Producing Outdoor Design Conditions Based on the Dependency between Meteorological Elements: Copula Approach
Authors: Zhichao Jiao, Craig Farnham, Jihui Yuan, Kazuo Emura
Abstract:
It is common to use the outdoor design weather data to select the air-conditioning capacity in the building design stage. The meteorological elements of outdoor design weather data are usually selected based on their excess frequency separately while the dependency between the elements is not well considered. It means that the simultaneous occurrence probability of these elements is smaller than the original excess frequency which may cause an overestimation of selecting air-conditioning capacity. Therefore, the copula approach which can capture the dependency between multivariate data was used to model the joint distributions of the meteorological elements, like air temperature and global solar radiation. We suggest a method based on the specific simultaneous occurrence probability of these two elements of selecting more credible outdoor design conditions. The hourly weather data at 12 noon from 2001 to 2010 in Tokyo, Japan are used to analyze the dependency structure and joint distribution, the Gaussian copula represents the dependence of data best. According to calculating the air temperature and global solar radiation in specific simultaneous occurrence probability and the common exceeding, the results show that both the air temperature and global solar radiation based on simultaneous occurrence probability are lower than these based on the conventional method in the same probability.
Keywords: Copula approach, Design weather database, energy conservation, HVAC.
Procedia APA BibTeX Chicago EndNote Harvard JSON MLA RIS XML ISO 690 PDF Downloads 368References:
[1] SHASE. Calculation Method of Design Thermal Peak Load, Tokyo: The Society of Heating, Air-Conditioning and Sanitary Engineers of Japan, 1989 (in Japanese).
[2] ASHRAE. Handbook-2017 Fundamentals, Atlanta: American Society of Heating, Refrigerating and Air-Conditioning Engineers, 2017.
[3] CIBSE. Environmental design: CIBSE guide A. London: Chartered Institution of Building Services E, 2006.
[4] Takeda H. Tokyo weather data for air-conditioning: Outdoor design conditions for heating and cooling loads by the TAC method. Energy and Buildings 1990; 15: 263-269.
[5] Yuan J, Emura K, Farnham C, et al. The creation of weather data for AC design using two weather indices for Osaka. Energy and Buildings 2017; 134: 248-258.
[6] Genest C and Favre AC. Everything You Always Wanted to Know About Copula Modeling but Were Afraid to Ask, Journal of Hydrological Engineering 2007; 12: 347–368.
[7] Nelsen RB. An Introduction to Copulas, New York: Springer, 2010.
[8] Gilchrist W. Statistical Modelling with Quantile Functions. Boca Raton: Chapman and Hall/CRC, 2019.
[9] Choroś B, Ibragimov R and Permiakova E. Copula Estimation. In: Jaworski P, Durante F, Härdle WK, et al.(eds) Copula Theory and Its Applications. Berlin Heidelberg: Springer, 2010.
[10] Akaike H. Information Theory and an Extension of the Maximum Likelihood Principle. In: Parzen E, Tanabe K and Kitagawa G (eds) Selected Papers of Hirotugu Akaike. New York: Springer, 1998, pp.199-213.
[11] Huard D, Évin G and Favre AC. Bayesian copula selection. Computational Statistics & Data Analysis 2006; 51: 809-822.
[12] Akasaka H, et al. Expanded AMeDAS Weather Data. Tokyo: Architectural Institute of Japan/Maruzen, 2003.
[13] Everitt BS and Hand DJ. Mixtures of normal distributions. Finite Mixture Distributions. Dordrecht: Springer Netherlands, 1981, pp.25-57.
[14] Tsai DM and Yang CH. A quantile–quantile plot based pattern matching for defect detection. Pattern Recognition Letters 2005; 26: 1948-1962.
[15] Holmgren EB. The P-P Plot as a Method for Comparing Treatment Effects. Journal of the American Statistical Association 1995; 90: 360-365.
[16] Nagler T, Schepsmeier U, Stoeber J, et al. VineCopula: Statistical inference of vine copulas. R package version 2.3.0, 2019.