Abstract
This article attempts answering the following problematic: How to model and classify energy consumption profiles over a large distributed territory to optimize the management of buildings’ consumption?
Doing case-by-case in depth auditing of thousands of buildings would require a massive amount of time and money as well as a significant number of qualified people.Thus, an automated method must be developed to establish a relevant and effective recommendations system.
To answer this problematic, pretopology is used to model the sites’ consumption profiles and a multi-criterion hierarchical classification algorithm, using the properties of pretopological space, has been developed in a Python library.
To evaluate the results, three data sets are used: A generated set of dots of various sizes in a 2D space, a generated set of time series and a set of consumption time series of 400 real consumption sites from a French Energy company.
On the point data set, the algorithm is able to identify the clusters of points using their position in space and their size as parameter. On the generated time series, the algorithm is able to identify the time series clusters using Pearson’s correlation with an Adjusted Rand Index (ARI) of 1.
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References
Ahat, M., Amor, S.B., Bui, M., Bui, A., Guérard, G., Petermann, C.: Smart grid and optimization. Am. J. Oper. Res. 03(01), 196–206 (2013). https://doi.org/10.4236/ajor.2013.31A019, http://www.scirp.org/journal/doi.aspx?DOI=10.4236/ajor.2013.31A019
Auray, J.P., Bonnevay, S., Bui, M., Duru, G., Lamure, M.: Prétopologie et applications : un état de l’art. Studia Informatica Universalis (Hermann), 7, 27–44 (2009)
Belmandt, Z.: Manuel de prétopologie et ses Applications. Hermès science publications (1993)
Boemi, S.-N., Tziogas, C.: Indicators for buildings’ energy performance. In: Boemi, S.-N., Irulegi, O., Santamouris, M. (eds.) Energy Performance of Buildings, pp. 79–93. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-20831-2_5
Bosom, J., Scius-Bertrand, A., Tran, H., Bui, M.: Multi-agent architecture of a MIBES for smart energy management. In: Hodoň, M., Eichler, G., Erfurth, C., Fahrnberger, G. (eds.) I4CS 2018. CCIS, vol. 863, pp. 18–32. Springer, Cham (2018). https://doi.org/10.1007/978-3-319-93408-2_2
Bosom, J.: Conception de microservices intelligents pour la supervision de systèmes sociotechniques: application aux systèmes énergétiques. Ph.D. thesis, Université Paris sciences et lettres (2020)
Fleischhacker, A., Lettner, G., Schwabeneder, D., Auer, H.: Portfolio optimization of energy communities to meet reductions in costs and emissions. Energy 173, 1092–1105 (2019). https://doi.org/10.1016/j.energy.2019.02.104,http://www.sciencedirect.com/science/article/pii/S0360544219303032
Gao, X., Malkawi, A.: A new methodology for building energy performance benchmarking: an approach based on intelligent clustering algorithm. Energy Build. 84, 607–616 (2014). https://doi.org/10.1016/j.enbuild.2014.08.030, http://www.sciencedirect.com/science/article/pii/S0378778814006720
Guerard, G., Pichon, B., Nehai, Z.: Demand-response: let the devices take our decisions. In: SMARTGREENS, pp. 119–126 (2017)
Guérard, G., Ben Amor, S., Bui, A.: A context-free smart grid model using pretopologic structure. In: 2015 International Conference on Smart Cities and Green ICT Systems (SMARTGREENS), pp. 1–7 (2015)
Habib, U., Zucker, G., Blochle, M., Judex, F., Haase, J.: Outliers detection method using clustering in buildings data. In: IECON 2015–41st Annual Conference of the IEEE Industrial Electronics Society, IEEE, pp. 000694–000700 (2015)
Iglesias, F., Kastner, W.: Analysis of similarity measures in times series clustering for the discovery of building energy patterns. Energies 6(2), 579–597 (2013)
Kriegel, H.P., Kröger, P., Sander, J., Zimek, A.: Density-based clustering. Wiley Interdisc. Rev.: Data Min. Knowl. Discovery 1(3), 231–240 (2011)
Laborde, J.: Pretopology, a mathematical tool for structuring complex systems: methods, algorithms and applications. Ph.D. thesis, EPHE (2019)
Largeron, C., Bonnevay, S.: A pretopological approach for structural analysis. Inf. Sci. 144(1–4), 169–185 (2002)
Le, T.V.: Classification prétopologique des données: application à l’analyse des trajectoires patients. Ph.D. thesis, Lyon 1 (2007)
Levy, L.N., Bosom, J., Guérard, G., Amor, S.B., Bui, M., Tran, H.: Application of pretopological hierarchical clustering for buildings portfolio. In: SMARTGREENS, pp. 228–235 (2021)
Li, K., Ma, Z., Robinson, D., Lin, W., Li, Z.: A data-driven strategy to forecast next-day electricity usage and peak electricity demand of a building portfolio using cluster analysis, cubist regression models and particle swarm optimization. J. Cleaner Prod. 273, 123115 (2020). https://doi.org/10.1016/j.jclepro.2020.123115, http://www.sciencedirect.com/science/article/pii/S0959652620331607
Li, K., Yang, R.J., Robinson, D., Ma, J., Ma, Z.: An agglomerative hierarchical clustering-based strategy using shared nearest neighbours and multiple dissimilarity measures to identify typical daily electricity usage profiles of university library buildings. Energy 174, 735–748 (2019). https://doi.org/10.1016/j.energy.2019.03.003, http://www.sciencedirect.com/science/article/pii/S0360544219304074
Lu, Y., Tian, Z., Peng, P., Niu, J., Li, W., Zhang, H.: GMM clustering for heating load patterns in-depth identification and prediction model accuracy improvement of district heating system. Energy Build. 190, 49–60 (2019). https://doi.org/10.1016/j.enbuild.2019.02.014, http://www.sciencedirect.com/science/article/pii/S0378778818308326
Marquant, J.F., Bollinger, L.A., Evins, R., Carmeliet, J.: A new combined clustering method to analyse the potential of district heating networks at large-scale. Energy 156, 73–83 (2018). https://doi.org/10.1016/j.energy.2018.05.027, http://www.sciencedirect.com/science/article/pii/S0360544218308478
Miller, C.: Screening meter data: characterization of temporal energy data from large groups of non-residential buildings. Ph.D. thesis, ETH Zurich (2016)
Mills, E.: Building commissioning: a golden opportunity for reducing energy costs and greenhouse gas emissions in the United States. Energy Effi. 4(2), 145–173 (2011). https://doi.org/10.1007/s12053-011-9116-8
Wang, S., Liu, H., Pu, H., Yang, H.: Spatial disparity and hierarchical cluster analysis of final energy consumption in china. Energy 197, 117195 (2020). https://doi.org/10.1016/j.energy.2020.117195
Xu, D., Tian, Y.: A comprehensive survey of clustering algorithms. Ann. Data Sci. 2(2), 165–193 (2015)
Acknowledgements
This paper is the result of research conducted at the energy data management company Energisme. We thank Energisme for the resources that have been made available to us and Julio Laborde for his assistance with the conception of our pretopological hierarchical algorithm library.
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Lévy, LN., Bosom, J., Guerard, G., Amor, S.B., Bui, M., Tran, H. (2022). Hierarchical Clustering of Complex Energy Systems Using Pretopology. In: Klein, C., Jarke, M., Helfert, M., Berns, K., Gusikhin, O. (eds) Smart Cities, Green Technologies, and Intelligent Transport Systems. VEHITS SMARTGREENS 2021 2021. Communications in Computer and Information Science, vol 1612. Springer, Cham. https://doi.org/10.1007/978-3-031-17098-0_5
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