{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,15]],"date-time":"2026-01-15T03:43:45Z","timestamp":1768448625302,"version":"3.49.0"},"reference-count":45,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2022,1,28]],"date-time":"2022-01-28T00:00:00Z","timestamp":1643328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>Industry 4.0 is a paradigm consisting of cyber-physical systems based on the interconnection between all sorts of machines, sensors, and actuators, generally known as things. The combination of energy technology and information and technology communication (ICT) enables measurement, control, and automation to be performed across the distributed grid with high time resolution. Through digital revolution in the energy sector, the term Energy 4.0 emerges in the future electric sector. The growth outlook for appliance usage is increasing and the appearance of renewable energy sources on the electric grid requires strategies to control demand and peak loads. Potential feedback for energy performance is the use of smart meters in conjunction with smart energy management; well-designed applications will successfully inform, engage, empower, and motivate consumers. This paper presents several hands-on tools for load forecasting, comparing previous works and verifying which show the best energy forecasting performance in a smart monitoring system. Simulations were performed based on forecasting of the hours ahead of the load for several households. Special attention was given to the accuracy of the forecasting model for weekdays and weekends. The development of the proposed methods, based on artificial neural networks (ANN), provides more reliable forecasting for a few hours ahead and peak loads.<\/jats:p>","DOI":"10.3390\/en15030957","type":"journal-article","created":{"date-parts":[[2022,1,29]],"date-time":"2022-01-29T01:41:59Z","timestamp":1643420519000},"page":"957","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Home Energy Forecast Performance Tool for Smart Living Services Suppliers under an Energy 4.0 and CPS Framework"],"prefix":"10.3390","volume":"15","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-7268-5908","authenticated-orcid":false,"given":"Filipe Martins","family":"Rodrigues","sequence":"first","affiliation":[{"name":"Instituto Superior de Engenharia de Lisboa, Instituto Polit\u00e9cnico de Lisboa, Rua Conselheiro Em\u00eddio Navarro, 1959-007 Lisboa, Portugal"},{"name":"IDMEC, Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal"}]},{"given":"Carlos","family":"Cardeira","sequence":"additional","affiliation":[{"name":"IDMEC, Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6628-4657","authenticated-orcid":false,"given":"Jo\u00e3o M. F.","family":"Calado","sequence":"additional","affiliation":[{"name":"Instituto Superior de Engenharia de Lisboa, Instituto Polit\u00e9cnico de Lisboa, Rua Conselheiro Em\u00eddio Navarro, 1959-007 Lisboa, Portugal"},{"name":"IDMEC, Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1081-2729","authenticated-orcid":false,"given":"Rui","family":"Melicio","sequence":"additional","affiliation":[{"name":"IDMEC, Instituto Superior T\u00e9cnico, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisboa, Portugal"},{"name":"ICT, Universidade de \u00c9vora, Rua Rom\u00e3o Ramalho, 59, 7000-645 \u00c9vora, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2022,1,28]]},"reference":[{"key":"ref_1","unstructured":"Hirsch-Kreinsen, H. (2016). \u201cIndustry 4.0\u201d as Promising Technology: Emergence, Semantics and Ambivalent Character, Technische Universit\u00e4t Dortmund."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"616","DOI":"10.1016\/J.ENG.2017.05.015","article-title":"Intelligent Manufacturing in the Context of Industry 4.0: A. Review","volume":"3","author":"Zhong","year":"2017","journal-title":"Engineering"},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"16","DOI":"10.1016\/j.enbuild.2017.02.039","article-title":"Services enabler architecture for smart grid and smart living services providers under Industry 4.0","volume":"141","author":"Batista","year":"2017","journal-title":"Energy Build."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Albach, H., Meffert, H., Pinkwart, A., and Reichwald, R. (2015). Management of Permanent Change\u2014New Challenges and Opportunities for Change Management. Management of Permanent Change, Springer Gabler.","DOI":"10.1007\/978-3-658-05014-6"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Gomes, I.L.R., Pousinho, H.M.I., Melicio, R., and Mendes, V.M.F. (2017, January 6\u20138). Optimization of Wind Power Producer Participation in Electricity Markets with Energy Storage in a Way of Energy 4.0. Proceedings of the International Joint Conference SOCO\u201917-CISIS\u201917-ICUTE\u201917, Le\u00f3n, Spain.","DOI":"10.1007\/978-3-319-67180-2_9"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"126427","DOI":"10.1016\/j.jclepro.2021.126427","article-title":"Industry 4.0 and opportunities for energy sustainability","volume":"295","author":"Ghobakhloo","year":"2021","journal-title":"J. Clean. Prod."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1016\/j.rser.2004.09.004","article-title":"A review of energy models","volume":"10","author":"Jebaraj","year":"2006","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"2583","DOI":"10.1016\/j.rser.2012.02.010","article-title":"On energy for sustainable development in Nigeria","volume":"16","author":"Oyedepo","year":"2012","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"1223","DOI":"10.1016\/j.rser.2011.08.014","article-title":"Energy models for demand forecasting\u2014A review","volume":"16","author":"Suganthi","year":"2012","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_10","unstructured":"Ozturk, Ilhan (2016). Energy dependency and energy security: The role of energy efficiency and renewable energy sources. Pak. Dev. Rev., 52, 309\u2013330."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"294","DOI":"10.1080\/15325008.2013.832439","article-title":"Smart home activities: A literature review","volume":"42","author":"Ahmed","year":"2014","journal-title":"Electr. Power Compon. Syst."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Soares, A., Gomes, A., and Antunes, C.H. (2012, January 16\u201318). Domestic load characterization for demand-responsive energy management systems. Proceedings of the 2012 IEEE International Symposium on Sustainable Systems and Technology (ISSST), Boston, MA, USA.","DOI":"10.1109\/ISSST.2012.6227976"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"26","DOI":"10.1016\/j.solener.2015.11.016","article-title":"A novel smart grid theory for optimal sizing of hybrid renewable energy systems","volume":"124","author":"Eltamaly","year":"2016","journal-title":"Sol. Energy"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Ryu, S., Noh, J., and Kim, H. (2016). Deep neural network based demand side short term load forecasting. Energies, 10.","DOI":"10.3390\/en10010003"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"402","DOI":"10.1016\/j.apenergy.2017.10.014","article-title":"Recent advances in the analysis of residential electricity consumption and applications of smart meter data","volume":"208","author":"Yildiz","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"220","DOI":"10.1016\/j.egypro.2014.12.383","article-title":"The daily and hourly energy consumption and load forecasting using artificial neural network method: A case study using a set of 93 households in Portugal","volume":"62","author":"Rodrigues","year":"2014","journal-title":"Energy Procedia"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Rodrigues, F., Cardeira, C., Calado, J.M.F., and Mel\u00edcio, R. (2016, January 4\u20135). Load profile analysis tool for electrical appliances in households. Proceedings of the Energy Economics Iberian Conference, Lisbon, Portugal.","DOI":"10.1016\/j.egypro.2016.12.117"},{"key":"ref_18","doi-asserted-by":"crossref","unstructured":"Rodrigues, F., Cardeira, C., Calado, J.M.F., and Mel\u00edcio, R. (2016, January 4\u20136). Energy household forecast with ANN for demand response and demand side management. Proceedings of the International Conference on Renewable Energies and Power Quality-ICREPQ, Renewable Energy and Power Quality Journal (RE&PQJ), Madrid, Spain.","DOI":"10.24084\/repqj14.559"},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Kagermann, H. (2015). Change through digitization\u2014Value creation in the age of Industry 4.0. Management of Permanent Change, Springer Gabler.","DOI":"10.1007\/978-3-658-05014-6_2"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"152","DOI":"10.1109\/COMST.2014.2341586","article-title":"A survey on demand response programs in smart grids: Pricing methods and optimization algorithms","volume":"17","author":"Vardakas","year":"2015","journal-title":"Commun. Surv. Tutor."},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Rahman, M.M., Shakeri, M., Tiong, S.K., Khatun, F., Amin, N., Pasupuleti, J., and Hasan, M.K. (2021). Prospective Methodologies in Hybrid Renewable Energy Systems for Energy Prediction Using Artificial Neural Networks. Sustainability, 13.","DOI":"10.3390\/su13042393"},{"key":"ref_22","unstructured":"Qiu, J., Liu, J., Hou, Y., and Zhang, J. (2011, January 15\u201317). Use of real-time\/historical database in smart grid. Proceedings of the 2011 International Conference on Electric Information and Control Engineering, Wuhan, China."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"1311","DOI":"10.1016\/j.rser.2015.10.117","article-title":"Load forecasting, dynamic pricing and DSM in smart grid: A review","volume":"54","author":"Khan","year":"2016","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"367","DOI":"10.1016\/j.ijepes.2005.12.007","article-title":"A neural network based several-hour-ahead electric load forecasting using similar days approach","volume":"28","author":"Mandal","year":"2006","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Berriel, R.F., Lopes, A.T., Rodrigues, A., Varej\u00e3o, F.M., and Oliveira-Santos, T. (2009). Monthly energy consumption forecast: A deep learning approach. Neural Networks (IJCNN), Proceedings of the International Joint Conference on IEEE, Anchorage, AK, USA, 14\u201319 May 2017, IEEE.","DOI":"10.1109\/IJCNN.2017.7966398"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Kyriakides, E., and Polycarpou, M. (2007). Short term electric load forecasting: A tutorial. Trends in Neural Computation, Springer.","DOI":"10.1007\/978-3-540-36122-0_16"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"1525","DOI":"10.1016\/S0196-8904(02)00148-6","article-title":"Artificial intelligence in short term electric load forecasting: A state-of-the-art survey for the researcher","volume":"44","author":"Metaxiotis","year":"2003","journal-title":"Energy Convers. Manag."},{"key":"ref_28","unstructured":"Tasre, M.B., Ghate, V.N., and Bedekar, P.P. (2012, January 30\u201331). Hourly load forecasting using artificial neural network for a small area. Proceedings of the 2012 International Conference on Advances in Engineering, Science and Management (ICAESM), Nagapattinam, India."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Tasre, M.B., Ghate, V.N., and Bedekar, P.P. (2012, January 30\u201331). Comparative analysis of hourly load forecast for a small load area. Proceedings of the 2012 International Conference on Computing, Electronics and Electrical Technologies (ICCEET), Nagapattinam, India.","DOI":"10.1109\/ICCEET.2012.6203746"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"654","DOI":"10.1016\/j.apenergy.2017.07.114","article-title":"Short-term residential load forecasting: Impact of calendar effects and forecast granularity","volume":"205","author":"Lusis","year":"2017","journal-title":"Appl. Energy"},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ghofrani, M., Hassanzadeh, M., Etezadi-Amoli, M., and Fadali, M.S. (2011, January 4\u20136). Smart meter based short-term load forecasting for residential consumers. Proceedings of the North American Power Symposium (NAPS), Boston, MA, USA.","DOI":"10.1109\/NAPS.2011.6025124"},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"380","DOI":"10.1016\/j.enbuild.2015.11.068","article-title":"Short-term residential electric load forecasting: A compressive spatio-temporal approach","volume":"111","author":"Tascikaraoglu","year":"2016","journal-title":"Energy Build."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"461","DOI":"10.1016\/j.rser.2013.10.022","article-title":"Demand response and smart grids\u2014A survey","volume":"30","author":"Siano","year":"2014","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Deng, R., Yang, Z., Chow, M.Y., and Chen, J. (2015). A survey on demand response in smart grids: Mathematical models and approaches. IEEE Transactions on Industrial Informatics, IEEE.","DOI":"10.1109\/TII.2015.2414719"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zebulum, R.S., Vellasco, M., Guedes, K., and Pacheco, M.A. (1995). Short-term load forecasting using neural nets. Natural to Artificial Neural Computation, Springer.","DOI":"10.1007\/3-540-59497-3_279"},{"key":"ref_36","first-page":"15","article-title":"Next 24-Hours Load Forecasting Using Artificial Neural Network (ANN) for the Western Area of Saudi Arabia","volume":"19","author":"Muhammad","year":"2008","journal-title":"Eng. Sci."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Rodrigues, F., Cardeira, C., and Calado, J.M.F. (2017). Neural Networks Applied to Short Term Load Forecasting: A Case Study. Smart Energy Control Systems for Sustainable Buildings, Springer.","DOI":"10.1007\/978-3-319-52076-6_8"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"1297","DOI":"10.1016\/j.epsr.2006.09.022","article-title":"Short-term electricity prices forecasting in a competitive market: A neural network approach","volume":"77","author":"Mariano","year":"2007","journal-title":"Electr. Power Syst. Res."},{"key":"ref_39","doi-asserted-by":"crossref","unstructured":"Catal\u00e3o, J.P.S., Mariano, S.J.P.S., Mendes, V.M.F., and Ferreira, L.A.F.M. (2007, January 5\u20138). An artificial neural network approach for short-term electricity prices forecasting. Proceedings of the International Conference on Intelligent Systems Applications to Power Systems, (ISAP 2007), Kaohsiung, Taiwan.","DOI":"10.1109\/ISAP.2007.4441655"},{"key":"ref_40","doi-asserted-by":"crossref","unstructured":"Catal\u00e3o, J.P.D.S., Pousinho, H.M.I., and Mendes, V.M.F. (2009, January 8\u201312). An artificial neural network approach for short-term wind power forecasting in Portugal. Proceedings of the 15th International Conference on Intelligent System Applications to Power Systems, (ISAP\u203209), Curitiba, Brazil.","DOI":"10.1109\/ISAP.2009.5352853"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"1200","DOI":"10.1109\/TNN.2002.1031951","article-title":"Neighborhood based Levenberg-Marquardt algorithm for neural network training","volume":"13","author":"Lera","year":"2002","journal-title":"IEEE Trans. Neural Netw."},{"key":"ref_42","doi-asserted-by":"crossref","unstructured":"Zayani, R., Bouallegue, R., and Roviras, D. (2008, January 25\u201329). Levenberg-Marquardt learning neural network for adaptive predistortion for time-varying HPA with memory in OFDM systems. Proceedings of the Signal Processing Conference, Lausanne, Switzerland.","DOI":"10.1155\/2008\/132729"},{"key":"ref_43","unstructured":"Lehmann, E.L., and Casella, G. (2006). Theory of Point Estimation, Springer. [2nd ed.]."},{"key":"ref_44","doi-asserted-by":"crossref","first-page":"1352","DOI":"10.1057\/jors.2014.103","article-title":"A better measure of relative prediction accuracy for model selection and model estimation","volume":"66","author":"Tofallis","year":"2015","journal-title":"J. Oper. Res. Soc."},{"key":"ref_45","first-page":"1","article-title":"Predictive abilities of bayesian regularization and Levenberg\u2013Marquardt algorithms in artificial neural networks: A comparative empirical study on social data","volume":"21","author":"Kayri","year":"2016","journal-title":"Math. Comput. Appl."}],"container-title":["Energies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1996-1073\/15\/3\/957\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T22:09:41Z","timestamp":1760134181000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1996-1073\/15\/3\/957"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,1,28]]},"references-count":45,"journal-issue":{"issue":"3","published-online":{"date-parts":[[2022,2]]}},"alternative-id":["en15030957"],"URL":"https:\/\/doi.org\/10.3390\/en15030957","relation":{},"ISSN":["1996-1073"],"issn-type":[{"value":"1996-1073","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,1,28]]}}}