Maagaard. (2017), Early Building Design: Informed decision-making by exploring multidimensional design space using sensitivity analysis. Energy and Buildings, 142: p. 8-22. Search in Google Scholar

Al-Saadi, S.N. and K.S. Al-Jabri. (2020), Optimization of envelope design for housing in hot climates using a genetic algorithm (GA) computational approach. Journal of Building Engineering, 32. Search in Google Scholar

Liu, Y., et al. (2019), Simulation Analysis and Scheme Optimization of Energy Consumption in Public Buildings. Advances in Civil Engineering, 2019. Search in Google Scholar

Delgarm, N., et al. (2018), Sensitivity analysis of building energy performance: A simulation-based approach using OFAT and variance-based sensitivity analysis methods. Journal of Building Engineering, 15: p. 181-193. Search in Google Scholar

Center, N.M.I. (2012), Data set of ground annual value in China (1981-2010). National Meteorological Information Center, Beijing. Search in Google Scholar

Mistry, M.N. (2019), Historical global gridded degree-days: A high-spatial resolution database of CDD and HDD. Geoscience Data Journal, 6(2). Search in Google Scholar

Zhao, W., Y.Y. Chen, and J.K. Liu. (2019), Reliability sensitivity analysis using axis orthogonal importance Latin hypercube sampling method. Advances in Mechanical Engineering, 11(3). Search in Google Scholar

Xu, J., et al. (2018), A general construction for nested Latin hypercube designs. Statistics & Probability Letters, 134: p. 134-140. Search in Google Scholar

McWilliams, T.P. (1987), SENSITIVITY ANALYSIS OF GEOLOGIC COMPUTER-MODELS-A FORMAL PROCEDURE BASED ON LATIN HYPERCUBE SAMPLING. Mathematical Geology, 19(2): p. 81-90. Search in Google Scholar

Seaholm, S.K., S.C. Wu, and E. Ackerman. (1986), LATIN HYPERCUBE SAMPLING AND THE ANALYSIS OF MONTE-CARLO MODEL PARAMETER SENSITIVITY. Biophysical Journal, 49(2): p. A275-A275. Search in Google Scholar

McKay, M.D., R.J. Beckman, and W.J. Conover. (1979), A COMPARISON OF THREE METHODS FOR SELECTING VALUES OF INPUT VARIABLES IN THE ANALYSIS OF OUTPUT FROM A COMPUTER CODE. Techno-metrics, 21(2): p. 239-245. Search in Google Scholar

Tzempelikos and Athanasios. (2017), Advances on daylighting and visual comfort research. Building and Environment. Search in Google Scholar

Alnaqi, A.A., et al. (2019), Prediction of energetic performance of a building integrated photovoltaic/thermal system thorough artificial neural network and hybrid particle swarm optimization models. Energy Conversion and Management, 183: p. 137-148. Search in Google Scholar

Wu, Y.C. and J.W. Feng. (2018), Development and Application of Artificial Neural Network. Wireless Personal Communications, 102(2): p. 1645-1656. Search in Google Scholar

Yadav, A.K. and S.S. Chandel. (2014), Solar radiation prediction using Artificial Neural Network techniques: A review. Renewable & Sustainable Energy Reviews, 33: p. 772-781. Search in Google Scholar

D, G.G. (1991), Interpreting neural-network connection weights. AI Expert, 6(4): p. 47. Search in Google Scholar

Cai, Y., Y. Xing, and D. Hu. (2008), ON SENSITIVITY ANALYSIS. Journal of Beijing Normal University (Natural Science), (01): p. 9-16. Search in Google Scholar

Piro, P., et al. (2019), A Comprehensive Approach to Stormwater Management Problems in the Next Generation Drainage Networks, in The Internet of Things for Smart Urban Ecosystems, F. Cicirelli, et al., Editors. Springer International Publishing: Cham. p. 275-304. Search in Google Scholar

Weselek, J. and U. Häussler-Combe. (2017), Sensitivity Studies Within a Reliability Analysis of Cross Sections with Carbon Concrete. Cham: Springer International Publishing. Search in Google Scholar

Borgonovo, E. (2007), A new uncertainty importance measure. Reliability Engineering & System Safety, 92(6): p. 771-784. Search in Google Scholar

Plischke, E., E. Borgonovo, and C.L. Smith. (2013), Global sensitivity measures from given data. European Journal of Operational Research, 226(3): p. 536-550. Search in Google Scholar

Rao, C.R. and Y. Wu. (2009), Linear model selection by cross-validation. Journal of Statistical Planning Inference, 128(1): p. 231-240. Search in Google Scholar

Seymour and Geisser. (1975), The Predictive Sample Reuse Method with Applications. Journal of the American Statistical Association. Search in Google Scholar

Empfohlene Artikel von Trend MD