{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,25]],"date-time":"2026-03-25T18:16:30Z","timestamp":1774462590903,"version":"3.50.1"},"reference-count":38,"publisher":"MDPI AG","issue":"18","license":[{"start":{"date-parts":[[2020,9,12]],"date-time":"2020-09-12T00:00:00Z","timestamp":1599868800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e a Tecnologia","doi-asserted-by":"publisher","award":["760"],"award-info":[{"award-number":["760"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>Society\u2019s concerns with electricity consumption have motivated researchers to improve on the way that energy consumption management is done. The reduction of energy consumption and the optimization of energy management are, therefore, two major aspects to be considered. Additionally, load forecast provides relevant information with the support of historical data allowing an enhanced energy management, allowing energy costs reduction. In this paper, the proposed consumption forecast methodology uses an Artificial Neural Network (ANN) and incremental learning to increase the forecast accuracy. The ANN is retrained daily, providing an updated forecasting model. The case study uses 16 months of data, split in 5-min periods, from a real industrial facility. The advantages of using the proposed method are illustrated with the numerical results.<\/jats:p>","DOI":"10.3390\/en13184774","type":"journal-article","created":{"date-parts":[[2020,9,13]],"date-time":"2020-09-13T21:11:32Z","timestamp":1600031492000},"page":"4774","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":32,"title":["Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning"],"prefix":"10.3390","volume":"13","author":[{"given":"Daniel","family":"Ramos","sequence":"first","affiliation":[{"name":"GECAD\u2014Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal"},{"name":"Polytechnic of Porto, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5982-8342","authenticated-orcid":false,"given":"Pedro","family":"Faria","sequence":"additional","affiliation":[{"name":"GECAD\u2014Research Group on Intelligent Engineering and Computing for Advanced Innovation and Development, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal"},{"name":"Polytechnic of Porto, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4560-9544","authenticated-orcid":false,"given":"Zita","family":"Vale","sequence":"additional","affiliation":[{"name":"Polytechnic of Porto, Rua DR. Antonio Bernardino de Almeida, 431, 4200-072 Porto, Portugal"}]},{"given":"Jo\u00e3o","family":"Mourinho","sequence":"additional","affiliation":[{"name":"SISTRADE\u2014Software Consulting, S.A., 4250-380 Porto, Portugal"}]},{"given":"Regina","family":"Correia","sequence":"additional","affiliation":[{"name":"SISTRADE\u2014Software Consulting, S.A., 4250-380 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2020,9,12]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"20","DOI":"10.1109\/CC.2015.7084360","article-title":"Investing and pricing with supply uncertainty in electricity market: A general view combining wholesale and retail market","volume":"12","author":"Li","year":"2015","journal-title":"China Commun."},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Hauteclocque, A. (2008, January 28\u201330). Legal uncertainty and competition policy: The case of long-term vertical contracting by dominant firms in the EU electricity markets. Proceedings of the 2008 5th International Conference on the European Electricity Market, Lisboa, Portugal.","DOI":"10.1109\/EEM.2008.4579098"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Faria, P., and Vale, Z. (2019). A Demand Response Approach to Scheduling Constrained Load Shifting. Energies, 12.","DOI":"10.3390\/en12091752"},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Zhou, B., Yan, J., Yang, D., Zheng, X., Xiong, Z., and Zhang, J. (2019, January 6\u20139). A Regional Smart Power Grid Distribution Transformer Planning Method Considering Life Cycle Cost. Proceedings of the 2019 4th International Conference on Intelligent Green Building and Smart Grid (IGBSG), Yi Chang, China.","DOI":"10.1109\/IGBSG.2019.8886277"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"5374","DOI":"10.1016\/j.energy.2011.06.049","article-title":"Demand response in electrical energy supply: An optimal real time pricing approach","volume":"36","author":"Faria","year":"2011","journal-title":"Energy"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Noppakant, A., Plangklang, B., and Marsong, S. (2019, January 16\u201318). The Study of Challenge and Issue of Building Demand Response. Proceedings of the 2019 International Conference on Power, Energy and Innovations (ICPEI), Pattaya, Thailand.","DOI":"10.1109\/ICPEI47862.2019.8945005"},{"key":"ref_7","unstructured":"Zhou, Q., Guan, W., and Sun, W. (2012, January 22\u201326). Impact of demand response contracts on load forecasting in a smart grid environment. Proceedings of the 2012 IEEE Power and Energy Society General Meeting, San Diego, CA, USA."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"55279","DOI":"10.1109\/ACCESS.2020.2981877","article-title":"Algorithm for Demand Response to Maximize the Penetration of Renewable Energy","volume":"8","author":"Silva","year":"2020","journal-title":"IEEE Access"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Bhuiyan, S.M.A., Khan, J.F., and Murphy, G.V. (April, January 30). Big data analysis of the electric power PMU data from smart grid. Proceedings of the SoutheastCon 2017, Charlotte, NC, USA.","DOI":"10.1109\/SECON.2017.7925277"},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"3","DOI":"10.1186\/s42162-018-0006-6","article-title":"Application of an optimization-based curtailment service provider in real-time simulation","volume":"1","author":"Abrishambaf","year":"2018","journal-title":"Energy Inform."},{"key":"ref_11","unstructured":"Aggarwal, C.C. (2015). Data Classification: Algorithms and Applications, CRC Press."},{"key":"ref_12","doi-asserted-by":"crossref","unstructured":"Suzuki, K. (2013). Artificial Neural Networks\u2014Architectures and Applications, Intech.","DOI":"10.5772\/3409"},{"key":"ref_13","doi-asserted-by":"crossref","unstructured":"Kramer, O. (2013). Dimensionality Reduction with Unsupervised Nearest Neighbors, Springer.","DOI":"10.1007\/978-3-642-38652-7"},{"key":"ref_14","unstructured":"Cunningham, P., and Delany, S.J. (2007). k-Nearest Neighbour Classifiers, UCD."},{"key":"ref_15","unstructured":"Cutler, A., Cutler, D.R., and Stevens, J.R. (2012). Ensemble Machine Learning: Methods and Applications, Springer."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Sahay, K.B., and Singh, K. (2018, January 7\u20139). Short-Term Price Forecasting by Using ANN Algorithms. Proceedings of the 2018 International Electrical Engineering Congress (iEECON), Krabi, Thailand.","DOI":"10.1109\/IEECON.2018.8712254"},{"key":"ref_17","doi-asserted-by":"crossref","unstructured":"Bhatt, G.A., and Gandhi, P.R. (2019, January 23\u201325). Statistical and ANN based prediction of wind power with uncertainty. Proceedings of the 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI), Tirunelveli, India.","DOI":"10.1109\/ICOEI.2019.8862551"},{"key":"ref_18","unstructured":"Mamun, M.A., and Nagasaka, K. (2004, January 5\u20138). Artificial neural networks applied to long-term electricity demand forecasting. Proceedings of the Fourth International Conference on Hybrid Intelligent Systems (HIS\u201904), Kitakyushu, Japan."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Bracale, A., Falco, P., and Carpinelli, G. (2018, January 24\u201326). Comparing Univariate and Multivariate Methods for Probabilistic Industrial Load Forecasting. Proceedings of the 2018 5th International Symposium on Environment-Friendly Energies and Applications (EFEA), Rome, Italy.","DOI":"10.1109\/EFEA.2018.8617111"},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Ramos, D., Faria, P., and Vale, Z. (2020, January 9\u201312). Electricity Consumption Forecast in an Industry Facility to Support Production Planning Update in Short Time. Proceedings of the 2020 IEEE International Conference on Environment and Electrical Engineering and 2020 IEEE Industrial and Commercial Power Systems Europe (EEEIC\/I&CPS Europe), Madrid, Spain.","DOI":"10.1109\/EEEIC\/ICPSEurope49358.2020.9160535"},{"key":"ref_21","unstructured":"(2020, May 29). Keras. Available online: https:\/\/www.tensorflow.org\/guide\/keras."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Bracale, A., Carpinelli, G., Falco, P., and Hong, T. (2017, January 26\u201329). Short-term industrial load forecasting: A case study in an Italian factory. Proceedings of the 2017 IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe), Torino, Italy.","DOI":"10.1109\/ISGTEurope.2017.8260176"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"432","DOI":"10.1016\/j.apenergy.2010.03.017","article-title":"The potential of demand-side management in energy-intensive industries for electricity markets in Germany","volume":"88","author":"Paulus","year":"2011","journal-title":"Appl. Energy"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"147","DOI":"10.1016\/j.ijforecast.2017.09.006","article-title":"Probabilistic forecasting of industrial electricity load with regime switching behavior","volume":"34","author":"Berk","year":"2018","journal-title":"Int. J. Forecast."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2886","DOI":"10.1109\/TII.2017.2711648","article-title":"Hour-Ahead Price Based Energy Management Scheme for Industrial Facilities","volume":"13","author":"Huang","year":"2017","journal-title":"IEEE Trans. Ind. Inform."},{"key":"ref_26","doi-asserted-by":"crossref","first-page":"443","DOI":"10.1016\/j.ijforecast.2006.01.001","article-title":"25 years of time series forecasting","volume":"22","author":"Gooijer","year":"2006","journal-title":"Int. J. Forecast."},{"key":"ref_27","unstructured":"S\u00e1nchez-S\u00e1nchez, P.A., Garc\u00eda-Gonz\u00e1lez, J.R., and Coronell, L.H.P. (2019). Encountered Problems of Time Series with Neural Networks: Models and Architectures. Recent Trends in Artificial Neural Networks-From Training to Prediction, Intech."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"1832","DOI":"10.1016\/j.procs.2018.05.136","article-title":"Effective Short-Term Forecasting for Daily Time Series with Complex Seasonal Patterns","volume":"132","author":"Naim","year":"2018","journal-title":"Procedia Comput. Sci."},{"key":"ref_29","doi-asserted-by":"crossref","unstructured":"Mahalakshmi, G., Sridevi, S., and Rajaram, S. (2016, January 7\u20139). A survey on forecasting of time series data. Proceedings of the 2016 International Conference on Computing Technologies and Intelligent Data Engineering, Kovilpatti, India.","DOI":"10.1109\/ICCTIDE.2016.7725358"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"334","DOI":"10.1016\/j.fcij.2018.10.003","article-title":"Time series forecasting using artificial neural networks methodologies: A systematic review","volume":"3","author":"Tealab","year":"2018","journal-title":"Future Comput. Inform. J."},{"key":"ref_31","doi-asserted-by":"crossref","unstructured":"Ashour, M.A.H., and Abbas, R.A. (2018, January 8\u201310). Improving Time Series\u2019 Forecast Errors by Using Recurrent Neural Networks. Proceedings of the 7th International Conference on Software and Computer Applications, Kuantan, Malaysia.","DOI":"10.1145\/3185089.3185151"},{"key":"ref_32","doi-asserted-by":"crossref","unstructured":"Wan, R., Mei, S., Wang, J., Liu, M., and Yang, F. (2019). Multivariate Temporal Convolutional Network: A Deep Neural Networks Approach for Multivariate Time Series Forecasting. Electronics, 8.","DOI":"10.3390\/electronics8080876"},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Bontempi, G., Taieb, S.B., and Borgne, Y.L. (2012). Machine Learning Strategies for Time Series Forecasting. European Business Intelligence Summer School, Springer.","DOI":"10.1007\/978-3-642-36318-4_3"},{"key":"ref_34","doi-asserted-by":"crossref","unstructured":"Nor, M.E., Safuan, H.M., Shab, N.F.M., Abdullah, M.A.A., Mohamad, N.A.I., and Muhammad, L. (2017, January 18\u201322). Neural network versus classical time series forecasting models. Proceedings of the AIP Conference Proceedings, Vladivostok, Russia.","DOI":"10.1063\/1.4982865"},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Mart\u00ednez-\u00c1lvarez, F., Troncoso, A., and Riquelme, J. (2017). Recent Advances in Energy Time Series Forecasting. Energies, 10.","DOI":"10.3390\/en10060809"},{"key":"ref_36","doi-asserted-by":"crossref","unstructured":"Ponce-Flores, M., Frausto-Sol\u00eds, J., Santamar\u00eda-Bonfil, G., P\u00e9rez-Ortega, J., and Gonz\u00e1lez-Barbosa, J. (2020). Time Series Complexities and Their Relationship to Forecasting Performance. Entropy, 22.","DOI":"10.3390\/e22010089"},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Divina, F., Torres, M., Vela, F., and Noguera, J. (2019). A Comparative Study of Time Series Forecasting Methods for Short Term Electric Energy Consumption Prediction in Smart Buildings. Energies, 12.","DOI":"10.3390\/en12101934"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"13162","DOI":"10.3390\/en81112361","article-title":"A Survey on Data Mining Techniques Applied to Electricity-Related Time Series Forecasting","volume":"8","author":"Troncoso","year":"2015","journal-title":"Energies"}],"container-title":["Energies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1996-1073\/13\/18\/4774\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T10:09:31Z","timestamp":1760177371000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1996-1073\/13\/18\/4774"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2020,9,12]]},"references-count":38,"journal-issue":{"issue":"18","published-online":{"date-parts":[[2020,9]]}},"alternative-id":["en13184774"],"URL":"https:\/\/doi.org\/10.3390\/en13184774","relation":{},"ISSN":["1996-1073"],"issn-type":[{"value":"1996-1073","type":"electronic"}],"subject":[],"published":{"date-parts":[[2020,9,12]]}}}