{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,14]],"date-time":"2026-05-14T06:07:03Z","timestamp":1778738823903,"version":"3.51.4"},"reference-count":98,"publisher":"Springer Science and Business Media LLC","issue":"15","license":[{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T00:00:00Z","timestamp":1740787200000},"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":["Neural Comput & Applic"],"published-print":{"date-parts":[[2025,5]]},"DOI":"10.1007\/s00521-025-11066-z","type":"journal-article","created":{"date-parts":[[2025,3,1]],"date-time":"2025-03-01T05:45:23Z","timestamp":1740807923000},"page":"9095-9127","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Application of spiking neural networks and traditional artificial neural networks for solar radiation forecasting in photovoltaic systems in Arab countries"],"prefix":"10.1007","volume":"37","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3430-9260","authenticated-orcid":false,"given":"Bahgat","family":"Ayasi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5438-7656","authenticated-orcid":false,"given":"Iago X.","family":"V\u00e1zquez","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0008-7254-8071","authenticated-orcid":false,"given":"Mohammed","family":"Saleh","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1583-2128","authenticated-orcid":false,"given":"Angel M.","family":"Garcia-Vico","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7008-5378","authenticated-orcid":false,"given":"Crist\u00f3bal J.","family":"Carmona","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,1]]},"reference":[{"issue":"3","key":"11066_CR1","doi-asserted-by":"publisher","first-page":"256","DOI":"10.1080\/23754931.2020.1870540","volume":"7","author":"S Hamada","year":"2021","unstructured":"Hamada S, Ghodieh A (2021) Mapping of solar energy potential in the west bank, palestine using geographic information systems. Papers Appl Geogr 7(3):256\u2013273. https:\/\/doi.org\/10.1080\/23754931.2020.1870540","journal-title":"Papers Appl Geogr"},{"key":"11066_CR2","unstructured":"United Nations: The 17 Goals. https:\/\/sdgs.un.org\/goals. Accessed: 15-Jan-2024 (2015)"},{"key":"11066_CR3","doi-asserted-by":"publisher","first-page":"2490","DOI":"10.1016\/j.rser.2017.09.008","volume":"82","author":"MA Omar","year":"2018","unstructured":"Omar MA, Mahmoud MM (2018) Grid connected pv home systems in palestine: a review on technical performance, effects and economic feasibility. Renew Sustain Energy Rev 82:2490\u20132497. https:\/\/doi.org\/10.1016\/j.rser.2017.09.008","journal-title":"Renew Sustain Energy Rev"},{"key":"11066_CR4","doi-asserted-by":"publisher","first-page":"318","DOI":"10.1016\/j.seta.2018.12.010","volume":"31","author":"YF Nassar","year":"2019","unstructured":"Nassar YF, Alsadi SY (2019) Assessment of solar energy potential in gaza strip-palestine. Sustain Energy Technol Assess 31:318\u2013328. https:\/\/doi.org\/10.1016\/j.seta.2018.12.010","journal-title":"Sustain Energy Technol Assess"},{"issue":"18","key":"11066_CR5","doi-asserted-by":"publisher","first-page":"13493","DOI":"10.3390\/su151813493","volume":"15","author":"Z Fan","year":"2023","unstructured":"Fan Z, Yan Z, Wen S (2023) Deep learning and artificial intelligence in sustainability: A review of sdgs, renewable energy, and environmental health. Sustainability 15(18):13493. https:\/\/doi.org\/10.3390\/su151813493","journal-title":"Sustainability"},{"issue":"5","key":"11066_CR6","doi-asserted-by":"publisher","first-page":"1","DOI":"10.3390\/su13052996","volume":"13","author":"T Khatib","year":"2021","unstructured":"Khatib T, Bazyan A, Assi H, Malhis S (2021) Palestine energy policy for photovoltaic generation: current status and what should be next? Sustainability (Switzerland) 13(5):1\u201319. https:\/\/doi.org\/10.3390\/su13052996","journal-title":"Sustainability (Switzerland)"},{"key":"11066_CR7","doi-asserted-by":"publisher","DOI":"10.1016\/j.jup.2024.101730","volume":"87","author":"A Biancardi","year":"2024","unstructured":"Biancardi A, D\u2019Adamo I, D\u2019Amore F, Moretti R (2024) Prosumers as drivers of sdg7 in palestine: net-benefit analysis of grid-connected photovoltaic systems. Utilities Policy 87:101730. https:\/\/doi.org\/10.1016\/j.jup.2024.101730","journal-title":"Utilities Policy"},{"key":"11066_CR8","doi-asserted-by":"publisher","first-page":"193","DOI":"10.3103\/S0003701X16030105","volume":"52","author":"EM Natsheh","year":"2016","unstructured":"Natsheh EM (2016) Power generation of solar pv systems in palestine. Appl Solar Energy 52:193\u2013196. https:\/\/doi.org\/10.3103\/S0003701X16030105","journal-title":"Appl Solar Energy"},{"key":"11066_CR9","doi-asserted-by":"publisher","unstructured":"Said N, Alsamamra H (2019) An overview of green buildings potential in palestine. Int J Sustain Green Energy 8(2): 11. https:\/\/doi.org\/10.11648\/j.ijrse.20190802.11","DOI":"10.11648\/j.ijrse.20190802.11"},{"issue":"O1","key":"11066_CR10","doi-asserted-by":"publisher","first-page":"36","DOI":"10.1007\/s00506-021-00748-3","volume":"36","author":"F Rohde","year":"2021","unstructured":"Rohde F, Gossen M, Wagner J, Santarius T (2021) Sustainability challenges of artificial intelligence and policy implications. \u00d6kologisches Wirtschaften-Fachzeitschrift 36(O1):36\u201340. https:\/\/doi.org\/10.1007\/s00506-021-00748-3","journal-title":"\u00d6kologisches Wirtschaften-Fachzeitschrift"},{"key":"11066_CR11","doi-asserted-by":"publisher","unstructured":"Lohn AJ, Musser M (December 2021) Ai and compute: How much longer can computing power drive artificial intelligence progress? Technical report, Center for Security and Emerging Technology. https:\/\/doi.org\/10.51593\/2021CA009 . https:\/\/cset.georgetown.edu\/wp-content\/uploads\/CSET-2021-CA009-AI-and-Compute.pdf","DOI":"10.51593\/2021CA009"},{"key":"11066_CR12","doi-asserted-by":"publisher","unstructured":"Pachot A, Patissier C (2023) Toward sustainable artificial intelligence: An overview of environmental protection uses and issues. Green and Low-Carbon Economy 00(00), 1\u20138 https:\/\/doi.org\/10.47852\/bonviewGLCE3202608","DOI":"10.47852\/bonviewGLCE3202608"},{"key":"11066_CR13","doi-asserted-by":"publisher","unstructured":"Energy Sector Management Assistance Program: Global Photovoltaic Power Potential by Country. World Bank. Available online: https:\/\/doi.org\/10.1596\/34102 (2020). https:\/\/doi.org\/10.1596\/34102","DOI":"10.1596\/34102"},{"key":"11066_CR14","unstructured":"Imamkulieva E (2022) In: Name, E. (ed.) Clean Energy in the Arab Countries, pp. 58\u201372. Monograph Compiler Publications, Publisher Location"},{"issue":"2","key":"11066_CR15","doi-asserted-by":"publisher","first-page":"77","DOI":"10.1080\/23754931.2020.1870540","volume":"16","author":"IA Ibrahim","year":"2023","unstructured":"Ibrahim IA (2023) Energy transition and sustainable development goal 7: a legal analysis in the context of the arab world. J World Energy Law Bus 16(2):77\u201390. https:\/\/doi.org\/10.1080\/23754931.2020.1870540","journal-title":"J World Energy Law Bus"},{"issue":"13","key":"11066_CR16","doi-asserted-by":"publisher","first-page":"5029","DOI":"10.3390\/en16135029","volume":"16","author":"E Chodakowska","year":"2023","unstructured":"Chodakowska E, Nazarko J, Nazarko Rabayah HS, Abendeh RM, Alawneh R (2023) Arima models in solar radiation forecasting in different geographic locations. Energies 16(13):5029. https:\/\/doi.org\/10.3390\/en16135029","journal-title":"Energies"},{"issue":"2","key":"11066_CR17","doi-asserted-by":"publisher","first-page":"236","DOI":"10.3934\/energy.2022013","volume":"10","author":"A Braik","year":"2022","unstructured":"Braik A, Makhalfih A, Hamid ASA, Sopian K, Ibrahim A (2022) Impact of photovoltaic grid-tied systems on national grid power factor in palestine. AIMS Energy 10(2):236\u2013253. https:\/\/doi.org\/10.3934\/energy.2022013","journal-title":"AIMS Energy"},{"issue":"12","key":"11066_CR18","doi-asserted-by":"publisher","first-page":"3531","DOI":"10.1016\/j.solener.2012.07.029","volume":"86","author":"D Yang","year":"2012","unstructured":"Yang D, Jirutitijaroen P, Walsh WM (2012) Hourly solar irradiance time series forecasting using cloud cover index. Sol Energy 86(12):3531\u20133543. https:\/\/doi.org\/10.1016\/j.solener.2012.07.029","journal-title":"Sol Energy"},{"issue":"2","key":"11066_CR19","doi-asserted-by":"publisher","first-page":"240","DOI":"10.3390\/sym11020240","volume":"11","author":"MH Alsharif","year":"2019","unstructured":"Alsharif MH, Younes MK, Kim J (2019) Time series arima model for prediction of daily and monthly average global solar radiation: The case study of seoul, south korea. Symmetry 11(2):240. https:\/\/doi.org\/10.3390\/sym11020240","journal-title":"Symmetry"},{"key":"11066_CR20","doi-asserted-by":"publisher","first-page":"145","DOI":"10.1016\/j.eswa.2017.04.039","volume":"83","author":"H Zamani Sabzi","year":"2017","unstructured":"Zamani Sabzi H, King JP, Abudu S (2017) Developing an intelligent expert system for streamflow prediction, integrated in a dynamic decision support system for managing multiple reservoirs: A case study. Expert Syst Appl 83:145\u2013163. https:\/\/doi.org\/10.1016\/j.eswa.2017.04.039","journal-title":"Expert Syst Appl"},{"issue":"8","key":"11066_CR21","doi-asserted-by":"publisher","first-page":"866","DOI":"10.1080\/10962247.2018.1459956","volume":"68","author":"BS Freeman","year":"2018","unstructured":"Freeman BS, Taylor G, Gharabaghi B, Th\u00e9 J (2018) Forecasting air quality time series using deep learning. J Air Waste Manag Assoc 68(8):866\u2013886. https:\/\/doi.org\/10.1080\/10962247.2018.1459956","journal-title":"J Air Waste Manag Assoc"},{"key":"11066_CR22","doi-asserted-by":"publisher","first-page":"509","DOI":"10.1016\/j.egypro.2018.12.007","volume":"159","author":"D Zafirakis","year":"2019","unstructured":"Zafirakis D, Tzanes G, Kaldellis JK (2019) Forecasting of wind power generation with the use of artificial neural networks and support vector regression models. Energy Proc 159:509\u2013514. https:\/\/doi.org\/10.1016\/j.egypro.2018.12.007","journal-title":"Energy Proc"},{"issue":"03","key":"11066_CR23","doi-asserted-by":"publisher","first-page":"2130001","DOI":"10.1142\/s0129065721300011","volume":"31","author":"P Lara-Ben\u00edtez","year":"2021","unstructured":"Lara-Ben\u00edtez P, Carranza-Garc\u00eda M, Riquelme JC (2021) An experimental review on deep learning architectures for time series forecasting. Int J Neural Syst 31(03):2130001. https:\/\/doi.org\/10.1142\/s0129065721300011","journal-title":"Int J Neural Syst"},{"key":"11066_CR24","first-page":"13693","volume":"34","author":"E Strubell","year":"2020","unstructured":"Strubell E, Ganesh A, McCallum A (2020) Energy and policy considerations for modern deep learning research. Proc AAAI Conf Artif Intell 34:13693\u201313696","journal-title":"Proc AAAI Conf Artif Intell"},{"key":"11066_CR25","unstructured":"Lacoste A, Luccioni A, Schmidt V, Dandres T (2019) Quantifying the carbon emissions of machine learning. arXiv preprint arXiv:1910.09700"},{"issue":"2","key":"11066_CR26","doi-asserted-by":"publisher","first-page":"87","DOI":"10.1007\/s12530-013-9074-9","volume":"4","author":"S Schliebs","year":"2013","unstructured":"Schliebs S, Kasabov N (2013) Evolving spiking neural network-a survey. Evol Syst 4(2):87\u201398. https:\/\/doi.org\/10.1007\/s12530-013-9074-9","journal-title":"Evol Syst"},{"issue":"9","key":"11066_CR27","doi-asserted-by":"publisher","first-page":"1659","DOI":"10.1016\/S0893-6080(97)00011-7","volume":"10","author":"W Maass","year":"1997","unstructured":"Maass W (1997) Networks of spiking neurons: the third generation of neural network models. Neural Netw 10(9):1659\u20131671","journal-title":"Neural Netw"},{"issue":"04","key":"11066_CR28","doi-asserted-by":"publisher","first-page":"295","DOI":"10.1142\/s0129065709002002","volume":"19","author":"S Ghosh-Dastidar","year":"2009","unstructured":"Ghosh-Dastidar S, Adeli H (2009) Spiking neural networks. Int J Neural Syst 19(04):295\u2013308. https:\/\/doi.org\/10.1142\/s0129065709002002","journal-title":"Int J Neural Syst"},{"key":"11066_CR29","doi-asserted-by":"publisher","unstructured":"Paugam-Moisy H, Boht\u00e9 SM (2012) Computing with Spiking Neuron Networks, pp. 335\u2013376. https:\/\/doi.org\/10.1007\/978-3-540-92910-9_10","DOI":"10.1007\/978-3-540-92910-9_10"},{"issue":"8","key":"11066_CR30","doi-asserted-by":"publisher","first-page":"3628","DOI":"10.1016\/j.asoc.2013.04.007","volume":"13","author":"S Kulkarni","year":"2013","unstructured":"Kulkarni S, Simon SP, Sundareswaran K (2013) A spiking neural network (snn) forecast engine for short-term electrical load forecasting. Appl Soft Comput 13(8):3628\u20133635. https:\/\/doi.org\/10.1016\/j.asoc.2013.04.007","journal-title":"Appl Soft Comput"},{"issue":"8","key":"11066_CR31","doi-asserted-by":"publisher","first-page":"103656","DOI":"10.1371\/journal.pone.0103656","volume":"9","author":"D Reid","year":"2014","unstructured":"Reid D, Hussain AJ, Tawfik H (2014) Financial time series prediction using spiking neural networks. PLoS ONE 9(8):103656. https:\/\/doi.org\/10.1371\/journal.pone.0103656","journal-title":"PLoS ONE"},{"issue":"1","key":"11066_CR32","doi-asserted-by":"publisher","first-page":"54","DOI":"10.3390\/en9010054","volume":"9","author":"G Sun","year":"2016","unstructured":"Sun G, Chen T, Wei Z, Sun Y, Zang H, Chen S (2016) A carbon price forecasting model based on variational mode decomposition and spiking neural networks. Energies 9(1):54. https:\/\/doi.org\/10.3390\/en9010054","journal-title":"Energies"},{"key":"11066_CR33","doi-asserted-by":"publisher","first-page":"4927","DOI":"10.1109\/ACCESS.2018.2887354","volume":"7","author":"A Zhang","year":"2019","unstructured":"Zhang A, Zhu W, Li J (2019) Spiking echo state convolutional neural network for robust time series classification. IEEE Access 7:4927\u20134935. https:\/\/doi.org\/10.1109\/ACCESS.2018.2887354","journal-title":"IEEE Access"},{"key":"11066_CR34","doi-asserted-by":"publisher","unstructured":"Fang H, Shrestha A, Qiu Q (2020) Multivariate time series classification using spiking neural networks. In: 2020 international joint conference on neural networks (IJCNN), pp. 1\u20136. https:\/\/doi.org\/10.1109\/IJCNN48605.2020.9206751 . IEEE","DOI":"10.1109\/IJCNN48605.2020.9206751"},{"key":"11066_CR35","doi-asserted-by":"publisher","first-page":"5023","DOI":"10.1016\/j.procs.2021.09.280","volume":"192","author":"K Matenczuk","year":"2021","unstructured":"Matenczuk K, Kozina A, Markowska A, Czerniachowska K, Kaczmarczyk K, Golec P, Hernes M, Lutos\u0142awski K, Kozierkiewicz A, Pietranik M, Rot A, Dyvak M (2021) Financial time series forecasting: Comparison of traditional and spiking neural networks. Proc Comput Sci 192:5023\u20135029. https:\/\/doi.org\/10.1016\/j.procs.2021.09.280","journal-title":"Proc Comput Sci"},{"issue":"19","key":"11066_CR36","doi-asserted-by":"publisher","first-page":"97256","DOI":"10.3390\/en15197256","volume":"15","author":"JM Gonz\u00e1lez Sope\u00f1a","year":"2022","unstructured":"Gonz\u00e1lez Sope\u00f1a JM, Pakrashi V, Ghosh B (2022) A spiking neural network based wind power forecasting model for neuromorphic devices. Energies 15(19):97256. https:\/\/doi.org\/10.3390\/en15197256","journal-title":"Energies"},{"key":"11066_CR37","unstructured":"Intel\u2019s Neuromorphic Computing Lab: Lava: A Software Framework for Neuromorphic Computing. http:\/\/lava-nc.org and https:\/\/github.com\/lava-nc\/lava. Accessed January 1, 2024 (2021)"},{"key":"11066_CR38","doi-asserted-by":"publisher","first-page":"114941","DOI":"10.1016\/j.eswa.2021.114941","volume":"177","author":"CS Lai","year":"2021","unstructured":"Lai CS, Zhong C, Pan K, Ng WWY, Lai LL (2021) A deep learning based hybrid method for hourly solar radiation forecasting. Expert Syst Appl 177:114941. https:\/\/doi.org\/10.1016\/j.eswa.2021.114941","journal-title":"Expert Syst Appl"},{"key":"11066_CR39","volume-title":"Time series analysis: forecasting and control","author":"GE Box","year":"2015","unstructured":"Box GE, Jenkins GM, Reinsel GC, Ljung GM (2015) Time series analysis: forecasting and control. Wiley, Hoboken, NJ"},{"key":"11066_CR40","doi-asserted-by":"publisher","first-page":"537","DOI":"10.1016\/j.sbspro.2016.11.010","volume":"235","author":"BC Ervural","year":"2016","unstructured":"Ervural BC, Beyca OF, Zaim S (2016) Model estimation of arma using genetic algorithms: a case study of forecasting natural gas consumption. Proc Soc Behav Sci 235:537\u2013545. https:\/\/doi.org\/10.1016\/j.sbspro.2016.11.010","journal-title":"Proc Soc Behav Sci"},{"issue":"4","key":"11066_CR41","first-page":"2679","volume":"34","author":"B Cayir Ervural","year":"2018","unstructured":"Cayir Ervural B, Ervural B (2018) Improvement of grey prediction models and their usage for energy demand forecasting. J Intell Fuzzy Syst 34(4):2679\u20132688","journal-title":"J Intell Fuzzy Syst"},{"issue":"8","key":"11066_CR42","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"},{"issue":"1","key":"11066_CR43","doi-asserted-by":"publisher","first-page":"61","DOI":"10.1177\/1094428116668035","volume":"20","author":"AT Jebb","year":"2016","unstructured":"Jebb AT, Tay L (2016) Introduction to time series analysis for organizational research: methods for longitudinal analyses. Organ Res Methods 20(1):61\u201394. https:\/\/doi.org\/10.1177\/1094428116668035","journal-title":"Organ Res Methods"},{"key":"11066_CR44","unstructured":"Siami-Namini S, Tavakoli N, Namin AS (2019) A comparative analysis of forecasting financial time series using ARIMA, LSTM, and BILSTM. http:\/\/export.arxiv.org\/pdf\/1911.09512"},{"key":"11066_CR45","doi-asserted-by":"publisher","first-page":"114513","DOI":"10.1016\/j.eswa.2020.114513","volume":"169","author":"R Yan","year":"2021","unstructured":"Yan R, Liao J, Yang J, Sun W, Nong M, Li F (2021) Multi-hour and multi-site air quality index forecasting in beijing using cnn, lstm, cnn-lstm, and spatiotemporal clustering. Expert Syst Appl 169:114513. https:\/\/doi.org\/10.1016\/j.eswa.2020.114513","journal-title":"Expert Syst Appl"},{"issue":"4","key":"11066_CR46","doi-asserted-by":"publisher","first-page":"2942","DOI":"10.3390\/su15042942","volume":"15","author":"PNLM Radzi","year":"2023","unstructured":"Radzi PNLM, Akhter MN, Mekhilef S, Shah NM (2023) Review on the application of photovoltaic forecasting using machine learning for very short-to long-term forecasting. Sustainability 15(4):2942. https:\/\/doi.org\/10.3390\/su15042942","journal-title":"Sustainability"},{"key":"11066_CR47","doi-asserted-by":"publisher","first-page":"513","DOI":"10.1016\/j.rser.2019.03.040","volume":"108","author":"M Sharifzadeh","year":"2019","unstructured":"Sharifzadeh M, Sikinioti-Lock A, Shah N (2019) Machine-learning methods for integrated renewable power generation: a comparative study of artificial neural networks, support vector regression, and gaussian process regression. Renew Sustain Energy Rev 108:513\u2013538. https:\/\/doi.org\/10.1016\/j.rser.2019.03.040","journal-title":"Renew Sustain Energy Rev"},{"key":"11066_CR48","doi-asserted-by":"publisher","first-page":"380","DOI":"10.1016\/j.enconman.2016.05.025","volume":"121","author":"M Rana","year":"2016","unstructured":"Rana M, Koprinska I, Agelidis VG (2016) Univariate and multivariate methods for very short-term solar photovoltaic power forecasting. Energy Convers Manag 121:380\u2013390. https:\/\/doi.org\/10.1016\/j.enconman.2016.05.025","journal-title":"Energy Convers Manag"},{"key":"11066_CR49","doi-asserted-by":"publisher","first-page":"109792","DOI":"10.1016\/j.rser.2020.109792","volume":"124","author":"R Ahmed","year":"2020","unstructured":"Ahmed R, Sreeram V, Mishra Y, Arif MD (2020) A review and evaluation of the state-of-the-art in pv solar power forecasting: techniques and optimization. Renew Sustain Energy Rev 124:109792. https:\/\/doi.org\/10.1016\/j.rser.2020.109792","journal-title":"Renew Sustain Energy Rev"},{"issue":"2194","key":"11066_CR50","doi-asserted-by":"publisher","first-page":"20200209","DOI":"10.1098\/rsta.2020.0209","volume":"379","author":"B Lim","year":"2021","unstructured":"Lim B, Zohren S (2021) Time-series forecasting with deep learning: a survey. Phil Trans R Soc A 379(2194):20200209. https:\/\/doi.org\/10.1098\/rsta.2020.0209","journal-title":"Phil Trans R Soc A"},{"key":"11066_CR51","doi-asserted-by":"publisher","first-page":"113315","DOI":"10.1016\/j.apenergy.2019.113315","volume":"251","author":"K Wang","year":"2019","unstructured":"Wang K, Qi X, Liu H (2019) A comparison of day-ahead photovoltaic power forecasting models based on deep learning neural network. Appl Energy 251:113315. https:\/\/doi.org\/10.1016\/j.apenergy.2019.113315","journal-title":"Appl Energy"},{"key":"11066_CR52","doi-asserted-by":"publisher","first-page":"119682","DOI":"10.1016\/j.apenergy.2022.119682","volume":"319","author":"X Ren","year":"2022","unstructured":"Ren X, Zhang F, Zhu H, Liu Y (2022) Quad-kernel deep convolutional neural network for intra-hour photovoltaic power forecasting. Appl Energy 319:119682. https:\/\/doi.org\/10.1016\/j.apenergy.2022.119682","journal-title":"Appl Energy"},{"key":"11066_CR53","doi-asserted-by":"publisher","first-page":"461","DOI":"10.1016\/j.energy.2018.01.177","volume":"148","author":"X Qing","year":"2018","unstructured":"Qing X, Niu Y (2018) Hourly day-ahead solar irradiance prediction using weather forecasts by lstm. Energy 148:461\u2013468","journal-title":"Energy"},{"issue":"2","key":"11066_CR54","doi-asserted-by":"publisher","first-page":"102","DOI":"10.1063\/1.5087462","volume":"11","author":"D Yang","year":"2019","unstructured":"Yang D (2019) A guideline to solar forecasting research practice: reproducible, operational, probabilistic or physically-based, ensemble, and skill (ropes). J Renew Sustain Energy 11(2):102","journal-title":"J Renew Sustain Energy"},{"issue":"1","key":"11066_CR55","doi-asserted-by":"publisher","first-page":"32","DOI":"10.3390\/info11010032","volume":"11","author":"K Yan","year":"2020","unstructured":"Yan K, Shen H, Wang L, Zhou H, Xu M, Mo Y (2020) Short-term solar irradiance forecasting based on a hybrid deep learning methodology. Information 11(1):32","journal-title":"Information"},{"issue":"4","key":"11066_CR56","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1109\/ICJECE.2021.3093369","volume":"44","author":"CN Obiora","year":"2021","unstructured":"Obiora CN, Hasan AN, Ali A, Alajarmeh N (2021) Forecasting hourly solar radiation using artificial intelligence techniques. IEEE Canad J Elect Comput Eng 44(4):497\u2013508","journal-title":"IEEE Canad J Elect Comput Eng"},{"issue":"3","key":"11066_CR57","doi-asserted-by":"publisher","first-page":"1435","DOI":"10.3390\/app12031435","volume":"12","author":"M Mukhtar","year":"2022","unstructured":"Mukhtar M, Oluwasanmi A, Yimen N, Qinxiu Z, Ukwuoma CC, Ezurike B, Bamisile O (2022) Development and comparison of two novel hybrid neural network models for hourly solar radiation prediction. Appl Sci 12(3):1435","journal-title":"Appl Sci"},{"key":"11066_CR58","doi-asserted-by":"publisher","DOI":"10.1017\/CBO9781107447615","volume-title":"Neuronal dynamics: from single neurons to networks and models of cognition","author":"W Gerstner","year":"2014","unstructured":"Gerstner W, Kistler WM, Naud R, Paninski L (2014) Neuronal dynamics: from single neurons to networks and models of cognition. Cambridge University Press, Cambridge, UK"},{"key":"11066_CR59","doi-asserted-by":"crossref","unstructured":"Gerstner W, Kistler WM (2002) Spiking Neuron Models: Single Neurons, Populations, Plasticity. Cambridge University Press, Cambridge, UK. http:\/\/catdir.loc.gov\/catdir\/samples\/cam031\/2002067657.pdf","DOI":"10.1017\/CBO9780511815706"},{"key":"11066_CR60","unstructured":"Paugam-Moisy H (2006) Spiking neuron networks a survey. Technical report, EPFL-REPORT-83371. http:\/\/publications.idiap.ch\/downloads\/reports\/2006\/paugam-idiap-rr-06-11.pdf"},{"issue":"6","key":"11066_CR61","doi-asserted-by":"publisher","first-page":"1569","DOI":"10.1109\/TNN.2003.820440","volume":"14","author":"EM Izhikevich","year":"2003","unstructured":"Izhikevich EM (2003) Simple model of spiking neurons. IEEE Trans Neural Netw 14(6):1569\u20131572. https:\/\/doi.org\/10.1109\/TNN.2003.820440","journal-title":"IEEE Trans Neural Netw"},{"key":"11066_CR62","unstructured":"Zambrano D, Bohte SM (2016) Fast and efficient asynchronous neural computation with adapting spiking neural networks. arXiv preprint arXiv:1609.02053"},{"key":"11066_CR63","doi-asserted-by":"publisher","first-page":"349","DOI":"10.1007\/s10827-007-0038-6","volume":"23","author":"R Brette","year":"2007","unstructured":"Brette R, Rudolph M, Carnevale T, Hines M, Beeman D, Bower JM, Destexhe A (2007) Simulation of networks of spiking neurons: a review of tools and strategies. J Comput Neurosci 23:349\u2013398","journal-title":"J Comput Neurosci"},{"key":"11066_CR64","unstructured":"Shrestha SB, Orchard G (2018) SLAYER: Spike layer error reassignment in time. https:\/\/arxiv.org\/pdf\/1810.08646"},{"key":"11066_CR65","unstructured":"Davies M (2021) Taking neuromorphic computing to the next level with Loihi2. Intel Labs\u2019 Loihi"},{"key":"11066_CR66","doi-asserted-by":"publisher","first-page":"119","DOI":"10.3389\/fnins.2020.00119","volume":"14","author":"C Lee","year":"2020","unstructured":"Lee C, Sarwar SS, Panda P, Srinivasan G, Roy K (2020) Enabling spike-based backpropagation for training deep neural network architectures. Front Neurosci 14:119. https:\/\/doi.org\/10.3389\/fnins.2020.00119","journal-title":"Front Neurosci"},{"key":"11066_CR67","doi-asserted-by":"publisher","first-page":"99","DOI":"10.3389\/fncom.2015.00099","volume":"9","author":"PU Diehl","year":"2015","unstructured":"Diehl PU, Cook M (2015) Unsupervised learning of digit recognition using spike-timing-dependent plasticity. Front Comput Neurosci 9:99. https:\/\/doi.org\/10.3389\/fncom.2015.00099","journal-title":"Front Comput Neurosci"},{"issue":"08","key":"11066_CR68","doi-asserted-by":"publisher","first-page":"204","DOI":"10.1142\/s0129065722500204","volume":"32","author":"L Long","year":"2022","unstructured":"Long L, Liu Q, Peng H, Yang Q, Luo X, Wang J, Song X (2022) A time series forecasting approach based on nonlinear spiking neural systems. Int J Neural Syst 32(08):204. https:\/\/doi.org\/10.1142\/s0129065722500204","journal-title":"Int J Neural Syst"},{"key":"11066_CR69","doi-asserted-by":"publisher","first-page":"118","DOI":"10.1016\/j.neunet.2018.03.019","volume":"103","author":"S Kulkarni","year":"2018","unstructured":"Kulkarni S, Rajendran B (2018) Spiking neural networks for handwritten digit recognition-supervised learning and network optimization. Neural Netw 103:118\u2013127. https:\/\/doi.org\/10.1016\/j.neunet.2018.03.019","journal-title":"Neural Netw"},{"key":"11066_CR70","doi-asserted-by":"publisher","first-page":"294","DOI":"10.1016\/j.neunet.2019.09.005","volume":"121","author":"L Deng","year":"2020","unstructured":"Deng L, Wu Y, Hu X, Liang L, Ding Y, Li G, Zhao G, Li P, Xie Y (2020) Rethinking the performance comparison between snns and anns. Neural Netw 121:294\u2013307. https:\/\/doi.org\/10.1016\/j.neunet.2019.09.005","journal-title":"Neural Netw"},{"key":"11066_CR71","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.neucom.2020.12.098","volume":"434","author":"C Tan","year":"2021","unstructured":"Tan C, \u0160arlija M, Kasabov N (2021) Neurosense: short-term emotion recognition and understanding based on spiking neural network modelling of spatio-temporal eeg patterns. Neurocomputing 434:137\u2013148. https:\/\/doi.org\/10.1016\/j.neucom.2020.12.098","journal-title":"Neurocomputing"},{"key":"11066_CR72","doi-asserted-by":"publisher","first-page":"117072","DOI":"10.1016\/j.energy.2020.117072","volume":"196","author":"H Wang","year":"2020","unstructured":"Wang H, Xue W, Liu Y, Peng J, Jiang H (2020) Probabilistic wind power forecasting based on spiking neural network. Energy 196:117072. https:\/\/doi.org\/10.1016\/j.energy.2020.117072","journal-title":"Energy"},{"key":"11066_CR73","doi-asserted-by":"publisher","unstructured":"Zhan G, Song Z, Fang T, Zhang Y, Le S, Zhang X, Kang X (2021) Applications of spiking neural network in brain computer interface. In: 2021 9th International Winter Conference on Brain-Computer Interface (BCI), pp. 1\u20136. https:\/\/doi.org\/10.1109\/BCI51272.2021.9385361 . IEEE","DOI":"10.1109\/BCI51272.2021.9385361"},{"key":"11066_CR74","doi-asserted-by":"publisher","first-page":"271","DOI":"10.1016\/j.neunet.2020.06.001","volume":"129","author":"G Capizzi","year":"2020","unstructured":"Capizzi G, Sciuto GL, Napoli C, Wo\u017aniak M, Susi G (2020) A spiking neural network-based long-term prediction system for biogas production. Neural Netw 129:271\u2013279. https:\/\/doi.org\/10.1016\/j.neunet.2020.06.001","journal-title":"Neural Netw"},{"key":"11066_CR75","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1007\/s12667-023-00518-4","volume":"4","author":"VK Thangaraj","year":"2023","unstructured":"Thangaraj VK, Nachimuthu DS, Francis VAR (2023) Wind speed forecasting at wind farm locations with an unique hybrid pso-alo based modified spiking neural network. Energy Syst 4:1\u201329. https:\/\/doi.org\/10.1007\/s12667-023-00518-4","journal-title":"Energy Syst"},{"key":"11066_CR76","unstructured":"Manna DL, Vicente-Sola A, Kirkland P, Bihl TJ, Di\u00a0Caterina G (2023) An approach to time series forecasting with spiking neural networks. Preprint"},{"issue":"4","key":"11066_CR77","doi-asserted-by":"publisher","first-page":"1709","DOI":"10.3390\/su16041709","volume":"16","author":"Y Gao","year":"2024","unstructured":"Gao Y, Wang J, Guo L, Peng H (2024) Short-term photovoltaic power prediction using nonlinear spiking neural p systems. Sustainability 16(4):1709. https:\/\/doi.org\/10.3390\/su16041709","journal-title":"Sustainability"},{"key":"11066_CR78","doi-asserted-by":"publisher","first-page":"51","DOI":"10.1016\/j.rser.2018.03.003","volume":"89","author":"M Sengupta","year":"2018","unstructured":"Sengupta M, Xie Y, Lopez A, Habte A, Maclaurin G, Shelby J (2018) The national solar radiation data base (nsrdb). Renew Sustain Energy Rev 89:51\u201360","journal-title":"Renew Sustain Energy Rev"},{"key":"11066_CR79","doi-asserted-by":"publisher","first-page":"638474","DOI":"10.3389\/fnins.2021.638474","volume":"15","author":"W Guo","year":"2021","unstructured":"Guo W, Fouda ME, Eltawil AM, Salama KN (2021) Neural coding in spiking neural networks: a comparative study for robust neuromorphic systems. Front Neurosci 15:638474. https:\/\/doi.org\/10.3389\/fnins.2021.638474","journal-title":"Front Neurosci"},{"key":"11066_CR80","doi-asserted-by":"crossref","unstructured":"Nair MV, Indiveri G (2019) An ultra-low power sigma-delta neuron circuit. In: 2019 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1\u20135. IEEE","DOI":"10.1109\/ISCAS.2019.8702500"},{"key":"11066_CR81","doi-asserted-by":"crossref","unstructured":"Kim Y, Park H, Moitra A, Bhattacharjee A, Venkatesha Y, Panda P (2022) Rate coding or direct coding: Which one is better for accurate, robust, and energy-efficient spiking neural networks? In: 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 71\u201375. IEEE","DOI":"10.1109\/ICASSP43922.2022.9747906"},{"key":"11066_CR82","doi-asserted-by":"publisher","first-page":"638474","DOI":"10.3389\/fnins.2021.638474","volume":"15","author":"W Guo","year":"2021","unstructured":"Guo W, Fouda ME, Eltawil AM, Salama KN (2021) Neural coding in spiking neural networks: a comparative study for robust neuromorphic systems. Front Neurosci 15:638474","journal-title":"Front Neurosci"},{"key":"11066_CR83","doi-asserted-by":"crossref","unstructured":"Pan Z, Wu J, Zhang M, Li H, Chua Y (2019) Neural population coding for effective temporal classification. In: 2019 International Joint Conference on Neural Networks (IJCNN), pp. 1\u20138. IEEE","DOI":"10.1109\/IJCNN.2019.8851858"},{"key":"11066_CR84","unstructured":"Corporation N (2023) NVIDIA System Management Interface. http:\/\/developer.download.nvidia.com\/compute\/DCGM\/docs\/nvidia-smi-367.38.pdf. Accessed: 20 February"},{"key":"11066_CR85","unstructured":"Liu L, Jiang H, He P, Chen W, Liu X, Gao J, Han J (2019) On the variance of the adaptive learning rate and beyond. arXiv preprint arXiv:1908.03265"},{"key":"11066_CR86","doi-asserted-by":"crossref","unstructured":"Kucik AS, Meoni G (2021) Investigating spiking neural networks for energy-efficient on-board ai applications: A case study in land cover and land use classification. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 2020\u20132030","DOI":"10.1109\/CVPRW53098.2021.00230"},{"key":"11066_CR87","unstructured":"Research AB (2024) KerasSpiking - Estimating model energy. https:\/\/www.nengo.ai\/keras-spiking\/examples\/model-energy.html. Last accessed on 12\/04\/2024"},{"key":"11066_CR88","doi-asserted-by":"crossref","unstructured":"Ali HAH, Dabbous A, Ibrahim A, Valle M (2023) Assessment of recurrent spiking neural networks on neuromorphic accelerators for naturalistic texture classification. In: 2023 18th Conference on Ph. D Research in Microelectronics and Electronics (PRIME)","DOI":"10.1109\/PRIME58259.2023.10161772"},{"key":"11066_CR89","doi-asserted-by":"crossref","unstructured":"Herbozo\u00a0Contreras LF, Huang Z, Yu L, Nikpour A, Kavehei O (2024) Biological plausible algorithm for seizure detection: Toward ai-enabled electroceuticals at the edge. APL Machine Learning 2(2)","DOI":"10.1063\/5.0192875"},{"issue":"1","key":"11066_CR90","doi-asserted-by":"publisher","first-page":"58","DOI":"10.1109\/TVLSI.2015.2392942","volume":"24","author":"B Degnan","year":"2015","unstructured":"Degnan B, Marr B, Hasler J (2015) Assessing trends in performance per watt for signal processing applications. IEEE Trans Very Large Scale Integr VLSI Syst 24(1):58\u201366","journal-title":"IEEE Trans Very Large Scale Integr VLSI Syst"},{"issue":"1","key":"11066_CR91","doi-asserted-by":"publisher","first-page":"82","DOI":"10.1109\/MM.2018.112130359","volume":"38","author":"M Davies","year":"2018","unstructured":"Davies M, Srinivasa N, Lin TH, Chinya G, Cao Y, Choday SH, Dimou G, Joshi P, Imam N, Jain S et al (2018) Loihi: a neuromorphic manycore processor with on-chip learning. IEEE Micro 38(1):82\u201399. https:\/\/doi.org\/10.1109\/MM.2018.112130359","journal-title":"IEEE Micro"},{"key":"11066_CR92","unstructured":"Zhu RJ, Wang Z, Gilpin L, Eshraghian JK (2024) Autonomous driving with spiking neural networks. ArXiv arXiv:2405.19687"},{"key":"11066_CR93","unstructured":"Vidya Wicaksana\u00a0Putra R, Shafique M (2020) FSpiNN: An Optimization Framework for Memory-and Energy-Efficient Spiking Neural Networks. arXiv e-prints, arXiv:2007"},{"issue":"8","key":"11066_CR94","doi-asserted-by":"publisher","first-page":"9393","DOI":"10.1109\/TPAMI.2023.3241201","volume":"45","author":"M Yao","year":"2023","unstructured":"Yao M, Zhao G, Zhang H, Hu Y, Deng L, Tian Y, Li G (2023) Attention spiking neural networks. IEEE Trans Pattern Anal Mach Intell 45(8):9393\u20139410","journal-title":"IEEE Trans Pattern Anal Mach Intell"},{"key":"11066_CR95","unstructured":"Han S, Mao H, Dally WJ (2015) Deep compression: Compressing deep neural networks with pruning, trained quantization and huffman coding. arXiv preprint arXiv:1510.00149"},{"key":"11066_CR96","doi-asserted-by":"publisher","first-page":"409662","DOI":"10.3389\/fnins.2018.409662","volume":"12","author":"M Pfeiffer","year":"2018","unstructured":"Pfeiffer M, Pfeil T (2018) Deep learning with spiking neurons: opportunities and challenges. Front Neurosci 12:409662. https:\/\/doi.org\/10.3389\/fnins.2018.409662","journal-title":"Front Neurosci"},{"issue":"7784","key":"11066_CR97","doi-asserted-by":"publisher","first-page":"607","DOI":"10.1038\/s41586-019-1677-2","volume":"575","author":"K Roy","year":"2019","unstructured":"Roy K, Jaiswal A, Panda P (2019) Towards spike-based machine intelligence with neuromorphic computing. Nature 575(7784):607\u2013617. https:\/\/doi.org\/10.1038\/s41586-019-1677-2","journal-title":"Nature"},{"issue":"5","key":"11066_CR98","doi-asserted-by":"publisher","first-page":"911","DOI":"10.1109\/JPROC.2021.3058954","volume":"109","author":"M Davies","year":"2021","unstructured":"Davies M, Wild A, Orchard G, Sandamirskaya Y, Guerra GAF, Joshi P, Risbud SR (2021) Advancing neuromorphic computing with loihi: a survey of results and outlook. Proc IEEE 109(5):911\u2013934. https:\/\/doi.org\/10.1109\/JPROC.2021.3058954","journal-title":"Proc IEEE"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11066-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11066-z\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11066-z.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,5,5]],"date-time":"2025-05-05T08:48:46Z","timestamp":1746434926000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11066-z"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,1]]},"references-count":98,"journal-issue":{"issue":"15","published-print":{"date-parts":[[2025,5]]}},"alternative-id":["11066"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11066-z","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,1]]},"assertion":[{"value":"22 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"3 February 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"1 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All authors describe no potential conflict of interest.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}