{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T13:54:00Z","timestamp":1773064440944,"version":"3.50.1"},"reference-count":69,"publisher":"Springer Science and Business Media LLC","issue":"5","license":[{"start":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T00:00:00Z","timestamp":1773014400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T00:00:00Z","timestamp":1773014400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"funder":[{"name":"the Key Project of Natural Science Foundation of Ningxia","award":["No.2024AAC02033"],"award-info":[{"award-number":["No.2024AAC02033"]}]},{"DOI":"10.13039\/100014718","name":"Innovative Research Group Project of the National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["No.12362005"],"award-info":[{"award-number":["No.12362005"]}],"id":[{"id":"10.13039\/100014718","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Ningxia higher education first-class discipline construction funding project","award":["NXYLXK2017B09"],"award-info":[{"award-number":["NXYLXK2017B09"]}]},{"name":"Major Special project of North Minzu University","award":["No.ZDZX201902"],"award-info":[{"award-number":["No.ZDZX201902"]}]},{"DOI":"10.13039\/501100012481","name":"Graduate Innovation Project of North Minzu University","doi-asserted-by":"publisher","award":["YCX24273"],"award-info":[{"award-number":["YCX24273"]}],"id":[{"id":"10.13039\/501100012481","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Appl Intell"],"published-print":{"date-parts":[[2026,4]]},"DOI":"10.1007\/s10489-026-07171-8","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T08:49:27Z","timestamp":1773046167000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["GTBAD: GVSAO-Transformer-BiLSTM-based time-series anomaly detection for photovoltaic power generation"],"prefix":"10.1007","volume":"56","author":[{"given":"Lin","family":"Zhu","sequence":"first","affiliation":[]},{"given":"Shaojuan","family":"Ma","sequence":"additional","affiliation":[]},{"given":"Changlin","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Xinyi","family":"Xu","sequence":"additional","affiliation":[]},{"given":"Haichuan","family":"Du","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2026,3,9]]},"reference":[{"key":"7171_CR1","doi-asserted-by":"publisher","first-page":"100545","DOI":"10.1016\/j.ref.2024.100545","volume":"48","author":"Q Hassan","year":"2024","unstructured":"Hassan Q, Viktor P, Al-Musawi TJ, Ali BM, Algburi S, Alzoubi HM, Al-Jiboory AK, Sameen AZ, Salman HM, Jaszczur M (2024) The renewable energy role in the global energy transformations. Renew Energy Focus 48:100545","journal-title":"Renew Energy Focus"},{"issue":"4","key":"7171_CR2","doi-asserted-by":"publisher","first-page":"590","DOI":"10.1109\/JPHOTOV.2023.3272071","volume":"13","author":"Q Liu","year":"2023","unstructured":"Liu Q, Hu Q, Zhou J, Yu D, Mo H (2023) Remaining useful life prediction of pv systems under dynamic environmental conditions. IEEE J Photovolt 13(4):590\u2013602","journal-title":"IEEE J Photovolt"},{"issue":"35","key":"7171_CR3","doi-asserted-by":"publisher","first-page":"24829","DOI":"10.1007\/s00521-023-09041-7","volume":"35","author":"GM El-Banby","year":"2023","unstructured":"El-Banby GM, Moawad NM, Abouzalm BA, Abouzaid WF, Ramadan E (2023) Photovoltaic system fault detection techniques: a review. Neural Comput Appl 35(35):24829\u201324842","journal-title":"Neural Comput Appl"},{"key":"7171_CR4","doi-asserted-by":"publisher","first-page":"154","DOI":"10.1016\/j.egyr.2023.09.159","volume":"9","author":"IA Zulfauzi","year":"2023","unstructured":"Zulfauzi IA, Dahlan NY, Sintuya H, Setthapun W (2023) Anomaly detection using k-means and long-short term memory for predictive maintenance of large-scale solar (lss) photovoltaic plant. Energy Rep 9:154\u2013158","journal-title":"Energy Rep"},{"key":"7171_CR5","doi-asserted-by":"crossref","unstructured":"Chen M, Zhao Z (2024) Optimization of Deep Learning Models for Non-stationary Time Series Data. In: 2024 International conference on intelligent algorithms for computational intelligence systems (IACIS) (IEEE), pp 1\u20135","DOI":"10.1109\/IACIS61494.2024.10721817"},{"issue":"5","key":"7171_CR6","doi-asserted-by":"publisher","first-page":"139","DOI":"10.1007\/s10142-024-01415-x","volume":"24","author":"K Borah","year":"2024","unstructured":"Borah K, Das HS, Seth S, Mallick K, Rahaman Z, Mallik S (2024) A review on advancements in feature selection and feature extraction for high-dimensional ngs data analysis. Funct Integrat Genom 24(5):139","journal-title":"Funct Integrat Genom"},{"issue":"19","key":"7171_CR7","doi-asserted-by":"publisher","first-page":"9313","DOI":"10.1007\/s10489-024-05673-x","volume":"54","author":"G Yang","year":"2024","unstructured":"Yang G, Wu J, Wang L, Wang Q, Liu X, Fu J (2024) A novel fusion feature imageization with improved extreme learning machine for network anomaly detection. Appl Intell 54(19):9313\u20139329","journal-title":"Appl Intell"},{"issue":"2","key":"7171_CR8","first-page":"2118","volume":"35","author":"Y Zhang","year":"2023","unstructured":"Zhang Y, Chen Y, Wang J, Pan Z (2023) Unsupervised deep anomaly detection for multi-sensor time-series signals. IEEE Trans Knowl Data Eng 35(2):2118\u20132132","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"24","key":"7171_CR9","doi-asserted-by":"publisher","first-page":"9630","DOI":"10.3390\/s22249630","volume":"22","author":"X Guo","year":"2022","unstructured":"Guo X, Mo Y, Yan K (2022) Short-term photovoltaic power forecasting based on historical information and deep learning methods. Sensors 22(24):9630","journal-title":"Sensors"},{"key":"7171_CR10","doi-asserted-by":"crossref","unstructured":"Souhe FGY, Mbey CF, Kakeu VJF, Meyo AE, Boum AT (2024) Optimized forecasting of photovoltaic power generation using hybrid deep learning model based on gru and svm. Electric Eng 1\u201320","DOI":"10.1007\/s00202-024-02492-8"},{"key":"7171_CR11","doi-asserted-by":"publisher","first-page":"4210","DOI":"10.1007\/s10489-024-05395-0","volume":"54","author":"J Chen","year":"2024","unstructured":"Chen J, Pi D, Wang X (2024) A two-stage adversarial transformer based approach for multivariate industrial time series anomaly detection. Appl Intell 54:4210\u20134229","journal-title":"Appl Intell"},{"key":"7171_CR12","doi-asserted-by":"publisher","first-page":"101769","DOI":"10.1016\/j.segan.2025.101769","volume":"43","author":"SM Miraftabzadeh","year":"2025","unstructured":"Miraftabzadeh SM, Longo M, Leva S, Matera N (2025) Data anomaly detection in photovoltaic power time-series via unsupervised deep learning with insufficient information. Sustain Energ Grids Netw 43:101769","journal-title":"Sustain Energ Grids Netw"},{"issue":"2","key":"7171_CR13","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439950","volume":"54","author":"G Pang","year":"2021","unstructured":"Pang G, Shen C, Cao L, Hengel AVD (2021) Deep learning for anomaly detection: A review. ACM Comput Surv (CSUR) 54(2):1\u201338","journal-title":"ACM Comput Surv (CSUR)"},{"key":"7171_CR14","doi-asserted-by":"crossref","unstructured":"Ghrib Z, Jaziri R, Romdhane R (2020) Hybrid approach for anomaly detection in time series data. In: 2020 international joint conference on neural networks (ijcnn) (IEEE), pp 1\u20137","DOI":"10.1109\/IJCNN48605.2020.9207013"},{"issue":"10","key":"7171_CR15","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.3390\/math9101122","volume":"9","author":"O Mandrikova","year":"2021","unstructured":"Mandrikova O, Fetisova N, Polozov Y (2021) Hybrid model for time series of complex structure with arima components. Mathematics 9(10):1122","journal-title":"Mathematics"},{"key":"7171_CR16","doi-asserted-by":"crossref","unstructured":"Abhishek IA, Dafinny T (2023) Anomaly detection in load forecasting using ARIMA and Autoencoder. In: 2023 IEEE Fifth International conference on advances in electronics, computers and communications (ICAECC) (IEEE), pp 01\u201306","DOI":"10.1109\/ICAECC59324.2023.10560105"},{"key":"7171_CR17","doi-asserted-by":"crossref","unstructured":"Saqib M, \u015eent\u00fcrk E, Sahu SA, Adil MA (2022) Comparisons of autoregressive integrated moving average (arima) and long short term memory (lstm) network models for ionospheric anomalies detection: a study on haiti (m w= 7.0) earthquake. Acta Geodaetica et Geophysica 1\u201319","DOI":"10.1007\/s40328-021-00371-3"},{"key":"7171_CR18","doi-asserted-by":"publisher","first-page":"101071","DOI":"10.1016\/j.aei.2020.101071","volume":"44","author":"H Lee","year":"2020","unstructured":"Lee H, Li G, Rai A, Chattopadhyay A (2020) Real-time anomaly detection framework using a support vector regression for the safety monitoring of commercial aircraft. Adv Eng Inform 44:101071","journal-title":"Adv Eng Inform"},{"key":"7171_CR19","doi-asserted-by":"publisher","first-page":"102302","DOI":"10.1016\/j.datak.2024.102302","volume":"151","author":"V Yepmo","year":"2024","unstructured":"Yepmo V, Smits G, Lesot MJ, Pivert O (2024) Leveraging an isolation forest to anomaly detection and data clustering. Data Knowl Eng 151:102302","journal-title":"Data Knowl Eng"},{"key":"7171_CR20","doi-asserted-by":"publisher","first-page":"116100","DOI":"10.1016\/j.eswa.2021.116100","volume":"189","author":"A Barbado","year":"2022","unstructured":"Barbado A, Corcho \u00d3, Benjamins R (2022) Rule extraction in unsupervised anomaly detection for model explainability: Application to oneclass svm. Expert Syst Appl 189:116100","journal-title":"Expert Syst Appl"},{"key":"7171_CR21","doi-asserted-by":"publisher","first-page":"107119","DOI":"10.1016\/j.patcog.2019.107119","volume":"100","author":"M Turkoz","year":"2020","unstructured":"Turkoz M, Kim S, Son Y, Jeong MK, Elsayed EA (2020) Generalized support vector data description for anomaly detection. Pattern Recogn 100:107119","journal-title":"Pattern Recogn"},{"key":"7171_CR22","doi-asserted-by":"publisher","first-page":"104923","DOI":"10.1016\/j.jpdc.2024.104923","volume":"192","author":"A Adesh","year":"2024","unstructured":"Adesh A, Shobha G, Shetty J, Xu L (2024) Local outlier factor for anomaly detection in hpcc systems. J Parallel Distrib Comput 192:104923","journal-title":"J Parallel Distrib Comput"},{"issue":"2","key":"7171_CR23","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1145\/3439950","volume":"54","author":"G Pang","year":"2021","unstructured":"Pang G, Shen C, Cao L, Hengel AVD (2021) Deep learning for anomaly detection: A review. ACM Comput Surv (CSUR) 54(2):1\u201338","journal-title":"ACM Comput Surv (CSUR)"},{"key":"7171_CR24","doi-asserted-by":"crossref","unstructured":"Neloy AA, Turgeon M (2024) A comprehensive study of auto-encoders for anomaly detection: Efficiency and trade-offs. Mach Learn Appl 100572","DOI":"10.1016\/j.mlwa.2024.100572"},{"issue":"3","key":"7171_CR25","doi-asserted-by":"publisher","first-page":"683","DOI":"10.1007\/s13042-022-01657-w","volume":"14","author":"C Zhang","year":"2023","unstructured":"Zhang C, Wang X, Zhang J, Li S, Zhang H, Liu C, Han P (2023) Vesc: a new variational autoencoder based model for anomaly detection. Int J Mach Learn Cybern 14(3):683\u2013696","journal-title":"Int J Mach Learn Cybern"},{"issue":"3","key":"7171_CR26","doi-asserted-by":"publisher","first-page":"1477","DOI":"10.1109\/TASE.2022.3141186","volume":"19","author":"M Maggipinto","year":"2022","unstructured":"Maggipinto M, Beghi A, Susto GA (2022) A deep convolutional autoencoder-based approach for anomaly detection with industrial, non-images, 2-dimensional data: A semiconductor manufacturing case study. IEEE Trans Autom Sci Eng 19(3):1477\u20131490","journal-title":"IEEE Trans Autom Sci Eng"},{"issue":"1","key":"7171_CR27","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s42400-022-00134-9","volume":"6","author":"H Torabi","year":"2023","unstructured":"Torabi H, Mirtaheri SL, Greco S (2023) Practical autoencoder based anomaly detection by using vector reconstruction error. Cybersecurity 6(1):1","journal-title":"Cybersecurity"},{"key":"7171_CR28","doi-asserted-by":"crossref","unstructured":"He S, Du M, Jiang X, Zhang W, Wang C (2024) Vaeat: Variational autoeencoder with adversarial training for multivariate time series anomaly detection. Inform Sci 120852","DOI":"10.1016\/j.ins.2024.120852"},{"key":"7171_CR29","doi-asserted-by":"publisher","first-page":"497","DOI":"10.1016\/j.neucom.2021.12.093","volume":"493","author":"X Xia","year":"2022","unstructured":"Xia X, Pan X, Li N, He X, Ma L, Zhang X, Ding N (2022) Gan-based anomaly detection: A review. Neurocomputing 493:497\u2013535","journal-title":"Neurocomputing"},{"key":"7171_CR30","doi-asserted-by":"crossref","unstructured":"Audibert J, Michiardi P, Guyard F, Marti S, Zuluaga MA (2020) Usad: Unsupervised anomaly detection on multivariate time series. In: Proceedings of the 26th ACM SIGKDD international conference on knowledge discovery & data mining, pp 3395\u20133404","DOI":"10.1145\/3394486.3403392"},{"key":"7171_CR31","doi-asserted-by":"crossref","unstructured":"Zhou B, Liu S, Hooi B, Cheng X, Ye J (2019) Beatgan: Anomalous rhythm detection using adversarially generated time series. In: IJCAI, vol. 2019, pp 4433\u20134439","DOI":"10.24963\/ijcai.2019\/616"},{"key":"7171_CR32","doi-asserted-by":"publisher","first-page":"108560","DOI":"10.1016\/j.compchemeng.2023.108560","volume":"182","author":"A Iqbal","year":"2024","unstructured":"Iqbal A, Amin R (2024) Time series forecasting and anomaly detection using deep learning. Comput Chem Eng 182:108560","journal-title":"Comput Chem Eng"},{"key":"7171_CR33","first-page":"102282","volume":"57","author":"HD Nguyen","year":"2021","unstructured":"Nguyen HD, Tran KP, Thomassey S, Hamad M (2021) Forecasting and anomaly detection approaches using lstm and lstm autoencoder techniques with the applications in supply chain management. Int J Inf Manage 57:102282","journal-title":"Int J Inf Manage"},{"issue":"8","key":"7171_CR34","doi-asserted-by":"publisher","first-page":"3127","DOI":"10.1109\/TNNLS.2019.2935975","volume":"31","author":"T Ergen","year":"2019","unstructured":"Ergen T, Kozat SS (2019) Unsupervised anomaly detection with lstm neural networks. IEEE Trans Neural Netw Learn Syst 31(8):3127\u20133141","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"5","key":"7171_CR35","doi-asserted-by":"publisher","first-page":"3469","DOI":"10.1109\/TII.2020.3022432","volume":"17","author":"X Zhou","year":"2020","unstructured":"Zhou X, Hu Y, Liang W, Ma J, Jin Q (2020) Variational lstm enhanced anomaly detection for industrial big data. IEEE Trans Industr Inf 17(5):3469\u20133477","journal-title":"IEEE Trans Industr Inf"},{"key":"7171_CR36","doi-asserted-by":"crossref","unstructured":"Fadili Y, El Yamani Y, Kilani J, El Kamoun N, Baddi Y, Bensalah F (2024) An Enhancing Timeseries Anomaly Detection Using LSTM and Bi-LSTM Architectures. In: 2024 11th International conference on wireless networks and mobile communications (WINCOM) (IEEE), pp 1\u20136","DOI":"10.1109\/WINCOM62286.2024.10655101"},{"key":"7171_CR37","unstructured":"Vaswani A, Shazeer N, Parmar N, Uszkoreit J, Jones L, Gomez AN, Kaiser L, Polosukhin I (2017) Attention is all you need. Adv Neural Inf Process Syst 2017-December, 5999\u20136009"},{"key":"7171_CR38","doi-asserted-by":"publisher","first-page":"106745","DOI":"10.1016\/j.neunet.2024.106745","volume":"180","author":"A Li","year":"2024","unstructured":"Li A, Li Y, Xu Y, Li X, Zhang C (2024) Multi-scale convolution enhanced transformer for multivariate long-term time series forecasting. Neural Netw 180:106745","journal-title":"Neural Netw"},{"key":"7171_CR39","doi-asserted-by":"crossref","unstructured":"Wang D, Ruan P, Xu D, Xie W, Chen X, Li H (2023) TranAD: A Deep Transformer Model for Fault Diagnosis of Lithium Batteries. In: 2023 International conference on smart electrical grid and renewable energy (SEGRE) (IEEE), pp 133\u2013139","DOI":"10.1109\/SEGRE58867.2023.00028"},{"key":"7171_CR40","doi-asserted-by":"publisher","first-page":"244","DOI":"10.1016\/j.future.2023.02.015","volume":"144","author":"F Zeng","year":"2023","unstructured":"Zeng F, Chen M, Qian C, Wang Y, Zhou Y, Tang W (2023) Multivariate time series anomaly detection with adversarial transformer architecture in the internet of things. Futur Gener Comput Syst 144:244\u2013255","journal-title":"Futur Gener Comput Syst"},{"issue":"6","key":"7171_CR41","doi-asserted-by":"publisher","first-page":"759","DOI":"10.3390\/e24060759","volume":"24","author":"S Guan","year":"2022","unstructured":"Guan S, Zhao B, Dong Z, Gao M, He Z (2022) Gtad: Graph and temporal neural network for multivariate time series anomaly detection. Entropy 24(6):759","journal-title":"Entropy"},{"key":"7171_CR42","doi-asserted-by":"crossref","unstructured":"Li B, M\u00fcller E (2023) Contrastive time series anomaly detection by temporal transformations. In: 2023 International joint conference on neural networks (IJCNN) (IEEE), pp 1\u20138","DOI":"10.1109\/IJCNN54540.2023.10191358"},{"issue":"8","key":"7171_CR43","first-page":"8980","volume":"36","author":"Z Yue","year":"2022","unstructured":"Yue Z, Wang Y, Duan J, Yang T, Huang C, Tong Y, Xu B (2022) Ts2vec: Towards universal representation of time series. Proceed AAAI Conf Artif Intell 36(8):8980\u20138987","journal-title":"Proceed AAAI Conf Artif Intell"},{"key":"7171_CR44","doi-asserted-by":"crossref","unstructured":"Gong D, Liu L, Le V, Saha B, Mansour MR, Venkatesh S, Hengel Avd (2019) Memorizing normality to detect anomaly: Memory-augmented deep autoencoder for unsupervised anomaly detection. In: Proceedings of the IEEE\/CVF international conference on computer vision, pp 1705\u20131714","DOI":"10.1109\/ICCV.2019.00179"},{"key":"7171_CR45","doi-asserted-by":"crossref","unstructured":"Pintilie I, Manolache A, Brad F (2023) Time series anomaly detection using diffusion-based models. In: 2023 IEEE International conference on data mining workshops (ICDMW) (IEEE), pp 570\u2013578","DOI":"10.1109\/ICDMW60847.2023.00080"},{"issue":"3","key":"7171_CR46","doi-asserted-by":"publisher","first-page":"359","DOI":"10.14778\/3632093.3632101","volume":"17","author":"Y Chen","year":"2023","unstructured":"Chen Y, Zhang C, Ma M, Liu Y, Ding R, Li B, He S, Rajmohan S, Lin Q, Zhang D (2023) Imdiffusion: Imputed diffusion models for multivariate time series anomaly detection. Proceed VLDB Endowm 17(3):359\u2013372","journal-title":"Proceed VLDB Endowm"},{"issue":"4","key":"7171_CR47","doi-asserted-by":"publisher","first-page":"3953","DOI":"10.1109\/TASE.2022.3141248","volume":"19","author":"RNA Algburi","year":"2022","unstructured":"Algburi RNA, Gao H, Al-Huda Z (2022) Improvement of an industrial robotic flaw detection system. IEEE Trans Autom Sci Eng 19(4):3953\u20133967","journal-title":"IEEE Trans Autom Sci Eng"},{"issue":"5","key":"7171_CR48","doi-asserted-by":"publisher","first-page":"600","DOI":"10.1049\/iet-smt.2019.0172","volume":"14","author":"RNA Algburi","year":"2020","unstructured":"Algburi RNA, Gao H (2020) Detecting feeble position oscillations from rotary encoder signal in an industrial robot via singular spectrum analysis. IET Sci Measurem Technol 14(5):600\u2013609","journal-title":"IET Sci Measurem Technol"},{"issue":"5","key":"7171_CR49","doi-asserted-by":"publisher","first-page":"1446","DOI":"10.1016\/j.cam.2010.08.030","volume":"235","author":"WF Abd-El-Wahed","year":"2011","unstructured":"Abd-El-Wahed WF, Mousa A, El-Shorbagy MA (2011) Integrating particle swarm optimization with genetic algorithms for solving nonlinear optimization problems. J Comput Appl Math 235(5):1446\u20131453","journal-title":"J Comput Appl Math"},{"issue":"7","key":"7171_CR50","doi-asserted-by":"publisher","first-page":"1743","DOI":"10.1109\/TCYB.2016.2556742","volume":"47","author":"M Mavrovouniotis","year":"2016","unstructured":"Mavrovouniotis M, M\u00fcller FM, Yang S (2016) Ant colony optimization with local search for dynamic traveling salesman problems. IEEE Trans Cybernetics 47(7):1743\u20131756","journal-title":"IEEE Trans Cybernetics"},{"key":"7171_CR51","doi-asserted-by":"publisher","first-page":"309","DOI":"10.1016\/j.neucom.2022.03.013","volume":"489","author":"M Sheng","year":"2022","unstructured":"Sheng M, Chen S, Liu W, Mao J, Liu X (2022) A differential evolution with adaptive neighborhood mutation and local search for multi-modal optimization. Neurocomputing 489:309\u2013322","journal-title":"Neurocomputing"},{"key":"7171_CR52","doi-asserted-by":"crossref","unstructured":"Li C, Lian Z, Zhang T (2020) An optimized bat algorithm combining local search and global search. In IOP Conference series: Earth and environmental science, vol. 571 (IOP Publishing), p 012018","DOI":"10.1088\/1755-1315\/571\/1\/012018"},{"key":"7171_CR53","doi-asserted-by":"publisher","first-page":"120069","DOI":"10.1016\/j.eswa.2023.120069","volume":"225","author":"L Deng","year":"2023","unstructured":"Deng L, Liu S (2023) Snow ablation optimizer: A novel metaheuristic technique for numerical optimization and engineering design. Expert Syst Appl 225:120069","journal-title":"Expert Syst Appl"},{"issue":"17","key":"7171_CR54","doi-asserted-by":"publisher","first-page":"4434","DOI":"10.3390\/en17174434","volume":"17","author":"Y Wu","year":"2024","unstructured":"Wu Y, Xiang C, Qian H, Zhou P (2024) Optimization of bi-lstm photovoltaic power prediction based on improved snow ablation optimization algorithm. Energies 17(17):4434","journal-title":"Energies"},{"issue":"4","key":"7171_CR55","doi-asserted-by":"publisher","first-page":"2287","DOI":"10.1007\/s00202-023-01806-6","volume":"105","author":"GS Eldeghady","year":"2023","unstructured":"Eldeghady GS, Kamal HA, Hassan MAM (2023) Fault diagnosis for pv system using a deep learning optimized via pso heuristic combination technique. Electr Eng 105(4):2287\u20132301","journal-title":"Electr Eng"},{"issue":"28","key":"7171_CR56","doi-asserted-by":"publisher","first-page":"17715","DOI":"10.1007\/s00521-024-10322-y","volume":"36","author":"V Sinap","year":"2024","unstructured":"Sinap V, Kumtepe A (2024) Cnn-based automatic detection of photovoltaic solar module anomalies in infrared images: a comparative study. Neural Comput Appl 36(28):17715\u201317736","journal-title":"Neural Comput Appl"},{"key":"7171_CR57","doi-asserted-by":"publisher","first-page":"103840","DOI":"10.1016\/j.cose.2024.103840","volume":"141","author":"P Luo","year":"2024","unstructured":"Luo P, Wang B, Tian J (2024) Ttsad: Tcn-transformer-svdd model for anomaly detection in air traffic ads-b data. Comput Sec 141:103840","journal-title":"Comput Sec"},{"key":"7171_CR58","doi-asserted-by":"publisher","first-page":"110791","DOI":"10.1016\/j.measurement.2022.110791","volume":"191","author":"X Wang","year":"2022","unstructured":"Wang X, Pi D, Zhang X, Liu H, Guo C (2022) Variational transformer-based anomaly detection approach for multivariate time series. Measurement 191:110791","journal-title":"Measurement"},{"key":"7171_CR59","doi-asserted-by":"publisher","first-page":"133418","DOI":"10.1016\/j.energy.2024.133418","volume":"311","author":"Z Li","year":"2024","unstructured":"Li Z, Zhang X, Gao W (2024) State of health estimation of lithium-ion battery during fast charging process based on bilstm-transformer. Energy 311:133418","journal-title":"Energy"},{"issue":"1","key":"7171_CR60","doi-asserted-by":"publisher","first-page":"577","DOI":"10.1038\/s41597-022-01696-6","volume":"9","author":"Y Chen","year":"2022","unstructured":"Chen Y, Xu J (2022) Solar and wind power data from the chinese state grid renewable energy generation forecasting competition. Scientif Data 9(1):577","journal-title":"Scientif Data"},{"issue":"8","key":"7171_CR61","first-page":"122","volume":"36","author":"Y Zhang","year":"2024","unstructured":"Zhang Y, Wei G (2024) Short-term photovoltaic power combination forecasting model considering feature selection. J Electric Power Syst Autom 36(8):122\u2013132","journal-title":"J Electric Power Syst Autom"},{"key":"7171_CR62","doi-asserted-by":"publisher","first-page":"111671","DOI":"10.1016\/j.asoc.2024.111671","volume":"159","author":"S Fu","year":"2024","unstructured":"Fu S, Gao X, Li B, Zhai F, Lu J, Xue B, Yu J, Xiao C (2024) Multivariate time series anomaly detection via separation, decomposition, and dual transformer-based autoencoder. Appl Soft Comput 159:111671","journal-title":"Appl Soft Comput"},{"issue":"5","key":"7171_CR63","first-page":"188","volume":"44","author":"T Wang","year":"2023","unstructured":"Wang T, Yu R, Mao W, Song X, Ma J (2023) Research on anomaly detection and correction of nuclear power plant operation data based on gru-mlp. Hedongli Gongcheng\/Nuclear Power Eng 44(5):188\u2013194","journal-title":"Hedongli Gongcheng\/Nuclear Power Eng"},{"key":"7171_CR64","doi-asserted-by":"crossref","unstructured":"Yang W, Zeng C (2022) A Hybrid Anomaly Detection Model Based on GANomaly in Cloud Environment. In: 2022 IEEE 5th International conference on big data and artificial intelligence (BDAI) (IEEE), pp 51\u201356","DOI":"10.1109\/BDAI56143.2022.9862656"},{"issue":"4","key":"7171_CR65","doi-asserted-by":"publisher","first-page":"3787","DOI":"10.1109\/JSEN.2022.3230361","volume":"23","author":"Y Wei","year":"2023","unstructured":"Wei Y, Jang-Jaccard J, Xu W, Sabrina F, Camtepe S, Boulic M (2023) Lstm-autoencoder-based anomaly detection for indoor air quality time-series data. IEEE Sens J 23(4):3787\u20133800","journal-title":"IEEE Sens J"},{"key":"7171_CR66","doi-asserted-by":"crossref","unstructured":"Yu Lr, Lu Qh, Xue Y (2024) Dtaad: Dual tcn-attention networks for anomaly detection in multivariate time series data. Knowl-Based Syst 295:111849","DOI":"10.1016\/j.knosys.2024.111849"},{"key":"7171_CR67","first-page":"1","volume":"71","author":"Z Li","year":"2022","unstructured":"Li Z, Sun Y, Yang L, Zhao Z, Chen X (2022) Unsupervised machine anomaly detection using autoencoder and temporal convolutional network. IEEE Trans Instrum Meas 71:1\u201313","journal-title":"IEEE Trans Instrum Meas"},{"key":"7171_CR68","doi-asserted-by":"crossref","unstructured":"Yang Y, Zhang C, Zhou T, Wen Q, Sun L (2023) Dcdetector: Dual attention contrastive representation learning for time series anomaly detection. In: Proceedings of the 29th ACM SIGKDD conference on knowledge discovery and data mining, pp 3033\u20133045","DOI":"10.1145\/3580305.3599295"},{"key":"7171_CR69","doi-asserted-by":"crossref","unstructured":"Dang W, Zhou B, Wei L, Zhang W, Yang Z, Hu S (2021) Ts-bert: Time series anomaly detection via pre-training model bert. In: International conference on computational science (Springer), pp 209\u2013223","DOI":"10.1007\/978-3-030-77964-1_17"}],"container-title":["Applied Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-026-07171-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10489-026-07171-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10489-026-07171-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T10:05:30Z","timestamp":1773050730000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10489-026-07171-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,9]]},"references-count":69,"journal-issue":{"issue":"5","published-print":{"date-parts":[[2026,4]]}},"alternative-id":["7171"],"URL":"https:\/\/doi.org\/10.1007\/s10489-026-07171-8","relation":{},"ISSN":["0924-669X","1573-7497"],"issn-type":[{"value":"0924-669X","type":"print"},{"value":"1573-7497","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,9]]},"assertion":[{"value":"7 February 2025","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"22 February 2026","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"9 March 2026","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 there are no known financial or personal relationships that could have influenced the work reported in this paper.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}],"article-number":"140"}}