{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,12,31]],"date-time":"2025-12-31T19:58:17Z","timestamp":1767211097661,"version":"build-2065373602"},"reference-count":30,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2020,2,16]],"date-time":"2020-02-16T00:00:00Z","timestamp":1581811200000},"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>Modeling and control of the heating feature of living spaces remain challenging tasks because of the intrinsic nonlinear nature of the involved processes as well as the strong nonlinearity of the entailed dynamic parameters in those processes. Although nowadays, adaptive heating controllers represent a crucial need for smart building energy management systems (SBEMS) as well as an appealing perspective for their effectiveness in optimizing energy efficiency, unfortunately, the leakage of models competent in handling the complexity of real living spaces\u2019 heating processes means the control strategies implemented in most SBEMSs are still conventional. Within this context and by considering that the living space\u2019s occupation rate (i.e., by users or residents) may affect the model and the issued heating control strategy of the concerned living space, we have investigated the design and implementation of a data-driven machine learning-based identification of the building\u2019s living space dynamic heating conduct, taking into account the occupancy (by the residents) of the heated space. In fact, the proposed modeling strategy takes advantage, on the one hand, of the forecasting capacity of the time-series of the nonlinear autoregressive exogenous (NARX) model, and on the other hand, from the multi-layer perceptron\u2019s (MLP) learning and generalization skills. The proposed approach has been implemented and applied for modeling the dynamic heating conduct of a real five-floor building\u2019s living spaces located at Senart Campus of University Paris-Est Cr\u00e9teil (UPEC), taking into account their occupancy (by users of this public building). The obtained results assessing the accuracy and addictiveness of the investigated hybrid machine learning-based approach are reported and discussed.<\/jats:p>","DOI":"10.3390\/s20041071","type":"journal-article","created":{"date-parts":[[2020,2,20]],"date-time":"2020-02-20T03:20:03Z","timestamp":1582168803000},"page":"1071","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["Data-Driven Living Spaces\u2019 Heating Dynamics Modeling in Smart Buildings using Machine Learning-Based Identification"],"prefix":"10.3390","volume":"20","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6101-9819","authenticated-orcid":false,"given":"Roozbeh","family":"Sadeghian Broujeny","sequence":"first","affiliation":[{"name":"Universit\u00e9 Paris-Est, LISSI Laboratory EA 3956, Senart-FB Institute of Technology, Campus de Senart, 36-37 Rue Charpak\u2013F-77567 Lieusaint, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kurosh","family":"Madani","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris-Est, LISSI Laboratory EA 3956, Senart-FB Institute of Technology, Campus de Senart, 36-37 Rue Charpak\u2013F-77567 Lieusaint, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdennasser","family":"Chebira","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris-Est, LISSI Laboratory EA 3956, Senart-FB Institute of Technology, Campus de Senart, 36-37 Rue Charpak\u2013F-77567 Lieusaint, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Veronique","family":"Amarger","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris-Est, LISSI Laboratory EA 3956, Senart-FB Institute of Technology, Campus de Senart, 36-37 Rue Charpak\u2013F-77567 Lieusaint, France"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Laurent","family":"Hurtard","sequence":"additional","affiliation":[{"name":"Universit\u00e9 Paris-Est, LISSI Laboratory EA 3956, Senart-FB Institute of Technology, Campus de Senart, 36-37 Rue Charpak\u2013F-77567 Lieusaint, France"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,2,16]]},"reference":[{"key":"ref_1","unstructured":"U.S. EIA (2019, September 04). Energy Use in Commercial Buildings-Energy Explained, Your Guide To Understanding Energy\u2014Energy Information Administration, Available online: https:\/\/www.eia.gov\/energyexplained\/index.php?page=us_energy_commercial."},{"key":"ref_2","unstructured":"(2019, September 04). Space Heating and Water Heating (EIA), Available online: https:\/\/www.eia.gov\/todayinenergy\/detail.php?id=37433."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Alhamoud, A., Ruettiger, F., Reinhardt, A., Englert, F., Burgstahler, D., B\u00f6hnstedt, D., Gottron, C., and Steinmetz, R. (2014, January 8\u201311). An Intelligent System for Energy Saving in Smart Home. Proceedings of the 39th Annual IEEE Conference on Local Computer Networks Workshops, Edmonton, AB, Canada.","DOI":"10.1109\/LCNW.2014.6927721"},{"key":"ref_4","unstructured":"James, D.H. (1934). The Radiation of Heat from the Human Body New York Hospital, PubMed Central (PMC)."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"609","DOI":"10.1364\/OPEX.14.000609","article-title":"Path-Dependent Human Identification Using a Pyroelectric Infrared Sensor and Fresnel Lens Arrays","volume":"14","author":"Fang","year":"2006","journal-title":"Opt. Express"},{"key":"ref_6","unstructured":"Johnson, C. (2012). Mathematical Physics of BlackBody Radiation, Icarus iDucation."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"e00655","DOI":"10.1016\/j.heliyon.2018.e00655","article-title":"Design and Simulation of an Automatic Room Heater Control System","volume":"4","author":"Zungeru","year":"2018","journal-title":"Heliyon"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Aaurav, K., and Ch andan, V. (2017, January 16\u201319). Gray-Box Approach for Thermal Modelling of Buildings for Applications in District Heating and Cooling Networks. Proceedings of the 8th International Conference on Future Energy Systems, Hong Kong, China.","DOI":"10.1145\/3077839.3084078"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Purdon, S., Jurdak, B.K.R., and Challen, G. (2013, January 21\u201324). Model-free HVAC control using occupant feedback. Proceedings of the 38th Annual IEEE Conference on Local Computer Networks, Sydney, Australia.","DOI":"10.1109\/LCNW.2013.6758502"},{"key":"ref_10","first-page":"611","article-title":"ANN-Based System Identification, Modelling and Control of Gas Turbines\u2014A Review","volume":"622","author":"Asgari","year":"2013","journal-title":"Adv. Mater. Res."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1063\/1.3060939","article-title":"Non-Linear Problems in Random Theory","volume":"Volume 12","author":"Wiener","year":"1959","journal-title":"Physics Today"},{"key":"ref_12","unstructured":"Schetzen, M. (1980). The Volterra And Wiener Theories of Nonlinear Systems, Wiley."},{"key":"ref_13","first-page":"116","article-title":"Fuzzy Identification of Systems and Its Application to Modeling and Control","volume":"5","author":"Takagi","year":"1985","journal-title":"IEEE Trans. SMC"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Boussaada, Z., Curea, O., Remaci, A., Camblong, H., and Mrabet Bellaaj, N. (2018). A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation. Energies, 11.","DOI":"10.3390\/en11030620"},{"key":"ref_15","unstructured":"Brus, L. (2005, January 28\u201331). Nonlinear Identification of a Solar Heating System. Proceedings of the IEEE Conference on Control Applications (CCA 2005), Toronto, ON, Canada."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"012022","DOI":"10.1088\/1757-899X\/51\/1\/012022","article-title":"Model Identification and Validation for a Heating System Using Matlab System Identification Toolbox","volume":"51","author":"Rabbani","year":"2013","journal-title":"IOP Conf. Ser. Mater. Sci. Eng."},{"key":"ref_17","first-page":"10708","article-title":"System Identification and Controller Design for Boiler and Heat Exchanger Set-Up","volume":"3","author":"Lahane","year":"2014","journal-title":"Int. J. Adv. Res. Electr. Electron. Instrum. Eng."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1016\/j.egypro.2017.07.426","article-title":"Pedersen, and Steffen Petersen. System Identification of Thermal Building Models for Demand Response\u2014A Practical Approach","volume":"122","author":"Knudsen","year":"2017","journal-title":"Energy Procedia"},{"key":"ref_19","unstructured":"(2019, September 04). Building Controls Virtual Test Bed, Available online: https:\/\/simulationresearch.lbl.gov\/projects\/building-controls-virtual-test-bed."},{"key":"ref_20","unstructured":"(2019, September 04). Open Modelica. Available online: https:\/\/www.openmodelica.org\/."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"75","DOI":"10.1016\/0005-1098(94)90230-5","article-title":"N4SID: Subspace Algorithms for the Identification of Combined Deterministic-Stochastic Systems","volume":"30","year":"1994","journal-title":"Automatica"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"331","DOI":"10.1016\/j.fss.2004.09.015","article-title":"NARMAX time series model prediction: Feed-forward and recurrent fuzzy neural network approaches","volume":"150","author":"Gao","year":"2005","journal-title":"Fuzzy Sets Sytems"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1125","DOI":"10.1080\/00207179308923046","article-title":"Constructing NARMAX using ARMAX","volume":"58","author":"Johansen","year":"1993","journal-title":"Int. J. Control"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Billings, S.A. (2013). Nonlinear System Identification: NARMAX Methods in the Time, Frequency, and Spatio-Temporal Domains, John Wiley & Sons.","DOI":"10.1002\/9781118535561"},{"key":"ref_25","unstructured":"Rumelhart, D.E., Hinton, G.E., and Williams, R.J. (1986). Learning Internal Representations by Error Propagation, MIT Press. Parallel Distributed Processing: Explorations in the Microstructure of Cognition."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Hastie, T., Tibshirani, R., and Friedman, J. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction, Springer.","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref_27","unstructured":"(2019, September 04). EnOcean\u2014The World of Energy Harvesting Wireless Technology, January 2016. Available online: https:\/\/www.enocean.com\/fileadmin\/redaktion\/pdf\/white_paper\/WhitePaper_Getting_Started_With_EnOcean_v1.0.pdf."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Sadeghian Broujeny, R., Madani, K., Chebira, A., and Hurtard, L. (2017, January 8\u201310). A multi-layer system for smart-buildings\u2019 functional and energy-efficiency awareness: Implementation on a real five-floors building. Proceedings of the 2017 IEEE 8th International Conference on Awareness Science and Technology (iCAST), Taichung, Taiwan.","DOI":"10.1109\/ICAwST.2017.8256529"},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Sadeghian Broujeny, R., Madani, K., Chebira, A., and Hurtard, L. (2019, January 18\u201321). A Machine-Learning Based Approach for Data-Driven Identification of Heating Dynamics of Buildings\u2019 Living-Spaces. Proceedings of the 10th International Conference on Intelligent Data Acquisition and Advanced Computing Systems (IEEE\/IDAACS 2019), Metz, France.","DOI":"10.1109\/IDAACS.2019.8924329"},{"key":"ref_30","unstructured":"WAGO Kontakttechnik GmbH and Co. (2019, September 04). KG, WAGO-I\/O-SYSTEM 750 Manueal, 2016. Available online: http:\/\/www.safetycontrol.ind.br\/imgs\/downloads\/manual-750-406-pdf-5b211d6fe9baa.pdf."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/4\/1071\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T08:58:15Z","timestamp":1760173095000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/20\/4\/1071"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,2,16]]},"references-count":30,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2020,2]]}},"alternative-id":["s20041071"],"URL":"https:\/\/doi.org\/10.3390\/s20041071","relation":{},"ISSN":["1424-8220"],"issn-type":[{"type":"electronic","value":"1424-8220"}],"subject":[],"published":{"date-parts":[[2020,2,16]]}}}