{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,12]],"date-time":"2026-03-12T18:22:53Z","timestamp":1773339773303,"version":"3.50.1"},"reference-count":43,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2024,5,20]],"date-time":"2024-05-20T00:00:00Z","timestamp":1716163200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2024,5,20]],"date-time":"2024-05-20T00:00:00Z","timestamp":1716163200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["SN COMPUT. SCI."],"DOI":"10.1007\/s42979-024-02926-x","type":"journal-article","created":{"date-parts":[[2024,5,20]],"date-time":"2024-05-20T12:01:42Z","timestamp":1716206502000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Forecasting Electricity Demand in Greece: A Functional Data Approach in High Dimensional Hourly Time Series"],"prefix":"10.1007","volume":"5","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6141-2020","authenticated-orcid":false,"given":"George","family":"Varelas","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4073-7256","authenticated-orcid":false,"given":"Giannis","family":"Tzimas","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1313-1750","authenticated-orcid":false,"given":"Panayiotis","family":"Alefragis","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2024,5,20]]},"reference":[{"key":"2926_CR1","volume-title":"Modelling prices in competitive electricity markets","author":"DW Bunn","year":"2004","unstructured":"Bunn DW. Modelling prices in competitive electricity markets. Hoboken: Wiley; 2004."},{"issue":"222","key":"2926_CR2","first-page":"1","volume":"90","author":"K Schmietendorf","year":"2017","unstructured":"Schmietendorf K, Peinke J, Kamps O. The impact of turbulent renewable energy production on power grid stability and quality. Eur Phys J B. 2017;90(222):1\u20136.","journal-title":"Eur Phys J B"},{"issue":"9","key":"2926_CR3","doi-asserted-by":"publisher","first-page":"3423","DOI":"10.3390\/en15093423","volume":"15","author":"F Jan","year":"2022","unstructured":"Jan F, Shah I, Ali S. Short-term electricity prices forecasting using functional time series analysis. Energies. 2022;15(9):3423.","journal-title":"Energies"},{"key":"2926_CR4","doi-asserted-by":"publisher","first-page":"1482","DOI":"10.1016\/j.csda.2011.12.016","volume":"56","author":"D Pigoli","year":"2012","unstructured":"Pigoli D, Sangalli LM. Wavelets in functional data analysis: estimation of multidimensional curves and their derivatives. Comput Stat Data Anal. 2012;56:1482\u201398.","journal-title":"Comput Stat Data Anal"},{"key":"2926_CR5","doi-asserted-by":"publisher","first-page":"133","DOI":"10.1016\/j.csda.2014.05.010","volume":"79","author":"M Yamamoto","year":"2014","unstructured":"Yamamoto M, Terada Y. Functional factorial K-means analysis. Comput Stat Data Anal. 2014;79:133\u201348.","journal-title":"Comput Stat Data Anal"},{"key":"2926_CR6","doi-asserted-by":"publisher","first-page":"161","DOI":"10.1007\/s10489-020-01765-6","volume":"51","author":"A Jimenez-Cordero","year":"2021","unstructured":"Jimenez-Cordero A, Meldonado S. Automatic feature scaling and selection for support vector machine classification with functional data. Appl Intell. 2021;51:161\u201384.","journal-title":"Appl Intell"},{"key":"2926_CR7","first-page":"2018","volume":"1\u201313","author":"J Huang","year":"2018","unstructured":"Huang J, Tang Y, Chen S. Energy demand forecasting: combining cointegration analysis and artificial intelligence algorithm. Math Problems Eng. 2018;1\u201313:2018.","journal-title":"Math Problems Eng"},{"key":"2926_CR8","doi-asserted-by":"publisher","first-page":"96","DOI":"10.1016\/j.ijepes.2016.01.034","volume":"80","author":"G Aneiros","year":"2016","unstructured":"Aneiros G, Vilar J, Ra\u00f1a P. Short-term forecast of daily curves of electricity demand and price. Int J Electr Power Energy Syst. 2016;80:96\u2013108.","journal-title":"Int J Electr Power Energy Syst"},{"issue":"1","key":"2926_CR9","doi-asserted-by":"publisher","first-page":"44","DOI":"10.3390\/en10010044","volume":"10","author":"W Li","year":"2017","unstructured":"Li W, Yang X, Li H, Su L. Hybrid forecasting approach based on GRNN neural network and SVR machine for electricity demand forecasting. Energies. 2017;10(1):44.","journal-title":"Energies"},{"key":"2926_CR10","doi-asserted-by":"publisher","first-page":"821","DOI":"10.1016\/j.energy.2018.07.168","volume":"161","author":"Q Wang","year":"2018","unstructured":"Wang Q, Li S, Li R. Forecasting energy demand in China and India: using single-linear, hybrid-linear, and non-linear time series forecast techniques. Energy. 2018;161:821\u201331.","journal-title":"Energy"},{"issue":"2","key":"2926_CR11","doi-asserted-by":"publisher","first-page":"867","DOI":"10.1109\/TPWRS.2005.846044","volume":"20","author":"RC Garcia","year":"2005","unstructured":"Garcia RC, Contreras J, van Akkeren M, Garcia JBC. A GARCH forecasting model to predict day-ahead electricity prices. IEEE Trans Power Syst. 2005;20(2):867\u201374.","journal-title":"IEEE Trans Power Syst"},{"issue":"11","key":"2926_CR12","first-page":"2017","volume":"10","author":"S Rehman","year":"1868","unstructured":"Rehman S, Cai Y, Fazal R, Walasai GD, Mirjat N. An integrated modeling approach for forecasting long-term energy demand in Pakistan. Energies. 1868;10(11):2017.","journal-title":"Energies"},{"issue":"9","key":"2926_CR13","doi-asserted-by":"publisher","first-page":"1413","DOI":"10.1016\/j.energy.2009.06.034","volume":"34","author":"V Bianco","year":"2009","unstructured":"Bianco V, Manca O, Nardini S. Electricity consumption forecasting in Italy using linear regression models. Energy. 2009;34(9):1413\u201321.","journal-title":"Energy"},{"key":"2926_CR14","doi-asserted-by":"publisher","first-page":"602","DOI":"10.1016\/j.apenergy.2014.07.064","volume":"132","author":"J Che","year":"2014","unstructured":"Che J, Wang J. Short-term load forecasting using a kernel-based support vector regression combination model. Appl Energy. 2014;132:602\u20139.","journal-title":"Appl Energy"},{"key":"2926_CR15","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1016\/j.aei.2017.11.002","volume":"35","author":"MS Al-Musaylh","year":"2018","unstructured":"Al-Musaylh MS, Deo RC, Adamowski JF, Li Y. Short-term electricity demand forecasting with MARS, SVR and Arima models using aggregated demand data in Queensland, Australia. Adv Eng Inform. 2018;35:1\u201316.","journal-title":"Adv Eng Inform"},{"key":"2926_CR16","first-page":"2021","volume":"1\u20139","author":"G Zhu","year":"2021","unstructured":"Zhu G, Peng S, Lao Y, Su Q, Sun Q. Short-term electricity consumption forecasting based on the EMD-Fbprophet-LSTM method. Math Problems Eng. 2021;1\u20139:2021.","journal-title":"Math Problems Eng"},{"key":"2926_CR17","doi-asserted-by":"publisher","first-page":"2158","DOI":"10.3390\/en15062158","volume":"15","author":"S Jamal Ahmed","year":"2022","unstructured":"Jamal Ahmed S, Omar F, Simon FY. Forecasting of electric load using a hybrid LSTM-neural prophet model. Energies. 2022;15:2158.","journal-title":"Energies"},{"key":"2926_CR18","doi-asserted-by":"publisher","first-page":"136","DOI":"10.1016\/j.ijepes.2015.11.046","volume":"77","author":"A Laouafi","year":"2016","unstructured":"Laouafi A, Mordjaoui M, Laouafi F, Boukelia TE. Daily peak electricity demand forecasting based on an adaptive hybrid two-stage methodology. Int J Electr Power Energy Syst. 2016;77:136\u201344.","journal-title":"Int J Electr Power Energy Syst"},{"key":"2926_CR19","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1155\/2020\/4181045","volume":"2020","author":"A Acakpovi","year":"2020","unstructured":"Acakpovi A, Ternor AT, Asabere NY, Adjei P, Iddrisu AS. Time series prediction of electricity demand using adaptive neuro-fuzzy inference systems. Math Problems Eng. 2020;2020:1\u201314.","journal-title":"Math Problems Eng"},{"key":"2926_CR20","doi-asserted-by":"publisher","first-page":"291","DOI":"10.1016\/j.apenergy.2016.12.130","volume":"190","author":"Z Yang","year":"2017","unstructured":"Yang Z, Ce L, Lian L. Electricity price forecasting by a hybrid model, combining wavelet transform, Arma and kernel-based extreme learning machine methods. Appl Energy. 2017;190:291\u2013305.","journal-title":"Appl Energy"},{"issue":"5","key":"2926_CR21","doi-asserted-by":"publisher","first-page":"931","DOI":"10.3390\/en12050931","volume":"12","author":"M Kim","year":"2019","unstructured":"Kim M, Choi W, Jeon Y, Liu L. A hybrid neural network model for power demand forecasting. Energies. 2019;12(5):931.","journal-title":"Energies"},{"key":"2926_CR22","doi-asserted-by":"publisher","first-page":"4107","DOI":"10.3390\/en14144107","volume":"14","author":"A Stratigakos","year":"2021","unstructured":"Stratigakos A, Bachoumis A, Vita V, Zafiropoulos E. Short-term net load forecasting with singular spectrum analysis and LSTM neural networks. Energies. 2021;14:4107.","journal-title":"Energies"},{"key":"2926_CR23","unstructured":"Ekonomou L, Oikonomou DS. Application and comparison of several artificial neural networks for forecasting the Hellenic daily electricity demand load. In: AIKED'08: proceedings of the 7th WSEAS international conference on artificial intelligence, knowledge engineering and data bases. 2008."},{"issue":"2","key":"2926_CR24","doi-asserted-by":"publisher","first-page":"512","DOI":"10.1016\/j.energy.2009.10.018","volume":"35","author":"L Ekonomou","year":"2010","unstructured":"Ekonomou L. Greek long-term energy consumption prediction using artificial neural networks. Energy. 2010;35(2):512\u20137.","journal-title":"Energy"},{"key":"2926_CR25","doi-asserted-by":"crossref","unstructured":"Rawal K, Ahmad A. A comparative analysis of supervised machine learning algorithms for electricity demand forecasting. In: Second international conference on power, control and computing technologies (ICPC2T), Raipur, India. 2022.","DOI":"10.1109\/ICPC2T53885.2022.9776960"},{"key":"2926_CR26","doi-asserted-by":"crossref","unstructured":"Atanane A, Benabbou L, Ouafi AE. Electricity demand forecasting: a systematic literature review. In: 14th International conference on intelligent systems: theories and applications (SITA), Morocco. 2023.","DOI":"10.1109\/SITA60746.2023.10373741"},{"issue":"5","key":"2926_CR27","doi-asserted-by":"publisher","first-page":"107305","DOI":"10.1016\/j.tej.2023.107305","volume":"36","author":"G Varelas","year":"2023","unstructured":"Varelas G, Tzimas G, Alefragis P. A new approach in forecasting Greek electricity demand: from high dimensional hourly series to univariate series transformation. Electr J. 2023;36(5):107305.","journal-title":"Electr J"},{"key":"2926_CR28","doi-asserted-by":"publisher","DOI":"10.1007\/b98888","volume-title":"Functional data analysis","author":"JO Ramsay","year":"2005","unstructured":"Ramsay JO, Silverman BW. Functional data analysis. Berlin: Springer; 2005."},{"key":"2926_CR29","volume-title":"Nonparametric functional data analysis","author":"F Ferraty","year":"2006","unstructured":"Ferraty F, Vieu P. Nonparametric functional data analysis. Berlin: Springer; 2006."},{"issue":"1","key":"2926_CR30","doi-asserted-by":"publisher","first-page":"152","DOI":"10.1080\/02664763.2012.740619","volume":"40","author":"HL Shang","year":"2013","unstructured":"Shang HL. Functional time series approach for forecasting very short-term electricity demand. J Appl Stat. 2013;40(1):152\u201368.","journal-title":"J Appl Stat"},{"issue":"1","key":"2926_CR31","doi-asserted-by":"publisher","first-page":"75","DOI":"10.32479\/ijeep.13728","volume":"13","author":"JB Mar\u00edn","year":"2023","unstructured":"Mar\u00edn JB, Marulanda LM, Duque FV. Analyzing electricity demand in Colombia: a functional time series approach. Int J Energy Econ Policy. 2023;13(1):75\u201384.","journal-title":"Int J Energy Econ Policy"},{"key":"2926_CR32","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1155\/2022\/3007572","volume":"2022","author":"I Shah","year":"2022","unstructured":"Shah I, Jan F, Ali S. Functional data approach for short-term electricity demand forecasting. Math Problems Eng. 2022;2022:1\u201314.","journal-title":"Math Problems Eng"},{"issue":"6","key":"2926_CR33","doi-asserted-by":"publisher","first-page":"e0218702","DOI":"10.1371\/journal.pone.0218702","volume":"14","author":"M Fontana","year":"2019","unstructured":"Fontana M, Tavoni M, Vantini S. Functional data analysis of high-frequency load curves reveals drivers of residential electricity consumption. PLoS ONE. 2019;14(6):e0218702.","journal-title":"PLoS ONE"},{"issue":"12","key":"2926_CR34","doi-asserted-by":"publisher","first-page":"2027","DOI":"10.1080\/02664760903214395","volume":"37","author":"J Antoch","year":"2010","unstructured":"Antoch J, Prchal L, Rosaria De Rosa M, Sarda P. Electricity consumption prediction with functional linear regression using spline estimators. J Appl Stat. 2010;37(12):2027\u201341.","journal-title":"J Appl Stat"},{"issue":"2","key":"2926_CR35","doi-asserted-by":"publisher","first-page":"1035","DOI":"10.1109\/TPWRS.2005.846054","volume":"20","author":"AJ Conejo","year":"2005","unstructured":"Conejo AJ, Plazas MA, Espinola R, Molina AB. Day-ahead electricity price forecasting using the wavelet transform and ARIMA models. IEEE Trans Power Syst. 2005;20(2):1035\u201342.","journal-title":"IEEE Trans Power Syst"},{"issue":"1","key":"2926_CR36","doi-asserted-by":"publisher","first-page":"545","DOI":"10.1109\/TPWRS.2017.2700287","volume":"33","author":"JP Gonz\u00e1lez","year":"2018","unstructured":"Gonz\u00e1lez JP, Mu\u00f1oz San Roque AMS, P\u00e9rez EA. Forecasting functional time series with a new Hilbertian ARMAX model: application to electricity price forecasting. IEEE Trans Power Syst. 2018;33(1):545\u201356.","journal-title":"IEEE Trans Power Syst"},{"issue":"2","key":"2926_CR37","doi-asserted-by":"publisher","first-page":"269","DOI":"10.1016\/j.jeconom.2017.08.008","volume":"201","author":"D Benatia","year":"2017","unstructured":"Benatia D, Carrasco M, Florens J-P. Functional linear regression with functional response. J Econom. 2017;201(2):269\u201391.","journal-title":"J Econom"},{"issue":"470","key":"2926_CR38","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1198\/016214504000001745","volume":"100","author":"F Yao","year":"2005","unstructured":"Yao F, M\u00fcller H-G, Wang J-L. Functional data analysis for sparse longitudinal data. J Am Stat Assoc. 2005;100(470):577\u201390.","journal-title":"J Am Stat Assoc"},{"key":"2926_CR39","volume-title":"Schaum\u2019s outline of differential geometry","author":"MM Lipschutz","year":"1974","unstructured":"Lipschutz MM. Schaum\u2019s outline of differential geometry. McGraw Hill Professional. 1974."},{"key":"2926_CR40","doi-asserted-by":"publisher","first-page":"377","DOI":"10.1007\/BF01404567","volume":"31","author":"P Craven","year":"1979","unstructured":"Craven P, Wahba G. Smoothing noisy data with spline functions: estimating the correct degree of smoothing by the method of generalized cross-validation. Numer Math. 1979;31:377\u2013403.","journal-title":"Numer Math"},{"issue":"1","key":"2926_CR41","doi-asserted-by":"publisher","first-page":"64","DOI":"10.32614\/RJ-2013-006","volume":"5","author":"HL Shang","year":"2013","unstructured":"Shang HL, Hyndman RJ. ftsa: an R package for analyzing functional time series. R J. 2013;5(1):64.","journal-title":"R J"},{"key":"2926_CR42","unstructured":"Hyndman R. Simple exponential smoothing. https:\/\/otexts.com\/fpp2\/ses.html. Accessed 25 June 2023."},{"key":"2926_CR43","unstructured":"Ucenic C, Atsalakis G. A neuro-fuzzy approach to forecast the electricity demand. In: Proceedings of the 2006 IASME\/WSEAS international conference on energy & environmental systems, Chalkida. 2006."}],"container-title":["SN Computer Science"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-02926-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s42979-024-02926-x\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s42979-024-02926-x.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,5,20]],"date-time":"2024-05-20T12:25:11Z","timestamp":1716207911000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s42979-024-02926-x"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,5,20]]},"references-count":43,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2024,6]]}},"alternative-id":["2926"],"URL":"https:\/\/doi.org\/10.1007\/s42979-024-02926-x","relation":{},"ISSN":["2661-8907"],"issn-type":[{"value":"2661-8907","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,5,20]]},"assertion":[{"value":"11 August 2023","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 April 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"20 May 2024","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Does not apply.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical and Informed Consent for Data Used."}},{"value":"The authors declare that there is no conflict of interest.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"566"}}