{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T10:15:34Z","timestamp":1778753734952,"version":"3.51.4"},"reference-count":43,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2020,2,6]],"date-time":"2020-02-06T00:00:00Z","timestamp":1580947200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The exponential growth in population and their overall reliance on the usage of electrical and electronic devices have increased the demand for energy production. It needs precise energy management systems that can forecast the usage of the consumers for future policymaking. Embedded smart sensors attached to electricity meters and home appliances enable power suppliers to effectively analyze the energy usage to generate and distribute electricity into residential areas based on their level of energy consumption. Therefore, this paper proposes a clustering-based analysis of energy consumption to categorize the consumers\u2019 electricity usage into different levels. First, a deep autoencoder that transfers the low-dimensional energy consumption data to high-level representations was trained. Second, the high-level representations were fed into an adaptive self-organizing map (SOM) clustering algorithm. Afterward, the levels of electricity energy consumption were established by conducting the statistical analysis on the obtained clustered data. Finally, the results were visualized in graphs and calendar views, and the predicted levels of energy consumption were plotted over the city map, providing a compact overview to the providers for energy utilization analysis.<\/jats:p>","DOI":"10.3390\/s20030873","type":"journal-article","created":{"date-parts":[[2020,2,7]],"date-time":"2020-02-07T03:13:27Z","timestamp":1581045207000},"page":"873","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":67,"title":["Deep Learning Assisted Buildings Energy Consumption Profiling Using Smart Meter Data"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7538-2689","authenticated-orcid":false,"given":"Amin","family":"Ullah","sequence":"first","affiliation":[{"name":"Intelligent Media Laboratory, Digital Contents Research Institute, Sejong University, Seoul 143-747, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kilichbek","family":"Haydarov","sequence":"additional","affiliation":[{"name":"Intelligent Media Laboratory, Digital Contents Research Institute, Sejong University, Seoul 143-747, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8201-7372","authenticated-orcid":false,"given":"Ijaz","family":"Ul Haq","sequence":"additional","affiliation":[{"name":"Intelligent Media Laboratory, Digital Contents Research Institute, Sejong University, Seoul 143-747, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4055-7412","authenticated-orcid":false,"given":"Khan","family":"Muhammad","sequence":"additional","affiliation":[{"name":"Department of Software, Sejong University, Seoul 143-747, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Seungmin","family":"Rho","sequence":"additional","affiliation":[{"name":"Department of Software, Sejong University, Seoul 143-747, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8139-7091","authenticated-orcid":false,"given":"Miyoung","family":"Lee","sequence":"additional","affiliation":[{"name":"Intelligent Media Laboratory, Digital Contents Research Institute, Sejong University, Seoul 143-747, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sung Wook","family":"Baik","sequence":"additional","affiliation":[{"name":"Intelligent Media Laboratory, Digital Contents Research Institute, Sejong University, Seoul 143-747, Korea"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Naspi, F., Arnesano, M., Stazi, F., D\u2019Orazio, M., and Revel, G.M. (2018). Measuring Occupants\u2019 Behaviour for Buildings\u2019 Dynamic Cosimulation. J. Sens., 2018.","DOI":"10.1155\/2018\/2756542"},{"key":"ref_2","unstructured":"Programme, U.N.E. (2019, September 09). Energy Efficiency for Buildings. Available online: http:\/\/www.studiocollantin.eu\/pdf\/UNEP%20Info%20sheet%20-%20EE%20Buildings.pdf."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"3350","DOI":"10.1016\/j.apm.2013.11.042","article-title":"Power supply-demand balance in a Smart Grid: An information sharing model for a market mechanism","volume":"38","author":"Larsen","year":"2014","journal-title":"Appl. Math. Model."},{"key":"ref_4","first-page":"1","article-title":"Measuring users-windows interactions in buildings: Behavioural models for the summer season","volume":"4","author":"Naspi","year":"2018","journal-title":"TEMA"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"371","DOI":"10.1016\/j.renene.2018.06.068","article-title":"Optimization of 4th generation distributed district heating system: Design and planning of combined heat and power","volume":"130","author":"Sameti","year":"2019","journal-title":"Renew. Energ."},{"key":"ref_6","first-page":"43","article-title":"A wireless sensor network for intelligent building energy management based on multi communication standards-A case study","volume":"17","author":"Grindvoll","year":"2012","journal-title":"J. Inf. Technol. Constr."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ai, S., Chakravorty, A., and Rong, C. (2019). Household Power Demand Prediction Using Evolutionary Ensemble Neural Network Pool with Multiple Network Structures. Sensors, 19.","DOI":"10.3390\/s19030721"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Kim, Y.-I., Ko, J.-M., and Choi, S.-H. (2011, January 11\u201315). Methods for generating TLPs (typical load profiles) for smart grid-based energy programs. Proceedings of the 2011 IEEE Symposium on Computational Intelligence Applications in Smart Grid (CIASG), Paris, France.","DOI":"10.1109\/CIASG.2011.5953331"},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5215","DOI":"10.3390\/en5125215","article-title":"Classification and clustering of electricity demand patterns in industrial parks","volume":"5","author":"Aguiar","year":"2012","journal-title":"Energies"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Ford, V., and Siraj, A. (2013, January 3\u20135). Clustering of smart meter data for disaggregation. Proceedings of the 2013 IEEE Global Conference on Signal and Information Processing, Austin, TX, USA.","DOI":"10.1109\/GlobalSIP.2013.6736926"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.apenergy.2014.08.111","article-title":"Clustering analysis of residential electricity demand profiles","volume":"135","author":"Rhodes","year":"2014","journal-title":"Appl. Energ."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.enbuild.2015.01.035","article-title":"A data-mining-based methodology to support MV electricity customers\u2019 characterization","volume":"91","author":"Ramos","year":"2015","journal-title":"Energy Build."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"18459","DOI":"10.1109\/ACCESS.2017.2712258","article-title":"Multi-layered clustering for power consumption profiling in smart grids","volume":"5","author":"Yoo","year":"2017","journal-title":"IEEE Access"},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"30386","DOI":"10.1109\/ACCESS.2019.2901257","article-title":"Predicting the energy consumption of residential buildings for regional electricity supply-side and demand-side management","volume":"7","author":"Cai","year":"2019","journal-title":"IEEE Access"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Nordahl, C., Boeva, V., Grahn, H., and Netz, M.P. (2019, January 12\u201314). Profiling of Household Residents\u2019 Electricity Consumption Behavior Using Clustering Analysis. Proceedings of the International Conference on Computational Science, Faro, Portugal.","DOI":"10.1007\/978-3-030-22750-0_78"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"59048","DOI":"10.1109\/ACCESS.2019.2914216","article-title":"A Novel Load Image Profile-Based Electricity Load Clustering Methodology","volume":"7","author":"Park","year":"2019","journal-title":"IEEE Access"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"3538","DOI":"10.1016\/j.apenergy.2010.05.015","article-title":"Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data","volume":"87","author":"Voukantsis","year":"2010","journal-title":"Appl. Energ."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1016\/j.jclepro.2018.12.067","article-title":"A shape-based clustering method for pattern recognition of residential electricity consumption","volume":"212","author":"Wen","year":"2019","journal-title":"J. Clean. Prod."},{"key":"ref_19","unstructured":"EERE (2019, September 09). DOE Buildings Performance Database, Sample Residential Data. Available online: https:\/\/openei.org\/datasets\/dataset\/doe-buildings-performance-database-sample-residential-data."},{"key":"ref_20","unstructured":"Georges Hebrail, A.B. (2019, August 13). Individual Household Electric Power Consumption Data Set. Available online: https:\/\/archive.ics.uci.edu\/ml\/datasets\/individual+household+electric+power+consumption."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"729","DOI":"10.1097\/EDE.0b013e3182576cdb","article-title":"Missing data: A systematic review of how they are reported and handled","volume":"23","author":"Eekhout","year":"2012","journal-title":"Epidemiology"},{"key":"ref_22","first-page":"45","article-title":"Min max normalization based data perturbation method for privacy protection","volume":"2","author":"Jain","year":"2011","journal-title":"IJCCT"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"70535","DOI":"10.1109\/ACCESS.2018.2880694","article-title":"Progress on Artificial Neural Networks for Big Data Analytics: A Survey","volume":"7","author":"Chiroma","year":"2019","journal-title":"IEEE Access"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"27","DOI":"10.1109\/TCYB.2015.2501373","article-title":"Coupled deep autoencoder for single image super-resolution","volume":"47","author":"Zeng","year":"2015","journal-title":"IEEE Trans. Cybern."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Cho, K. (2013). Boltzmann machines and denoising autoencoders for image denoising. arXiv.","DOI":"10.1007\/978-3-642-40728-4_76"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Gondara, L. (2016, January 12). Medical image denoising using convolutional denoising autoencoders. Proceedings of the 2016 IEEE 16th International Conference on Data Mining Workshops (ICDMW), Barcelona, Spain.","DOI":"10.1109\/ICDMW.2016.0041"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.patrec.2017.07.016","article-title":"A study of deep convolutional auto-encoders for anomaly detection in videos","volume":"105","author":"Ribeiro","year":"2018","journal-title":"Pattern Recognit. Lett."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1038\/s41587-019-0224-x","article-title":"Deep learning enables rapid identification of potent DDR1 kinase inhibitors","volume":"37","author":"Zhavoronkov","year":"2019","journal-title":"Nat. Biotechnol."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/j.future.2019.01.029","article-title":"Action recognition using optimized deep autoencoder and CNN for surveillance data streams of non-stationary environments","volume":"96","author":"Ullah","year":"2019","journal-title":"Future Gener. Comput. Syst."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Ul Haq, I., Ullah, A., Muhammad, K., Lee, M.Y., and Baik, S.W. (2019). Personalized Movie Summarization Using Deep CNN-Assisted Facial Expression Recognition. Complexity, 2019.","DOI":"10.1155\/2019\/3581419"},{"key":"ref_31","first-page":"247","article-title":"Enhancing K-means algorithm with initial cluster centers derived from data partitioning along the data axis with the highest variance","volume":"2","author":"Deelers","year":"2007","journal-title":"IJCS"},{"key":"ref_32","unstructured":"Ester, M., Kriegel, H.-P., Sander, J., and Xu, X. (1996, January 2\u20134). A density-based algorithm for discovering clusters in large spatial databases with noise. Proceedings of the KDD, Portland, OR, USA."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Ankerst, M., Breunig, M.M., Kriegel, H.-P., and Sander, J. (1999, January 1\u20133). OPTICS: Ordering points to identify the clustering structure. Proceedings of the ACM Sigmod Record, Philadelphia, PA, USA.","DOI":"10.1145\/304182.304187"},{"key":"ref_34","unstructured":"Forest, F., Lebbah, M., Azzag, H., and Lacaille, J. (2019, August 10). Deep Embedded SOM: Joint Representation Learning and Self-Organization. Available online: http:\/\/florentfo.rest\/files\/ESANN-2019-DeepEmbeddedSOM-full-paper.pdf."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"1568","DOI":"10.4249\/scholarpedia.1568","article-title":"Kohonen network","volume":"2","author":"Kohonen","year":"2007","journal-title":"Scholarpedia"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"53","DOI":"10.1016\/0377-0427(87)90125-7","article-title":"Silhouettes: A graphical aid to the interpretation and validation of cluster analysis","volume":"20","author":"Rousseeuw","year":"1987","journal-title":"J. Comput. Appl. Math."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/03610927408827101","article-title":"A dendrite method for cluster analysis","volume":"3","author":"Harabasz","year":"1974","journal-title":"Commun. Stat.-Theory Methods"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"224","DOI":"10.1109\/TPAMI.1979.4766909","article-title":"A cluster separation measure","volume":"1","author":"Davies","year":"1979","journal-title":"IEEE Trans. Pattern Anal. Mach. Intel."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"236","DOI":"10.1080\/01621459.1963.10500845","article-title":"Hierarchical grouping to optimize an objective function","volume":"58","author":"Ward","year":"1963","journal-title":"JASA"},{"key":"ref_40","unstructured":"Ding, C., and He, X. (2002, January 9\u201312). Cluster merging and splitting in hierarchical clustering algorithms. Proceedings of the 2002 IEEE International Conference on Data Mining, Maebashi TERRSA, Maebashi, Japan."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"103","DOI":"10.1145\/235968.233324","article-title":"BIRCH: An efficient data clustering method for very large databases","volume":"25","author":"Zhang","year":"1996","journal-title":"ACM Sigmod Rec."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"759","DOI":"10.1016\/0893-6080(91)90056-B","article-title":"Fuzzy ART: Fast stable learning and categorization of analog patterns by an adaptive resonance system","volume":"4","author":"Carpenter","year":"1991","journal-title":"Neural Netw."},{"key":"ref_43","doi-asserted-by":"crossref","unstructured":"Ba\u00e7\u00e3o, F., Lobo, V., and Painho, M. (2005, January 22). Self-organizing maps as substitutes for k-means clustering. Proceedings of the International Conference on Computational Science, Heidelberg, Berlin.","DOI":"10.1007\/11428862_65"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/3\/873\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:55:28Z","timestamp":1760172928000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/3\/873"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,6]]},"references-count":43,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["s20030873"],"URL":"https:\/\/doi.org\/10.3390\/s20030873","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,2,6]]}}}