{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,12]],"date-time":"2026-06-12T14:03:30Z","timestamp":1781273010904,"version":"3.54.1"},"reference-count":45,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-017"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-037"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-012"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-029"},{"start":{"date-parts":[[2026,6,1]],"date-time":"2026-06-01T00:00:00Z","timestamp":1780272000000},"content-version":"stm-asf","delay-in-days":0,"URL":"https:\/\/doi.org\/10.15223\/policy-004"}],"funder":[{"DOI":"10.13039\/501100012154","name":"Graduate Research and Innovation Projects of Jiangsu Province","doi-asserted-by":"publisher","award":["SJCX25_0568"],"award-info":[{"award-number":["SJCX25_0568"]}],"id":[{"id":"10.13039\/501100012154","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["52305375"],"award-info":[{"award-number":["52305375"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Engineering Applications of Artificial Intelligence"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.engappai.2026.114438","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T13:29:52Z","timestamp":1773062992000},"page":"114438","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":2,"special_numbering":"C","title":["Power load forecasting based on time-frequency domain feature fusion"],"prefix":"10.1016","volume":"173","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3705-3686","authenticated-orcid":false,"given":"Wenhua","family":"Jiao","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0005-2766-1372","authenticated-orcid":false,"given":"Xiao","family":"Han","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Ce","family":"Yu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiaowei","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qing","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xiang","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bin","family":"Wang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lijuan","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-4733-6524","authenticated-orcid":false,"given":"Feiyi","family":"Xu","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.engappai.2026.114438_bib1","series-title":"Proceedings of the European Conference on Artificial Intelligence (ECAI)","first-page":"1688","article-title":"TimeMachine: a time series is worth 4 mambas for long-term forecasting","author":"Ahamed","year":"2024"},{"key":"10.1016\/j.engappai.2026.114438_bib2","doi-asserted-by":"crossref","first-page":"3654","DOI":"10.1016\/j.egyr.2024.09.056","article-title":"Review on smart grid load forecasting for smart energy management using machine learning and deep learning techniques","volume":"12","author":"Biswal","year":"2024","journal-title":"Energy Rep."},{"key":"10.1016\/j.engappai.2026.114438_bib3","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110781","article-title":"Transformer-based deep probabilistic network for load forecasting","volume":"152","author":"Bouhamed","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"2","key":"10.1016\/j.engappai.2026.114438_bib4","doi-asserted-by":"crossref","first-page":"1035","DOI":"10.1109\/TPWRS.2005.846054","article-title":"Day-ahead electricity price forecasting using the wavelet transform and ARIMA models","volume":"20","author":"Conejo","year":"2005","journal-title":"IEEE Trans. Power Syst."},{"issue":"4","key":"10.1016\/j.engappai.2026.114438_bib5","doi-asserted-by":"crossref","first-page":"1409","DOI":"10.1016\/j.ijforecast.2018.09.007","article-title":"Neural networks for GEFCom2017 probabilistic load forecasting","volume":"35","author":"Dimoulkas","year":"2019","journal-title":"Int. J. Forecast."},{"key":"10.1016\/j.engappai.2026.114438_bib6","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110980","article-title":"Short-term electricity-load forecasting by deep learning: a comprehensive survey","volume":"154","author":"Dong","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114438_bib7","doi-asserted-by":"crossref","first-page":"3405","DOI":"10.1016\/j.egyr.2024.09.023","article-title":"A method for short-term electric load forecasting based on the FMLP-ITransformer model","volume":"12","author":"Fang","year":"2024","journal-title":"Energy Rep."},{"issue":"Part A","key":"10.1016\/j.engappai.2026.114438_bib8","article-title":"A comparative analysis of machine learning techniques for short-term grid power forecasting and uncertainty analysis of wave energy converters","volume":"138","author":"Fontana Crespo","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114438_bib9","series-title":"Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition (CVPR)","first-page":"6639","article-title":"Dynamic fusion with intra-and inter-modality attention flow for visual question answering","author":"Gao","year":"2019"},{"key":"10.1016\/j.engappai.2026.114438_bib10","series-title":"Mamba: Linear-Time Sequence Modeling with Selective State Spaces","author":"Gu","year":"2023"},{"key":"10.1016\/j.engappai.2026.114438_bib11","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.110268","article-title":"Hybrid long short-term memory and bidirectional multichannel network cascaded with split convolution for short-term load forecasting","volume":"147","author":"Hasanat","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"9","key":"10.1016\/j.engappai.2026.114438_bib12","doi-asserted-by":"crossref","first-page":"9447","DOI":"10.1109\/TII.2022.3228383","article-title":"Day-ahead peak load probability density forecasting based on QRLSTM-DF considering exogenous factors","volume":"19","author":"He","year":"2023","journal-title":"IEEE Trans. Ind. Inf."},{"issue":"A","key":"10.1016\/j.engappai.2026.114438_bib13","article-title":"Short-term electric load forecasting using particle swarm optimization-based convolutional neural network","volume":"126","author":"Hong","year":"2023","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"4","key":"10.1016\/j.engappai.2026.114438_bib14","doi-asserted-by":"crossref","first-page":"1389","DOI":"10.1016\/j.ijforecast.2019.02.006","article-title":"Global energy forecasting competition 2017: hierarchical probabilistic load forecasting","volume":"35","author":"Hong","year":"2019","journal-title":"Int. J. Forecast."},{"key":"10.1016\/j.engappai.2026.114438_bib15","doi-asserted-by":"crossref","first-page":"144919","DOI":"10.1109\/ACCESS.2024.3469369","article-title":"Short-term power load dynamic scheduling based on GWO-TCN-GRU optimization algorithm","volume":"12","author":"Hu","year":"2024","journal-title":"IEEE Access"},{"issue":"8","key":"10.1016\/j.engappai.2026.114438_bib16","doi-asserted-by":"crossref","first-page":"10520","DOI":"10.1109\/TII.2024.3394609","article-title":"Day-ahead probabilistic load forecasting: a multi-information fusion and noncrossing quantiles method","volume":"20","author":"Huang","year":"2024","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.engappai.2026.114438_bib17","doi-asserted-by":"crossref","first-page":"106296","DOI":"10.1109\/ACCESS.2022.3211941","article-title":"Short-term electricity load forecasting based on temporal fusion transformer model","volume":"10","author":"Huy","year":"2022","journal-title":"IEEE Access"},{"issue":"1","key":"10.1016\/j.engappai.2026.114438_bib18","doi-asserted-by":"crossref","first-page":"841","DOI":"10.1109\/TSG.2017.2753802","article-title":"Short-term residential load forecasting based on LSTM recurrent neural network","volume":"10","author":"Kong","year":"2019","journal-title":"IEEE Trans. Smart Grid"},{"key":"10.1016\/j.engappai.2026.114438_bib19","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2024.124155","article-title":"Time and frequency-domain feature fusion network for multivariate time series classification","volume":"252","author":"Lei","year":"2024","journal-title":"Expert Syst. Appl."},{"issue":"4","key":"10.1016\/j.engappai.2026.114438_bib20","doi-asserted-by":"crossref","first-page":"1748","DOI":"10.1016\/j.ijforecast.2021.03.012","article-title":"Temporal fusion transformers for interpretable multi-horizon time series forecasting","volume":"37","author":"Lim","year":"2021","journal-title":"Int. J. Forecast."},{"issue":"6","key":"10.1016\/j.engappai.2026.114438_bib21","doi-asserted-by":"crossref","first-page":"5373","DOI":"10.1109\/TSG.2021.3093515","article-title":"Spatial-Temporal residential short-term load forecasting via graph neural networks","volume":"12","author":"Lin","year":"2021","journal-title":"IEEE Trans. Smart Grid"},{"key":"10.1016\/j.engappai.2026.114438_bib22","series-title":"Proceedings of the International Conference on Learning Representations","article-title":"Itransformer: inverted transformers are effective for time series forecasting","author":"Liu","year":"2023"},{"key":"10.1016\/j.engappai.2026.114438_bib23","doi-asserted-by":"crossref","DOI":"10.1016\/j.ijepes.2025.110512","article-title":"TDCN: a novel temporal depthwise convolutional network for short-term load forecasting","volume":"165","author":"Liu","year":"2025","journal-title":"Int. J. Electr. Power Energy Syst."},{"key":"10.1016\/j.engappai.2026.114438_bib24","first-page":"69","article-title":"Multi-scale one-dimensional convolution tool wear monitoring based on multi-model fusion learning skills","volume":"70","author":"Ma","year":"2023","journal-title":"J. Manuf. Process."},{"issue":"1","key":"10.1016\/j.engappai.2026.114438_bib25","doi-asserted-by":"crossref","first-page":"1421","DOI":"10.1109\/TPWRS.2023.3266369","article-title":"Forwardformer: efficient transformer with multi-scale forward self-attention for day-ahead load forecasting","volume":"39","author":"Qu","year":"2024","journal-title":"IEEE Trans. Power Syst."},{"key":"10.1016\/j.engappai.2026.114438_bib26","article-title":"A Survey of mamba","author":"Qu","year":"2024","journal-title":"Computing Research Repository (CoRR)"},{"issue":"1","key":"10.1016\/j.engappai.2026.114438_bib27","doi-asserted-by":"crossref","first-page":"1932","DOI":"10.1109\/TPWRS.2023.3271325","article-title":"A novel sequence to sequence data modelling based CNN-LSTM algorithm for three years ahead monthly peak load forecasting","volume":"39","author":"Rubasinghe","year":"2024","journal-title":"IEEE Trans. Power Syst."},{"issue":"3","key":"10.1016\/j.engappai.2026.114438_bib28","doi-asserted-by":"crossref","first-page":"1177","DOI":"10.1109\/TSTE.2017.2774195","article-title":"Direct Interval forecast of uncertain wind power based on recurrent neural networks","volume":"9","author":"Shi","year":"2018","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"10.1016\/j.engappai.2026.114438_bib29","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2025.111380","article-title":"Power generation forecasting based on hybrid deep learning models for a multi-energy power generation system","volume":"157","author":"Song","year":"2025","journal-title":"Eng. Appl. Artif. Intell."},{"issue":"4","key":"10.1016\/j.engappai.2026.114438_bib30","doi-asserted-by":"crossref","first-page":"2937","DOI":"10.1109\/TPWRS.2019.2963109","article-title":"Ultra-short-term industrial power demand forecasting using LSTM based hybrid ensemble learning","volume":"35","author":"Tan","year":"2020","journal-title":"IEEE Trans. Power Syst."},{"key":"10.1016\/j.engappai.2026.114438_bib31","doi-asserted-by":"crossref","first-page":"61958","DOI":"10.1109\/ACCESS.2023.3273596","article-title":"Short-term power load forecasting based on vmd-pyraformer-adan","volume":"11","author":"Tang","year":"2023","journal-title":"IEEE Access"},{"key":"10.1016\/j.engappai.2026.114438_bib32","doi-asserted-by":"crossref","first-page":"160660","DOI":"10.1109\/ACCESS.2019.2950957","article-title":"Application of bidirectional recurrent neural network combined with deep belief network in short-term load forecasting","volume":"7","author":"Tang","year":"2019","journal-title":"IEEE Access"},{"issue":"3","key":"10.1016\/j.engappai.2026.114438_bib33","doi-asserted-by":"crossref","first-page":"1984","DOI":"10.1109\/TPWRS.2020.3028133","article-title":"Short-term load forecasting for industrial customers based on TCN-LightGBM","volume":"36","author":"Wang","year":"2021","journal-title":"IEEE Trans. Power Syst."},{"key":"10.1016\/j.engappai.2026.114438_bib34","article-title":"State space model for new-generation network alternative to transformers: a survey","author":"Wang","year":"2024","journal-title":"CoRR"},{"issue":"2","key":"10.1016\/j.engappai.2026.114438_bib35","first-page":"104","article-title":"Time-series forecasting of chlorophyll-a in coastal areas using LSTM GRU and attention-based RNN models","volume":"41","author":"Wu","year":"2023","journal-title":"Journal of Environmental Informatics"},{"issue":"2","key":"10.1016\/j.engappai.2026.114438_bib36","doi-asserted-by":"crossref","first-page":"2508","DOI":"10.1109\/TII.2023.3292532","article-title":"SecTCN: privacy-preserving short-term residential electrical load forecasting","volume":"20","author":"Wu","year":"2024","journal-title":"IEEE Trans. Ind. Inf."},{"key":"10.1016\/j.engappai.2026.114438_bib37","article-title":"DTMamba: dual twin mamba for time series forecasting","author":"Wu","year":"2024","journal-title":"Computing Research Repository (CoRR)"},{"key":"10.1016\/j.engappai.2026.114438_bib38","article-title":"SST: multi-scale hybrid mamba-transformer experts for long-short range time series forecasting","author":"Xu","year":"2024","journal-title":"Computing Research Repository (CoRR)"},{"key":"10.1016\/j.engappai.2026.114438_bib39","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2021.104645","article-title":"Deep-learning-based short-term electricity load forecasting: a real case application","volume":"109","author":"Yazici","year":"2022","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.engappai.2026.114438_bib40","series-title":"Proceedings of the Advances in Neural Information Processing Systems (Neurips)","article-title":"Frequency-domain MLPs are more effective learners in time series forecasting","author":"Yi","year":"2023"},{"key":"10.1016\/j.engappai.2026.114438_bib41","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2020.116328","article-title":"Multi-temporal-spatial-scale temporal convolution network for short-term load forecasting of power systems","volume":"283","author":"Yin","year":"2021","journal-title":"Appl. Energy"},{"key":"10.1016\/j.engappai.2026.114438_bib42","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"11121","article-title":"Are transformers effective for time series forecasting","author":"Zeng","year":"2023"},{"key":"10.1016\/j.engappai.2026.114438_bib43","series-title":"Proceedings of the 11th International Conference on Learning Representations","article-title":"Crossformer: transformer utilizing cross-dimension dependency for multivariate time series forecasting","author":"Zhang","year":"2023"},{"key":"10.1016\/j.engappai.2026.114438_bib44","first-page":"1","article-title":"Spatial and temporal attention-enabled transformer network for multivariate short-term residential load forecasting","volume":"72","author":"Zhao","year":"2023","journal-title":"IEEE Trans. Instrum. Meas."},{"key":"10.1016\/j.engappai.2026.114438_bib45","series-title":"Proceedings of the AAAI Conference on Artificial Intelligence","first-page":"11106","article-title":"Informer: beyond efficient transformer for long sequence time-series forecasting","author":"Zhou","year":"2021"}],"container-title":["Engineering Applications of Artificial Intelligence"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626007190?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0952197626007190?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,4,14]],"date-time":"2026-04-14T00:21:41Z","timestamp":1776126101000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0952197626007190"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":45,"alternative-id":["S0952197626007190"],"URL":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114438","relation":{},"ISSN":["0952-1976"],"issn-type":[{"value":"0952-1976","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"Power load forecasting based on time-frequency domain feature fusion","name":"articletitle","label":"Article Title"},{"value":"Engineering Applications of Artificial Intelligence","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.engappai.2026.114438","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"114438"}}