{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,7]],"date-time":"2026-03-07T04:39:28Z","timestamp":1772858368453,"version":"3.50.1"},"reference-count":66,"publisher":"Springer Science and Business Media LLC","issue":"7","license":[{"start":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T00:00:00Z","timestamp":1742601600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T00:00:00Z","timestamp":1742601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100014597","name":"Universidade da Coru\u00f1a","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100014597","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2025,5]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>In recent years, growing concern about climate change and the need to reduce greenhouse gas emissions have highlighted the role of energy efficiency and sustainability on the global agenda. Energy policies are decisive in establishing regulatory frameworks and incentives to address these challenges, leading to an inclusive and more resilient energy transition. In this context, geothermal energy is an essential source of renewable, low-emission energy, capable of providing heat and electricity sustainably. The present research focuses on a bioclimatic house\u2019s geothermal energy system based on a heating pump and a horizontal heat exchanger. The main aim is to predict the generated thermal power of the heat pump using historical data from several sensors. In particular, two approaches were proposed with both uni-variate and multi-variate scenarios. Several deep learning techniques were applied: LSTM, GRU, 1D-CNN, CNN-LSTM, and CNN-GRU, obtaining satisfactory results over the whole dataset, which comprised one year of data acquisition. Specifically, promising results have been achieved using hybrid methods combining recurrent-based and convolutional neural networks.<\/jats:p>","DOI":"10.1007\/s10489-025-06457-7","type":"journal-article","created":{"date-parts":[[2025,3,22]],"date-time":"2025-03-22T23:16:00Z","timestamp":1742685360000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["A new deep learning-based approach for predicting the geothermal heat pump\u2019s thermal power of a real bioclimatic house"],"prefix":"10.1007","volume":"55","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-0994-1961","authenticated-orcid":false,"given":"Francisco","family":"Zayas-Gato","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-6771-5211","authenticated-orcid":false,"given":"Antonio","family":"D\u00edaz-Longueira","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-6706-5519","authenticated-orcid":false,"given":"Paula","family":"Arcano-Bea","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0134-5660","authenticated-orcid":false,"given":"\u00c1lvaro","family":"Michelena","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-2333-8405","authenticated-orcid":false,"given":"Jose Luis","family":"Calvo-Rolle","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0625-359X","authenticated-orcid":false,"given":"Esteban","family":"Jove","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,22]]},"reference":[{"key":"6457_CR1","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.enconman.2017.04.019","volume":"143","author":"MD Al-Falahi","year":"2017","unstructured":"Al-Falahi MD, Jayasinghe S, Enshaei H (2017) A review on recent size optimization methodologies for standalone solar and wind hybrid renewable energy system. Energy Conver Manage 143:252\u2013274","journal-title":"Energy Conver Manage"},{"key":"6457_CR2","first-page":"1","volume":"5","author":"JG Olivier","year":"2017","unstructured":"Olivier JG, Schure K, Peters J et al (2017) Trends in global co2 and total greenhouse gas emissions. PBL Netherlands Environment Assess Agency 5:1\u201311","journal-title":"PBL Netherlands Environment Assess Agency"},{"key":"6457_CR3","doi-asserted-by":"publisher","unstructured":"Gielen D, Boshell F, Saygin D, Bazilian MD, Wagner N, Gorini R (2019) The role of renewable energy in the global energy transformation. Energy Strat Rev 24:38\u201350. https:\/\/doi.org\/10.1016\/J.ESR.2019.01.006","DOI":"10.1016\/J.ESR.2019.01.006"},{"key":"6457_CR4","unstructured":"Union E (2024) Renewable energy targets"},{"key":"6457_CR5","unstructured":"Nations U (2024) Global Sustainable Development Report (GSDR) 2023"},{"key":"6457_CR6","doi-asserted-by":"publisher","unstructured":"Ang TZ, Salem M, Kamarol M, Das HS, Nazari MA, Prabaharan N (2022) A comprehensive study of renewable energy sources: Classifications, challenges and suggestions. Energy Strat Rev 43:100939. https:\/\/doi.org\/10.1016\/J.ESR.2022.100939","DOI":"10.1016\/J.ESR.2022.100939"},{"key":"6457_CR7","doi-asserted-by":"publisher","unstructured":"Gupta J, Chakraborty M (2020) Energy efficiency in buildings. Sustainable Fuel Technologies Handbook, 457\u2013480. https:\/\/doi.org\/10.1016\/B978-0-12-822989-7.00016-0","DOI":"10.1016\/B978-0-12-822989-7.00016-0"},{"key":"6457_CR8","doi-asserted-by":"publisher","unstructured":"Feist W, Schnieders J, Dorer V, Haas A (2005) Re-inventing air heating: Convenient and comfortable within the frame of the passive house concept. Energy Build 37. https:\/\/doi.org\/10.1016\/j.enbuild.2005.06.020","DOI":"10.1016\/j.enbuild.2005.06.020"},{"key":"6457_CR9","doi-asserted-by":"publisher","unstructured":"Hacene MAB, Sari NEC (2020) Energy efficient design optimization of a bioclimatic house. Indoor and Built Environment 29. https:\/\/doi.org\/10.1177\/1420326X19856668","DOI":"10.1177\/1420326X19856668"},{"key":"6457_CR10","doi-asserted-by":"publisher","unstructured":"Manzano-Agugliaro F, Montoya FG, Sabio-Ortega A, Garc\u00eda-Cruz A (2015) Review of bioclimatic architecture strategies for achieving thermal comfort. Renew Sustain Energy Rev 49:736\u2013755. https:\/\/doi.org\/10.1016\/J.RSER.2015.04.095","DOI":"10.1016\/J.RSER.2015.04.095"},{"key":"6457_CR11","doi-asserted-by":"publisher","unstructured":"Zoure AN, Genovese PV (2022) Development of bioclimatic passive designs for office building in burkina faso. Sustainability (Switzerland) 14 https:\/\/doi.org\/10.3390\/su14074332","DOI":"10.3390\/su14074332"},{"key":"6457_CR12","doi-asserted-by":"publisher","unstructured":"Ahmed A, Ge T, Peng J, Yan WC, Tee BT, You S (2022) Assessment of the renewable energy generation towards net-zero energy buildings: A review. Energy Build 256:111755. https:\/\/doi.org\/10.1016\/J.ENBUILD.2021.111755","DOI":"10.1016\/J.ENBUILD.2021.111755"},{"key":"6457_CR13","doi-asserted-by":"publisher","unstructured":"Chel A, Kaushik G (2018) Renewable energy technologies for sustainable development of energy efficient building. Alexandria Eng J 57:655\u2013669. https:\/\/doi.org\/10.1016\/J.AEJ.2017.02.027","DOI":"10.1016\/J.AEJ.2017.02.027"},{"key":"6457_CR14","doi-asserted-by":"publisher","unstructured":"Christopher S, Vikram MP, Bakli C, Thakur AK, Ma Y, Ma Z, Xu H, Cuce PM, Cuce E, Singh P (2023) Renewable energy potential towards attainment of net-zero energy buildings status \u2013 a critical review. J Cleaner Product 405:136942. https:\/\/doi.org\/10.1016\/J.JCLEPRO.2023.136942","DOI":"10.1016\/J.JCLEPRO.2023.136942"},{"key":"6457_CR15","doi-asserted-by":"crossref","unstructured":"Jenssen T (2013) Glances at Renewable and Sustainable Energy. Springer, ???","DOI":"10.1007\/978-1-4471-5137-1"},{"key":"6457_CR16","doi-asserted-by":"publisher","first-page":"49","DOI":"10.1016\/j.energy.2018.12.207","volume":"171","author":"B Baruque","year":"2019","unstructured":"Baruque B, Porras S, Jove E, Calvo-Rolle JL (2019) Geothermal heat exchanger energy prediction based on time series and monitoring sensors optimization. Energy 171:49\u201360","journal-title":"Energy"},{"key":"6457_CR17","doi-asserted-by":"publisher","unstructured":"Ozgener L (2011) A review on the experimental and analytical analysis of earth to air heat exchanger (eahe) systems in turkey. Renew Sustain Energy Rev 15:4483\u20134490. https:\/\/doi.org\/10.1016\/J.RSER.2011.07.103","DOI":"10.1016\/J.RSER.2011.07.103"},{"key":"6457_CR18","doi-asserted-by":"crossref","unstructured":"Kaka\u00e7 S, Liu H, Pramuanjaroenkij A (2002) Heat Exchangers: Selection, Rating, and Thermal Design. CRC press, ???","DOI":"10.1201\/9781420053746"},{"key":"6457_CR19","unstructured":"Sauer HJ, Howell RH (1983) Heat pump systems"},{"key":"6457_CR20","doi-asserted-by":"publisher","unstructured":"Yan B, Hao F, Meng X (2021) When artificial intelligence meets building energy efficiency, a review focusing on zero energy building. Artif Intell Rev 54. https:\/\/doi.org\/10.1007\/s10462-020-09902-w","DOI":"10.1007\/s10462-020-09902-w"},{"key":"6457_CR21","doi-asserted-by":"publisher","unstructured":"Mehmood MU, Chun D, Zeeshan Han H, Jeon G, Chen K (2019) A review of the applications of artificial intelligence and big data to buildings for energy-efficiency and a comfortable indoor living environment. Energy and Buildings 202:109383. https:\/\/doi.org\/10.1016\/J.ENBUILD.2019.109383","DOI":"10.1016\/J.ENBUILD.2019.109383"},{"key":"6457_CR22","doi-asserted-by":"publisher","DOI":"10.1016\/j.rser.2021.110969","author":"GH Merabet","year":"2021","unstructured":"Merabet GH, Essaaidi M, Haddou MB, Qolomany B, Qadir J, Anan M, Al-Fuqaha A, Abid MR, Benhaddou D (2021). Intell Build Contr Syst Thermal Comfort Energy-Efficiency: A Syst Rev Artif Intell-Assist Technique. https:\/\/doi.org\/10.1016\/j.rser.2021.110969","journal-title":"Intell Build Contr Syst Thermal Comfort Energy-Efficiency: A Syst Rev Artif Intell-Assist Technique"},{"key":"6457_CR23","doi-asserted-by":"publisher","first-page":"62","DOI":"10.1002\/2475-8876.12135","volume":"3","author":"B Nepal","year":"2020","unstructured":"Nepal B, Yamaha M, Yokoe A, Yamaji T (2020) Electricity load forecasting using clustering and arima model for energy management in buildings. Japan Architect Rev 3:62\u201376","journal-title":"Japan Architect Rev"},{"key":"6457_CR24","doi-asserted-by":"publisher","first-page":"6356","DOI":"10.1007\/s11227-020-03540-3","volume":"77","author":"MA Alduailij","year":"2021","unstructured":"Alduailij MA, Petri I, Rana O, Alduailij MA, Aldawood AS (2021) Forecasting peak energy demand for smart buildings. The J Supercomput 77:6356\u20136380","journal-title":"The J Supercomput"},{"key":"6457_CR25","doi-asserted-by":"publisher","first-page":"1934","DOI":"10.3390\/en12101934","volume":"12","author":"F Divina","year":"2019","unstructured":"Divina F, Torres MG, Vela FAG, Noguera JLV (2019) A comparative study of time series forecasting methods for short term electric energy consumption prediction in smart buildings. Energies 12:1934","journal-title":"Energies"},{"key":"6457_CR26","unstructured":"Gangwani P, Soni J, Upadhyay H, Joshi S (2020) A deep learning approach for modeling of geothermal energy prediction. Int J Comput Sci Inf Secur (IJCSIS) 18(1)"},{"key":"6457_CR27","doi-asserted-by":"publisher","unstructured":"Ahmed N, Assadi M, Zhang Q, \u015aliwa T (2024) Data-driven insights for improved heating and cooling predictions: Impact of input parameters on multivariate deep learning algorithms using geothermal borehole field data. Appl Thermal Eng 245:122870. https:\/\/doi.org\/10.1016\/J.APPLTHERMALENG.2024.122870","DOI":"10.1016\/J.APPLTHERMALENG.2024.122870"},{"key":"6457_CR28","doi-asserted-by":"publisher","unstructured":"Chaoran W, (Bill) YX, Chanjuan H (2024) Performance prediction of a ground source heat pump system using denoised long short-term memory neural network optimised by fast non-dominated sorting genetic algorithm-ii. Geothermics 120:103002. https:\/\/doi.org\/10.1016\/J.GEOTHERMICS.2024.103002","DOI":"10.1016\/J.GEOTHERMICS.2024.103002"},{"key":"6457_CR29","doi-asserted-by":"crossref","unstructured":"Li Y, Peng G, Du T, Jiang L, Kong X-Z (2024) Advancing fractured geothermal system modeling with artificial neural network and bidirectional gated recurrent unit. Appl Energy 372:123826","DOI":"10.1016\/j.apenergy.2024.123826"},{"key":"6457_CR30","doi-asserted-by":"crossref","unstructured":"Eom YH, Chung Y, Park M, Hong SB, Kim MS (2021) Deep learning-based prediction method on performance change of air source heat pump system under frosting conditions. Energy 228:120542","DOI":"10.1016\/j.energy.2021.120542"},{"key":"6457_CR31","doi-asserted-by":"crossref","unstructured":"Yang Y, Zhang Y, Cheng Y, Lei Z, Gao X, Huang Y, Ma Y (2023) Using one-dimensional convolutional neural networks and data augmentation to predict thermal production in geothermal fields. J Cleaner Product 387:135879","DOI":"10.1016\/j.jclepro.2023.135879"},{"key":"6457_CR32","doi-asserted-by":"publisher","unstructured":"Jiang A, Qin Z, Faulder D, Cladouhos TT, Jafarpour B (2022) Recurrent neural networks for short-term and long-term prediction of geothermal reservoirs. Geothermics 104:102439. https:\/\/doi.org\/10.1016\/j.geothermics.2022.102439","DOI":"10.1016\/j.geothermics.2022.102439"},{"key":"6457_CR33","doi-asserted-by":"crossref","unstructured":"Zhang W, Zhou H, Bao X, Cui H (2023) Outlet water temperature prediction of energy pile based on spatial-temporal feature extraction through cnn-lstm hybrid model. Energy 264:126190","DOI":"10.1016\/j.energy.2022.126190"},{"key":"6457_CR34","doi-asserted-by":"crossref","unstructured":"Ahmed N, Assadi M, Zhang Q (2023) Investigating the impact of borehole field data\u2019s input parameters on the forecasting accuracy of multivariate hybrid deep learning models for heating and cooling. Energy and Buildings 301:113706","DOI":"10.1016\/j.enbuild.2023.113706"},{"key":"6457_CR35","doi-asserted-by":"crossref","unstructured":"Porras S, Jove E, Baruque B, Calvo-Rolle JL (2021) Analysis of the seasonality in a geothermal system using projectionist and clustering methods. In: Hybrid Artificial Intelligent Systems: 16th International Conference, HAIS 2021, Bilbao, Spain, 22\u201324 September, 2021, Proceedings 16, pp 500\u2013510. Springer","DOI":"10.1007\/978-3-030-86271-8_42"},{"key":"6457_CR36","doi-asserted-by":"crossref","unstructured":"Baruque B, Jove E, Porras S, Calvo-Rolle JL (2019) Study of data pre-processing for short-term prediction of heat exchanger behaviour using time series. In: Hybrid Artificial Intelligent Systems: 14th International Conference, HAIS 2019, Le\u00f3n, Spain, 4\u20136 September, 2019, Proceedings 14, pp 38\u201349 . Springer","DOI":"10.1007\/978-3-030-29859-3_4"},{"key":"6457_CR37","doi-asserted-by":"publisher","unstructured":"Massey FJ (1951) The kolmogorov-smirnov test for goodness of fit. J American Stat Ass 46 . https:\/\/doi.org\/10.1080\/01621459.1951.10500769","DOI":"10.1080\/01621459.1951.10500769"},{"issue":"10","key":"6457_CR38","doi-asserted-by":"publisher","first-page":"1149","DOI":"10.3390\/electronics10101149","volume":"10","author":"P Oliveira","year":"2021","unstructured":"Oliveira P, Fernandes B, Analide C, Novais P (2021) Forecasting energy consumption of wastewater treatment plants with a transfer learning approach for sustainable cities. Electron 10(10):1149","journal-title":"Electron"},{"key":"6457_CR39","doi-asserted-by":"publisher","unstructured":"Schober P, Schwarte LA (2018) Correlation coefficients: Appropriate use and interpretation. Anesthesia and Analgesia 126. https:\/\/doi.org\/10.1213\/ANE.0000000000002864","DOI":"10.1213\/ANE.0000000000002864"},{"key":"6457_CR40","doi-asserted-by":"publisher","unstructured":"Wenya L (2021) Cooling, heating and electric load forecasting for integrated energy systems based on cnn-lstm. https:\/\/doi.org\/10.1109\/ICPRE52634.2021.9635244","DOI":"10.1109\/ICPRE52634.2021.9635244"},{"issue":"8","key":"6457_CR41","doi-asserted-by":"publisher","first-page":"1735","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):1735\u20131780","journal-title":"Neural Comput"},{"key":"6457_CR42","doi-asserted-by":"crossref","unstructured":"Siami-Namini S, Tavakoli N, Namin AS (2018) A comparison of arima and lstm in forecasting time series. In: 2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA), pp 1394\u20131401. IEEE","DOI":"10.1109\/ICMLA.2018.00227"},{"issue":"4","key":"6457_CR43","doi-asserted-by":"publisher","first-page":"235","DOI":"10.2478\/jaiscr-2019-0006","volume":"9","author":"A Shewalkar","year":"2019","unstructured":"Shewalkar A, Nyavanandi D, Ludwig SA (2019) Performance evaluation of deep neural networks applied to speech recognition: Rnn, lstm and gru. J Artif Intell Soft Comput Res 9(4):235\u2013245","journal-title":"J Artif Intell Soft Comput Res"},{"issue":"10","key":"6457_CR44","doi-asserted-by":"publisher","first-page":"2451","DOI":"10.1162\/089976600300015015","volume":"12","author":"FA Gers","year":"2000","unstructured":"Gers FA, Schmidhuber J, Cummins F (2000) Learning to forget: Continual prediction with lstm. Neural Comput 12(10):2451\u20132471","journal-title":"Neural Comput"},{"issue":"7","key":"6457_CR45","doi-asserted-by":"publisher","first-page":"1406","DOI":"10.3390\/pr10071406","volume":"10","author":"V L\u00f3pez","year":"2022","unstructured":"L\u00f3pez V, Jove E, Zayas Gato F, Pinto-Santos F, Pi\u00f1\u00f3n-Pazos AJ, Casteleiro-Roca J-L, Quintian H, Calvo-Rolle JL (2022) Intelligent model for power cells state of charge forecasting in ev. Process 10(7):1406","journal-title":"Process"},{"issue":"7","key":"6457_CR46","doi-asserted-by":"publisher","first-page":"1235","DOI":"10.1162\/neco_a_01199","volume":"31","author":"Y Yu","year":"2019","unstructured":"Yu Y, Si X, Hu C, Zhang J (2019) A review of recurrent neural networks: Lstm cells and network architectures. Neural Comput 31(7):1235\u20131270","journal-title":"Neural Comput"},{"key":"6457_CR47","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1007\/978-3-642-46466-9_18","volume-title":"Competition and Cooperation in Neural Nets","author":"K Fukushima","year":"1982","unstructured":"Fukushima K, Miyake S (1982) Neocognitron: A self-organizing neural network model for a mechanism of visual pattern recognition. In: Amari S-I, Arbib MA (eds) Competition and Cooperation in Neural Nets. Springer, Berlin, Heidelberg, pp 267\u2013285"},{"issue":"11","key":"6457_CR48","doi-asserted-by":"publisher","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y Lecun","year":"1998","unstructured":"Lecun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proceed IEEE 86(11):2278\u20132324. https:\/\/doi.org\/10.1109\/5.726791","journal-title":"Proceed IEEE"},{"issue":"1","key":"6457_CR49","doi-asserted-by":"publisher","first-page":"106","DOI":"10.1113\/jphysiol.1962.sp006837","volume":"160","author":"DH Hubel","year":"1962","unstructured":"Hubel DH, Wiesel TN (1962) Receptive fields, binocular interaction and functional architecture in the cat\u2019s visual cortex. The J Physio 160(1):106","journal-title":"The J Physio"},{"key":"6457_CR50","doi-asserted-by":"publisher","DOI":"10.1109\/ICARCV.2014.7064414","author":"Q Li","year":"2014","unstructured":"Li Q, Cai W, Wang X, Zhou Y, Feng DD, Chen M (2014). Medical image classification with convolutional neural network. https:\/\/doi.org\/10.1109\/ICARCV.2014.7064414","journal-title":"Medical image classification with convolutional neural network."},{"key":"6457_CR51","doi-asserted-by":"publisher","unstructured":"Chen C, Liu MY, Tuzel O, Xiao J (2017) R-cnn for small object detection, vol 10115 LNCS. https:\/\/doi.org\/10.1007\/978-3-319-54193-8_14","DOI":"10.1007\/978-3-319-54193-8_14"},{"key":"6457_CR52","doi-asserted-by":"publisher","DOI":"10.1109\/BIGCOMP.2017.7881726","author":"D Xishuang","year":"2017","unstructured":"Xishuang D, Lijun Q, Lei H (2017). Short-term load forecasting in smart grid: A combined cnn and k-means clustering approach. https:\/\/doi.org\/10.1109\/BIGCOMP.2017.7881726","journal-title":"Short-term load forecasting in smart grid: A combined cnn and k-means clustering approach."},{"key":"6457_CR53","doi-asserted-by":"publisher","unstructured":"Hussain D, Hussain T, Khan AA, Naqvi SAA, Jamil A (2020) A deep learning approach for hydrological time-series prediction: A case study of gilgit river basin. Earth Sci Inf 13. https:\/\/doi.org\/10.1007\/s12145-020-00477-2","DOI":"10.1007\/s12145-020-00477-2"},{"key":"6457_CR54","doi-asserted-by":"publisher","unstructured":"Liu H, Mi X, Li Y (2018) Smart deep learning based wind speed prediction model using wavelet packet decomposition, convolutional neural network and convolutional long short term memory network. Energy Convers Manage 166. https:\/\/doi.org\/10.1016\/j.enconman.2018.04.021","DOI":"10.1016\/j.enconman.2018.04.021"},{"key":"6457_CR55","doi-asserted-by":"publisher","unstructured":"Kiranyaz S, Avci O, Abdeljaber O, Ince T, Gabbouj M, Inman DJ (2021) 1d convolutional neural networks and applications: A survey. Mechanic Syst Signal Process 151. https:\/\/doi.org\/10.1016\/j.ymssp.2020.107398","DOI":"10.1016\/j.ymssp.2020.107398"},{"key":"6457_CR56","doi-asserted-by":"publisher","unstructured":"Rick R, Berton L (2022) Energy forecasting model based on cnn-lstm-ae for many time series with unequal lengths. Eng Appl Artif Intell 113:104998. https:\/\/doi.org\/10.1016\/J.ENGAPPAI.2022.104998","DOI":"10.1016\/J.ENGAPPAI.2022.104998"},{"key":"6457_CR57","doi-asserted-by":"publisher","unstructured":"Ferreira LB, Cunha FF (2020) Multi-step ahead forecasting of daily reference evapotranspiration using deep learning. Comput Electron Agri 178:105728. https:\/\/doi.org\/10.1016\/J.COMPAG.2020.105728","DOI":"10.1016\/J.COMPAG.2020.105728"},{"key":"6457_CR58","doi-asserted-by":"publisher","unstructured":"Liu H, Mi X, Li Y, Duan Z, Xu Y (2019) Smart wind speed deep learning based multi-step forecasting model using singular spectrum analysis, convolutional gated recurrent unit network and support vector regression. Renew Energy 143:842\u2013854. https:\/\/doi.org\/10.1016\/J.RENENE.2019.05.039","DOI":"10.1016\/J.RENENE.2019.05.039"},{"issue":"9","key":"6457_CR59","doi-asserted-by":"publisher","first-page":"2578","DOI":"10.1175\/1520-0493(1993)121<2578:OESFTA>2.0.CO;2","volume":"121","author":"J Sullivan","year":"1993","unstructured":"Sullivan J, Gandin L, Gruber A, Baker W (1993) Observation error statistics for noaa-10 temperature and height retrievals. Monthly Weather Rev 121(9):2578\u20132587","journal-title":"Monthly Weather Rev"},{"key":"6457_CR60","doi-asserted-by":"crossref","unstructured":"Emanuel KA, \u017divkovi\u0107-Rothman M (1999) Development and evaluation of a convection scheme for use in climate models. J Atmosph Sci 56(11):1766\u20131782. https:\/\/doi.org\/10.1175\/1520-0469(1999)056h1766:DAEOACi2.0.CO;2 Cited by: 620; All Open Access, Bronze Open Access","DOI":"10.1175\/1520-0469(1999)056<1766:DAEOAC>2.0.CO;2"},{"issue":"2","key":"6457_CR61","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1002\/met.1550","volume":"23","author":"J Hyrkk\u00e4nen","year":"2016","unstructured":"Hyrkk\u00e4nen J, Kilpinen J, Nurmi P, Kaurola J, Brockmann M (2016) Error characteristics of temperature forecast in finland for the period 1979\u20132011 in relation to various weather patterns. Meteorologic Appl 23(2):244\u2013253","journal-title":"Meteorologic Appl"},{"key":"6457_CR62","doi-asserted-by":"publisher","unstructured":"Shcherbakov MV, Brebels A, Shcherbakova NL, Tyukov AP, Janovsky TA, Kamaev VA (2013) A survey of forecast error measures. World Appl Sci J 24. https:\/\/doi.org\/10.5829\/idosi.wasj.2013.24.itmies.80032","DOI":"10.5829\/idosi.wasj.2013.24.itmies.80032"},{"issue":"1","key":"6457_CR63","doi-asserted-by":"publisher","first-page":"79","DOI":"10.3354\/cr030079","volume":"30","author":"CJ Willmott","year":"2005","unstructured":"Willmott CJ, Matsuura K (2005) Advantages of the mean absolute error (mae) over the root mean square error (rmse) in assessing average model performance. Climate Res 30(1):79\u201382","journal-title":"Climate Res"},{"key":"6457_CR64","doi-asserted-by":"publisher","unstructured":"Chicco D, Warrens MJ, Jurman G (2021) The coefficient of determination r-squared is more informative than smape, mae, mape, mse and rmse in regression analysis evaluation. Peer J Comput Sci 7. https:\/\/doi.org\/10.7717\/PEERJ-CS.623","DOI":"10.7717\/PEERJ-CS.623"},{"issue":"7","key":"6457_CR65","first-page":"557","volume":"20","author":"S Wright","year":"1921","unstructured":"Wright S (1921) Correlation and causation. J Agri Res 20(7):557","journal-title":"J Agri Res"},{"issue":"22","key":"6457_CR66","doi-asserted-by":"publisher","first-page":"5026","DOI":"10.3390\/s19225026","volume":"19","author":"A Dehghani","year":"2019","unstructured":"Dehghani A, Sarbishei O, Glatard T, Shihab E (2019) A quantitative comparison of overlapping and non-overlapping sliding windows for human activity recognition using inertial sensors. Sensors 19(22):5026","journal-title":"Sensors"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06457-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-025-06457-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-025-06457-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,19]],"date-time":"2025-09-19T19:33:34Z","timestamp":1758310414000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-025-06457-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,22]]},"references-count":66,"journal-issue":{"issue":"7","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["6457"],"URL":"https:\/\/doi.org\/10.1007\/s10489-025-06457-7","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,22]]},"assertion":[{"value":"22 March 2025","order":1,"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 competing interests to declare that are relevant to the content of this article.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing Interests"}}],"article-number":"557"}}