{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,26]],"date-time":"2026-03-26T15:39:16Z","timestamp":1774539556519,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T00:00:00Z","timestamp":1671753600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["2020R1I1A3070744"],"award-info":[{"award-number":["2020R1I1A3070744"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Energy consumption is increasing daily, and with that comes a continuous increase in energy costs. Predicting future energy consumption and building an effective energy management system for smart homes has become essential for many industrialists to solve the problem of energy wastage. Machine learning has shown significant outcomes in the field of energy management systems. This paper presents a comprehensive predictive-learning based framework for smart home energy management systems. We propose five modules: classification, prediction, optimization, scheduling, and controllers. In the classification module, we classify the category of users and appliances by using k-means clustering and support vector machine based classification. We predict the future energy consumption and energy cost for each user category using long-term memory in the prediction module. We define objective functions for optimization and use grey wolf optimization and particle swarm optimization for scheduling appliances. For each case, we give priority to user preferences and indoor and outdoor environmental conditions. We define control rules to control the usage of appliances according to the schedule while prioritizing user preferences and minimizing energy consumption and cost. We perform experiments to evaluate the performance of our proposed methodology, and the results show that our proposed approach significantly reduces energy cost while providing an optimized solution for energy consumption that prioritizes user preferences and considers both indoor and outdoor environmental factors.<\/jats:p>","DOI":"10.3390\/s23010127","type":"journal-article","created":{"date-parts":[[2022,12,23]],"date-time":"2022-12-23T03:55:21Z","timestamp":1671767721000},"page":"127","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":10,"title":["A Comprehensive Predictive-Learning Framework for Optimal Scheduling and Control of Smart Home Appliances Based on User and Appliance Classification"],"prefix":"10.3390","volume":"23","author":[{"given":"Wafa","family":"Shafqat","sequence":"first","affiliation":[{"name":"Division of Information and Communication Engineering, Kongju National University, Cheonan 331717, Republic of Korea"}]},{"given":"Kyu-Tae","family":"Lee","sequence":"additional","affiliation":[{"name":"Division of Information and Communication Engineering, Kongju National University, Cheonan 331717, Republic of Korea"}]},{"given":"Do-Hyeun","family":"Kim","sequence":"additional","affiliation":[{"name":"Computer Engineering Department, Ara Campus, Jeju National University, Jeju-si 63243, Republic of Korea"}]}],"member":"1968","published-online":{"date-parts":[[2022,12,23]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"77","DOI":"10.17993\/3cemp.2021.100448.77-105","article-title":"An optimized deep neural network-based financial statement fraud detection in text mining","volume":"10","author":"Yadav","year":"2021","journal-title":"3c Empres. Investig. Y Pensam. Cr\u00edtico"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"525","DOI":"10.2478\/amns.2021.2.00111","article-title":"Research on a reference signal optimisation algorithm for indoor Bluetooth positioning","volume":"6","author":"Luo","year":"2021","journal-title":"Appl. Math. Nonlinear Sci."},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Ramesh, J., Al-Ali, A.R., Al Nabulsi, A., Osman, A., and Shaaban, M. (2022, January 7\u20139). Deep Learning Approach for Smart Home Appliances Monitoring and Classification. Proceedings of the 2022 IEEE International Conference on Consumer Electronics (ICCE), Las Vegas, NV, USA.","DOI":"10.1109\/ICCE53296.2022.9730441"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Kang, S., and Yoon, J.W. (2016, January 13\u201316). Classification of home appliance by using Probabilistic KNN with sensor data. Proceedings of the 2016 Asia-Pacific Signal and Information Processing Association Annual Summit and Conference (APSIPA), Jeju, Korea.","DOI":"10.1109\/APSIPA.2016.7820745"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Rashid, R.A., Chin, L., Sarijari, M.A., Sudirman, R., and Ide, T. (2019, January 2\u20135). Machine learning for smart energy monitoring of home appliances using IoT. Proceedings of the 2019 Eleventh International Conference on Ubiquitous and Future Networks (ICUFN), Zagreb, Croatia.","DOI":"10.1109\/ICUFN.2019.8806026"},{"key":"ref_6","unstructured":"Veloso, A.F.d.S., de Oliveira, R.G., Rodrigues, A.A., Rabelo, R.A., and Rodrigues, J.J. (2019, January 20\u201324). Cognitive smart plugs for signature identification of residential home appliance load using machine learning: From theory to practice. Proceedings of the 2019 IEEE International Conference on Communications Workshops (ICC Workshops), Shanghai, China."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"5609","DOI":"10.1109\/TSG.2018.2888581","article-title":"Non-intrusive load monitoring by voltage\u2013current trajectory enabled transfer learning","volume":"10","author":"Liu","year":"2018","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_8","doi-asserted-by":"crossref","unstructured":"Alam, M., Reaz, M., Ali, M.M., Samad, S., Hashim, F., and Hamzah, M. (2010, January 3\u20135). Human activity classification for smart home: A multiagent approach. Proceedings of the 2010 IEEE Symposium on Industrial Electronics and Applications (ISIEA), Penang, Malaysia.","DOI":"10.1109\/ISIEA.2010.5679411"},{"key":"ref_9","unstructured":"Mozer, M.C. (1998, January 23\u201325). The neural network house: An environment hat adapts to its inhabitants. Proceedings of the AAAI Spring Symposium, Intelligent Environments, Palo Alto, CA, USA."},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Zheng, H., Wang, H., and Black, N. (2008, January 6\u20138). Human activity detection in smart home environment with self-adaptive neural networks. Proceedings of the 2008 IEEE International Conference on Networking, Sensing and Control, Sanya, China.","DOI":"10.1109\/ICNSC.2008.4525459"},{"key":"ref_11","unstructured":"Krumm, J., Harris, S., Meyers, B., Brumitt, B., Hale, M., and Shafer, S. (2000, January 1). Multi-camera multi-person tracking for easyliving. Proceedings of the Proceedings third IEEE International Workshop on Visual Surveillance, Dublin, Ireland."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Brumitt, B., Meyers, B., Krumm, J., Kern, A., and Shafer, S. (2000, January 25\u201327). Easyliving: Technologies for intelligent environments. Proceedings of the International Symposium on Handheld and Ubiquitous Computing, Bristol, UK.","DOI":"10.1007\/3-540-39959-3_2"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1109\/MIS.2008.33","article-title":"Proactive fuzzy control and adaptation methods for smart homes","volume":"23","author":"Vainio","year":"2008","journal-title":"IEEE Intell. Syst."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"387","DOI":"10.1109\/TITB.2007.904157","article-title":"Behavioral patterns of older adults in assisted living","volume":"12","author":"Virone","year":"2008","journal-title":"IEEE Trans. Inf. Technol. Biomed."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"314","DOI":"10.1109\/TSG.2011.2114678","article-title":"Wireless sensor networks for cost-efficient residential energy management in the smart grid","volume":"2","author":"Mouftah","year":"2011","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"2364","DOI":"10.1109\/TSG.2013.2254506","article-title":"Load scheduling for household energy consumption optimization","volume":"4","author":"Agnetis","year":"2013","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"134","DOI":"10.1109\/TSG.2010.2053053","article-title":"Coordinated scheduling of residential distributed energy resources to optimize smart home energy services","volume":"1","author":"Pedrasa","year":"2010","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Shafqat, W., Malik, S., Lee, K.T., and Kim, D.H. (2021). PSO based optimized ensemble learning and feature selection approach for efficient energy forecast. Electronics, 10.","DOI":"10.3390\/electronics10182188"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"218245","DOI":"10.1109\/ACCESS.2020.3042534","article-title":"Optimal control based on scheduling for comfortable smart home environment","volume":"8","author":"Malik","year":"2020","journal-title":"IEEE Access"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Malik, S., Shafqat, W., Lee, K.T., and Kim, D.H. (2021). A Feature Selection-Based Predictive-Learning Framework for Optimal Actuator Control in Smart Homes. Actuators, 10.","DOI":"10.3390\/act10040084"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"108126","DOI":"10.1016\/j.ijepes.2022.108126","article-title":"Optimization scheduling of home appliances in smart home: A model based on a niche technology with sharing mechanism","volume":"141","author":"Lu","year":"2022","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Zhu, Z., Tang, J., Lambotharan, S., Chin, W.H., and Fan, Z. (2012, January 16\u201320). An integer linear programming based optimization for home demand-side management in smart grid. Proceedings of the 2012 IEEE PES Innovative Smart Grid Technologies (ISGT), Washington, DC, USA.","DOI":"10.1109\/GLOCOMW.2011.6162372"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Makhadmeh, S.N., Khader, A.T., Al-Betar, M.A., and Naim, S. (2018, January 23\u201325). An optimal power scheduling for smart home appliances with smart battery using grey wolf optimizer. Proceedings of the 2018 8th IEEE International Conference on Control System, Computing and Engineering (ICCSCE), Penang, Malaysia.","DOI":"10.1109\/ICCSCE.2018.8685003"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"3643","DOI":"10.1007\/s12652-018-1085-8","article-title":"Multi-objective power scheduling problem in smart homes using grey wolf optimiser","volume":"10","author":"Makhadmeh","year":"2019","journal-title":"J. Ambient Intell. Humaniz. Comput."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Anwar ul Hassan, C., Khan, M.S., Ghafar, A., Aimal, S., Asif, S., and Javaid, N. (2017, January 24\u201326). Energy optimization in smart grid using grey wolf optimization algorithm and bacterial foraging algorithm. Proceedings of the International Conference on Intelligent Networking and Collaborative Systems, Toronto, ON, Canada.","DOI":"10.1007\/978-3-319-65636-6_15"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Javaid, N., Javaid, S., Abdul, W., Ahmed, I., Almogren, A., Alamri, A., and Niaz, I.A. (2017). A hybrid genetic wind driven heuristic optimization algorithm for demand side management in smart grid. Energies, 10.","DOI":"10.3390\/en10030319"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"217","DOI":"10.3390\/en8010217","article-title":"Optimal scheduling of domestic appliances via MILP","volume":"8","author":"Bradac","year":"2014","journal-title":"Energies"},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Shareef, H., Al-Hassan, E., and Sirjani, R. (2020). Wireless home energy management system with smart rule-based controller. Appl. Sci., 10.","DOI":"10.3390\/app10134533"},{"key":"ref_29","unstructured":"(2022, June 23). Smart Home Activity Data. Available online: https:\/\/github.com\/RiccardoBonesi\/SmartHouse."},{"key":"ref_30","unstructured":"(2022, June 23). User Appliance Energy Usage and Cost. Available online: https:\/\/www.siliconvalleypower.com\/residents\/save-energy\/appliance-energy-use-chart."},{"key":"ref_31","unstructured":"(2022, June 24). Outdoor Environment Data, Available online: https:\/\/www.ncei.noaa.gov\/maps\/daily-summaries\/."},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Shewale, A., Mokhade, A., Funde, N., and Bokde, N.D. (2020). An overview of demand response in smart grid and optimization techniques for efficient residential appliance scheduling problem. Energies, 13.","DOI":"10.3390\/en13164266"},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"111908","DOI":"10.1016\/j.enbuild.2022.111908","article-title":"A hybrid RF-LSTM based on CEEMDAN for improving the accuracy of building energy consumption prediction","volume":"259","author":"Karijadi","year":"2022","journal-title":"Energy Build."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Fu, Q., Li, K., Chen, J., Wang, J., Lu, Y., and Wang, Y. (2022). Building energy consumption prediction using a deep-forest-based DQN method. Buildings, 12.","DOI":"10.3390\/buildings12020131"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"110929","DOI":"10.1016\/j.enbuild.2021.110929","article-title":"Hourly energy consumption prediction of an office building based on ensemble learning and energy consumption pattern classification","volume":"241","author":"Dong","year":"2021","journal-title":"Energy Build."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1246","DOI":"10.1016\/j.egyr.2021.02.006","article-title":"Prediction of home energy consumption based on gradient boosting regression tree","volume":"7","author":"Nie","year":"2021","journal-title":"Energy Rep."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1065","DOI":"10.1038\/s41598-022-04923-7","article-title":"Proposing a hybrid metaheuristic optimization algorithm and machine learning model for energy use forecast in non-residential buildings","volume":"12","author":"Ngo","year":"2022","journal-title":"Sci. Rep."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"973","DOI":"10.1007\/s12083-021-01267-3","article-title":"EM_WOA: A budget-constrained energy consumption optimization approach for workflow scheduling in clouds","volume":"15","author":"Zhang","year":"2022","journal-title":"Peer-Netw. Appl."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"8701","DOI":"10.1016\/j.egyr.2022.06.053","article-title":"Sewage treatment system for improving energy efficiency based on particle swarm optimization algorithm","volume":"8","author":"Su","year":"2022","journal-title":"Energy Rep."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"101250","DOI":"10.1016\/j.csite.2021.101250","article-title":"Building energy optimization using grey wolf optimizer (GWO)","volume":"27","author":"Ghalambaz","year":"2021","journal-title":"Case Stud. Therm. Eng."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"108762","DOI":"10.1016\/j.knosys.2022.108762","article-title":"Multi-objective scheduling of IoT-enabled smart homes for energy management based on Arithmetic Optimization Algorithm: A Node-RED and NodeMCU module-based technique","volume":"247","author":"Bahmanyar","year":"2022","journal-title":"Knowl.-Based Syst."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"52178","DOI":"10.1109\/ACCESS.2022.3174073","article-title":"IESR: Instant Energy Scheduling Recommendations for Cost Saving in Smart Homes","volume":"10","author":"Fakhar","year":"2022","journal-title":"IEEE Access"},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"107159","DOI":"10.1016\/j.ijepes.2021.107159","article-title":"An optimal scheduling scheme for smart home electricity considering demand response and privacy protection","volume":"132","author":"Zhang","year":"2021","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"100793","DOI":"10.1016\/j.swevo.2020.100793","article-title":"A novel hybrid grey wolf optimizer with min-conflict algorithm for power scheduling problem in a smart home","volume":"60","author":"Makhadmeh","year":"2021","journal-title":"Swarm Evol. Comput."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/127\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T01:49:00Z","timestamp":1760147340000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/1\/127"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,12,23]]},"references-count":45,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2023,1]]}},"alternative-id":["s23010127"],"URL":"https:\/\/doi.org\/10.3390\/s23010127","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,12,23]]}}}