ORIGINAL ARTICLE
A Statistical Approach for Selecting Buildings for Experimental Measurement of HVAC Needs
More details
Hide details
1
Corresponding author: Technical University of Wroclaw, Institute of Air-Conditioning and District Heating, Norwida st. 4/6, 50-373 Wroclaw, Poland
2
Corresponding author: University of Zielona Gora, Institute of Environmental Engineering, Z. Szafrana st. 15, 65-516 Zielona Gora, Poland
Online publication date: 2017-05-17
Publication date: 2017-03-28
Civil and Environmental Engineering Reports 2017;24(1):99-116
KEYWORDS
ABSTRACT
This article presents a statistical methodology for selecting representative buildings for experimentally evaluating the performance of HVAC systems, especially in terms of energy consumption. The proposed approach is based on the k-means method. The algorithm for this method is conceptually simple, allowing it to be easily implemented. The method can be applied to large quantities of data with unknown distributions. The method was tested using numerical experiments to determine the hourly, daily, and yearly heat values and the domestic hot water demands of residential buildings in Poland. Due to its simplicity, the proposed approach is very promising for use in engineering applications and is applicable to testing the performance of many HVAC systems.
REFERENCES (18)
1.
Gunel M. H., Ilgin H. E.: A proposal for the classification of structural systems of tall buildings, Building and Environment 42 (2007), pp. 2667-2675.
2.
Theodoridou K., Papadopoulos A., Hegger M.: A typological classification of the Greek residential building stock, Energy and Buildings 43 (2011), pp. 2779-2787.
3.
Theodoridou L., Papadopoulos A., Hegger M.: Statistical analysis of the Greek residential building stock, Energy and Buildings 43 (2011), pp. 2422-2428.
4.
Fovell R. G., Fovell M. Y. C.: Climate zones of the conterminous United States defined using cluster analysis, Journal of Climate 6 (1993), pp. 2103-2135.
5.
Lau C., Lam J. C., Yan L.: Climate classification and passive solar design implications in China, Energy Conversion and Management 48 (2007), pp. 2006-2015.
6.
Wan K., Li D., Yang L., Lam J.: Climate classifications and building energy use implications in China, Energy and Buildings 42 (2010), pp. 1463-1471.
7.
Cohen S. C. M., de Castro L. N.: Data clustering with particle swarms, IEEE Congress on Evolutionary Computation, 2006, pp. 1792-1798.
8.
Ward J. H. Jr.: Hierarchical grouping to optimize an objective function, Journal of the American Statistical Association 58 (1963), pp. 236-244.
9.
Dempster A. P., Laird N. M., Rubin D. B.: Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society Series B (Methodological) 39 (1977), pp. 1-38.
10.
Lloyd S. P.: Least square quantization in PCM, IEEE Transactions on Information Theory 28 (1982), pp. 129-137.
11.
MacQueen J. B.: Some methods for classification and analysis of multivariate observations, in: Proceedings of the 5th Berkeley Symposium on Mathematical Statistics and Probability. University of California Press, Berkeley, 1967, pp. 281-297.
12.
Steinhaus H.: Sur la division des corps materiels en parties, Bulletin of the Polish Academy of Sciences Mathematics 4 (1956), pp. 801-804.
13.
Finding the Right Number of Clusters in k-Means and EM Clustering: v- Fold Cross-Validation, in: Electronic Statistics Textbook, StatSoft, Tulsa, 2010.
14.
Rousseu P.J.: Silhouettes: a Graphical Aid to the Interpretation and Validation of Cluster Analysis, Computational and Applied Mathematics 20 (1987), pp. 53-65.
15.
Sugar C. A., James G.M.: Finding the number of clusters in a data set: an information theoretic approach, Journal of the American Statistical Association 98 (2003), pp. 750-763.
16.
Bartlett J. E., Kortlik J. W., Higgins C.: Organizational research: determining appropriate sample size for survey research, Information Technology, Learning, and Performance Journal 19 (2001), pp. 43-50.
17.
Kish L.: Survey Sampling, Wiley, New York, 1995.
18.
Lohr S.L.: Sampling: Design and Analysis. Duxbury Press, Pacific Grove, 1999.