{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,8,12]],"date-time":"2025-08-12T21:44:02Z","timestamp":1755035042537,"version":"3.32.0"},"reference-count":33,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T00:00:00Z","timestamp":1736208000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T00:00:00Z","timestamp":1736208000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Energy Inform"],"DOI":"10.1186\/s42162-024-00441-0","type":"journal-article","created":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T10:12:17Z","timestamp":1736244737000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Arrears behavior prediction of power users based on BP neural network and multi-scale feature learning: a refined risk assessment framework"],"prefix":"10.1186","volume":"8","author":[{"given":"Liang","family":"Yu","sequence":"first","affiliation":[]},{"given":"Yuanshen","family":"Hong","sequence":"additional","affiliation":[]},{"given":"Hua","family":"Lin","sequence":"additional","affiliation":[]},{"given":"Xu","family":"Jiang","sequence":"additional","affiliation":[]},{"given":"Ziming","family":"Song","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,1,7]]},"reference":[{"key":"441_CR1","doi-asserted-by":"crossref","unstructured":"Gong B, An A, Shi Y, Guan H, Jia W, Yang F (2024) An interpretable hybrid Spatiotemporal Fusion Method for Ultra-short-term Photovoltaic Power Prediction. Energy, 308, 132969","DOI":"10.1016\/j.energy.2024.132969"},{"issue":"1","key":"441_CR2","doi-asserted-by":"publisher","first-page":"6653","DOI":"10.1038\/s41598-024-57398-z","volume":"14","author":"M Guermoui","year":"2024","unstructured":"Guermoui M, Fezzani A, Mohamed Z, Rabehi A, Ferkous K, Bailek N, Ghoneim SS (2024) An analysis of case studies for advancing photovoltaic power forecasting through multi-scale fusion techniques. Sci Rep 14(1):6653","journal-title":"Sci Rep"},{"issue":"5","key":"441_CR3","doi-asserted-by":"publisher","first-page":"2998","DOI":"10.3390\/app13052998","volume":"13","author":"S Yang","year":"2023","unstructured":"Yang S, Zhu K, Li F, Weng L, Cheng L (2023) MFAMNet: Multi-scale feature attention mixture network for short-term load forecasting. Appl Sci 13(5):2998","journal-title":"Appl Sci"},{"issue":"8","key":"441_CR4","first-page":"6217","volume":"34","author":"SI Khan","year":"2022","unstructured":"Khan SI, Shahrior A, Karim R, Hasan M, Rahman A (2022) MultiNet: a deep neural network approach for detecting breast cancer through multi-scale feature fusion. J King Saud University-Computer Inform Sci 34(8):6217\u20136228","journal-title":"J King Saud University-Computer Inform Sci"},{"issue":"1","key":"441_CR5","doi-asserted-by":"publisher","first-page":"7394","DOI":"10.1038\/s41598-022-11206-8","volume":"12","author":"C Chen","year":"2022","unstructured":"Chen C, Zhao X, Wang J, Li D, Guan Y, Hong J (2022) Dynamic graph convolutional network for assembly behavior recognition based on attention mechanism and multi-scale feature fusion. Sci Rep 12(1):7394","journal-title":"Sci Rep"},{"key":"441_CR6","doi-asserted-by":"publisher","first-page":"106912","DOI":"10.1016\/j.engappai.2023.106912","volume":"126","author":"J Liang","year":"2023","unstructured":"Liang J, Mao Z, Liu F, Kong X, Zhang J, Jiang Z (2023) Multi-sensor signals multi-scale fusion method for fault detection of high-speed and high-power diesel engine under variable operating conditions. Eng Appl Artif Intell 126:106912","journal-title":"Eng Appl Artif Intell"},{"key":"441_CR7","doi-asserted-by":"publisher","first-page":"105791","DOI":"10.1016\/j.bspc.2023.105791","volume":"90","author":"D Wang","year":"2024","unstructured":"Wang D, Ye Y, Zhang B, Sun J, Zhang C (2024) IMSF-Net: an improved multi-scale information fusion network for PPG-based blood pressure estimation. Biomed Signal Process Control 90:105791","journal-title":"Biomed Signal Process Control"},{"key":"441_CR8","doi-asserted-by":"publisher","first-page":"69","DOI":"10.1016\/j.jmsy.2023.07.007","volume":"70","author":"W Ma","year":"2023","unstructured":"Ma W, Liu X, Yue C, Wang L, Liang SY (2023) Multi-scale one-dimensional convolution tool wear monitoring based on multi-model fusion learning skills. J Manuf Syst 70:69\u201398","journal-title":"J Manuf Syst"},{"issue":"12","key":"441_CR9","doi-asserted-by":"publisher","first-page":"1997","DOI":"10.1049\/rsn2.12312","volume":"16","author":"Q Xiang","year":"2022","unstructured":"Xiang Q, Wang X, Lai J, Song Y, Li R, Lei L (2022) Multi-scale group\u2010fusion convolutional neural network for high\u2010resolution range profile target recognition. IET Radar Sonar Navig 16(12):1997\u20132016","journal-title":"IET Radar Sonar Navig"},{"issue":"7","key":"441_CR10","doi-asserted-by":"publisher","first-page":"3594","DOI":"10.1109\/TNNLS.2021.3112235","volume":"34","author":"K Jiang","year":"2021","unstructured":"Jiang K, Wang Z, Yi P, Chen C, Wang G, Han Z, Xiong Z (2021) Multi-scale hybrid fusion network for single image deraining. IEEE Trans Neural Networks Learn Syst 34(7):3594\u20133608","journal-title":"IEEE Trans Neural Networks Learn Syst"},{"key":"441_CR11","doi-asserted-by":"publisher","first-page":"105347","DOI":"10.1016\/j.engappai.2022.105347","volume":"116","author":"AM Roy","year":"2022","unstructured":"Roy AM (2022) Adaptive transfer learning-based multiscale feature fused deep convolutional neural network for EEG MI multiclassification in brain\u2013computer interface. Eng Appl Artif Intell 116:105347","journal-title":"Eng Appl Artif Intell"},{"issue":"23","key":"441_CR12","doi-asserted-by":"publisher","first-page":"3035","DOI":"10.3390\/math9233035","volume":"9","author":"F Deng","year":"2021","unstructured":"Deng F, Bi Y, Liu Y, Yang S (2021) Deep-learning-based remaining useful life prediction based on a multi-scale dilated convolution network. Mathematics 9(23):3035","journal-title":"Mathematics"},{"key":"441_CR13","doi-asserted-by":"publisher","first-page":"102747","DOI":"10.1016\/j.bspc.2021.102747","volume":"68","author":"M Riyad","year":"2021","unstructured":"Riyad M, Khalil M, Adib A (2021) A novel multi-scale convolutional neural network for motor imagery classification. Biomed Signal Process Control 68:102747","journal-title":"Biomed Signal Process Control"},{"key":"441_CR14","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1186\/s12938-021-00848-w","volume":"20","author":"F Jiang","year":"2021","unstructured":"Jiang F, Hong C, Cheng T, Wang H, Xu B, Zhang B (2021) Attention-based multi-scale features fusion for unobtrusive atrial fibrillation detection using ballistocardiogram signal. Biomed Eng Online 20:1\u201321","journal-title":"Biomed Eng Online"},{"key":"441_CR15","doi-asserted-by":"crossref","unstructured":"Fan GF, Li JW, Peng LL, Huang HP, Hong WC (2024) The bi-long short-term memory based on multiscale and mesoscale feature extraction for electric load forecasting. Appl Soft Comput, 162, 111853","DOI":"10.1016\/j.asoc.2024.111853"},{"issue":"26","key":"441_CR16","doi-asserted-by":"publisher","first-page":"9356","DOI":"10.1021\/acs.iecr.2c00797","volume":"61","author":"NL Jian","year":"2022","unstructured":"Jian NL, Zabiri H, Ramasamy M (2022) Data-based modeling of a Nonexplicit two-time scale process via multiple time-scale recurrent neural networks. Ind Eng Chem Res 61(26):9356\u20139365","journal-title":"Ind Eng Chem Res"},{"issue":"2","key":"441_CR17","doi-asserted-by":"publisher","first-page":"47","DOI":"10.26480\/aim.02.2022.47.51","volume":"6","author":"MS Younis","year":"2022","unstructured":"Younis MS, Elfargani (2022) The benefits of Artificial Intelligence in Construction projects. Acta Informatica Malaysia 6(2):47\u201351","journal-title":"Acta Informatica Malaysia"},{"issue":"12","key":"441_CR18","doi-asserted-by":"publisher","first-page":"4798","DOI":"10.1109\/TCSVT.2021.3055197","volume":"31","author":"F Li","year":"2021","unstructured":"Li F, Zhang Y, Cosman PC (2021) MMMNet: an end-to-end multi-task deep convolution neural network with multi-scale and multi-hierarchy fusion for blind image quality assessment. IEEE Trans Circuits Syst Video Technol 31(12):4798\u20134811","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"issue":"10","key":"441_CR19","doi-asserted-by":"publisher","first-page":"10748","DOI":"10.1109\/TKDE.2023.3268199","volume":"35","author":"L Chen","year":"2023","unstructured":"Chen L, Chen D, Shang Z, Wu B, Zheng C, Wen B, Zhang W (2023) Multi-scale adaptive graph neural network for multivariate time series forecasting. IEEE Trans Knowl Data Eng 35(10):10748\u201310761","journal-title":"IEEE Trans Knowl Data Eng"},{"issue":"12","key":"441_CR20","doi-asserted-by":"publisher","first-page":"5688","DOI":"10.3390\/s23125688","volume":"23","author":"X Cao","year":"2023","unstructured":"Cao X, Guo X, Duan Y, Zhang F, Fan H, Xu X (2023) Health status recognition method for rotating machinery based on multi-scale hybrid features and improved convolutional neural networks. Sensors 23(12):5688","journal-title":"Sensors"},{"key":"441_CR21","doi-asserted-by":"publisher","first-page":"379","DOI":"10.1016\/j.isatra.2020.10.054","volume":"110","author":"Z Xu","year":"2021","unstructured":"Xu Z, Li C, Yang Y (2021) Fault diagnosis of rolling bearings using an improved multi-scale convolutional neural network with feature attention mechanism. ISA Trans 110:379\u2013393","journal-title":"ISA Trans"},{"key":"441_CR22","doi-asserted-by":"publisher","first-page":"100888","DOI":"10.1016\/j.rineng.2023.100888","volume":"17","author":"A Nazir","year":"2023","unstructured":"Nazir A, Shaikh AK, Shah AS, Khalil A (2023) Forecasting energy consumption demand of customers in smart grid using temporal Fusion Transformer (TFT). Results Eng 17:100888","journal-title":"Results Eng"},{"key":"441_CR23","doi-asserted-by":"publisher","first-page":"252","DOI":"10.1016\/j.apm.2022.10.006","volume":"114","author":"J Ying","year":"2023","unstructured":"Ying J, Liu J, Chen J, Cao S, Hou M, Chen Y (2023) Multi-scale fusion network: a new deep learning structure for elliptic interface problems. Appl Math Model 114:252\u2013269","journal-title":"Appl Math Model"},{"issue":"9","key":"441_CR24","doi-asserted-by":"publisher","first-page":"2404","DOI":"10.3390\/en14092404","volume":"14","author":"NY Jayalakshmi","year":"2021","unstructured":"Jayalakshmi NY, Shankar R, Subramaniam U, Baranilingesan I, Karthick A, Stalin B, Ghosh A (2021) Novel multi-time scale deep learning algorithm for solar irradiance forecasting. Energies 14(9):2404","journal-title":"Energies"},{"key":"441_CR25","doi-asserted-by":"crossref","unstructured":"Bakkouri I, Afdel K, Benois-Pineau J, Initiative GC F. T. A. S. D. N. (2022). BG-3DM2F: bidirectional gated 3D multi-scale feature fusion for Alzheimer\u2019s disease diagnosis. Multimedia Tools Appl, 81(8), 10743\u201310776","DOI":"10.1007\/s11042-022-12242-2"},{"key":"441_CR26","doi-asserted-by":"publisher","first-page":"486","DOI":"10.1109\/TNSRE.2022.3153252","volume":"30","author":"G Hajian","year":"2022","unstructured":"Hajian G, Morin E (2022) Deep multi-scale fusion of convolutional neural networks for EMG-based movement estimation. IEEE Trans Neural Syst Rehabil Eng 30:486\u2013495","journal-title":"IEEE Trans Neural Syst Rehabil Eng"},{"key":"441_CR27","doi-asserted-by":"publisher","first-page":"120","DOI":"10.1016\/j.ins.2020.12.032","volume":"554","author":"S Behera","year":"2021","unstructured":"Behera S, Misra R, Sillitti A (2021) Multiscale deep bidirectional gated recurrent neural networks based prognostic method for complex non-linear degradation systems. Inf Sci 554:120\u2013144","journal-title":"Inf Sci"},{"issue":"4","key":"441_CR28","doi-asserted-by":"publisher","first-page":"2533","DOI":"10.1007\/s00034-023-02575-0","volume":"43","author":"F Niknejad Mazandarani","year":"2024","unstructured":"Niknejad Mazandarani F, Babyn P, Alirezaie J (2024) Low-dose CT image denoising with a residual multi-scale feature Fusion Convolutional neural network and enhanced perceptual loss. Circuits Syst Signal Process 43(4):2533\u20132559","journal-title":"Circuits Syst Signal Process"},{"issue":"15","key":"441_CR29","doi-asserted-by":"publisher","first-page":"5092","DOI":"10.3390\/s21155092","volume":"21","author":"TDT Phan","year":"2021","unstructured":"Phan TDT, Kim SH, Yang HJ, Lee GS (2021) EEG-based emotion recognition by convolutional neural network with multi-scale kernels. Sensors 21(15):5092","journal-title":"Sensors"},{"key":"441_CR30","doi-asserted-by":"publisher","first-page":"9295","DOI":"10.1109\/ACCESS.2023.3237028","volume":"11","author":"N Taghinezhad","year":"2023","unstructured":"Taghinezhad N, Yazdi M (2023) A new unsupervised video anomaly detection using multi-scale feature memorization and multipath temporal information prediction. IEEE Access 11:9295\u20139310","journal-title":"IEEE Access"},{"issue":"4","key":"441_CR31","doi-asserted-by":"publisher","first-page":"041006","DOI":"10.1115\/1.4056433","volume":"23","author":"V Sharma","year":"2023","unstructured":"Sharma V, Sharma D, Anand A (2023) Hybrid Multi-scale Convolutional Long Short-Term Memory Network for Remaining Useful Life Prediction and Offset Analysis. J Comput Inf Sci Eng 23(4):041006","journal-title":"J Comput Inf Sci Eng"},{"issue":"1","key":"441_CR32","doi-asserted-by":"publisher","first-page":"11","DOI":"10.3390\/vibration6010002","volume":"6","author":"T Saghi","year":"2022","unstructured":"Saghi T, Bustan D, Aphale SS (2022) Bearing fault diagnosis based on multi-scale CNN and bidirectional GRU. Vibration 6(1):11\u201328","journal-title":"Vibration"},{"issue":"16","key":"441_CR33","doi-asserted-by":"publisher","first-page":"7731","DOI":"10.3390\/app11167731","volume":"11","author":"R Zeng","year":"2021","unstructured":"Zeng R, Liao M (2021) 6mAPred-MSFF: a deep learning model for predicting DNA N6-methyladenine sites across species based on a multi-scale feature fusion mechanism. Appl Sci 11(16):7731","journal-title":"Appl Sci"}],"container-title":["Energy Informatics"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42162-024-00441-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1186\/s42162-024-00441-0\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1186\/s42162-024-00441-0.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,1,7]],"date-time":"2025-01-07T11:03:57Z","timestamp":1736247837000},"score":1,"resource":{"primary":{"URL":"https:\/\/energyinformatics.springeropen.com\/articles\/10.1186\/s42162-024-00441-0"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,1,7]]},"references-count":33,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["441"],"URL":"https:\/\/doi.org\/10.1186\/s42162-024-00441-0","relation":{},"ISSN":["2520-8942"],"issn-type":[{"value":"2520-8942","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,1,7]]},"assertion":[{"value":"12 September 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"24 November 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"7 January 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 no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Consent for publication"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"<!--Emphasis Type='Bold' removed-->Ethical approval"}}],"article-number":"4"}}