{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T03:27:45Z","timestamp":1781234865974,"version":"3.54.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"23","license":[{"start":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T00:00:00Z","timestamp":1625443200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"},{"start":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T00:00:00Z","timestamp":1625443200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springer.com\/tdm"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput & Applic"],"published-print":{"date-parts":[[2021,12]]},"DOI":"10.1007\/s00521-021-06266-2","type":"journal-article","created":{"date-parts":[[2021,7,5]],"date-time":"2021-07-05T07:02:56Z","timestamp":1625468576000},"page":"16713-16731","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":27,"title":["Application of deep learning and chaos theory for load forecasting in Greece"],"prefix":"10.1007","volume":"33","author":[{"given":"K.","family":"Stergiou","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9580-0702","authenticated-orcid":false,"given":"T. E.","family":"Karakasidis","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2021,7,5]]},"reference":[{"key":"6266_CR1","doi-asserted-by":"publisher","unstructured":"Marino DL, Amarasinghe K, Manic M (2016) Building energy load forecasting using Deep Neural Networks. In: IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society. https:\/\/doi.org\/10.1109\/iecon.2016.7793413","DOI":"10.1109\/iecon.2016.7793413"},{"key":"6266_CR2","doi-asserted-by":"publisher","first-page":"990","DOI":"10.4236\/jmf.2017.74055","volume":"7","author":"P Papaioannou","year":"2017","unstructured":"Papaioannou P, Papaioannou G, Stratigakos A, Dikaiakos C (2017) Dynamic conditional correlation between electricity, energy (Commodity) and financial markets during the financial crisis in Greece. J Math Finance 7:990\u20131033. https:\/\/doi.org\/10.4236\/jmf.2017.74055","journal-title":"J Math Finance"},{"key":"6266_CR3","doi-asserted-by":"crossref","unstructured":"Kiartzis SJ, Bakirtzis AG, Petridis V (1994) Short term load forecasting using neural networks, Aristotle University of Thessaloniki, Electric Power Systems Research","DOI":"10.1016\/0378-7796(95)00920-D"},{"issue":"3","key":"6266_CR4","doi-asserted-by":"publisher","first-page":"868","DOI":"10.1016\/j.asoc.2009.10.004","volume":"10","author":"D Niu","year":"2010","unstructured":"Niu D, Liu D, Wu DD (2010) A soft computing system for day-ahead electricity price forecasting. Appl Soft Comput 10(3):868\u2013875","journal-title":"Appl Soft Comput"},{"issue":"4","key":"6266_CR5","doi-asserted-by":"publisher","first-page":"915","DOI":"10.1016\/j.rser.2008.02.002","volume":"13","author":"M Lei","year":"2009","unstructured":"Lei M, Shiyan L, Chuanwen J, Hongling L, Yan Z (2009) A review on the forecasting of wind speed and generated power. Renew Sustain Energy Rev 13(4):915\u2013920. https:\/\/doi.org\/10.1016\/j.rser.2008.02.002","journal-title":"Renew Sustain Energy Rev"},{"key":"6266_CR6","unstructured":"Stamatakis P (2008) Forecasting the consumption of electricity load with neural networks and genetic algorithms, Thesis"},{"key":"6266_CR7","doi-asserted-by":"publisher","first-page":"495","DOI":"10.1016\/S0925-2312(02)00870-6","volume":"51","author":"AK Topalli","year":"2003","unstructured":"Topalli AK, Erkmenb I (2003) A hybrid learning for neural networks applied to short term load forecasting. Neurocomputing 51:495\u2013500","journal-title":"Neurocomputing"},{"issue":"4","key":"6266_CR8","doi-asserted-by":"publisher","first-page":"405","DOI":"10.1016\/j.asoc.2004.02.005","volume":"4","author":"AS Otavio","year":"2004","unstructured":"Otavio AS, Carpinteiro A, Agnaldo JR, Reis A, Alexandre PA, da Silva B (2004) A hierarchical neural model in short-term load forecasting. Appl Soft Comput 4(4):405\u2013412","journal-title":"Appl Soft Comput"},{"issue":"6","key":"6266_CR9","doi-asserted-by":"publisher","first-page":"367","DOI":"10.1016\/j.ijepes.2005.12.007","volume":"28","author":"P Mandal","year":"2006","unstructured":"Mandal P, Senjyu T, Urasaki N, Funabashi T (2006) A neural network based several-hour-ahead electric load forecasting using similar days approach. Int J Electr Power Energy Syst 28(6):367\u2013373","journal-title":"Int J Electr Power Energy Syst"},{"key":"6266_CR10","doi-asserted-by":"publisher","unstructured":"Hong T, Gui M, Baran ME, Willis HL (2010) Modeling and forecasting hourly electric load by multiple linear regression with interactions. in: IEEE PES general meeting. https:\/\/doi.org\/10.1109\/pes.2010.5589959","DOI":"10.1109\/pes.2010.5589959"},{"issue":"4","key":"6266_CR11","doi-asserted-by":"publisher","first-page":"700","DOI":"10.1016\/j.ijforecast.2009.05.015","volume":"26","author":"A Goia","year":"2010","unstructured":"Goia A, May C, Fusai G (2010) Functional clustering and linear regression for peak load forecasting. Int J Forecast 26(4):700\u2013711. https:\/\/doi.org\/10.1016\/j.ijforecast.2009.05.015","journal-title":"Int J Forecast"},{"key":"6266_CR12","doi-asserted-by":"publisher","unstructured":"Hong T, Wang P, Willis HL (2011) A Na\u00efve multiple linear regression benchmark for short term load forecasting. In: 2011 IEEE power and energy society general meeting. https:\/\/doi.org\/10.1109\/pes.2011.6038881","DOI":"10.1109\/pes.2011.6038881"},{"issue":"11","key":"6266_CR13","doi-asserted-by":"publisher","first-page":"5897","DOI":"10.3390\/en6115897","volume":"6","author":"S Voronin","year":"2013","unstructured":"Voronin S, Partanen J (2013) Price forecasting in the day-ahead energy market by an iterative method with separate normal price and price spike frameworks. Energies 6(11):5897\u20135920. https:\/\/doi.org\/10.3390\/en6115897","journal-title":"Energies"},{"key":"6266_CR14","doi-asserted-by":"publisher","first-page":"132","DOI":"10.1016\/j.apenergy.2016.03.089","volume":"172","author":"P Panapakidis Ioannis","year":"2016","unstructured":"Panapakidis Ioannis P, Dragoumas Athanasios S (2016) Day \u2013 ahead electricity price forecasting via the application of artificial neural network based models. Appl Energy 172:132\u2013151","journal-title":"Appl Energy"},{"issue":"8","key":"6266_CR15","doi-asserted-by":"publisher","first-page":"845","DOI":"10.1103\/physrevlett.59.845","volume":"59","author":"JD Farmer","year":"1987","unstructured":"Farmer JD, Sidorowich JJ (1987) Predicting chaotic time series. Phys Rev Lett 59(8):845\u2013848. https:\/\/doi.org\/10.1103\/physrevlett.59.845","journal-title":"Phys Rev Lett"},{"key":"6266_CR16","doi-asserted-by":"crossref","unstructured":"Tkachenko R, Izonin I (2019) Model and principles for the implementation of neural-like structures based on geometric data transformations. In: Hu Z, Petoukhov S, Dychka I, He M (eds) Advances in intelligent systems and computing, vol 754. Springer, pp 578\u2013587","DOI":"10.1007\/978-3-319-91008-6_58"},{"key":"6266_CR17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-20521-8_39","volume-title":"Advances in computational intelligence, IWANN 2019, LNCS","author":"I Izonin","year":"2019","unstructured":"Izonin I, Tkachenko R, Kryvinska N, Tkachenko P, Gregu\u0161 M (2019) Multiple linear regression based on coefficients identification using non-iterative sgtm neural-like structure. In: Rojas I, Joya G, Catala A (eds) Advances in computational intelligence, IWANN 2019, LNCS, vol 11506. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-030-20521-8_39"},{"issue":"4","key":"6266_CR18","doi-asserted-by":"publisher","first-page":"46","DOI":"10.3390\/data3040046","volume":"3","author":"R Tkachenko","year":"2018","unstructured":"Tkachenko R, Izonin I, Vitynskyi P, Lotoshynska N, Pavlyuk O (2018) Development of the non-iterative supervised learning predictor based on the ito decomposition and SGTM neural-like structure for managing medical insurance costs. Data 3(4):46. https:\/\/doi.org\/10.3390\/data3040046","journal-title":"Data"},{"issue":"4","key":"6266_CR19","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1007\/s40095-016-0220-6","volume":"7","author":"F Fazelpour","year":"2016","unstructured":"Fazelpour F, Tarashkar N, Rosen MA (2016) Short-term wind speed forecasting using artificial neural networks for Tehran, Iran. Int J Energy Environ Eng 7(4):377\u2013390. https:\/\/doi.org\/10.1007\/s40095-016-0220-6","journal-title":"Int J Energy Environ Eng"},{"key":"6266_CR20","doi-asserted-by":"publisher","first-page":"308","DOI":"10.1016\/j.procs.2017.11.374","volume":"122","author":"W He","year":"2017","unstructured":"He W (2017) Load forecasting via deep neural networks. Proc Comput Sci 122:308\u2013314. https:\/\/doi.org\/10.1016\/j.procs.2017.11.374","journal-title":"Proc Comput Sci"},{"key":"6266_CR21","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-017-3225-z","author":"M Abdel-Nasser","year":"2017","unstructured":"Abdel-Nasser M, Mahmoud K (2017) Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-017-3225-z","journal-title":"Neural Comput Appl"},{"key":"6266_CR22","doi-asserted-by":"publisher","first-page":"386","DOI":"10.1016\/j.apenergy.2018.02.069","volume":"221","author":"J Lago","year":"2018","unstructured":"Lago J, De Ridder F, De Schutter B (2018) Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms. Appl Energy 221:386\u2013405","journal-title":"Appl Energy"},{"key":"6266_CR23","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-018-3724-6","author":"D Popa","year":"2018","unstructured":"Popa D, Pop F, Serbanescu C, Castiglione A (2018) Deep learning model for home automation and energy reduction in a smart home environment platform. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-018-3724-6","journal-title":"Neural Comput Appl"},{"issue":"7","key":"6266_CR24","doi-asserted-by":"publisher","first-page":"1636","DOI":"10.3390\/en11071636","volume":"11","author":"S Bouktif","year":"2018","unstructured":"Bouktif S, Fiaz A, Ouni A, Serhani M (2018) Optimal deep learning LSTM Model for electric load forecasting using feature selection and genetic algorithm: comparison with machine learning approaches. Energies 11(7):1636. https:\/\/doi.org\/10.3390\/en11071636","journal-title":"Energies"},{"key":"6266_CR25","doi-asserted-by":"publisher","DOI":"10.1109\/access.2019.2937098","author":"H Cheng","year":"2019","unstructured":"Cheng H, Xie Z, Shi Y, Xiong N (2019) Multi-step data prediction in wireless sensor networks based on one-dimensional CNN and bidirectional LSTM. IEEE Access. https:\/\/doi.org\/10.1109\/access.2019.2937098","journal-title":"IEEE Access"},{"key":"6266_CR26","doi-asserted-by":"publisher","DOI":"10.1007\/s00521-020-04856-0","author":"NMM Bendaoud","year":"2020","unstructured":"Bendaoud NMM, Farah N (2020) Using deep learning for short-term load forecasting. Neural Comput Appl. https:\/\/doi.org\/10.1007\/s00521-020-04856-0","journal-title":"Neural Comput Appl"},{"key":"6266_CR27","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1007\/BF02478259","volume":"5","author":"WS McCulloch","year":"1943","unstructured":"McCulloch WS, Pitts W (1943) A logical calculus of ideas immanent in nervous activity. Bull Math Biophys 5:115","journal-title":"Bull Math Biophys"},{"issue":"8","key":"6266_CR28","doi-asserted-by":"publisher","first-page":"1735e80","DOI":"10.1162\/neco.1997.9.8.1735","volume":"9","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S, Schmidhuber J (1997) Long short-term memory. Neural Comput 9(8):1735e80","journal-title":"Neural Comput"},{"issue":"1","key":"6266_CR29","doi-asserted-by":"publisher","first-page":"841","DOI":"10.1109\/TSG.2017.2753802","volume":"10","author":"W Kong","year":"2017","unstructured":"Kong W, Dong ZY, Jia Y, Hill D, Zhang Y (2017) Short-Term Residential Load Forecasting Based on LSTM Recurrent Neural Network. IEEE TransSmart Grid 10(1):841\u2013851","journal-title":"IEEE TransSmart Grid"},{"issue":"8","key":"6266_CR30","doi-asserted-by":"publisher","first-page":"3103","DOI":"10.3390\/su12083103","volume":"12","author":"H Son","year":"2020","unstructured":"Son H, Kim C (2020) A deep learning approach to forecasting monthly demand for residential-sector electricity. Sustainability 12(8):3103. https:\/\/doi.org\/10.3390\/su12083103","journal-title":"Sustainability"},{"key":"6266_CR31","doi-asserted-by":"publisher","unstructured":"Graves A, Jaitly N, Mohamed A (2013) Hybrid speech recognition with Deep Bidirectional LSTM. In: 2013 IEEE workshop on automatic speech recognition and understanding. https:\/\/doi.org\/10.1109\/asru.2013.6707742","DOI":"10.1109\/asru.2013.6707742"},{"key":"6266_CR32","doi-asserted-by":"publisher","first-page":"2673","DOI":"10.1109\/78.650093","volume":"45","author":"M Schuster","year":"1997","unstructured":"Schuster M, Paliwal K (1997) Bidirectional recurrent neural networks. Signal Process IEEE Trans 45:2673\u20132681. https:\/\/doi.org\/10.1109\/78.650093","journal-title":"Signal Process IEEE Trans"},{"key":"6266_CR33","doi-asserted-by":"publisher","unstructured":"Fu R, Zhang Z, Li L (2016) Using LSTM and GRU neural network methods for traffic flow prediction. In: 2016 31st Youth Academic Annual Conference of Chinese Association of Automation (YAC). https:\/\/doi.org\/10.1109\/yac.2016.7804912","DOI":"10.1109\/yac.2016.7804912"},{"key":"6266_CR34","volume-title":"Nonlinear Time Series Analysis","author":"H Kantz","year":"2004","unstructured":"Kantz H, Schreiber H (2004) Nonlinear Time Series Analysis, 2nd edn. Cambridge University Press, Cambridge","edition":"2"},{"key":"6266_CR35","doi-asserted-by":"publisher","unstructured":"Kumar H, Saini S (2016) Chaotic characterization of electric load demand time series & load forecasting by using GA trained artificial neural network. In: 2016 International conference on signal processing, communication, power and embedded system (SCOPES). https:\/\/doi.org\/10.1109\/scopes.2016.7955674","DOI":"10.1109\/scopes.2016.7955674"},{"key":"6266_CR36","doi-asserted-by":"publisher","first-page":"205","DOI":"10.1088\/0143-0807\/26\/1\/021","volume":"26","author":"S Kodba","year":"2005","unstructured":"Kodba S, Perc M, Marhl M (2005) Detecting chaos from a time series. Eur J Phys 26:205\u2013215. https:\/\/doi.org\/10.1088\/0143-0807\/26\/1\/021","journal-title":"Eur J Phys"},{"key":"6266_CR37","doi-asserted-by":"publisher","DOI":"10.18637\/jss.v007.i09","author":"D Bayarri","year":"2002","unstructured":"Bayarri D, Belaire-Franch J (2002) Recurrence plots in nonlinear time series analysis: free software. J Stat Softw. https:\/\/doi.org\/10.18637\/jss.v007.i09","journal-title":"J Stat Softw"},{"key":"6266_CR38","doi-asserted-by":"publisher","DOI":"10.1155\/2018\/1452683","author":"M Alfaro","year":"2018","unstructured":"Alfaro M, Fuertes G, Vargas E, Sep\u00falveda J, Veloso-Poblete M (2018) Forecast of chaotic series in a horizon superior to the inverse of the maximum lyapunov exponent. Complexity. https:\/\/doi.org\/10.1155\/2018\/1452683","journal-title":"Complexity"},{"key":"6266_CR39","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-4470-8","volume-title":"Building machine learning and deep learning models on google cloud platform","author":"E Bisong","year":"2019","unstructured":"Bisong E (2019) Introduction to Scikit-learn. Building machine learning and deep learning models on google cloud platform. Apress, Berkeley, CA"},{"key":"6266_CR40","doi-asserted-by":"publisher","unstructured":"Dahl GE, Sainath TN, Hinton GE (2013) Improving deep neural networks for LVCSR using rectified linear units and dropout. In: 2013 IEEE International Conference on Acoustics, Speech and Signal Processing. https:\/\/doi.org\/10.1109\/icassp.2013.6639346","DOI":"10.1109\/icassp.2013.6639346"},{"issue":"1","key":"6266_CR41","first-page":"1929","volume":"15","author":"N Srivastava","year":"2014","unstructured":"Srivastava N, Hinton G, Krizhevsky A, Sutskever I, Salakhutdinov R (2014) Dropout: a simple way to prevent neural networks from overfitting. J Mach Learn Res 15(1):1929\u20131958","journal-title":"J Mach Learn Res"},{"key":"6266_CR42","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/1428104","volume":"2020","author":"L Wu","year":"2020","unstructured":"Wu L, Kong C, Hao X, Chen W (2020) A short-term load forecasting method based on GRU-CNN hybrid neural network model. Math Probl Eng 2020:1\u201310. https:\/\/doi.org\/10.1155\/2020\/1428104","journal-title":"Math Probl Eng"},{"key":"6266_CR43","unstructured":"Huffaker RG (2010) Phase space reconstruction from time series data: where history meets theory. In: 2010 International European Forum, 8\u201312 Feb 2010, Innsbruck-Igls, Austria, International European Forum on System Dynamics and Innovation in Food Networks"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06266-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-021-06266-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-021-06266-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2021,11,3]],"date-time":"2021-11-03T18:16:42Z","timestamp":1635963402000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-021-06266-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,7,5]]},"references-count":43,"journal-issue":{"issue":"23","published-print":{"date-parts":[[2021,12]]}},"alternative-id":["6266"],"URL":"https:\/\/doi.org\/10.1007\/s00521-021-06266-2","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2021,7,5]]},"assertion":[{"value":"11 October 2020","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 June 2021","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"5 July 2021","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors have no conflicts of interest to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}