{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,31]],"date-time":"2026-01-31T00:36:10Z","timestamp":1769819770314,"version":"3.49.0"},"reference-count":35,"publisher":"Springer Science and Business Media LLC","issue":"4","license":[{"start":{"date-parts":[[2022,10,25]],"date-time":"2022-10-25T00:00:00Z","timestamp":1666656000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2022,10,25]],"date-time":"2022-10-25T00:00:00Z","timestamp":1666656000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"DOI":"10.13039\/501100007601","name":"Horizon 2020","doi-asserted-by":"publisher","award":["723274"],"award-info":[{"award-number":["723274"]}],"id":[{"id":"10.13039\/501100007601","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Mobile Netw Appl"],"published-print":{"date-parts":[[2023,8]]},"DOI":"10.1007\/s11036-022-02050-1","type":"journal-article","created":{"date-parts":[[2022,10,25]],"date-time":"2022-10-25T14:03:18Z","timestamp":1666706598000},"page":"1293-1305","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["The Fuzzy Logic Predictive Model for Remote Increasing Energy Efficiency"],"prefix":"10.1007","volume":"28","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-1198-5274","authenticated-orcid":false,"given":"Stella","family":"Hrehov\u00e1","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2576-7238","authenticated-orcid":false,"given":"Jozef","family":"Hus\u00e1r","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1925-4038","authenticated-orcid":false,"given":"Lucia","family":"Knap\u010d\u00edkov\u00e1","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2022,10,25]]},"reference":[{"key":"2050_CR1","doi-asserted-by":"publisher","first-page":"2317","DOI":"10.3390\/en13092317","volume":"13","author":"KI Papageorgiou","year":"2020","unstructured":"Papageorgiou KI, Papageorgiou E, Poczeta K, Bochtis D, Stamoulis G (2020) Forecasting of day-ahead natural gas consumption demand in greece using adaptive neuro-fuzzy inference system. Energies 13:2317. https:\/\/doi.org\/10.3390\/en13092317","journal-title":"Energies"},{"key":"2050_CR2","doi-asserted-by":"publisher","unstructured":"Hosovsky A, Pitel J, Mizakova J, Zidek K (2018) Introductory analysis of gas comsumption time series in nonresidental buildings fro prediction purposes using Wavelet decomposition. MM Sci J V:58. https:\/\/doi.org\/10.17973\/MMSJ.2018_12_201858","DOI":"10.17973\/MMSJ.2018_12_201858"},{"key":"2050_CR3","doi-asserted-by":"publisher","first-page":"1645","DOI":"10.1016\/j.future.2013.01.010","volume":"29","author":"J Gubbi","year":"2013","unstructured":"Gubbi J, Buyya R, Marusic S, Palaniswami M (2013) Internet of Things (IoT): A vision, architectural elements, and future directions. Futur Gener Comput Syst 29:1645\u20131660. https:\/\/doi.org\/10.1016\/j.future.2013.01.010","journal-title":"Futur Gener Comput Syst"},{"key":"2050_CR4","doi-asserted-by":"publisher","first-page":"713","DOI":"10.3390\/atmos12060713","volume":"12","author":"O Taylan","year":"2021","unstructured":"Taylan O, Alkabaa AS, Alamoudi M, Basahel A, Balubaid M, Andejany M, Alidrisi H (2021) Air quality modeling for sustainable clean environment using ANFIS and machine learning approaches. Atmosphere 12:713. https:\/\/doi.org\/10.3390\/atmos12060713","journal-title":"Atmosphere"},{"key":"2050_CR5","doi-asserted-by":"publisher","unstructured":"Hamrol A, Ciszak O, Legutko S, Jurczyk M (2018) Development of an intelligent and automated system for lean industrial production, adding maximum productivity and efficiency in the production process. Adv Manuf 131\u2013140, Lecture Notes in Mechanical Engineering. Springer, Cham. https:\/\/doi.org\/10.1007\/978-3-319-68619-6_13","DOI":"10.1007\/978-3-319-68619-6_13"},{"key":"2050_CR6","doi-asserted-by":"publisher","first-page":"2669","DOI":"10.1016\/j.enconman.2005.02.004","volume":"46","author":"PF Pai","year":"2005","unstructured":"Pai PF, Hong WC (2005) Support vector machines with simulated annealing algorithms in electricity load forecasting. Energy Convers Manag 46:2669\u20132688. https:\/\/doi.org\/10.1016\/j.enconman.2005.02.004","journal-title":"Energy Convers Manag"},{"key":"2050_CR7","doi-asserted-by":"publisher","first-page":"44","DOI":"10.1109\/59.910780","volume":"16","author":"HS Hippert","year":"2001","unstructured":"Hippert HS, Pedreira CE, Souza RC (2001) Neural networks for short-term load forecasting: A review and evaluation. IEEE Trans Power Syst 16:44\u201355. https:\/\/doi.org\/10.1109\/59.910780","journal-title":"IEEE Trans Power Syst"},{"issue":"1","key":"2050_CR8","doi-asserted-by":"publisher","first-page":"108","DOI":"10.1177\/0143624419843647","volume":"41","author":"K Li","year":"2020","unstructured":"Li K, Tan G, Xue W, Denzer A (2020) A state of the art review on the prediction of building energy consumption using data-driven technique and evolutionary algorithms. Build Serv Eng Res Technol 41(1):108\u2013127. https:\/\/doi.org\/10.1177\/0143624419843647","journal-title":"Build Serv Eng Res Technol"},{"key":"2050_CR9","doi-asserted-by":"publisher","first-page":"3586","DOI":"10.1016\/j.rser.2012.02.049","volume":"16","author":"H Zhao","year":"2012","unstructured":"Zhao H, Magoules F (2012) A review on the prediction of building energy consumption. Renew Sustain Energy Rev 16:3586\u20133592. https:\/\/doi.org\/10.1016\/j.rser.2012.02.049","journal-title":"Renew Sustain Energy Rev"},{"key":"2050_CR10","doi-asserted-by":"publisher","unstructured":"Andelkovic\u00a0AS, Bajatovic D (2020) Integration of weather forecast and artificial intelligence for a shortterm city-scale natural gas consumption prediction. J Clean Prod 266:122096.\u00a0https:\/\/doi.org\/10.1016\/j.jclepro.2020.122096","DOI":"10.1016\/j.jclepro.2020.122096"},{"key":"2050_CR11","doi-asserted-by":"publisher","unstructured":"Brown R H, Kharouf P, Feng X, Piessens LP, Nestor D (1994) Development of feed-forward network models to predict gas consumption. In: Proceedings of the 1994 IEEE International Conference on Neural Networks (ICNN'94) 1:802\u2013805. Orlando, FL, USA. https:\/\/doi.org\/10.1109\/ICNN.1994.374281","DOI":"10.1109\/ICNN.1994.374281"},{"key":"2050_CR12","doi-asserted-by":"publisher","unstructured":"Brown RH, Matin I (1995) Development of artificial neural network models to predict daily gas consumption. In: Proceedings of the IECON, '95 - 21st Annual Conference on IEEE Industrial Electronics 2:1389\u20131394. Orlando, FL, USA. https:\/\/doi.org\/10.1109\/IECON.1995.484153","DOI":"10.1109\/IECON.1995.484153"},{"key":"2050_CR13","doi-asserted-by":"publisher","first-page":"6409","DOI":"10.3390\/su12166409","volume":"12","author":"A Anagnostis","year":"2020","unstructured":"Anagnostis A, Papageorgiou E, Bochtis D (2020) Application of artificial neural networks for natural gas consumption forecasting. Sustainability 12:6409. https:\/\/doi.org\/10.3390\/su12166409","journal-title":"Sustainability"},{"issue":"5","key":"2050_CR14","doi-asserted-by":"publisher","first-page":"695","DOI":"10.3906\/elk-1101-1029","volume":"20","author":"\u00d6F Demirel","year":"2012","unstructured":"Demirel \u00d6F, Zaim S, \u00c7ali\u0161kan A, \u00d6zuyar P (2012) Forecasting natural gas consumption in Istanbul using neural networks and multivariate time series methods. Turk J Electr Eng Comput Sci 20(5):695\u2013711. https:\/\/doi.org\/10.3906\/elk-1101-1029","journal-title":"Turk J Electr Eng Comput Sci"},{"key":"2050_CR15","doi-asserted-by":"publisher","unstructured":"Ho\u0161ovsk\u00fd A, Pite\u013e J, Ad\u00e1mek M, Mi\u017e\u00e1kov\u00e1 J, \u017didek K (2020) Comparative study of week-ahead forecasting of daily gas consumption in buildings using regression ARMA\/SARMA and genetic-algorithm-optimized regression wavelet neural network models. J Build Eng 34:101955. https:\/\/doi.org\/10.1016\/j.jobe.2020.101955","DOI":"10.1016\/j.jobe.2020.101955"},{"key":"2050_CR16","doi-asserted-by":"publisher","first-page":"1246","DOI":"10.1016\/j.rser.2008.09.015","volume":"13","author":"AI Dounis","year":"2009","unstructured":"Dounis AI, Caraiscos C (2009) Advanced control systems engineering for energy and comfort management in a building environment\u2014A review. Renew Sustain Energy Rev 13:1246\u20131261. https:\/\/doi.org\/10.1016\/j.rser.2008.09.015","journal-title":"Renew Sustain Energy Rev"},{"key":"2050_CR17","doi-asserted-by":"publisher","first-page":"197","DOI":"10.1016\/J.ENCONMAN.2009.09.012","volume":"51","author":"WS Lee","year":"2010","unstructured":"Lee WS (2010) Evaluating and ranking energy performance of office buildings using fuzzy measure and fuzzy integral. Energy Convers Manag 51:197\u2013203. https:\/\/doi.org\/10.1016\/J.ENCONMAN.2009.09.012","journal-title":"Energy Convers Manag"},{"key":"2050_CR18","doi-asserted-by":"publisher","unstructured":"Dong N, Chang JF, Wu AG, Gao ZK (2020) A novel convolutional neural network framework based solar irradiance prediction method. Electr Power Energy Syst 114:105411. https:\/\/doi.org\/10.1016\/j.ijepes.2019.105411","DOI":"10.1016\/j.ijepes.2019.105411"},{"key":"2050_CR19","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.seta.2018.01.001","volume":"25","author":"A Khosravi","year":"2018","unstructured":"Khosravi A, Koury RNN, Machado L, Pabon JJG (2018) Prediction of wind speed and wind direction using artificial neural network, support vector regression and adaptive neuro-fuzzy inference system. Sustain Energy Technol Assess 25:146\u2013160. https:\/\/doi.org\/10.1016\/j.seta.2018.01.001","journal-title":"Sustain Energy Technol Assess"},{"key":"2050_CR20","doi-asserted-by":"publisher","first-page":"9221","DOI":"10.1016\/j.eswa.2015.08.010","volume":"42","author":"S Barak","year":"2015","unstructured":"Barak S, Dahooie JH, Tich\u00fd T (2015) Wrapper ANFIS-ICA method to do stock market timing and feature selection on the basis of Japanese Candlestick. Exp Syst Appl 42:9221\u20139235. https:\/\/doi.org\/10.1016\/j.eswa.2015.08.010","journal-title":"Exp Syst Appl"},{"key":"2050_CR21","doi-asserted-by":"publisher","first-page":"92","DOI":"10.1016\/j.ijepes.2016.03.012","volume":"82","author":"S Barak","year":"2016","unstructured":"Barak S, Sadegh SS (2016) Forecasting energy consumption using ensemble ARIMA-ANFIS hybrid algorithm. Int J Electr Power Energy Syst 82:92\u2013104. https:\/\/doi.org\/10.1016\/j.ijepes.2016.03.012","journal-title":"Int J Electr Power Energy Syst"},{"key":"2050_CR22","first-page":"470","volume":"65","author":"J Singh","year":"2006","unstructured":"Singh J, Singh N, Sharma JK (2006) Fuzzy modeling and control of HVAC systems \u2013 A review. J Sci Ind Res 65:470\u2013476","journal-title":"J Sci Ind Res"},{"key":"2050_CR23","doi-asserted-by":"publisher","first-page":"6731","DOI":"10.3390\/app11156731","volume":"11","author":"S Markulik","year":"2021","unstructured":"Markulik S, \u0160olc M, Petr\u00edk J, Bal\u00e1\u017eikov\u00e1 M, Bla\u0161ko P, Kliment J, Bez\u00e1k M (2021) Application of FTA analysis for calculation of the probability of the failure of the pressure leaching process. Appl Sci 11:6731. https:\/\/doi.org\/10.3390\/app11156731","journal-title":"Appl Sci"},{"key":"2050_CR24","doi-asserted-by":"publisher","first-page":"720","DOI":"10.3390\/app10020720","volume":"10","author":"\u00c0 Nebot","year":"2020","unstructured":"Nebot \u00c0, Mugica F (2020) Energy performance forecasting of residential buildings using fuzzy approaches. Appl Sci 10:720. https:\/\/doi.org\/10.3390\/app10020720","journal-title":"Appl Sci"},{"key":"2050_CR25","doi-asserted-by":"publisher","first-page":"359","DOI":"10.1016\/j.measurement.2017.06.002","volume":"10916","author":"H Pa\u010daiov\u00e1","year":"2017","unstructured":"Pa\u010daiov\u00e1 H, Sinay J, Turisov\u00e1 R, Hajduov\u00e1 Z, Markulik \u0160 (2017) Measuring the qualitative factors on copper wire surface. Measurement 10916:359\u2013365. https:\/\/doi.org\/10.1016\/j.measurement.2017.06.002","journal-title":"Measurement"},{"key":"2050_CR26","doi-asserted-by":"publisher","first-page":"619","DOI":"10.1016\/j.proeng.2017.05.388","volume":"190","author":"I Corn\u00fd","year":"2017","unstructured":"Corn\u00fd I (2017) Overview of progressive evaluation methods for monitoring of heat production and distribution. Proc Eng 190:619\u2013626. https:\/\/doi.org\/10.1016\/j.proeng.2017.05.388","journal-title":"Proc Eng"},{"key":"2050_CR27","doi-asserted-by":"publisher","first-page":"1628","DOI":"10.3390\/w12061628","volume":"12","author":"SL Zubaidi","year":"2020","unstructured":"Zubaidi SL, Al-Bugharbee H, Ortega-Martorell S, Gharghan SK, Olier I, Hashim KS, Al-Bdairi NSS, Kot P (2020) A novel methodology for prediction urban water demand by wavelet denoising and adaptive neuro-fuzzy inference system approach. Water 12:1628. https:\/\/doi.org\/10.3390\/w12061628","journal-title":"Water"},{"issue":"3","key":"2050_CR28","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2020.121160","volume":"261","author":"L Xu","year":"2020","unstructured":"Xu L, Huang Ch, Li Ch, Wang J, Liu H, Wang X (2020) A novel intelligent reasoning system to estimate energy consumption and optimize cutting parameters toward sustainable machining. J Clean Prod 261(3):121160. https:\/\/doi.org\/10.1016\/j.jclepro.2020.121160","journal-title":"J Clean Prod"},{"issue":"1","key":"2050_CR29","doi-asserted-by":"publisher","first-page":"813","DOI":"10.3233\/JIFS-200962","volume":"40","author":"S Kazemia","year":"2021","unstructured":"Kazemia S, Mavi RK, Emrouznejad A, Kiani M (2021) N. Fuzzy clustering of homogeneous decision making units with common weights in data envelopment analysis. J Intell Fuzzy Syst 40(1):813\u2013832. https:\/\/doi.org\/10.3233\/JIFS-200962","journal-title":"J Intell Fuzzy Syst"},{"key":"2050_CR30","doi-asserted-by":"publisher","unstructured":"Pavlenko I, Simonovskiy V, Ivanov V, Zajac J, Pitel J (2019) Application of Artificial Neural Network for Identification of Bearing Stiffness Characteristics in Rotor Dynamics Analysis. Adv Des Simul Manuf 325\u2013335.\u00a0https:\/\/doi.org\/10.1007\/978-3-319-93587-4_34","DOI":"10.1007\/978-3-319-93587-4_34"},{"key":"2050_CR31","unstructured":"Mandal SN, Choudhury JP, Chaudhuri SRB (2012) In Search of Suitable Fuzzy Membership Function in Prediction of Time Series Data. Int J Comput Sci 9(3):293\u2013302. https:\/\/www.ijcsi.org\/papers\/IJCSI-9-3-3-293-302.pdf"},{"key":"2050_CR32","doi-asserted-by":"publisher","first-page":"239","DOI":"10.3390\/sym13020239","volume":"13","author":"SH Khairuddin","year":"2021","unstructured":"Khairuddin SH, Hasan MH, Hashmani MA, Azam MH (2021) Generating clustering-based interval fuzzy type-2 triangular and trapezoidal membership functions: A structured literature review. Symmetry 13:239. https:\/\/doi.org\/10.3390\/sym13020239","journal-title":"Symmetry"},{"key":"2050_CR33","doi-asserted-by":"publisher","first-page":"12","DOI":"10.1057\/jos.2012.20","volume":"7","author":"RG Sargen","year":"2013","unstructured":"Sargen RG (2013) Verification and validation of simulation models. J Simul 7:12\u201324. https:\/\/doi.org\/10.1057\/jos.2012.20","journal-title":"J Simul"},{"key":"2050_CR34","doi-asserted-by":"publisher","unstructured":"Li Y, Portmann E (2012) A fuzzy risk attitude classification based on prospect theory. In: 2012 International conference on Fuzzy Theory and Its Applications (iFUZZY2012), IEEE, 137\u2013143. https:\/\/doi.org\/10.1109\/iFUZZY.2012.6409689","DOI":"10.1109\/iFUZZY.2012.6409689"},{"key":"2050_CR35","doi-asserted-by":"publisher","first-page":"2839","DOI":"10.17973\/MMSJ.2019_03_201875","volume":"2019","author":"J Kascak","year":"2019","unstructured":"Kascak J, Baron P, Torok J, Pollak M, Teliskova M (2019) Macrostructure Digitalization of the Roadway Surface Profiles. MM Sci J 2019:2839\u20132844. https:\/\/doi.org\/10.17973\/MMSJ.2019_03_201875","journal-title":"MM Sci J"}],"container-title":["Mobile Networks and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11036-022-02050-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11036-022-02050-1\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11036-022-02050-1.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,8,19]],"date-time":"2024-08-19T17:06:44Z","timestamp":1724087204000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11036-022-02050-1"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2022,10,25]]},"references-count":35,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2023,8]]}},"alternative-id":["2050"],"URL":"https:\/\/doi.org\/10.1007\/s11036-022-02050-1","relation":{},"ISSN":["1383-469X","1572-8153"],"issn-type":[{"value":"1383-469X","type":"print"},{"value":"1572-8153","type":"electronic"}],"subject":[],"published":{"date-parts":[[2022,10,25]]},"assertion":[{"value":"9 February 2022","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"25 October 2022","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"Not applicable. Without conflicts of interest\/competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of Interest\/Competing Interests"}}]}}