{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T18:36:09Z","timestamp":1772217369155,"version":"3.50.1"},"reference-count":42,"publisher":"Springer Science and Business Media LLC","issue":"19","license":[{"start":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T00:00:00Z","timestamp":1747180800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T00:00:00Z","timestamp":1747180800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100009367","name":"Mansoura University","doi-asserted-by":"crossref","id":[{"id":"10.13039\/501100009367","id-type":"DOI","asserted-by":"crossref"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Neural Comput &amp; Applic"],"published-print":{"date-parts":[[2025,7]]},"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Expanding the world\u2019s economy leads to higher requirements for energy storage. Traditional energy resources decline at the same time that environmental contamination levels increase in the world. Wind power is the most potential energy resource supported by its status as a significant renewable energy system. Wind power generation has become a popular and exciting method among nations worldwide for generating renewable energy. High wind power generation unpredictability results in unavoidable errors throughout the wind power prediction process, creating substantial difficulties in the optimal management of power systems. Wind power prediction errors remain unavoidable, but appropriate wind power uncertainty models help power system operators reduce their adverse impact on operational decision-making performance. In this paper, developed an appropriate machine learning model that efficiently forecasted wind power data through time series analysis. The long Short-Term Memory (LSTM), Gated Reference Unit (GRU), and Autoregressive Integrated Moving Average (ARIMA) are the machine algorithms used in this investigation. This paper proposed an X-double LSTM, which integrates explainable artificial intelligence (XAI) and long short-term memory (LSTM). The XAI-Shapley Additive Explanations (SHAP) model is modified to pinpoint the crucial elements affecting the power generation forecasting model\u2019s accuracy in cutting-edge solar systems. Nine metrics are used to assess the efficacy of the proposed X-double LSTM model: root mean square error, mean bias error, correlation coefficient, relative root mean square error, Nash\u2013Sutcliffe efficiency, mean square error, mean absolute deviation, coefficient of multiple determination, and Willmott index of agreement. For MSE, RMSE, MAE, MBE, r, R2, RRMSE, NSE, and WI, the suggested model improves by 0.000 11, 0.011, 0.008, 0.008, 0.99, 0.98, 2.5, 0.98, and 0.98, respectively. Other machine learning methods, including the Transformer model, single-layer LSTM networks, Autoregressive Moving Average (ARMA) models, Gated Recurrent Unit (GRU) networks, and Bidirectional (Bi-LSTM) networks, are compared to the performance of double LSTM. The twin LSTM model was demonstrated to perform better. The simulations and experimental findings show that the suggested model can precisely estimate wind power. Within the Google Collab environment, the suggested model uses TensorFlow and Keras.<\/jats:p>","DOI":"10.1007\/s00521-025-11230-5","type":"journal-article","created":{"date-parts":[[2025,5,14]],"date-time":"2025-05-14T17:55:04Z","timestamp":1747245304000},"page":"14589-14611","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Explainable artificial intelligence for wind power forecasting model based on long short-term memory"],"prefix":"10.1007","volume":"37","author":[{"given":"Mona Ahmed","family":"Yassen","sequence":"first","affiliation":[]},{"given":"El-Sayed M.","family":"El-Kenawy","sequence":"additional","affiliation":[]},{"given":"Mohamed Gamal","family":"Abdel-Fattah","sequence":"additional","affiliation":[]},{"given":"Islam","family":"Ismail","sequence":"additional","affiliation":[]},{"given":"Hossam El-Deen Salah","family":"Mostafa","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,5,14]]},"reference":[{"key":"11230_CR1","doi-asserted-by":"publisher","first-page":"132306","DOI":"10.1016\/j.physd.2019.132306","volume":"404","author":"A Sherstinsky","year":"2020","unstructured":"Sherstinsky A (2020) Fundamentals of recurrent neural network (rnn) and long short-term memory (lstm) network. Phys D: Nonlinear Phenom 404:132306","journal-title":"Phys D: Nonlinear Phenom"},{"key":"11230_CR2","doi-asserted-by":"publisher","first-page":"100722","DOI":"10.1016\/j.envc.2023.100722","volume":"11","author":"S Qureshi","year":"2023","unstructured":"Qureshi S et al (2023) Short-term forecasting of wind power generation using artificial intelligence. Environ Challenges 11:100722. https:\/\/doi.org\/10.1016\/j.envc.2023.100722","journal-title":"Environ Challenges"},{"key":"11230_CR3","doi-asserted-by":"publisher","first-page":"5373","DOI":"10.1109\/TSG.2021.3093515","volume":"12","author":"W Lin","year":"2021","unstructured":"Lin W, Wu D, Boulet B (2021) Spatial-temporal residential short-term load forecasting via graph neural networks. IEEE Transactions on Smart Grid 12:5373\u20135384","journal-title":"IEEE Transactions on Smart Grid"},{"key":"11230_CR4","doi-asserted-by":"publisher","first-page":"106826","DOI":"10.1016\/j.engappai.2023.106826","volume":"126","author":"W Cao","year":"2023","unstructured":"Cao W, Liu Y, Mei H, Shang H, Yu Y (2023) Short-term district power load self-prediction based on improved xgboost model. Eng Appl Artif Intell 126:106826","journal-title":"Eng Appl Artif Intell"},{"key":"11230_CR5","unstructured":"Long AW (2016) Learning to place one foot in front of the other: investigation of action and perception in human split-belt walking. Ph.D. thesis, The Johns Hopkins University."},{"key":"11230_CR6","doi-asserted-by":"publisher","first-page":"124750","DOI":"10.1016\/j.energy.2022.124750","volume":"257","author":"C Tian","year":"2022","unstructured":"Tian C, Niu T, Wei W (2022) Developing a wind power forecasting system based on deep learning with attention mechanism. Energy 257:124750","journal-title":"Energy"},{"key":"11230_CR7","doi-asserted-by":"publisher","first-page":"124384","DOI":"10.1016\/j.energy.2022.124384","volume":"254","author":"D Niu","year":"2022","unstructured":"Niu D, Sun L, Yu M, Wang K (2022) Point and interval forecasting of ultra-short-term wind power based on a data-driven method and hybrid deep learning model. Energy 254:124384","journal-title":"Energy"},{"key":"11230_CR8","doi-asserted-by":"publisher","first-page":"129496","DOI":"10.1016\/j.energy.2023.129496","volume":"285","author":"X Dong","year":"2023","unstructured":"Dong X et al (2023) Transferable wind power probabilistic forecasting based on multi-domain adversarial networks. Energy 285:129496","journal-title":"Energy"},{"key":"11230_CR9","doi-asserted-by":"publisher","first-page":"929","DOI":"10.1016\/j.egyr.2021.10.102","volume":"8","author":"B He","year":"2022","unstructured":"He B et al (2022) A combined model for short-term wind power forecasting based on the analysis of numerical weather prediction data. Energy Rep 8:929\u2013939","journal-title":"Energy Rep"},{"key":"11230_CR10","doi-asserted-by":"publisher","first-page":"120291","DOI":"10.1016\/j.apenergy.2022.120291","volume":"329","author":"Y Li","year":"2023","unstructured":"Li Y, Wang R, Li Y, Zhang M, Long C (2023) Wind power forecasting considering data privacy protection: A federated deep reinforcement learning approach. Appl Energy 329:120291","journal-title":"Appl Energy"},{"key":"11230_CR11","doi-asserted-by":"publisher","first-page":"106151","DOI":"10.1016\/j.asoc.2020.106151","volume":"90","author":"F Shahid","year":"2020","unstructured":"Shahid F, Khan A, Zameer A, Arshad J, Safdar K (2020) Wind power prediction using a three stage genetic ensemble and auxiliary predictor. Appl Soft Comput 90:106151","journal-title":"Appl Soft Comput"},{"key":"11230_CR12","doi-asserted-by":"publisher","first-page":"124095","DOI":"10.1016\/j.energy.2022.124095","volume":"253","author":"X Pan","year":"2022","unstructured":"Pan X, Wang L, Wang Z, Huang C (2022) Short-term wind speed forecasting based on spatial-temporal graph transformer networks. Energy 253:124095","journal-title":"Energy"},{"key":"11230_CR13","doi-asserted-by":"publisher","first-page":"123857","DOI":"10.1016\/j.energy.2022.123857","volume":"250","author":"S Yin","year":"2022","unstructured":"Yin S, Liu H (2022) Wind power prediction based on outlier correction, ensemble reinforcement learning, and residual correction. Energy 250:123857","journal-title":"Energy"},{"key":"11230_CR14","first-page":"5577547","volume":"2021","author":"AT Peiris","year":"2021","unstructured":"Peiris AT, Jayasinghe J, Rathnayake U (2021) Forecasting wind power generation using artificial neural network:\u201cpawan danawi\u201d\u2014a case study from sri lanka. J Electr Comput Eng 2021:5577547","journal-title":"J Electr Comput Eng"},{"key":"11230_CR15","doi-asserted-by":"publisher","first-page":"125","DOI":"10.3390\/en14010125","volume":"14","author":"I Delgado","year":"2020","unstructured":"Delgado I, Fahim M (2020) Wind turbine data analysis and lstm-based prediction in scada system. Energies 14:125","journal-title":"Energies"},{"key":"11230_CR16","doi-asserted-by":"publisher","first-page":"351","DOI":"10.1016\/j.renene.2021.10.070","volume":"183","author":"G L\u00f3pez","year":"2022","unstructured":"L\u00f3pez G, Arboleya P (2022) Short-term wind speed forecasting over complex terrain using linear regression models and multivariable lstm and narx networks in the andes mountains, ecuador. Renew Energy 183:351\u2013368. https:\/\/doi.org\/10.1016\/j.renene.2021.10.070","journal-title":"Renew Energy"},{"key":"11230_CR17","doi-asserted-by":"publisher","first-page":"742","DOI":"10.1016\/j.renene.2022.01.041","volume":"186","author":"F Sun","year":"2022","unstructured":"Sun F, Jin T (2022) A hybrid approach to multi-step, short-term wind speed forecasting using correlated features. Renewable Energy 186:742\u2013754. https:\/\/doi.org\/10.1016\/j.renene.2022.01.041","journal-title":"Renewable Energy"},{"key":"11230_CR18","doi-asserted-by":"publisher","first-page":"107776","DOI":"10.1016\/j.epsr.2022.107776","volume":"206","author":"B Xiong","year":"2022","unstructured":"Xiong B et al (2022) Short-term wind power forecasting based on attention mechanism and deep learning. Electr Power Syst Res 206:107776. https:\/\/doi.org\/10.1016\/j.epsr.2022.107776","journal-title":"Electr Power Syst Res"},{"key":"11230_CR19","doi-asserted-by":"publisher","first-page":"137","DOI":"10.1016\/j.apenergy.2019.01.010","volume":"178","author":"C Yu","year":"2018","unstructured":"Yu C, Li Y, Bao Y, Tang H, Zhai G (2018) A novel framework for wind speed prediction based on recurrent neural networks and support vector machine. Energy Convers Manag 178:137\u2013145. https:\/\/doi.org\/10.1016\/j.apenergy.2019.01.010","journal-title":"Energy Convers Manag"},{"key":"11230_CR20","doi-asserted-by":"publisher","first-page":"122024","DOI":"10.1016\/j.energy.2021.122024","volume":"238","author":"Z Shang","year":"2022","unstructured":"Shang Z, He Z, Chen Y, Chen Y, Xu M (2022) Short-term wind speed forecasting system based on multivariate time series and multi-objective optimization. Energy 238:122024. https:\/\/doi.org\/10.1016\/j.energy.2021.122024","journal-title":"Energy"},{"key":"11230_CR21","doi-asserted-by":"publisher","unstructured":"Praveena R, Dhanalakshmi K (2018) Wind power forecasting in short-term using fuzzy k-means clustering and neural network. In: 2018 international conference on intelligent computing and communication for smart world (I2C2SW), 336\u2013339, https:\/\/doi.org\/10.1109\/I2C2SW45816.2018.8997350 (IEEE, 2018).","DOI":"10.1109\/I2C2SW45816.2018.8997350"},{"key":"11230_CR22","doi-asserted-by":"crossref","unstructured":"Ghanbarzadeh A, Noghrehabadi A, Behrang MA, Assareh E (2009) Wind speed prediction based on simple meteorological data using artificial neural network. In: 2009 7th IEEE international conference on industrial informatics, 664\u2013667 (IEEE, 2009).","DOI":"10.1109\/INDIN.2009.5195882"},{"key":"11230_CR23","doi-asserted-by":"publisher","first-page":"127914","DOI":"10.1016\/j.energy.2023.127914","volume":"278","author":"AA Abdoos","year":"2023","unstructured":"Abdoos AA, Abdoos H, Kazemitabar J, Mobashsher MM, Khaloo H (2023) An intelligent hybrid method based on monte carlo simulation for short-term probabilistic wind power prediction. Energy 278:127914","journal-title":"Energy"},{"key":"11230_CR24","doi-asserted-by":"publisher","first-page":"119608","DOI":"10.1016\/j.apenergy.2022.119608","volume":"323","author":"Z Ma","year":"2022","unstructured":"Ma Z, Mei G (2022) A hybrid attention-based deep learning approach for wind power prediction. Appl Energy 323:119608","journal-title":"Appl Energy"},{"key":"11230_CR25","doi-asserted-by":"publisher","first-page":"120069","DOI":"10.1016\/j.energy.2021.120069","volume":"223","author":"F Shahid","year":"2021","unstructured":"Shahid F, Zameer A, Muneeb M (2021) A novel genetic lstm model for wind power forecast. Energy 223:120069","journal-title":"Energy"},{"key":"11230_CR26","doi-asserted-by":"publisher","first-page":"208","DOI":"10.1016\/j.enconman.2016.09.002","volume":"127","author":"R Azimi","year":"2016","unstructured":"Azimi R, Ghofrani M, Ghayekhloo M (2016) A hybrid wind power forecasting model based on data mining and wavelets analysis. Energy Conv Manag 127:208\u2013225","journal-title":"Energy Conv Manag"},{"key":"11230_CR27","doi-asserted-by":"publisher","first-page":"3227","DOI":"10.3390\/en11113227","volume":"11","author":"X Shi","year":"2018","unstructured":"Shi X et al (2018) Hourly day-ahead wind power prediction using the hybrid model of variational model decomposi- tion and long short-term memory. Energies 11:3227","journal-title":"Energies"},{"key":"11230_CR28","doi-asserted-by":"publisher","first-page":"529","DOI":"10.1016\/j.renene.2017.03.064","volume":"109","author":"A Lahouar","year":"2017","unstructured":"Lahouar A, Slama JBH (2017) Hour-ahead wind power forecast based on random forests. Renew Energy 109:529\u2013541","journal-title":"Renew Energy"},{"key":"11230_CR29","doi-asserted-by":"publisher","first-page":"1556","DOI":"10.1016\/j.rser.2010.11.036","volume":"15","author":"S Vel\u00e1zquez","year":"2011","unstructured":"Vel\u00e1zquez S, Carta JA, Mat\u00edas J (2011) Influence of the input layer signals of anns on wind power estimation for a target site: A case study. Renew Sustain Energy Rev 15:1556\u20131566","journal-title":"Renew Sustain Energy Rev"},{"key":"11230_CR30","doi-asserted-by":"crossref","unstructured":"Su Y. et al. (2019) A lstm based wind power forecasting method considering wind frequency components and the wind turbine states. In: 2019 22nd International Conference on Electrical Machines and Systems (ICEMS), 1\u20136 (IEEE, 2019).","DOI":"10.1109\/ICEMS.2019.8921671"},{"key":"11230_CR31","doi-asserted-by":"crossref","unstructured":"Zu X, Song R (2018) Short-term wind power prediction method based on wavelet packet decomposition and improved gru. J Phys Conf Ser, vol. 1087, 022034 (IOP Publishing, 2018).","DOI":"10.1088\/1742-6596\/1087\/2\/022034"},{"key":"11230_CR32","doi-asserted-by":"publisher","first-page":"4417","DOI":"10.3390\/app9204417","volume":"9","author":"S Mujeeb","year":"2019","unstructured":"Mujeeb S et al (2019) Exploiting deep learning for wind power forecasting based on big data analytics. Appl Sci 9:4417","journal-title":"Appl Sci"},{"key":"11230_CR33","doi-asserted-by":"publisher","DOI":"10.1162\/neco.1997.9.8.1735","volume-title":"Long short-term memory","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S (1997) Long short-term memory. Neural Comput MIT-Press. https:\/\/doi.org\/10.1162\/neco.1997.9.8.1735"},{"key":"11230_CR34","doi-asserted-by":"publisher","unstructured":"Graves A, Mohamed A-r, Hinton G (2013) Speech recognition with deep recurrent neural networks. In: 2013 IEEE international conference on acoustics, speech and signal processing, 6645\u20136649, https:\/\/doi.org\/10.1109\/icassp. 2013.6638947 (Ieee, 2013).","DOI":"10.1109\/icassp"},{"key":"11230_CR35","doi-asserted-by":"publisher","first-page":"8552","DOI":"10.3390\/s23208552","volume":"23","author":"J Zhang","year":"2023","unstructured":"Zhang J et al (2023) Research on none-line-of-sight\/line-of-sight identification method based on convolutional neural network-channel attention module. Sensors 23:8552","journal-title":"Sensors"},{"key":"11230_CR36","doi-asserted-by":"publisher","first-page":"177","DOI":"10.1016\/j.procs.2022.12.125","volume":"216","author":"BA Sunjaya","year":"2023","unstructured":"Sunjaya BA, Permai SD, Gunawan AAS (2023) Forecasting of covid-19 positive cases in indonesia using long short-term memory (lstm). Procedia Comput Sci 216:177\u2013185","journal-title":"Procedia Comput Sci"},{"key":"11230_CR37","doi-asserted-by":"crossref","unstructured":"Yang S, Yu X, Zhou Y (2020) Lstm and gru neural network performance comparison study: Taking yelp review dataset as an example. In: 2020 International workshop on electronic communication and artificial intelligence (IWECAI), 98\u2013101 (IEEE, 2020).","DOI":"10.1109\/IWECAI50956.2020.00027"},{"key":"11230_CR38","doi-asserted-by":"crossref","unstructured":"Lai S, Ye C, Zhou HJH (2021) Chinese stock trend prediction based on multi-feature learning and model fusion. In: 2021 IEEE International Conference on Smart Data Services (SMDS), 18\u201323 (IEEE, 2021).","DOI":"10.1109\/SMDS53860.2021.00013"},{"key":"11230_CR39","doi-asserted-by":"crossref","unstructured":"Boissonneault D, Hensen E (2024) Fake news detection with large language models on the liar dataset.","DOI":"10.21203\/rs.3.rs-4465815\/v1"},{"key":"11230_CR40","doi-asserted-by":"crossref","unstructured":"Subbiah SS, Paramasivan SK, Arockiasamy K, Senthivel S, Thangavel M (2023) Deep learning for wind speed forecasting using bi-lstm with selected features. Intell Autom Soft Comput 35.","DOI":"10.32604\/iasc.2023.030480"},{"key":"11230_CR41","unstructured":"Chung J, Gulcehre C, Cho K, Bengio Y (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555"},{"key":"11230_CR42","doi-asserted-by":"crossref","unstructured":"Li X, Sabas JF, Mend\u00e9z VD (2022) Wind energy forecasting using multiple arima models. In: 2022 IEEE 18th International Conference on Automation Science and Engineering (CASE), 2034\u20132039 (IEEE, 2022).","DOI":"10.1109\/CASE49997.2022.9926516"}],"container-title":["Neural Computing and Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11230-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s00521-025-11230-5\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s00521-025-11230-5.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,27]],"date-time":"2025-06-27T08:29:47Z","timestamp":1751012987000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s00521-025-11230-5"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,14]]},"references-count":42,"journal-issue":{"issue":"19","published-print":{"date-parts":[[2025,7]]}},"alternative-id":["11230"],"URL":"https:\/\/doi.org\/10.1007\/s00521-025-11230-5","relation":{},"ISSN":["0941-0643","1433-3058"],"issn-type":[{"value":"0941-0643","type":"print"},{"value":"1433-3058","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,14]]},"assertion":[{"value":"10 January 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"26 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"14 May 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":"The authors declare that they have no conflicts of interest to report regarding the present study.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflicts of interest"}}]}}