{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,8]],"date-time":"2026-02-08T04:14:44Z","timestamp":1770524084806,"version":"3.49.0"},"reference-count":27,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T00:00:00Z","timestamp":1637020800000},"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 Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/50021\/2020"],"award-info":[{"award-number":["UIDB\/50021\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001871","name":"Funda\u00e7\u00e3o para a Ci\u00eancia e Tecnologia","doi-asserted-by":"publisher","award":["UIDB\/00760\/2020"],"award-info":[{"award-number":["UIDB\/00760\/2020"]}],"id":[{"id":"10.13039\/501100001871","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Energies"],"abstract":"<jats:p>The higher share of renewable energy sources in the electrical grid and the electrification of significant sectors, such as transport and heating, are imposing a tremendous challenge on the operation of the energy system due to the increase in the complexity, variability and uncertainties associated with these changes. The recent advances of computational technologies and the ever-growing data availability allowed the development of sophisticated and efficient algorithms that can process information at a very fast pace. In this sense, the use of machine learning models has been gaining increased attention from the electricity sector as it can provide accurate forecasts of system behaviour from energy generation to consumption, helping all the stakeholders to optimize their activities. This work develops and proposes a methodology to enhance load demand forecasts using a machine learning model, namely a feed-forward neural network (FFNN), by incorporating an error correction step that involves the prediction of the initial forecast errors by another FFNN. The results showed that the proposed methodology was able to significantly improve the quality of load demand forecasts, demonstrating a better performance than the benchmark models.<\/jats:p>","DOI":"10.3390\/en14227644","type":"journal-article","created":{"date-parts":[[2021,11,16]],"date-time":"2021-11-16T11:32:03Z","timestamp":1637062323000},"page":"7644","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Electrical Load Demand Forecasting Using Feed-Forward Neural Networks"],"prefix":"10.3390","volume":"14","author":[{"given":"Eduardo","family":"Machado","sequence":"first","affiliation":[{"name":"Instituto Superior T\u00e9cnico-IST, Universidade de Lisboa, 1049-001 Lisbon, Portugal"},{"name":"Department of Materials, Energy Efficiency and Complementary Generation, Electrical Energy Research Center (Cepel), University City, Fund\u00e3o Island, Rio de Janeiro 21941-911, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8248-080X","authenticated-orcid":false,"given":"Tiago","family":"Pinto","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"}]},{"given":"Vanessa","family":"Guedes","sequence":"additional","affiliation":[{"name":"Department of Materials, Energy Efficiency and Complementary Generation, Electrical Energy Research Center (Cepel), University City, Fund\u00e3o Island, Rio de Janeiro 21941-911, Brazil"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5906-4744","authenticated-orcid":false,"given":"Hugo","family":"Morais","sequence":"additional","affiliation":[{"name":"Instituto Superior T\u00e9cnico-IST, Universidade de Lisboa, 1049-001 Lisbon, Portugal"},{"name":"INESC-ID, Department of Electrical and Computer Engineering, Instituto Superior T\u00e9cnico-IST, Universidade de Lisboa, 1049-001 Lisbon, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2021,11,16]]},"reference":[{"key":"ref_1","unstructured":"Intergovernmental Panel on Climate Change (2021, July 08). Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. Available online: https:\/\/www.ipcc.ch\/report\/ar5\/syr\/."},{"key":"ref_2","unstructured":"European Parliament (2009). Directive 2009\/28\/EC of the European Parliament and of the Council of 23 April 2009 on the Promotion of the Use of Energy from Renewable Sources."},{"key":"ref_3","unstructured":"European Parliament and of the Council (2021, July 08). Framework for Achieving Climate Neutrality and Amending Regulation (EU) 2018\/1999 (European Climate Law). Available online: https:\/\/eur-lex.europa.eu\/legal-content\/EN\/TXT\/?uri=CELEX:52020PC0080."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Intergovernmental Panel on Climate Change (2014). Climate Change 2014 Mitigation of Climate Change, Cambridge University Press.","DOI":"10.1017\/CBO9781107415416"},{"key":"ref_5","unstructured":"International Energy Agency (IEA) (2021, July 21). Secure Energy Transitions in the Power Sector. Available online: https:\/\/www.iea.org\/reports\/%0Asecure-energy-transitions-in-the-power-sector."},{"key":"ref_6","unstructured":"International Energy Agency (IEA) (2021, July 21). Power Systems in Transition. Available online: https:\/\/www.iea.org\/reports\/power-systems-in-transition."},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"297","DOI":"10.1016\/j.rser.2018.02.002","article-title":"Forecasting methods in energy planning models","volume":"88","author":"Debnath","year":"2018","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"1352","DOI":"10.1016\/j.rser.2015.04.065","article-title":"A review on artificial intelligence based load demand forecasting techniques for smart grid and buildings","volume":"50","author":"Raza","year":"2015","journal-title":"Renew. Sustain. Energy Rev."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"102010","DOI":"10.1016\/j.scs.2019.102010","article-title":"A review on machine learning forecasting growth trends and their real-time applications in different energy systems","volume":"54","author":"Ahmad","year":"2020","journal-title":"Sustain. Cities Soc."},{"key":"ref_10","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_11","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_12","doi-asserted-by":"crossref","unstructured":"Solyali, D. (2020). A Comparative Analysis of Machine Learning Approaches for Short-\/Long-Term Electricity Load Forecasting in Cyprus. Sustainability, 12.","DOI":"10.3390\/su12093612"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"257","DOI":"10.1016\/j.scs.2017.08.009","article-title":"Electrical load forecasting models: A critical systematic review","volume":"35","author":"Kuster","year":"2017","journal-title":"Sustain. Cities Soc."},{"key":"ref_14","doi-asserted-by":"crossref","first-page":"107173","DOI":"10.1016\/j.epsr.2021.107173","article-title":"Convolutional and recurrent neural network based model for short-term load forecasting","volume":"195","author":"Eskandari","year":"2021","journal-title":"Electr. Power Syst. Res."},{"key":"ref_15","unstructured":"Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning, MIT Press Book."},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Ahmad, A., Javaid, N., Mateen, A., Awais, M., and Khan, Z.A. (2019). Short-Term Load Forecasting in Smart Grids: An Intelligent Modular Approach. Energies, 12.","DOI":"10.3390\/en12010164"},{"key":"ref_17","unstructured":"Anzai, Y. (2012). Pattern Recognition and Machine Learning, Elsevier."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"118","DOI":"10.1016\/j.neucom.2015.12.004","article-title":"Forecasting electricity load with advanced wavelet neural networks","volume":"182","author":"Rana","year":"2016","journal-title":"Neurocomputing"},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"3807","DOI":"10.1016\/j.aej.2021.02.050","article-title":"A combined deep learning application for short term load forecasting","volume":"60","author":"Ozer","year":"2021","journal-title":"Alexandria Eng. J."},{"key":"ref_20","doi-asserted-by":"crossref","unstructured":"Karthika, S., Margaret, V., and Balaraman, K. (2017, January 21\u201322). Hybrid short term load forecasting using ARIMA-SVM. Proceedings of the 2017 Innovations in Power and Advanced Computing Technologies (i-PACT), Vellore, India.","DOI":"10.1109\/IPACT.2017.8245060"},{"key":"ref_21","doi-asserted-by":"crossref","unstructured":"Orr\u00f9, P.F., Zoccheddu, A., Sassu, L., Mattia, C., Cozza, R., and Arena, S. (2020). Machine Learning Approach Using MLP and SVM Algorithms for the Fault Prediction of a Centrifugal Pump in the Oil and Gas Industry. Sustainability, 12.","DOI":"10.3390\/su12114776"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"3341","DOI":"10.1109\/TSG.2016.2628061","article-title":"A Short-Term and High-Resolution Distribution System Load Forecasting Approach Using Support Vector Regression With Hybrid Parameters Optimization","volume":"9","author":"Jiang","year":"2018","journal-title":"IEEE Trans. Smart Grid"},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1007\/978-3-030-23946-6_1","article-title":"Energy Consumption Forecasting Using Ensemble Learning Algorithms","volume":"Volume 1004","author":"Vale","year":"2020","journal-title":"Advances in Intelligent Systems and Computing"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Jozi, A., Pinto, T., Praca, I., and Vale, Z. (2018, January 18\u201321). Day-ahead forecasting approach for energy consumption of an office building using support vector machines. Proceedings of the 2018 IEEE Symposium Series on Computational Intelligence (SSCI), Bangalore, India.","DOI":"10.1109\/SSCI.2018.8628734"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"114368","DOI":"10.1016\/j.apenergy.2019.114368","article-title":"Short-term electrical load forecasting based on error correction using dynamic mode decomposition","volume":"261","author":"Kong","year":"2020","journal-title":"Appl. Energy"},{"key":"ref_26","unstructured":"Aur\u00e9lien, G. (2017). Hands-On Machine Learning With Scikit-Learn, Keras, and Tensorflow\u2014Concepts, Tools, and Techniques to Build Intelligent Systems, O\u2019Reilly Media, Inc."},{"key":"ref_27","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A Method for Stochastic Optimization. arXiv."}],"container-title":["Energies"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1996-1073\/14\/22\/7644\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,11]],"date-time":"2025-10-11T07:30:49Z","timestamp":1760167849000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1996-1073\/14\/22\/7644"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,11,16]]},"references-count":27,"journal-issue":{"issue":"22","published-online":{"date-parts":[[2021,11]]}},"alternative-id":["en14227644"],"URL":"https:\/\/doi.org\/10.3390\/en14227644","relation":{},"ISSN":["1996-1073"],"issn-type":[{"value":"1996-1073","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,11,16]]}}}