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[1] Energy Efficiency: Buildings, International Energy Agency, www.iea.org/topics/ener- gyefficiency/buildings (accessed 17 March, 2019).
[2] Han, J., Kamber, M. & Pie, J., Data Mining Concepts and Techniques, Academic press, Morgan Kaufmann Publication, 2012.
[3] Arai, K. & Barakbah, A.R., Hierarchial K-means: An algorithm for centroids initialization for K-means. Reports of the Faculty of Science and Engineering, Saga University, 36(1), 2007.
[4] Santamouris, M., Mihalakakou, G., Patargias, P., Gaitani, N., Sfakianaki, K., Papaglastra, M., Pavlou, C., Doukas, P., Primikiri, E., Geros, V., Mitoula, R., Zerefos, S. & Assimakopoulos, M.N., Using intelligent clustering technique to classify the energy performances of School Buildings. Energy and Buildings, 39(1), pp. 45–51, 2007.
[5] Amri, Y., Fadhilah, A.L., Setani, N. & Rani, S., Analysis clustering of electricity usage profile using k-means algorithm. In IOP Conference Series: Materials Science and Engineering, 105(1), p. 012020, IOP Publishing, 2016. 899x/105/1/012020 [Crossref]
[6] Damayanti, R., Abdullah, A.G. & Purnama, W., Electricity load profile analysis using clustering techniques. IOP Conference Series: Materials Science and Engineering, 180, p. 012081, 2017. [Crossref]
[7] Fahim, A.M., Salem, A.M. & Ramadan, M.A., Efficent enhanced k-means clustering algotithm. Journal of Zhejiang University-Sciences A, 7(10), pp. 1626–1633, 2006. [Crossref]
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Open Access
Research article

Energy Conservation in University Buildings by Energy Pattern Analysis Using Clustering Technique

bishnu nepal,
motoi yamaha,
hiroya sahashi
Chubu University, Japan
International Journal of Energy Production and Management
|
Volume 4, Issue 2, 2019
|
Pages 158-167
Received: N/A,
Revised: N/A,
Accepted: N/A,
Available online: N/A
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Abstract:

The energy demand of the building sector is increasing rapidly, driven by the improved access to energy in developing countries, greater ownership and use of energy-consuming devices, and rapid growth in building floor area. Energy demands in the building sector account for more than 30% of the total energy consumption and more than 55% of the global electricity demand. Efforts to develop sustain- able buildings are progressing but are still not keeping up with the growing building sector and the rising demand for energy. Analyzing the energy consumption pattern of the buildings and planning for energy conservation in existing buildings are essential. In this research we proposed a method to analyze the energy pattern of university buildings using K-means clustering method. Energy consumption in Science, non-science and office buildings of university is analyzed and their respective base energy, energy consumption due to human activities and air-conditioning energy consumption is calculated. The proposed method is successful in classifying the energy consumption and will prove to be helpful in the planning of energy conservation in buildings.

Keywords: accuracy measurement, clustering, base and peak energy, energy conservation, energy consumption pattern analysis, K-means
Data Availability

The data used to support the findings of this study are available from the corresponding author upon request.

Conflicts of Interest

The authors declare that they have no conflicts of interest.

References
[1] Energy Efficiency: Buildings, International Energy Agency, www.iea.org/topics/ener- gyefficiency/buildings (accessed 17 March, 2019).
[2] Han, J., Kamber, M. & Pie, J., Data Mining Concepts and Techniques, Academic press, Morgan Kaufmann Publication, 2012.
[3] Arai, K. & Barakbah, A.R., Hierarchial K-means: An algorithm for centroids initialization for K-means. Reports of the Faculty of Science and Engineering, Saga University, 36(1), 2007.
[4] Santamouris, M., Mihalakakou, G., Patargias, P., Gaitani, N., Sfakianaki, K., Papaglastra, M., Pavlou, C., Doukas, P., Primikiri, E., Geros, V., Mitoula, R., Zerefos, S. & Assimakopoulos, M.N., Using intelligent clustering technique to classify the energy performances of School Buildings. Energy and Buildings, 39(1), pp. 45–51, 2007.
[5] Amri, Y., Fadhilah, A.L., Setani, N. & Rani, S., Analysis clustering of electricity usage profile using k-means algorithm. In IOP Conference Series: Materials Science and Engineering, 105(1), p. 012020, IOP Publishing, 2016. 899x/105/1/012020 [Crossref]
[6] Damayanti, R., Abdullah, A.G. & Purnama, W., Electricity load profile analysis using clustering techniques. IOP Conference Series: Materials Science and Engineering, 180, p. 012081, 2017. [Crossref]
[7] Fahim, A.M., Salem, A.M. & Ramadan, M.A., Efficent enhanced k-means clustering algotithm. Journal of Zhejiang University-Sciences A, 7(10), pp. 1626–1633, 2006. [Crossref]
Cite this:
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Nepal, B., Yamaha, M., & Sahashi, H. (2019). Energy Conservation in University Buildings by Energy Pattern Analysis Using Clustering Technique. Int. J. Energy Prod. Manag., 4(2), 158-167. https://doi.org/10.2495/EQ-V4-N2-158-167
B. Nepal, M. Yamaha, and H. Sahashi, "Energy Conservation in University Buildings by Energy Pattern Analysis Using Clustering Technique," Int. J. Energy Prod. Manag., vol. 4, no. 2, pp. 158-167, 2019. https://doi.org/10.2495/EQ-V4-N2-158-167
@research-article{Nepal2019EnergyCI,
title={Energy Conservation in University Buildings by Energy Pattern Analysis Using Clustering Technique},
author={Bishnu Nepal and Motoi Yamaha and Hiroya Sahashi},
journal={International Journal of Energy Production and Management},
year={2019},
page={158-167},
doi={https://doi.org/10.2495/EQ-V4-N2-158-167}
}
Bishnu Nepal, et al. "Energy Conservation in University Buildings by Energy Pattern Analysis Using Clustering Technique." International Journal of Energy Production and Management, v 4, pp 158-167. doi: https://doi.org/10.2495/EQ-V4-N2-158-167
Bishnu Nepal, Motoi Yamaha and Hiroya Sahashi. "Energy Conservation in University Buildings by Energy Pattern Analysis Using Clustering Technique." International Journal of Energy Production and Management, 4, (2019): 158-167. doi: https://doi.org/10.2495/EQ-V4-N2-158-167
NEPAL B， YAMAHA M， SAHASHI H. Energy Conservation in University Buildings by Energy Pattern Analysis Using Clustering Technique[J]. International Journal of Energy Production and Management， 2019， 4(2)： 158-167. https://doi.org/10.2495/EQ-V4-N2-158-167