{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,8,7]],"date-time":"2026-08-07T16:44:34Z","timestamp":1786121074903,"version":"3.56.0"},"reference-count":124,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2023,1,28]],"date-time":"2023-01-28T00:00:00Z","timestamp":1674864000000},"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>Smart grids are able to forecast customers\u2019 consumption patterns, i.e., their energy demand, and consequently electricity can be transmitted after taking into account the expected demand. To face today\u2019s demand forecasting challenges, where the data generated by smart grids is huge, modern data-driven techniques need to be used. In this scenario, Deep Learning models are a good alternative to learn patterns from customer data and then forecast demand for different forecasting horizons. Among the commonly used Artificial Neural Networks, Long Short-Term Memory networks\u2014based on Recurrent Neural Networks\u2014are playing a prominent role. This paper provides an insight into the importance of the demand forecasting issue, and other related factors, in the context of smart grids, and collects some experiences of the use of Deep Learning techniques, for demand forecasting purposes. To have an efficient power system, a balance between supply and demand is necessary. Therefore, industry stakeholders and researchers should make a special effort in load forecasting, especially in the short term, which is critical for demand response.<\/jats:p>","DOI":"10.3390\/s23031467","type":"journal-article","created":{"date-parts":[[2023,1,30]],"date-time":"2023-01-30T02:28:34Z","timestamp":1675045714000},"page":"1467","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":77,"title":["An Insight of Deep Learning Based Demand Forecasting in Smart Grids"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7054-0369","authenticated-orcid":false,"given":"Javier Manuel","family":"Aguiar-P\u00e9rez","sequence":"first","affiliation":[{"name":"Departamento de Teor\u00eda de la Se\u00f1al y Comunicaciones e Ingenier\u00eda Telem\u00e1tica, Universidad de Valladolid, ETSI Telecomunicaci\u00f3n, Paseo de Bel\u00e9n 15, 47011 Valladolid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mar\u00eda \u00c1ngeles","family":"P\u00e9rez-Ju\u00e1rez","sequence":"additional","affiliation":[{"name":"Departamento de Teor\u00eda de la Se\u00f1al y Comunicaciones e Ingenier\u00eda Telem\u00e1tica, Universidad de Valladolid, ETSI Telecomunicaci\u00f3n, Paseo de Bel\u00e9n 15, 47011 Valladolid, Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,1,28]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1016\/j.asej.2020.05.004","article-title":"Recent advancement in smart grid technology: Future prospects in the electrical power network","volume":"12","author":"Butt","year":"2021","journal-title":"Ain Shams Eng. J."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Cecati, C., Mokryani, G., Piccolo, A., and Siano, P. (2010, January 7\u201310). An Overview on the Smart Grid Concept. Proceedings of the IECON 2010\u201436th Annual Conference on IEEE Industrial Electronics Society, Glendale, CA, USA.","DOI":"10.1109\/IECON.2010.5675310"},{"key":"ref_3","unstructured":"Vakulenko, I., Saher, L., Lyulyov, O., and Pimonenko, T. (2021, January 22\u201323). A Systematic Literature Review of Smart Grids. Proceedings of the 1st Conference on Traditional and Renewable Energy Sources: Perspectives and Paradigms for the 21st Century (TRESP 2021), Prague, Czech Republic."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"998","DOI":"10.1109\/SURV.2012.010912.00035","article-title":"A survey on cyber security for smart grid communications","volume":"14","author":"Yan","year":"2012","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_5","first-page":"135","article-title":"Demand forecasting in smart grid","volume":"132","author":"Azad","year":"2013","journal-title":"Green Energy Technol."},{"key":"ref_6","unstructured":"Prabadevi, B., Pham, Q.-V., Liyanage, M., Deepa, N., Vvss, M., Reddy, S., Madikunta, P.K.R., Khare, N., Gadekallu, T.R., and Hwang, W.H. (2021). Deep learning for intelligent demand response and smart grids: A comprehensive survey. arXiv."},{"key":"ref_7","doi-asserted-by":"crossref","unstructured":"Ferreira, H.C., Lampe, L., Newbury, J., and Swart, T.G. (2010). Power Line Communications. Theory and Applications for Narrowband and Broadband Communications Over Power Lines, John Wiley and Sons.","DOI":"10.1002\/9780470661291"},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1186\/s42162-021-00148-6","article-title":"A scoping review of deep neural networks for electric load forecasting","volume":"4","author":"Vanting","year":"2021","journal-title":"Energy Inform."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1460","DOI":"10.1109\/SURV.2014.032014.00094","article-title":"A survey on electric power demand forecasting: Future trends in smart grids, microgrids and smart buildings","volume":"16","author":"Aguiar","year":"2014","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"150","DOI":"10.1016\/j.apenergy.2012.02.027","article-title":"Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short-term multiple loads forecasting","volume":"96","author":"Javed","year":"2012","journal-title":"Appl. Energy"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"51","DOI":"10.1016\/j.tej.2015.06.001","article-title":"Demand forecasting in the smart grid paradigm: Features and challenges","volume":"28","author":"Khodayar","year":"2015","journal-title":"Electr. J."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"116754","DOI":"10.1016\/j.apenergy.2021.116754","article-title":"Artificial intelligence to support the integration of variable renewable energy sources to the power system","volume":"290","author":"Boza","year":"2021","journal-title":"Appl. Energy"},{"key":"ref_13","unstructured":"Lasseter, R., Akhil, A., Mamy, C., Stephens, J., Dagle, J., Guttromson, R., Meliopoulous, A.S., Yinger, R., and Eto, J. (2022, November 20). Integration of Distributed Energy Resources. The CERTS Microgrid Concept. White Paper, Available online: https:\/\/certs.lbl.gov\/publications\/integration-distributed-energy."},{"key":"ref_14","unstructured":"Anduaga, J., Boyra, M., Cobelo, I., Garc\u00eda, E., Gil, A., Jimeno, J., Laresgoiti, I., Oyarzabal, J.M., Rodr\u00edguez, R., and S\u00e1nchez, E. (2008). La Microrred, Una Alternativa de Futuro Para un Suministro Energ\u00e9tico Integral, TECNALIA, Corporaci\u00f3n Tecnol\u00f3gica."},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"326","DOI":"10.1080\/02763869.2016.1189787","article-title":"Smart buildings: An introduction to the library of the future","volume":"35","author":"Hoy","year":"2016","journal-title":"Med. Ref. Serv. Q."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"944","DOI":"10.1109\/SURV.2011.101911.00087","article-title":"Smart grid\u2014The new and improved power grid: A Survey","volume":"14","author":"Fang","year":"2012","journal-title":"IEEE Commun. Surv. Tutor."},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Singh, A., Nasiruddin, I., Khatoon, S., Muazzam, M., and Chaturvedi, D.K. (2012, January 17\u201319). Load Forecasting Techniques and Methodologies: A Review. Proceedings of the 2nd International Conference on Power, Control and Embedded Systems (ICPCES), Allahabad, India.","DOI":"10.1109\/ICPCES.2012.6508132"},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1186\/s43067-020-00021-8","article-title":"Electricity load forecasting: A systematic review","volume":"7","author":"Nti","year":"2020","journal-title":"J. Electr. Syst. Inf. Technol."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Ahmed Mir, A., Alghassab Kafait, M., Ullah, K., Ali Khan, Z., Lu, Y., and Imran, M. (2020). A review of electricity demand forecasting in low and middle income countries: The demand determinants and horizons. Sustainability, 12.","DOI":"10.3390\/su12155931"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"522","DOI":"10.1016\/j.ejor.2015.12.030","article-title":"Forecasting day-ahead electricity load using a multiple equation time series approach","volume":"251","author":"Clements","year":"2016","journal-title":"Eur. J. Oper. Res."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"55785","DOI":"10.1109\/ACCESS.2020.2981817","article-title":"A deep learning method for short-term residential load forecasting in smart grid","volume":"8","author":"Hong","year":"2020","journal-title":"IEEE Access"},{"key":"ref_22","first-page":"19","article-title":"A study of load demand forecasting models in electric power system operation and planning","volume":"10","author":"Phuangpornpitak","year":"2016","journal-title":"GMSARN Int. J."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"135","DOI":"10.1016\/j.apenergy.2016.01.050","article-title":"A combined model based on multiple seasonal patterns and modified firefly algorithm for electrical load forecasting","volume":"167","author":"Xiao","year":"2016","journal-title":"Appl. Energy"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xue, B., and Geng, J. (2012, January 12\u201314). Dynamic Transverse Correction Method of Middle and Long-term Energy Forecasting based on Statistic of Forecasting Errors. Proceedings of the 10th Conference on Power and Energy (IPEC), Ho Chi Minh, Vietnam.","DOI":"10.1109\/ASSCC.2012.6523273"},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Khatoon, S., Nasiruddin, I., Singh, A., and Gupta, P. (2014, January 5\u20137). Effects of Various Factors on Electric Load Forecasting: An Overview. Proceedings of the IEEE Power India International Conference (PIICON), Delhi, India.","DOI":"10.1109\/34084POWERI.2014.7117763"},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"44","DOI":"10.1109\/59.910780","article-title":"Neural networks for short-term load forecasting: A review and evaluation","volume":"16","author":"Hippert","year":"2001","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"305","DOI":"10.7763\/JOCET.2014.V2.145","article-title":"Factor affecting short term load forecasting","volume":"2","author":"Fahad","year":"2014","journal-title":"J. Clean Energy Technol."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"465","DOI":"10.4236\/epe.2015.710045","article-title":"Review on people\u2019s lifestyle and energy consumption of Asian communities: Case study of Indonesia, Thailand, and China","volume":"7","author":"Novianto","year":"2015","journal-title":"Energy Power Eng."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"2927","DOI":"10.3390\/en6062927","article-title":"Experimental analysis of the input variables\u2019 relevance to forecast next day\u2019s aggregated electric demand using neural networks","volume":"6","author":"Aguiar","year":"2013","journal-title":"Energies"},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Issi, F., and Kaplan, O. (2018). The determination of load profiles and power consumptions of home appliances. Energies, 11.","DOI":"10.3390\/en11030607"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"209915","DOI":"10.1109\/ACCESS.2020.3019698","article-title":"An approach of electrical load profile analysis based on time series data mining","volume":"8","author":"Shi","year":"2020","journal-title":"IEEE Access"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"114915","DOI":"10.1016\/j.apenergy.2020.114915","article-title":"Electric load forecasting based on deep learning and optimized by heuristic algorithm in smart grid","volume":"269","author":"Hafeez","year":"2020","journal-title":"Appl. Energy"},{"key":"ref_33","first-page":"382","article-title":"A new approach to forecasting daily peak loads","volume":"75","author":"Gillies","year":"1956","journal-title":"Trans. Am. Inst. Electr. Eng. Part III Power Appar. Syst."},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"442","DOI":"10.1109\/59.76685","article-title":"Electric load forecasting using an artificial neural network","volume":"6","author":"Park","year":"1991","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"858","DOI":"10.1109\/59.496166","article-title":"A neural network short term load forecasting model for the Greek power system","volume":"11","author":"Bakirtzis","year":"1995","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"336","DOI":"10.1109\/59.221223","article-title":"Neural network based short term load forecasting","volume":"8","author":"Lu","year":"1993","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"1956","DOI":"10.1109\/59.331456","article-title":"An implementation of a neural network based load forecasting models for the EMS","volume":"9","author":"Papalexopoulos","year":"1994","journal-title":"IEEE Trans. Power Syst."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1","DOI":"10.2478\/jagi-2019-0002","article-title":"On defining artificial intelligence","volume":"10","author":"Wang","year":"2019","journal-title":"J. Artif. Gen. Intell."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Ongsulee, P. (2017, January 22\u201324). Artificial Intelligence, Machine Learning and Deep Learning. Proceedings of the 15th International Conference on ICT and Knowledge Engineering (ICT&KE), Bangkok, Thailand.","DOI":"10.1109\/ICTKE.2017.8259629"},{"key":"ref_40","unstructured":"Lawless, W.F., Mittu, R., and Sofge, D.A. (2020). Human-Machine Shared Contexts, Academic Press."},{"key":"ref_41","unstructured":"Cohen, S. (2021). Artificial Intelligence and Deep Learning in Pathology, Elsevier."},{"key":"ref_42","unstructured":"Fitzek, F.H.P., Granelli, F., and Seeling, P. (2020). Computing in Communication Networks, From Theory to Practice, Academic Press."},{"key":"ref_43","unstructured":"Delua, J. (2022, November 15). Supervised vs. Unsupervised Learning: What\u2019s the Difference?. Available online: https:\/\/www.ibm.com\/cloud\/blog\/supervised-vs-unsupervised-learning."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"160","DOI":"10.1007\/s42979-021-00592-x","article-title":"Machine learning: Algorithms, real-world applications and research directions","volume":"2","author":"Sarker","year":"2021","journal-title":"SN Comput. Sci."},{"key":"ref_45","first-page":"14","article-title":"Introduction to machine learning, neural networks, and deep learning","volume":"9","author":"Choi","year":"2020","journal-title":"Transl. Vis. Sci. Technol."},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Berry, M., Mohamed, A., and Yap, B. (2020). Supervised and Unsupervised Learning for Data Science, Springer.","DOI":"10.1007\/978-3-030-22475-2"},{"key":"ref_47","doi-asserted-by":"crossref","unstructured":"Satinet, C., and Fouss, F. (2022). A supervised machine learning classification framework for clothing products\u2019 sustainability. Sustainability, 14.","DOI":"10.3390\/su14031334"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"675","DOI":"10.1016\/j.beth.2020.05.002","article-title":"Supervised machine learning: A brief primer","volume":"51","author":"Jiang","year":"2020","journal-title":"Behav. Ther."},{"key":"ref_49","unstructured":"\u0160koda, P., and Adam, F. (2020). Knowledge Discovery in Big Data from Astronomy and Earth Observation, Elsevier."},{"key":"ref_50","first-page":"6774","article-title":"A study on unsupervised learning algorithms analysis in machine learning","volume":"12","author":"Uddamari","year":"2021","journal-title":"Turk. J. Comput. Math. Educ."},{"key":"ref_51","first-page":"34","article-title":"A survey on unsupervised machine learning algorithms for automation, classification and maintenance","volume":"119","author":"Khanum","year":"2015","journal-title":"Int. J. Comput. Appl."},{"key":"ref_52","unstructured":"Avinash, K., William, G., and Ryan, B. (2020, January 7\u201310). Network Attack Detection Using an Unsupervised Machine Learning Algorithm. Proceedings of the Hawaii International Conference on System Sciences (HICSS), Maui, HI, USA."},{"key":"ref_53","unstructured":"Kong, L., Huang, T., Zhu, Y., and Yu, S. (2020). Big Data in Astronomy, Elsevier."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1007\/s10994-019-05855-6","article-title":"A survey on semi-supervised learning","volume":"109","author":"Hoos","year":"2020","journal-title":"Mach. Learn."},{"key":"ref_55","unstructured":"ZhongKaizhu, G., and Huang, H. (2018). Semisupervised Learning: Background, Applications and Future Directions, Nova Science Publishers."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"106628","DOI":"10.1016\/j.cmpb.2022.106628","article-title":"Semisupervised learning for medical image classification using imbalanced training data","volume":"216","author":"Huynh","year":"2022","journal-title":"Comput. Methods Programs Biomed."},{"key":"ref_57","first-page":"4223","article-title":"A study of reinforcement learning applications & its algorithms","volume":"9","author":"Kaur","year":"2020","journal-title":"Int. J. Sci. Technol. Res."},{"key":"ref_58","doi-asserted-by":"crossref","unstructured":"Mohammed, M., Khan, M.B., and Bashier Mohammed, B.E. (2016). Machine Learning: Algorithms and Applications, CRC Press.","DOI":"10.1201\/9781315371658"},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"Tanveer, J., Haider, A., Ali, R., and Kim, A. (2022). An overview of reinforcement learning algorithms for handover management in 5G ultra-dense small cell networks. Appl. Sci., 12.","DOI":"10.3390\/app12010426"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"e18477","DOI":"10.2196\/18477","article-title":"Reinforcement learning for clinical decision support in critical care: Comprehensive review","volume":"22","author":"Liu","year":"2020","journal-title":"J. Med. Internet Res."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"417","DOI":"10.1142\/S1793351X16500045","article-title":"Deep learning","volume":"10","author":"Hao","year":"2016","journal-title":"Int. J. Semant. Comput."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1007\/s12551-018-0449-9","article-title":"Machine learning: Applications of artificial intelligence to imaging and diagnosis","volume":"11","author":"Nichols","year":"2019","journal-title":"Biophys. Rev."},{"key":"ref_63","unstructured":"Nriagu, J. (2019). Encyclopedia of Environmental Health, Elsevier. [2nd ed.]."},{"key":"ref_64","unstructured":"Zhou, S.K., Rueckert, D., and Fichtinger, G. (2020). Handbook of Medical Image Computing and Computer Assisted Intervention, Elsevier."},{"key":"ref_65","doi-asserted-by":"crossref","unstructured":"He, Z. (2020, January 18\u201320). Deep Learning in Image Classification: A Survey Report. Proceedings of the 2nd International Conference on Information Technology and Computer Application (ITCA), Guangzhou, China.","DOI":"10.1109\/ITCA52113.2020.00043"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"5455","DOI":"10.1007\/s10462-020-09825-6","article-title":"A survey of the recent architectures of deep convolutional neural networks","volume":"53","author":"Khan","year":"2020","journal-title":"Artif. Intell. Rev."},{"key":"ref_67","doi-asserted-by":"crossref","unstructured":"Cho, K., van Merrienboer, B., Bahdanau, D., and Bengio, Y. (2014). On the Properties of Neural Machine Translation: Encoder-Decoder Approaches. arXiv.","DOI":"10.3115\/v1\/W14-4012"},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","article-title":"Finding structure in time","volume":"14","author":"Elman","year":"1990","journal-title":"Cogn. Sci."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Toha, S.F., and Tokhi, M.O. (2008, January 9\u201310). MLP and Elman Recurrent Neural Network Modelling for the TRMS. Proceedings of the 7th IEEE International Conference on Cybernetic Intelligent Systems, London, UK.","DOI":"10.1109\/UKRICIS.2008.4798969"},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Liu, Y., Guo, J., Cai, C., Wang, Y., and Jia, L. (2018, January 12\u201314). Short-Term Forecasting of Rail Transit Passenger Flow Based on Long Short-Term Memory Neural Network. Proceedings of the International Conference on Intelligent Rail Transportation (ICIRT), Singapore.","DOI":"10.1109\/ICIRT.2018.8641683"},{"key":"ref_71","doi-asserted-by":"crossref","unstructured":"Zhao, Z., Liang, Y., and Jin, X. (2018, January 26\u201328). Handling Large-Scale Action Space in Deep Q Network. Proceedings of the International Conference on Artificial Intelligence and Big Data (ICAIBD), Chengdu, China.","DOI":"10.1109\/ICAIBD.2018.8396173"},{"key":"ref_72","doi-asserted-by":"crossref","unstructured":"Xie, W., Ouyang, Y., Ouyang, J., Rong, W., and Xiong, Z. (2016, January 15\u201318). User Occupation Aware Conditional Restricted Boltzmann Machine Based Recommendation. Proceedings of the IEEE International Conference on Internet of Things (iThings) and IEEE Green Computing and Communications (GreenCom) and IEEE Cyber, Physical and Social Computing (CPSCom) and IEEE Smart Data (SmartData), Chengdu, China.","DOI":"10.1109\/iThings-GreenCom-CPSCom-SmartData.2016.109"},{"key":"ref_73","first-page":"1025","article-title":"Two distributed-state models for generating high-dimensional time series","volume":"12","author":"Taylor","year":"2011","journal-title":"J. Mach. Learn. Res."},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"1527","DOI":"10.1162\/neco.2006.18.7.1527","article-title":"A fast learning algorithm for deep belief nets","volume":"18","author":"Hinton","year":"2006","journal-title":"Neural Comput."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"4912","DOI":"10.1109\/JIOT.2020.2975847","article-title":"Development of an IoT-driven building environment for prediction of electric energy consumption","volume":"7","author":"Bedi","year":"2020","journal-title":"IEEE Internet Things J."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"520","DOI":"10.13044\/j.sdewes.d7.0306","article-title":"Application of statistical and artificial intelligence techniques for medium-term electrical energy forecasting: A case study for a regional hospital","volume":"8","author":"Timur","year":"2020","journal-title":"J. Sustain. Dev. Energy Water Environ. Syst."},{"key":"ref_77","doi-asserted-by":"crossref","unstructured":"Selvi, M.V., and Mishra, S. (2018, January 13\u201315). Investigation of Weather Influence in Day-Ahead Hourly Electric Load Power Forecasting with New Architecture Realized in Multivariate Linear Regression Artificial Neural Network Techniques. Proceedings of the 8th IEEE India International Conference on Power Electronics (IICPE), Jaipur, India.","DOI":"10.1109\/IICPE.2018.8709498"},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"5603","DOI":"10.1109\/TIA.2020.3009313","article-title":"Investigation of performance of electric load power forecasting in multiple time horizons with new architecture realized in multivariate linear regression and feed-forward neural network techniques","volume":"56","author":"Selvi","year":"2020","journal-title":"IEEE Trans. Ind. Appl."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"91463","DOI":"10.1109\/ACCESS.2019.2924685","article-title":"Machine learning based integrated feature selection approach for improved electricity demand forecasting in decentralized energy systems","volume":"7","author":"Eseye","year":"2019","journal-title":"IEEE Access"},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"106073","DOI":"10.1016\/j.epsr.2019.106073","article-title":"Load demand forecasting of residential buildings using a deep learning model","volume":"179","author":"Wen","year":"2020","journal-title":"Electr. Power Syst. Res."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"122116","DOI":"10.1016\/j.energy.2021.122116","article-title":"Using deep learning and meteorological parameters to forecast the photovoltaic generators intra-hour output power interval for smart grid control","volume":"239","author":"Galarza","year":"2022","journal-title":"Energy"},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Taleb, I., Guerard, G., Fauberteau, F., and Nguyen, N.A. (2022). Flexible deep learning method for energy forecasting. Energies, 15.","DOI":"10.3390\/en15113926"},{"key":"ref_83","doi-asserted-by":"crossref","unstructured":"Xu, A., Tian, M.-W., Firouzi, B., Alattas, K.A., Mohammadzadeh, A., and Ghaderpour, E. (2022). A new deep learning Restricted Boltzmann Machine for energy consumption forecasting. Sustainability, 14.","DOI":"10.3390\/su141610081"},{"key":"ref_84","first-page":"629","article-title":"A hybrid model for forecasting the consumption of electrical energy in a smart grid","volume":"6","author":"Ele","year":"2022","journal-title":"J. Eng."},{"key":"ref_85","doi-asserted-by":"crossref","first-page":"14992","DOI":"10.1109\/ACCESS.2021.3053069","article-title":"A Pyramid-CNN based deep learning model for power load forecasting of similar-profile energy customers based on clustering","volume":"9","author":"Aurangzeb","year":"2021","journal-title":"IEEE Access"},{"key":"ref_86","doi-asserted-by":"crossref","first-page":"8298","DOI":"10.1109\/TIE.2020.3009604","article-title":"Deep learning-based forecasting approach in smart grids with microclustering and bidirectional LSTM network","volume":"68","author":"Jahangir","year":"2021","journal-title":"IEEE Trans. Ind. Electron."},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"205","DOI":"10.1504\/IJCAT.2022.124940","article-title":"Efficient residential load forecasting using deep learning approach","volume":"68","author":"Mubashar","year":"2022","journal-title":"Int. J. Comput. Appl. Technol."},{"key":"ref_88","doi-asserted-by":"crossref","unstructured":"Rosato, A., Araneo, R., Andreotti, A., and Panella, M. (2019, January 11\u201314). 2-D Convolutional Deep Neural Network for Multivariate Energy Time Series Prediction. Proceedings of the 2019 IEEE International Conference on Environment and Electrical Engineering and 2019 IEEE Industrial and Commercial Power Systems Europe (EEEIC\/I&CPS Europe), Genova, Italy.","DOI":"10.1109\/EEEIC.2019.8783304"},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1109\/TSG.2020.3014055","article-title":"An edge-cloud integrated solution for buildings demand response using reinforcement learning","volume":"12","author":"Zhang","year":"2020","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_90","doi-asserted-by":"crossref","unstructured":"Aurangzeb, K., and Alhussein, M. (2020, January 10). Deep Learning Framework for Short-term Power Load Forecasting, a Case Study of Individual Household Energy Customer. Proceedings of the 2019 International Conference on Advances in the Emerging Computing Technologies (AECT), Al Madinah Al Munawwarah, Saudi Arabia.","DOI":"10.1109\/AECT47998.2020.9194153"},{"key":"ref_91","unstructured":"Escobar, E., Rodr\u00edguez Licea, M.A., Rostro-Gonzalez, H., Espinoza Calderon, A., Barranco Guti\u00e9rrez, A.I., and P\u00e9rez-Pinal, F.J. (2020, January 4\u20136). Comparative Analysis of Multivariable Deep Learning Models for Forecasting in Smart Grids. Proceedings of the 2020 IEEE International Autumn Meeting on Power, Electronics and Computing (ROPEC), Ixtapa, Mexico."},{"key":"ref_92","doi-asserted-by":"crossref","unstructured":"Hafeez, G., Alimgeer, K.S., Wadud, Z., Shafiq, Z., Ali Khan, M.U., Khan, I., Khan, F.A., and Derhab, A. (2020). A novel accurate and fast converging deep learning-based model for electrical energy consumption forecasting in a smart grid. Energies, 13.","DOI":"10.3390\/en13092244"},{"key":"ref_93","doi-asserted-by":"crossref","unstructured":"Nguyen, V.-B., Duong, M.-T., and Le, M.-H. (2020, January 27\u201328). Electricity Demand Forecasting for Smart Grid based on Deep Learning Approach. Proceedings of the 2020 5th International Conference on Green Technology and Sustainable Development (GTSD), Ho Chi Minh City, Vietnam.","DOI":"10.1109\/GTSD50082.2020.9303164"},{"key":"ref_94","doi-asserted-by":"crossref","unstructured":"Rosato, A., Succetti, F., Araneo, R., Andreotti, A., Mitolo, M., and Panella, M. (July, January 29). A Combined Deep Learning Approach for Time Series Prediction in Energy Environments. Proceedings of the 2020 IEEE\/IAS 56th Industrial and Commercial Power Systems Technical Conference (I&CPS), Las Vegas, NV, USA.","DOI":"10.1109\/ICPS48389.2020.9176818"},{"key":"ref_95","unstructured":"Qi, X., Zheng, X., and Chen, Q. (2020, January 7\u20139). A Short-term Load Forecasting of Integrated Energy System based on CNN-LSTM. Proceedings of the 2020 International Conference on Energy, Environment and Bioengineering (ICEEB 2020), Xi\u2019an, China."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"3146","DOI":"10.1109\/TSG.2020.2967430","article-title":"Deep reinforcement learning method for demand response management of interruptible load","volume":"11","author":"Wang","year":"2020","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"118019","DOI":"10.1016\/j.energy.2020.118019","article-title":"Modified deep learning and reinforcement learning for an incentive-based demand response model","volume":"205","author":"Wen","year":"2020","journal-title":"Energy"},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"4703","DOI":"10.1109\/TII.2019.2942353","article-title":"Bayesian deep learning-based probabilistic load forecasting in smart grids","volume":"16","author":"Yang","year":"2020","journal-title":"IEEE Trans Ind. Inf."},{"key":"ref_99","doi-asserted-by":"crossref","unstructured":"Amin, P., Cherkasova, L., Aitken, R., and Kache, V. (2019, January 8\u201313). Automating Energy Demand Modeling and Forecasting Using Smart Meter Data. Proceedings of the 2019 IEEE International Congress on Internet of Things (ICIOT), Milan, Italy.","DOI":"10.1109\/ICIOT.2019.00032"},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Atef, S., and Eltawil, A.B. (2019, January 12\u201315). A Comparative Study Using Deep Learning and Support Vector Regression for Electricity Price Forecasting in Smart Grids. Proceedings of the IEEE 6th International Conference on Industrial Engineering and Applications (ICIEA), Tokyo, Japan.","DOI":"10.1109\/IEA.2019.8715213"},{"key":"ref_101","doi-asserted-by":"crossref","unstructured":"Chan, S., Oktavianti, I., and Puspita, V. (2019, January 17\u201319). A Deep Learning CNN and AI-Tuned SVM for Electricity Consumption Forecasting: Multivariate Time Series Data. Proceedings of the 2019 IEEE 10th annual information technology, Electronics and Mobile Communication Conference (IEMCON), Vancouver, BC, Canada.","DOI":"10.1109\/IEMCON.2019.8936260"},{"key":"ref_102","unstructured":"Hafeez, G., Javaid, N., Riaz, M., Ali, A., Umar, K., and Iqbal, Q.Z. (2020, January 1\u20133). Day Ahead Electric Load Forecasting by an Intelligent Hybrid Model based on Deep Learning for Smart Grid. Proceedings of the 14th International Conference on Complex, Intelligent and Software Intensive Systems (CISIS-2020), Lodz, Poland."},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Kaur, D., Kumar, R., Kumar, N., and Guizani, M. (2019, January 9\u201313). Smart Grid Energy Management using RNN-LSTM: A Deep Learning-Based Approach. Proceedings of the IEEE Global Communications Conference (GLOBECOM), Big Island, HI, USA.","DOI":"10.1109\/GLOBECOM38437.2019.9013850"},{"key":"ref_104","first-page":"31","article-title":"Application of deep learning long short-term memory in energy demand forecasting","volume":"1000","author":"Jalili","year":"2019","journal-title":"Commun. Comput. Inf. Sci."},{"key":"ref_105","unstructured":"Barolli, L., Xhafa, F., and Hussain, O. Innovative Mobile and Internet Services in Ubiquitous Computing, Springer."},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"72","DOI":"10.1016\/j.energy.2019.05.230","article-title":"Predicting residential energy consumption using CNN-LSTM neural networks","volume":"182","author":"Kim","year":"2019","journal-title":"Energy"},{"key":"ref_107","doi-asserted-by":"crossref","unstructured":"Kim, T., and Cho, S. (2019, January 10\u201313). Particle Swarm Optimization-based CNN-LSTM Networks for Forecasting Energy Consumption. Proceedings of the 2019 IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand.","DOI":"10.1109\/CEC.2019.8789968"},{"key":"ref_108","doi-asserted-by":"crossref","first-page":"937","DOI":"10.1016\/j.apenergy.2018.12.061","article-title":"Incentive-based demand response for smart grid with reinforcement learning and deep neural network","volume":"236","author":"Lu","year":"2019","journal-title":"Appl. Energy"},{"key":"ref_109","doi-asserted-by":"crossref","unstructured":"Pramono, S.H., Rohmatillah, M., Maulana, E., Hasanah, R.N., and Hario, F. (2019). Deep learning-based short-term load forecasting for supporting demand response program in hybrid energy system. Energies, 12.","DOI":"10.3390\/en12173359"},{"key":"ref_110","doi-asserted-by":"crossref","unstructured":"Rahman, S., Alam, M.G.R., and Rahman, M.M. (2019, January 18\u201320). Deep Learning based Ensemble Method for Household Energy Demand Forecasting of Smart Home. Proceedings of the 2019 22nd International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh.","DOI":"10.1109\/ICCIT48885.2019.9038565"},{"key":"ref_111","doi-asserted-by":"crossref","unstructured":"Syed, D., Refaat, S.S., Abu-Rub, H., Bouhali, O., Zainab, A., and Xie, L. (2019, January 9\u201312). Averaging Ensembles Model for Forecasting of Short-Term Load in Smart Grids. Proceedings of the IEEE International Conference on Big Data (Big Data), Los Angeles, CA, USA.","DOI":"10.1109\/BigData47090.2019.9006183"},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Ustundag Soykan, E., Bilgin, Z., Ersoy, M.A., and Tomur, E. (2019, January 9\u201313). Differentially Private Deep Learning for Load Forecasting on Smart Grid. Proceedings of the 2019 IEEE Globecom Workshops (GC Wkshps), Waikoloa, HI, USA.","DOI":"10.1109\/GCWkshps45667.2019.9024520"},{"key":"ref_113","doi-asserted-by":"crossref","unstructured":"Vesa, A.V., Ghitescu, N., Pop, C., Antal, M., Cioara, T., Anghel, I.A., and Salomie, I. (2019, January 5\u20137). Stacking Ulti-Learning Ensemble Model for Predicting Near Real Time Energy Consumption Demand of Residential Buildings. Proceedings of the 2019 IEEE 15th International Conference on Intelligent Computer Communication and Processing (ICCP), Cluj-Napoca, Romania.","DOI":"10.1109\/ICCP48234.2019.8959572"},{"key":"ref_114","doi-asserted-by":"crossref","first-page":"116324","DOI":"10.1016\/j.energy.2019.116324","article-title":"Deep ensemble learning based probabilistic load forecasting in smart grids","volume":"189","author":"Yang","year":"2019","journal-title":"Energy"},{"key":"ref_115","doi-asserted-by":"crossref","unstructured":"Zahid, M., Ahmed, F., Javaid, N., Abbasi, R.A., Zainab Kazmi, H.S., Javaid, A., Bilal, M., Akbar, M., and Ilahi, M. (2019). Electricity price and load forecasting using enhanced convolutional neural network enhanced support vector regression in smart grids. Electronics, 8.","DOI":"10.3390\/electronics8020122"},{"key":"ref_116","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1109\/TETCI.2018.2880511","article-title":"Modeling and forecasting short-term power load with copula model and deep belief network","volume":"3","author":"Ouyang","year":"2019","journal-title":"IEEE Trans. Emerg. Top. Comput. Intell."},{"key":"ref_117","doi-asserted-by":"crossref","unstructured":"Hafeez, G., Javaid, N., Ullah, S., Iqbal, Q.Z., Khan, M., Rehman, A.U., and Ullah, Z. (2018, January 4\u20136). Short-Term Load Forecasting based on Deep Learning for Smart Grid Applications. Proceedings of the 12th International Conference on Innovative Mobile and Internet Services in Ubiquitous Computing (IMIS-2018), Matsue, Japan.","DOI":"10.1007\/978-3-319-93554-6_25"},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Koprinska, I., Wu, D., and Wang, Z. (2018, January 8\u201313). Convolutional Neural Networks for Energy Time Series Forecasting. Proceedings of the 2018 International Joint Conference on Neural Networks (IJCNN), Rio de Janeiro, Brazil.","DOI":"10.1109\/IJCNN.2018.8489399"},{"key":"ref_119","doi-asserted-by":"crossref","unstructured":"Kuo, P.H., and Huang, C.J. (2018). A high precision artificial neural networks model for short-Term energy load forecasting. Energies, 11.","DOI":"10.3390\/en11010213"},{"key":"ref_120","doi-asserted-by":"crossref","first-page":"5271","DOI":"10.1109\/TSG.2017.2686012","article-title":"Deep learning of household load forecasting\u2014A novel pooling deep RNN","volume":"9","author":"Shi","year":"2018","journal-title":"IEEE Trans Smart Grid"},{"key":"ref_121","unstructured":"Ghaderi, A., Sanandaji, B.M., and Ghaderi, F. Deep forecast: Deep learning-based spatio-temporal forecasting. arXiv, 2017."},{"key":"ref_122","doi-asserted-by":"crossref","unstructured":"Jar\u00e1bek, T., Laurinec, P., and Luck\u00e1, M. (2017, January 14\u201316). Energy Load Forecast using S2S Deep Neural Networks with k-Shape Clustering. Proceedings of the 2017 IEEE 14th International Scientific Conference on Informatics 2017, Poprad, Slovakia.","DOI":"10.1109\/INFORMATICS.2017.8327236"},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Li, L., Ota, K., and Dong, M. (2017, January 21\u201323). Everything is Image: CNN-Based Short-Term Electrical Load Forecasting for Smart Grid. Proceedings of the 2017 14th International Symposium on Pervasive Systems, Algorithms and Networks & 2017 11th International Conference on Frontier of Computer Science and Technology & 2017 Third International Symposium of Creative Computing (ISPAN-FCST-ISCC), Exeter, UK.","DOI":"10.1109\/ISPAN-FCST-ISCC.2017.78"},{"key":"ref_124","doi-asserted-by":"crossref","unstructured":"Zhan, J., Huang, J., Niu, L., Peng, X., Deng, D., and Cheng, S. (2014, January 7\u201310). Study of the Key Technologies of Electric Power Big Data and Its Application Prospects in Smart Grid. Proceedings of the IEEE PES Asia-Pacific Power and Energy Engineering Conference (APPEEC), Hong Kong, China.","DOI":"10.1109\/APPEEC.2014.7066162"}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1467\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T18:18:12Z","timestamp":1760120292000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/3\/1467"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,1,28]]},"references-count":124,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2023,2]]}},"alternative-id":["s23031467"],"URL":"https:\/\/doi.org\/10.3390\/s23031467","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,1,28]]}}}