LOAD PROFILING OF COMMERCIAL AND RESIDENTIAL BUILDING USING CLUSTERING TECHNIQUE

Abayomi Olawumi; Folasade Dahunsi

doi:10.4314/njt.v42i2.14

Authors

Abayomi Olawumi Federal University of Technology Akure
F.M. Dahunsi Federal University of Technology Akure

DOI:

https://doi.org/10.4314/njt.v42i2.14

Keywords:

data mining, clustering, machine learning, consumption data

Abstract

Data mining is a promising tool used in processing energy data collected from energy consumers. The knowledge derived from energy data is very pertinent in the formulation of various demand-side management programs. This paper uses clustering techniques to segment the energy consumption patterns of residential and commercial buildings; situated at different geographical locations. The two (2) commonly used clustering techniques: K-Means and Agglomerative Hierarchical Clustering, were employed. The result indicates that the choice of clustering technique for load profiling is highly subjective to the nature of the dataset. Hence, using Davies-Bouldin Index (DB) Index and Silhouette Index (SI) as clustering indicators to select an optimum number of clusters and the best clustering technique. Hierarchical clustering was identified as the most appropriate clustering for the two buildings.

Author Biographies

Abayomi Olawumi, Federal University of Technology Akure

Abayomi Olawumi is a research assistant at the Smart Energy Research Laboratory at the Federal University of Technology Akure, he is simultaneously taking a Masters Programme in communication engineering at the departent of Electrical and Electronics Engineering at the same citadel of knowledge.

F.M. Dahunsi, Federal University of Technology Akure

Dr.(Mrs.) Folasade Dahunsi is an associate professor in computer engineering at the Federal University of Technology,Akure, with major research interest in communication networks and system.

References

Kane, S, N., Mishra, A., and Dutta, A. K. “Electrical Load Profile Analysis Using Clustering Techniques”, in Journal of Physics: Conference Series, 2016, vol. 755, no. 1, doi: 10.1088/1742-6596/755/1/011001.

Zhou, K., Fu, C., and Yang, S. “Big data driven smart energy management: From big data to big insights”, vol. 56, no. 2016, pp. 215–225, 2020, doi: 10.1016/j.rser.2015.11.050.

Liu, X., Ding, Y., Tang, H., and Xiao, F. “A data mining-based framework for the identification of daily electricity usage patterns and anomaly detection in building electricity consumption data”, Energy Build., vol. 231, no. xxxx, p. 110601, 2021, doi: 10.1016/j.enbuild.2 020.110601.

Oseni, M. O. “An analysis of the power sector performance in Nigeria”, Renew. Sustain. Energy Rev., vol. 15, no. 9, pp. 4765–4774, 2011, doi: 10.1016/j.rser.2011.07.075.

Wang, Y., Chen, Q., Kang, C., Zhang, M., Wang, K., and Zhao, Y. “Load Profiling and Its Application to Demand Response: A Review”, Tsinghua Sci. Technol., vol. 20, no. 2, pp. 117–129, 2015, doi: 10.1109/TST.2015.7085625.

Gunsay, M., Bilir, C., and Poyrazoglu, G. “Load Profile Segmentation for Electricity Market Settlement”, in 2020 17th International Conference on the European Energy Market (EEM), 2020, pp. 1–5, doi: 10.1109/EEM4980 2.2020.9221889.

Il Kim, Y., Ko, J. M., and Choi, S, H. “Methods for generating TLPs (Typical Load Profiles) for smart grid-based energy programs”, IEEE SSCI 2011 - Symp. Ser. Comput. Intell. - CIASG 2011 2011 IEEE Symp. Comput. Intell. Appl. Smart Grid, pp. 49–54, 2011, doi: 10.1109/CIASG.20 11.5953331.

C. C. A.; Chandan K. Reddy, DATA Algorithms and Applications. Minnesota, U.S.A., 2014.

Khan, Z. A., Jayaweera, D., and Alvarez-alvarado, M. S., “A novel approach for load profiling in smart power grids using smart meter data”, Electr. Power Syst. Res., vol. 165, no. August, pp. 191–198, 2018, doi: 10.101 6/j.epsr.2018.09.013.

Viegas, J. L., Vieira, S. M., Melício, R., Mendes, V. M. F., and Sousa, J. M. C. “Classification of new electricity customers based on surveys and smart metering data”, Energy, vol. 107, no. 2016, pp. 804–817, 2016, doi: 10.1016/j.energy.2016.04.065.

Lu, S., Lin, G., Liu, H., Ye, C., Que, H., and Ding, Y. I. “A Weekly Load Data Mining Approach Based on Hidden Markov Model”, IEEE Access, vol. 7, pp. 34609–34619, 2019, doi: 10.1109/ACCESS.2019.2901197.

Zhan, S., Liu, Z., Chong, A., and Yan, D. “Building categorization revisited: A clustering-based approach to using smart meter data for building energy benchmarking”, Appl. Energy, vol. 269, no. February, 2020, doi: 10.1016/j.apenergy.2020.114920.