{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,5]],"date-time":"2026-02-05T22:23:45Z","timestamp":1770330225886,"version":"3.49.0"},"reference-count":30,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T00:00:00Z","timestamp":1620777600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T00:00:00Z","timestamp":1620777600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2022,1]]},"DOI":"10.1007\/s10489-021-02501-4","type":"journal-article","created":{"date-parts":[[2021,5,12]],"date-time":"2021-05-12T22:02:26Z","timestamp":1620856946000},"page":"835-845","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A complete consumer behaviour learning model for real-time demand response implementation in smart grid"],"prefix":"10.1007","volume":"52","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2197-2231","authenticated-orcid":false,"given":"Swati","family":"Sharda","sequence":"first","affiliation":[]},{"given":"Mukhtiar","family":"Singh","sequence":"additional","affiliation":[]},{"given":"Kapil","family":"Sharma","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2021,5,12]]},"reference":[{"key":"2501_CR1","doi-asserted-by":"publisher","first-page":"134","DOI":"10.1016\/j.enbuild.2017.02.004","volume":"141","author":"R Brown","year":"2017","unstructured":"Brown R, Ghavami N, Siddiqui H-U-R, Adjrad M, Ghavami M, Dudley S (2017) Occupancy based household energy disaggregation using ultra wideband radar and electrical signature profiles. Energy and Buildings 141:134\u2013141","journal-title":"Energy and Buildings"},{"issue":"3","key":"2501_CR2","doi-asserted-by":"publisher","first-page":"1881","DOI":"10.1109\/TPWRS.2019.2946701","volume":"35","author":"Z Cao","year":"2020","unstructured":"Cao Z, Wan C, Zhang Z, Li F, Song Y (2020) Hybrid ensemble deep learning for deterministic and probabilistic low-voltage load forecasting. IEEE Transactions on Power Systems 35(3):1881\u20131897","journal-title":"IEEE Transactions on Power Systems"},{"key":"2501_CR3","doi-asserted-by":"crossref","unstructured":"Chen, T., and Guestrin, C. Xgboost. Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining - KDD -16 (2016)","DOI":"10.1145\/2939672.2939785"},{"issue":"3","key":"2501_CR4","doi-asserted-by":"publisher","first-page":"72","DOI":"10.1109\/MCE.2019.2956197","volume":"9","author":"L Ciabattoni","year":"2020","unstructured":"Ciabattoni L, Comodi G, Ferracuti F, Foresi G (2020) Ai-powered home electrical appliances as enabler of demand-side flexibility. IEEE Consumer Electronics Magazine 9(3):72\u201378","journal-title":"IEEE Consumer Electronics Magazine"},{"key":"2501_CR5","doi-asserted-by":"crossref","unstructured":"Elattar, E.E., Sabiha, N.A., and Alsharef, M. Short term electric load forecasting using hybrid algorithm for smart cities. Appl Intell 50 (2020), 3379-3399","DOI":"10.1007\/s10489-020-01728-x"},{"issue":"6","key":"2501_CR6","doi-asserted-by":"publisher","first-page":"4380","DOI":"10.1109\/JIOT.2018.2866998","volume":"5","author":"SM Errapotu","year":"2018","unstructured":"Errapotu SM, Wang J, Gong Y, Cho J, Pan M, Han Z (2018) Safe: Secure appliance scheduling for flexible and efficient energy consumption for smart home iot. IEEE Internet of Things Journal 5(6):4380\u20134391","journal-title":"IEEE Internet of Things Journal"},{"key":"2501_CR7","doi-asserted-by":"publisher","first-page":"57","DOI":"10.1016\/j.rser.2017.01.043","volume":"72","author":"N Good","year":"2017","unstructured":"Good N, Ellis KA, Mancarella P (2017) Review and classification of barriers and enablers of demand response in the smart grid. Renewable and Sustainable Energy Reviews 72:57\u201372","journal-title":"Renewable and Sustainable Energy Reviews"},{"key":"2501_CR8","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. Deep Learning. MIT Press, 2016. http:\/\/www.deeplearningbook.org"},{"key":"2501_CR9","doi-asserted-by":"crossref","unstructured":"Greff, K., Srivastava, R.\u00a0K., Koutnk, J., Steunebrink, B.\u00a0R., and Schmidhuber, J. Lstm: A search space odyssey. IEEE Transactions on Neural Networks and Learning Systems 28, 10 (2017), 2222\u20132232","DOI":"10.1109\/TNNLS.2016.2582924"},{"key":"2501_CR10","doi-asserted-by":"publisher","first-page":"1186","DOI":"10.1016\/j.energy.2018.07.090","volume":"160","author":"Z Guo","year":"2018","unstructured":"Guo Z, Zhou K, Zhang X, Yang S (2018) A deep learning model for short-term power load and probability density forecasting. Energy 160:1186\u20131200","journal-title":"Energy"},{"issue":"8","key":"2501_CR11","doi-asserted-by":"publisher","first-page":"17351780","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long Short-Term Memory. Neural Computation. 9(8):17351780","journal-title":"Neural Computation."},{"key":"2501_CR12","unstructured":"Ji Y, Buechler E, Rajagopal R (2019) Data-driven load modeling and forecasting of residential appliances. IEEE Transactions on Smart Grid 1\u20131"},{"key":"2501_CR13","doi-asserted-by":"publisher","first-page":"150007","DOI":"10.1038\/sdata.2015.7","volume":"2","author":"J Kelly","year":"2015","unstructured":"Kelly J, Knottenbelt W (2015) The UK-DALE dataset, domestic appliance-level electricity demand and whole-house demand from five UK homes. Scientific Data 2:150007","journal-title":"Scientific Data"},{"issue":"1","key":"2501_CR14","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1109\/TSG.2017.2753802","volume":"10","author":"W Kong","year":"2019","unstructured":"Kong W, Dong ZY, Jia Y, Hill DJ, Xu Y, Zhang Y (2019) Short-term residential load forecasting based on lstm recurrent neural network. IEEE Transactions on Smart Grid 10(1):841\u2013851","journal-title":"IEEE Transactions on Smart Grid"},{"key":"2501_CR15","doi-asserted-by":"crossref","unstructured":"Kumar, S., Hussain, L., Banarjee, S., and Reza, M. Energy load forecasting using deep learning approach-lstm and gru in spark cluster. In 2018 Fifth International Conference on Emerging Applications of Information Technology (EAIT) (2018), pp.\u00a01\u20134","DOI":"10.1109\/EAIT.2018.8470406"},{"key":"2501_CR16","first-page":"9","volume":"20","author":"T Le","year":"2020","unstructured":"Le T, Vo MT, Kieu T, Hwang E, Rho S, Baik SW (2020) Multiple electric energy consumption forecasting using a cluster-based strategy for transfer learning in smart building. Sensors 20:9","journal-title":"Sensors"},{"key":"2501_CR17","first-page":"20","volume":"9","author":"T Le","year":"2019","unstructured":"Le T, Vo MT, Vo B, Hwang E, Rho S, Baik SW (2019) Improving electric energy consumption prediction using cnn and bi-lstm. Applied Sciences 9:20","journal-title":"Applied Sciences"},{"key":"2501_CR18","doi-asserted-by":"publisher","first-page":"118874","DOI":"10.1016\/j.energy.2020.118874","volume":"214","author":"M Massaoudi","year":"2021","unstructured":"Massaoudi M, Refaat SS, Chihi I, Trabelsi M, Oueslati FS, Abu-Rub H (2021) A novel stacked generalization ensemble-based hybrid lgbm-xgb-mlp model for short-term load forecasting. Energy 214:118874","journal-title":"Energy"},{"key":"2501_CR19","doi-asserted-by":"crossref","unstructured":"Monacchi, A., Egarter, D., Elmenreich, W., D\u2019Alessandro, S., and Tonello, A.\u00a0M. Greend: An energy consumption dataset of households in italy and austria. In 2014 IEEE International Conference on Smart Grid Communications (SmartGridComm) (Nov 2014), pp.\u00a0511\u2013516","DOI":"10.1109\/SmartGridComm.2014.7007698"},{"key":"2501_CR20","doi-asserted-by":"crossref","unstructured":"Ponocko, J., and Milanovic, J. Forecasting demand flexibility of aggregated residential load using smart meter data. IEEE Transactions on Power Systems PP (01 2018), 1\u20131","DOI":"10.1109\/TPWRS.2018.2799903"},{"key":"2501_CR21","doi-asserted-by":"publisher","first-page":"143759","DOI":"10.1109\/ACCESS.2020.3009537","volume":"8","author":"M Sajjad","year":"2020","unstructured":"Sajjad M, Khan ZA, Ullah A, Hussain T, Ullah W, Lee MY, Baik SW (2020) A novel cnn-gru-based hybrid approach for short-term residential load forecasting. IEEE Access 8:143759\u2013143768","journal-title":"IEEE Access"},{"key":"2501_CR22","unstructured":"Sharda, S. link. https:\/\/github.com\/SwatiSharda\/Appliance-power-forecasting"},{"issue":"2","key":"2501_CR23","first-page":"911","volume":"6","author":"Z Sheng","year":"2020","unstructured":"Sheng Z, Wang H, Chen G (2020) Convolutional residual network to short-term load forecasting. Appl Intell 6(2):911\u2013918","journal-title":"Appl Intell"},{"key":"2501_CR24","doi-asserted-by":"crossref","unstructured":"Shi H, Xu M, Li R (2018) Deep learning for household load forecasting a novel pooling deep rnn. IEEE Transactions on Smart Grid 9(5):5271\u20135280","DOI":"10.1109\/TSG.2017.2686012"},{"key":"2501_CR25","doi-asserted-by":"publisher","first-page":"106025","DOI":"10.1016\/j.epsr.2019.106025","volume":"178","author":"G Sideratos","year":"2020","unstructured":"Sideratos G, Ikonomopoulos A, Hatziargyriou ND (2020) A novel fuzzy-based ensemble model for load forecasting using hybrid deep neural networks. Electric Power Systems Research 178:106025","journal-title":"Electric Power Systems Research"},{"key":"2501_CR26","doi-asserted-by":"publisher","first-page":"3","DOI":"10.1109\/JSEN.2020.3032357","volume":"20","author":"A Ullah","year":"2020","unstructured":"Ullah A, Haydarov K, Ul Haq I, Muhammad K, Rho S, Lee M, Baik SW (2020) Deep learning assisted buildings energy consumption profiling using smart meter data. Sensors 20:3","journal-title":"Sensors"},{"key":"2501_CR27","doi-asserted-by":"publisher","first-page":"123369","DOI":"10.1109\/ACCESS.2019.2963045","volume":"8","author":"FUM Ullah","year":"2020","unstructured":"Ullah FUM, Ullah A, Haq IU, Rho S, Baik SW (2020) Short-term prediction of residential power energy consumption via cnn and multi-layer bi-directional lstm networks. IEEE Access 8:123369\u2013123380","journal-title":"IEEE Access"},{"key":"2501_CR28","doi-asserted-by":"publisher","first-page":"10","DOI":"10.1016\/j.apenergy.2018.10.078","volume":"235","author":"Y Wang","year":"2019","unstructured":"Wang Y, Gan D, Sun M, Zhang N, Lu Z, Kang C (2019) Probabilistic individual load forecasting using pinball loss guided lstm. Applied Energy 235:10\u201320","journal-title":"Applied Energy"},{"key":"2501_CR29","doi-asserted-by":"crossref","unstructured":"Xu, W., Peng, H., and X., Z. A hybrid modelling method for time series forecasting based on a linear regression model and deep learning. Appl Intell 49 (2019), 3002-3015","DOI":"10.1007\/s10489-019-01426-3"},{"key":"2501_CR30","doi-asserted-by":"crossref","unstructured":"Zhao B, Xiao S, Lu H, Liu J (2017) Waveforms, classification based on convolutional neural networks. In, (2017) IEEE 2nd Advanced Information Technology. Electronic and Automation Control Conference (IAEAC). 162\u2013165","DOI":"10.1109\/IAEAC.2017.8053998"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02501-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-021-02501-4\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-021-02501-4.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,1,14]],"date-time":"2022-01-14T06:40:00Z","timestamp":1642142400000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-021-02501-4"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,5,12]]},"references-count":30,"journal-issue":{"issue":"1","published-print":{"date-parts":[[2022,1]]}},"alternative-id":["2501"],"URL":"https:\/\/doi.org\/10.1007\/s10489-021-02501-4","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,5,12]]},"assertion":[{"value":"3 May 2021","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"12 May 2021","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}}]}}