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http://dx.doi.org/10.6110/KJACR.2017.29.11.580

A Study of the Possibility of Building Energy Saving through the Building Data : A Case Study of Macro to Micro Building Energy Analysis  

Cho, Soo Youn (Department of Architectural Engineering, Yonsei University)
Leigh, Seung-Bok (Department of Architectural Engineering, Yonsei University)
Publication Information
Korean Journal of Air-Conditioning and Refrigeration Engineering / v.29, no.11, 2017 , pp. 580-591 More about this Journal
Abstract
In accordance with 2015 Paris agreement, each individual country around the world should voluntarily propose not only its (individual) reduction target, but also actively develop and present expansion targets of its scope and concrete reduction goals exceeding the previous ones. Accordingly, it is necessary to prepare a macroscopic, long-range strategy for reducing energy consumption and greenhouse gas emissions, which can cover a single building, town, city and eventually even a province. The purpose of this research is to gather and compile government-acquired data from various sources and (in accordance with contents and specificity), combine building data by stages by using multi-variable matrix and then analyze the significance of combined data for each stage. The first order data presents the probability and the cost effectiveness of energy saving on the scale of a city or a province, based only upon general information, size and power consumption of buildings. The second order data can identify a pattern of energy consumption for a building of a specific purpose and which tends to consume a larger amount of energy during one particular season (than others). Finally, the third order data can derive influential factors (base load, humidity) from the energy consumption pattern of a building, and thus propose an informed and practical energy-saving method to be applied in real time.
Keywords
Multi-variable matrix; Data order; Building energy influential factor; Potential building energy savings;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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1 Choi, M. S. and Choi, D. Y., 2014, Building Energy Consumption Sampling Survey, Korea Energy Economics Institute, Vol. 14-29, pp. 7-10.
2 Beak, Y. R., 2002, Thermal energy analysis program of building, Journal of Mechanical Science and Technology, Vol. 42, pp. 20-21.
3 Yu, Y. B. and Yu, D. H., 2014, A Review of Fault Detection and Diagnosis Methodologies on Air-handling Units, Energy and Buildings, Vol. 82, pp. 550-562.   DOI
4 Woo, H.-J. and Leigh, S.-B., 2016, A Study on Classifying Building Energy Consumption Pattern using Actual Building Data, Journal of the Architectural Institute of Korea Planning & Design, Vol. 32, No. 5, pp. 143-151.   DOI
5 Forina, M., Armanino, C., and Raggio, V., 2002, Clustering with dendrograms on interpretation variables, Analytica Chimica Acta, Vol. 454, pp. 13-19.   DOI
6 Jeong, S.-H., Kim, H.-Y., Lee, H.-N., and Leigh, S.-B., 2015, A Validation Study of Remote Energy Diagnosis Algorithm Performance through Actual Building Energy Data Analysis, Journal of the Architectural Institute of Korea Planning & Design, Vol. 31, No. 5, pp. 137-145.   DOI
7 Kim, G.-S., Kim, Y.-M., Kim, J.-S., and Oh, S.-G., 2014, A Study on Data Quantification Simulation Model for Public Office Green Remodeling, Journal of the Architectural Institute of Korea Planning & Design, Vol. 30, No. 10, pp. 53-62.   DOI
8 Kong, D.-S., Kwak, Y.-H., and Huh, J.-H., 2012, Artificial Neural Network based Energy Demand Prediction for the Urban District Energy Planning, Journal of the Architectural Institute of Korea Planning & Design, Vol. 26, No. 2, pp. 221-230.
9 Lee, T.-K., Noh, K.-C., and Oh, M.-D., 2014, Study on Electric Power Consumption in University Building using Multiple Regression Analysis, The Society of Air-Conditioning And Refrigerating Engineers of Korea, Vol. 11, pp. 151-154.
10 Jung, K.-T., Yoon, S.-M., Moon, H.-J., and Yeo, W.-H., 2012, A Study on Building Energy Consumption Pattern Analysis Using Data Mining, The International Journal of The Korea Institute of Ecological Architecture and Environment, Vol. 12, No. 2, pp. 77-82.
11 Cha, S. B., Kim, H. B., Oh, H. C., Yoon, J. H., and Kim, W. K., 2008, Multivariate Analysis, Baeksan Publishing.
12 James, G., Witten, D., and Hastie, T., and Tibshirani, R., 2015, An introduction to Statistical Learning with application in R, Berlin, Springer, pp. 263-275.
13 Lee, J.-S. and Seong, S.-Y., 2016, An Investigation into Supply Characteristics and Spatial Clustering Pattern of Office Buildings in Seoul : Major Office Buildings between 2003 and 2012, Journal of Korea Planning Association, Vol. 51, No. 3, pp. 83-96.   DOI
14 Lovett, T., Lee, J. H., Gabe-Thomas, E., Natarajan, S., Brown, M., Padget, J., and Coley, D., 2016, Designing sensor sets for capturing energy events in buildings, Building and Environment Vol. 110, pp. 11-22.   DOI
15 Marsland, S., 2016, Machine Learning : An algorithm perspective, Oxford, Taylor and Francis Group, pp. 327-365.
16 Han, J., Kamber, M., and Pei, J., 2012, Data Mining, New York, Elsvier, pp. 137-146.
17 Ryu, C.-H., 2015, R Visualization, Seoul, Insight, pp. 2-7.
18 Shmueli, G., Petel, N. R., and Bruce, P. C., 2010, Data mining for business intelligence, New York, Wiley, pp. 91-97.
19 Seo, M.-G., 2014, Practical Data Processing and Analysis Using R, Seoul, Gilbut, pp. 415-416.