{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,13]],"date-time":"2026-03-13T21:14:24Z","timestamp":1773436464461,"version":"3.50.1"},"reference-count":40,"publisher":"Springer Science and Business Media LLC","issue":"6","license":[{"start":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T00:00:00Z","timestamp":1687392000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T00:00:00Z","timestamp":1687392000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Energy Efficiency"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s12053-023-10125-5","type":"journal-article","created":{"date-parts":[[2023,6,22]],"date-time":"2023-06-22T10:14:52Z","timestamp":1687428892000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A new NIALM system design based on neural network architecture and adaptive springy particle swarm optimization algorithm"],"prefix":"10.1007","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3242-1451","authenticated-orcid":false,"given":"Saeid","family":"Rastegar","sequence":"first","affiliation":[]},{"given":"Rui","family":"Ara\u00fajo","sequence":"additional","affiliation":[]},{"given":"Milad","family":"Malekzadeh","sequence":"additional","affiliation":[]},{"given":"Alvaro","family":"Gomes","sequence":"additional","affiliation":[]},{"given":"Humberto","family":"Jorge","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2023,6,22]]},"reference":[{"key":"10125_CR1","doi-asserted-by":"crossref","unstructured":"Angelis, G.-F., Timplalexis, C., Krinidis, S., Ioannidis, D., & Tzovaras, D. (2022). NILM applications: Literature review of learning approaches, recent developments and challenges. Energy and Buildings, 111951.","DOI":"10.1016\/j.enbuild.2022.111951"},{"key":"10125_CR2","doi-asserted-by":"crossref","unstructured":"Ara\u00fajo Kuhn Pereira, A., Menezes, R. J. A., Jadidi, A., De Jong, P., & de Castro Lima, A. C. (2020). Development of an electronic device with wireless interface for measuring and monitoring residential electrical loads using the non-invasive method. Energy Efficiency,13(7), 1281\u20131298.","DOI":"10.1007\/s12053-020-09887-z"},{"key":"10125_CR3","doi-asserted-by":"publisher","first-page":"2250","DOI":"10.1016\/j.egypro.2017.12.626","volume":"142","author":"M Azaza","year":"2017","unstructured":"Azaza, M., & Wallin, F. (2017). Evaluation of classification methodologies and features selection from smart meter data. Energy Procedia, 142, 2250\u20132256.","journal-title":"Energy Procedia"},{"key":"10125_CR4","unstructured":"Basu, K. (2014). Classifcation techniques for non-intrusive load monitoring and prediction of residential loads. PhD thesis."},{"key":"10125_CR5","doi-asserted-by":"publisher","first-page":"1461","DOI":"10.1016\/j.enbuild.2017.11.054","volume":"158","author":"R Bonfigli","year":"2018","unstructured":"Bonfigli, R., Felicetti, A., Princip, E., Fagiani, M., Squartini, S., & Piazza, F. (2018). Denoising autoencoders for non-intrusive load monitoring: Improvements and comparative evaluation. Energy and Buildings, 158, 1461\u20131474.","journal-title":"Energy and Buildings"},{"key":"10125_CR6","doi-asserted-by":"publisher","first-page":"1590","DOI":"10.1016\/j.apenergy.2017.08.203","volume":"208","author":"R Bonfigli","year":"2017","unstructured":"Bonfigli, R., Principi, E., Fagiani, M., Severinic, M., Squartini, S., & Piazza, F. (2017). Non-intrusive load monitoring by using active and reactive power in additive factorial hidden Markov models. Applied Energy, 208, 1590\u20131607.","journal-title":"Applied Energy"},{"key":"10125_CR7","doi-asserted-by":"publisher","first-page":"392","DOI":"10.1016\/j.enbuild.2018.11.013","volume":"183","author":"AS Bouhouras","year":"2019","unstructured":"Bouhouras, A. S., Gkaidatzis, P. A., Panagiotou, E., Poulakis, N., & Christoforidis, G. C. (2019). A NILM algorithm with enhanced disaggregation scheme under harmonic current vectors. Energy and Buildings, 183, 392\u2013407.","journal-title":"Energy and Buildings"},{"key":"10125_CR8","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1007\/s12053-016-9460-9","volume":"10","author":"X Cipriano","year":"2017","unstructured":"Cipriano, X., Vellido, A., Cipriano, J., Mart\u00ed-Herrero, J., & Danov, S. (2017). Influencing factors in energy use of housing blocks: A new methodology, based on clustering and energy simulations, for decision making in energy refurbishment projects. Energy Efficiency, 10, 359\u2013382.","journal-title":"Energy Efficiency"},{"key":"10125_CR9","doi-asserted-by":"crossref","unstructured":"Dash, S., & Sahoo, N. (2022). Electric energy disaggregation via non-intrusive load monitoring: A state-of-the-art systematic review. Electric Power Systems Research,213, 108673.","DOI":"10.1016\/j.epsr.2022.108673"},{"key":"10125_CR10","doi-asserted-by":"crossref","unstructured":"Deng, X., Liu, Q., Deng, Y., & Mahadevan, S. (2016). An improved method to construct basic probability assignment based on the confusion matrix for classification problem. Information Sciences,340(C), 250\u2013261.","DOI":"10.1016\/j.ins.2016.01.033"},{"key":"10125_CR11","doi-asserted-by":"publisher","first-page":"325","DOI":"10.1016\/j.amc.2014.03.082","volume":"236","author":"K Elsayed","year":"2014","unstructured":"Elsayed, K., & Lacor, C. (2014). Robust parameter design optimization using kriging, RBF and RBFNN with gradient-based and evolutionary optimization techniques. Applied Mathematics and Computation, 236, 325\u2013344.","journal-title":"Applied Mathematics and Computation"},{"key":"10125_CR12","doi-asserted-by":"publisher","first-page":"66","DOI":"10.1016\/j.neucom.2011.10.037","volume":"96","author":"M Figueiredo","year":"2012","unstructured":"Figueiredo, M., Almeida, A. D., & Ribeiro, B. (2012). Home electrical signal disaggregation for non-intrusive load monitoring (NILM) systems. Neurocomputing, 96, 66\u201373.","journal-title":"Neurocomputing"},{"issue":"1","key":"10125_CR13","doi-asserted-by":"publisher","first-page":"151","DOI":"10.1117\/1.1525793","volume":"12","author":"IK Fodor","year":"2003","unstructured":"Fodor, I. K., & Kamath, C. (2003). Denoising through wavelet shrinkage: An empirical study. Journal of Electronic Imaging, 12(1), 151\u2013161.","journal-title":"Journal of Electronic Imaging"},{"issue":"8","key":"10125_CR14","doi-asserted-by":"publisher","first-page":"1868","DOI":"10.1080\/00207721.2014.955552","volume":"47","author":"M Gan","year":"2016","unstructured":"Gan, M., Chen, C. L. P., Chen, L., & Zhang, C.-Y. (2016). Exploiting the interpretability and forecasting ability of the RBF-AR model for nonlinear time series. International Journal of Systems Science, 47(8), 1868\u20131876.","journal-title":"International Journal of Systems Science"},{"key":"10125_CR15","doi-asserted-by":"publisher","first-page":"80","DOI":"10.1016\/j.neucom.2016.11.010","volume":"225","author":"H-G Han","year":"2017","unstructured":"Han, H.-G., Guo, Y.-N., & Qiao, J.-F. (2017). Self-organization of a recurrent RBF neural network using an information-oriented algorithm. Neurocomputing, 225, 80\u201391.","journal-title":"Neurocomputing"},{"key":"10125_CR16","unstructured":"W. Hart, G., Kern, E. C., & Schweppe, F. C (1989). Non-intrusive appliance monitor apparatus. Google Patents. US Patent 4,858,141."},{"issue":"12","key":"10125_CR17","doi-asserted-by":"publisher","first-page":"1870","DOI":"10.1109\/5.192069","volume":"80","author":"GW Hart","year":"1992","unstructured":"Hart, G. W. (1992). Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80(12), 1870\u20131891.","journal-title":"Proceedings of the IEEE"},{"issue":"2","key":"10125_CR18","first-page":"812","volume":"8","author":"N Henao","year":"2017","unstructured":"Henao, N., Agbossou, K., Kelouwani, S., Dub\u00e9, Y., & Fournier, M. (2017). Approach in nonintrusive type I load monitoring using subtractive clustering. IEEE Transactions on Smart Grid, 8(2), 812\u2013821.","journal-title":"IEEE Transactions on Smart Grid"},{"key":"10125_CR19","doi-asserted-by":"crossref","unstructured":"Iwayemi, A., & Zhou, C (2014). Leveraging smart meters for residential energy disaggregation. In 2014 IEEE PES General Meeting Conference and Exposition (pp 1\u20135).","DOI":"10.1109\/PESGM.2014.6939461"},{"key":"10125_CR20","doi-asserted-by":"publisher","first-page":"470","DOI":"10.1016\/j.asoc.2015.05.048","volume":"35","author":"P Kanirajan","year":"2015","unstructured":"Kanirajan, P., & Kumar, V. S. (2015). Power quality disturbance detection and classification using wavelet and RBFNN. Applied Soft Computing, 35, 470\u2013481.","journal-title":"Applied Soft Computing"},{"key":"10125_CR21","doi-asserted-by":"crossref","unstructured":"Kelly, J., & Knottenbelt, W. (2015). Neural NILM: Deep neural networks applied to energy disaggregation. In Proceedings of the 2nd ACM International Conference on Embedded Systems for Energy-Efficient Built Environments (pp. 55\u201364). ACM.","DOI":"10.1145\/2821650.2821672"},{"key":"10125_CR22","doi-asserted-by":"crossref","unstructured":"Kennedy, J., & Eberhart, R. (1995). Particle swarm optimization. In Proceedings of the IEEE International Conference on Neural Networks (pp. 1942\u20131945).","DOI":"10.1109\/ICNN.1995.488968"},{"key":"10125_CR23","doi-asserted-by":"publisher","first-page":"241","DOI":"10.1016\/j.epsr.2015.12.014","volume":"133","author":"S Lin","year":"2016","unstructured":"Lin, S., Zhao, L., Li, F., Liu, Q., Li, D., & Fu, Y. (2016). A nonintrusive load identification method for residential applications based on quadratic programming. Electric Power Systems Research, 133, 241\u2013248.","journal-title":"Electric Power Systems Research"},{"key":"10125_CR24","doi-asserted-by":"publisher","first-page":"776","DOI":"10.1016\/j.procs.2021.02.128","volume":"183","author":"Y Li","year":"2021","unstructured":"Li, Y., Yang, Y., Sima, K., Li, B., Sun, T., & Li, X. (2021). Non-intrusive load monitoring based on harmonic characteristics. Procedia Computer Science, 183, 776\u2013782.","journal-title":"Procedia Computer Science"},{"key":"10125_CR25","doi-asserted-by":"crossref","unstructured":"Makonin, S., Popowich, F., Bartram, L., Gill, B., & Baji\u0107, I. V. (2013). AMPDS: A public dataset for load disaggregation and eco-feedback research. In 2013 IEEE Electrical Power & Energy Conference (pp. 1\u20136). IEEE.","DOI":"10.1109\/EPEC.2013.6802949"},{"key":"10125_CR26","doi-asserted-by":"publisher","first-page":"809","DOI":"10.1007\/s12053-014-9306-2","volume":"8","author":"S Makonin","year":"2015","unstructured":"Makonin, S., & Popowich, F. (2015). Nonintrusive load monitoring (NILM) performance evaluation: A unified approach for accuracy reporting. Energy Efficiency, 8, 809\u2013814.","journal-title":"Energy Efficiency"},{"key":"10125_CR27","doi-asserted-by":"publisher","first-page":"441","DOI":"10.1007\/s12053-018-9682-0","volume":"12","author":"A M\u00e9jean","year":"2019","unstructured":"M\u00e9jean, A., Guivarch, C., Lef\u00e8vre, J., & Hamdi-Cherif, M. (2019). The transition in energy demand sectors to limit global warming to 1.5 c. Energy Efficiency, 12, 441-462.","journal-title":"Energy Efficiency"},{"key":"10125_CR28","doi-asserted-by":"crossref","unstructured":"Pereira, L., & Nunes, N. (2020). An empirical exploration of performance metrics for event detection algorithms in non-intrusive load monitoring. Sustainable Cities and Society,62, 102399.","DOI":"10.1016\/j.scs.2020.102399"},{"key":"10125_CR29","doi-asserted-by":"crossref","unstructured":"Qu, L., Kong, Y., Li, M., Dong, W., Zhang, F., & Zou, H. (2023). A residual convolutional neural network with multi-block for appliance recognition in non-intrusive load identification. Energy and Buildings,281, 112749.","DOI":"10.1016\/j.enbuild.2022.112749"},{"key":"10125_CR30","doi-asserted-by":"crossref","unstructured":"Ramadan, R., Huang, Q., Bamisile, O., & Zalhaf, A. S. (2022). Intelligent home energy management using internet of things platform based on NILM technique. Sustainable Energy, Grids and Networks,31, 100785.","DOI":"10.1016\/j.segan.2022.100785"},{"key":"10125_CR31","doi-asserted-by":"publisher","first-page":"98","DOI":"10.1016\/j.enbuild.2017.06.042","volume":"151","author":"N Sadeghianpourhamimi","year":"2017","unstructured":"Sadeghianpourhamimi, N., Ruyssinck, J., Deschrijver, D., Dhaene, T., & Develder, C. (2017). Comprehensive feature selection for appliance classification in NILM. Energy and Buildings, 151, 98\u2013106.","journal-title":"Energy and Buildings"},{"key":"10125_CR32","doi-asserted-by":"publisher","first-page":"328","DOI":"10.1016\/j.neucom.2015.02.090","volume":"170","author":"F Saraiva","year":"2015","unstructured":"Saraiva, F., Bernardes, W. M. S., & Asada, E. N. (2015). A framework for classification of non-linear loads in smart grids using artificial neural networks and multi-agent systems. Neurocomputing, 170, 328\u2013338.","journal-title":"Neurocomputing"},{"key":"10125_CR33","doi-asserted-by":"crossref","unstructured":"Shi, Y., & Eberhart, R. C. (1998). A modified particle swarm optimizer. In Proceedings of the IEEE International Conference on Evolutionary Computation, USA (pp. 69\u201373).","DOI":"10.1109\/ICEC.1998.699146"},{"key":"10125_CR34","doi-asserted-by":"crossref","unstructured":"Su, Y. C., Lian, K. L., & Chang, H. H. (2011). Feature selection of non-intrusive load monitoring system using STFT and wavelet transform. In 2011 IEEE 8th International Conference on e-Business Engineering (pp. 293\u2013298). IEEE.","DOI":"10.1109\/ICEBE.2011.49"},{"issue":"2","key":"10125_CR35","doi-asserted-by":"publisher","first-page":"457","DOI":"10.1109\/JAS.2017.7510820","volume":"5","author":"H Yang","year":"2018","unstructured":"Yang, H., & Liu, J. (2018). An adaptive RBF neural network control method for a class of nonlinear systems. IEEE\/CAA Journal of Automatica Sinica, 5(2), 457\u2013462.","journal-title":"IEEE\/CAA Journal of Automatica Sinica"},{"key":"10125_CR36","first-page":"34","volume":"14","author":"CC Yang","year":"2017","unstructured":"Yang, C. C., Soh, C. S., & Yap, V. V. (2017). A non-intrusive appliance load monitoring for efficient energy consumption based on naive bayes classifier. Sustainable Computing: Informatics and Systems, 14, 34\u201342.","journal-title":"Sustainable Computing: Informatics and Systems"},{"issue":"1","key":"10125_CR37","doi-asserted-by":"publisher","first-page":"239","DOI":"10.1007\/s12053-017-9561-0","volume":"11","author":"CC Yang","year":"2018","unstructured":"Yang, C. C., Soh, C. S., & Yap, V. V. (2018). A systematic approach in appliance disaggregation using k-nearest neighbours and Naive Bayes classifiers for energy efficiency. Energy Efficiency, 11(1), 239\u2013259.","journal-title":"Energy Efficiency"},{"key":"10125_CR38","doi-asserted-by":"crossref","unstructured":"Yan, L., Sheikholeslami, M., Gong, W., Tian, W., & Li, Z. (2022). Challenges for real-world applications of nonintrusive load monitoring and opportunities for machine learning approaches. The Electricity Journal, 35(5)","DOI":"10.1016\/j.tej.2022.107136"},{"key":"10125_CR39","doi-asserted-by":"publisher","first-page":"287","DOI":"10.1016\/j.enbuild.2014.05.049","volume":"81","author":"K Yan","year":"2014","unstructured":"Yan, K., Shen, W., Mulumba, T. M., & Afshari, A. (2014). ARX model based fault detection and diagnosis for chillers using support vector machines. Energy and Buildings, 81, 287\u20132952.","journal-title":"Energy and Buildings"},{"issue":"3","key":"10125_CR40","doi-asserted-by":"publisher","first-page":"606","DOI":"10.35833\/MPCE.2020.000569","volume":"10","author":"Z Zhou","year":"2021","unstructured":"Zhou, Z., Xiang, Y., Xu, H., Wang, Y., & Shi, D. (2021). Unsupervised learning for non-intrusive load monitoring in smart grid based on spiking deep neural network. Journal of Modern Power Systems and Clean Energy, 10(3), 606\u2013616.","journal-title":"Journal of Modern Power Systems and Clean Energy"}],"container-title":["Energy Efficiency"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12053-023-10125-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s12053-023-10125-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s12053-023-10125-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,8,19]],"date-time":"2023-08-19T08:27:30Z","timestamp":1692433650000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s12053-023-10125-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,6,22]]},"references-count":40,"journal-issue":{"issue":"6","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["10125"],"URL":"https:\/\/doi.org\/10.1007\/s12053-023-10125-5","relation":{},"ISSN":["1570-646X","1570-6478"],"issn-type":[{"value":"1570-646X","type":"print"},{"value":"1570-6478","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,6,22]]},"assertion":[{"value":"4 April 2022","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 May 2023","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 June 2023","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"52"}}