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

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

[2] 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

[3] 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

[4] 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

[5] 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

[6] 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

[7] 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

[8] (2001), DOE releases EnergyPlus program. Ashrae Journal, 43(6): p. 6-6. Search in Google Scholar

[9] 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

[10] 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

[11] 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

[12] 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

[13] 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

[14] 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

[15] 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

[16] 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

[17] 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

[18] 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

[19] 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

[20] 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

[21] 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

[22] 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

[23] 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

[24] 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

[25] 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

[26] 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

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

[28] 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

[29] 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

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

[31] 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

[32] 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

[33] 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

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

[35] 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

[36] 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

[37] 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

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

[39] 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

[40] 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

[41] 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

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

[43] 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

[44] 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

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