{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T09:54:01Z","timestamp":1774259641477,"version":"3.50.1"},"reference-count":50,"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\/501100006469","name":"Fund for the Development of Science and Technology","doi-asserted-by":"publisher","award":["0150\/2022\/A"],"award-info":[{"award-number":["0150\/2022\/A"]}],"id":[{"id":"10.13039\/501100006469","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100015260","name":"Macau University of Science and Technology","doi-asserted-by":"publisher","award":["FRG-22-074-FIE"],"award-info":[{"award-number":["FRG-22-074-FIE"]}],"id":[{"id":"10.13039\/501100015260","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Applied Soft Computing"],"published-print":{"date-parts":[[2026,6]]},"DOI":"10.1016\/j.asoc.2026.115007","type":"journal-article","created":{"date-parts":[[2026,3,9]],"date-time":"2026-03-09T17:00:13Z","timestamp":1773075613000},"page":"115007","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"C","title":["A robust framework for long-term photovoltaic power prediction through dual-mode modeling with transfer learning"],"prefix":"10.1016","volume":"195","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5613-509X","authenticated-orcid":false,"given":"Menggang","family":"Kou","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0712-9355","authenticated-orcid":false,"given":"Jianzhou","family":"Wang","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6522-6655","authenticated-orcid":false,"given":"Yilin","family":"Zhou","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0009-0002-8800-8383","authenticated-orcid":false,"given":"Runze","family":"Li","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1547-5503","authenticated-orcid":false,"given":"Zhiwu","family":"Li","sequence":"additional","affiliation":[]}],"member":"78","reference":[{"key":"10.1016\/j.asoc.2026.115007_bib0005","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2023.128669","article-title":"Multi-timescale photovoltaic power forecasting using an improved stacking ensemble algorithm based lstm-informer model","volume":"283","author":"Cao","year":"2023","journal-title":"Energy"},{"key":"10.1016\/j.asoc.2026.115007_bib0010","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1038\/s41467-023-44666-1","article-title":"Accurate nowcasting of cloud cover at solar photovoltaic plants using geostationary satellite images","volume":"15","author":"Xia","year":"2024","journal-title":"Nature Commun."},{"key":"10.1016\/j.asoc.2026.115007_bib0015","doi-asserted-by":"crossref","DOI":"10.1016\/j.apm.2024.115643","article-title":"A method for accurate prediction of photovoltaic power based on multi-objective optimization and data integration strategy","volume":"136","author":"Li","year":"2024","journal-title":"Appl. Math. Model."},{"key":"10.1016\/j.asoc.2026.115007_bib0020","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2024.115290","article-title":"Levelized cost quantification of energy flexibility in high-density cities and evaluation of demand-side technologies for providing grid services","volume":"211","author":"Zang","year":"2025","journal-title":"Renew. Sustain. Energy Rev."},{"key":"10.1016\/j.asoc.2026.115007_bib0025","article-title":"Robust spinning reserve scheduling for power systems incorporating building energy flexibility by considering load rebound","volume":"114","author":"Han","year":"2025","journal-title":"J. Energy Storage"},{"issue":"5","key":"10.1016\/j.asoc.2026.115007_bib0030","doi-asserted-by":"crossref","first-page":"5283","DOI":"10.1109\/TIA.2023.3284776","article-title":"Small-sample solar power interval prediction based on instance-based transfer learning","volume":"59","author":"Long","year":"2023","journal-title":"IEEE Trans. Ind. Appl."},{"key":"10.1016\/j.asoc.2026.115007_bib0035","doi-asserted-by":"crossref","DOI":"10.1016\/j.enconman.2024.119114","article-title":"Enhanced photovoltaic power generation forecasting for newly-built plants via physics-infused transfer learning with domain adversarial neural networks","volume":"322","author":"Liao","year":"2024","journal-title":"Energy Convers. Manag."},{"key":"10.1016\/j.asoc.2026.115007_bib0040","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2024.124738","article-title":"An error-corrected deep autoformer model via bayesian optimization algorithm and secondary decomposition for photovoltaic power prediction","volume":"377","author":"Chen","year":"2025","journal-title":"Appl. Energy."},{"key":"10.1016\/j.asoc.2026.115007_bib0045","doi-asserted-by":"crossref","first-page":"1979","DOI":"10.1109\/TSTE.2023.3268100","article-title":"Solar-mixer: an efficient end-to-end model for long-sequence photovoltaic power generation time series forecasting","volume":"14","author":"Zhang","year":"2023","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"10.1016\/j.asoc.2026.115007_bib0050","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2023.122169","article-title":"De-trend first, attend next: a mid-term pv forecasting system with attention mechanism and encoder\u2013decoder structure","volume":"353","author":"Niu","year":"2024","journal-title":"Appl. Energy"},{"issue":"4","key":"10.1016\/j.asoc.2026.115007_bib0055","first-page":"174","article-title":"Multistep prediction model for photovoltaic power generation based on time convolution and dlinear","volume":"46","author":"Wang","year":"2025","journal-title":"Dianli Jianshe\/Electr. Power Constr."},{"key":"10.1016\/j.asoc.2026.115007_bib0060","doi-asserted-by":"crossref","DOI":"10.1016\/j.jclepro.2024.141690","article-title":"Tfeformer: a new temporal frequency ensemble transformer for day-ahead photovoltaic power prediction","volume":"448","author":"Yu","year":"2024","journal-title":"J. Clean. Prod."},{"key":"10.1016\/j.asoc.2026.115007_bib0065","doi-asserted-by":"crossref","DOI":"10.1109\/TSTE.2025.3549225","article-title":"Photovoltaic power prediction considering multifactorial dynamic effects: a dynamic locally featured embedding-based broad learning system","volume":"16","author":"Gu","year":"2025","journal-title":"IEEE Trans. Sustain. Energy"},{"key":"10.1016\/j.asoc.2026.115007_bib0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.enconman.2024.119442","article-title":"Tackling the duck curve in renewable power system: a multi-task learning model with itransformer for net-load forecasting","volume":"326","author":"Pei","year":"2025","journal-title":"Energy Convers. Manag."},{"key":"10.1016\/j.asoc.2026.115007_bib0075","first-page":"469","article-title":"Timexer: Empowering transformers for time series forecasting with exogenous variables","volume":"37","author":"Wang","year":"2024","journal-title":"Adv. Neural Inf. Process. Syst."},{"key":"10.1016\/j.asoc.2026.115007_bib0080","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1016\/j.renene.2023.03.029","article-title":"Short-term pv power forecast methodology based on multi-scale fluctuation characteristics extraction","volume":"208","author":"Zhu","year":"2023","journal-title":"Renew. Energy"},{"key":"10.1016\/j.asoc.2026.115007_bib0085","doi-asserted-by":"crossref","first-page":"33","DOI":"10.3390\/en17163958","article-title":"A parallel prediction model for photovoltaic power using multi-level attention and similar day clustering","volume":"17","author":"Gao","year":"2024","journal-title":"Energies"},{"key":"10.1016\/j.asoc.2026.115007_bib0090","first-page":"1","article-title":"Pso\u2013lstm\u2013markov coupled photovoltaic power prediction based on sunny, cloudy and rainy weather","author":"Ge","year":"2024","journal-title":"J. Electr. Eng. Technol."},{"key":"10.1016\/j.asoc.2026.115007_bib0095","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2024.109116","article-title":"Deep learning model for short-term photovoltaic power forecasting based on variational mode decomposition and similar day clustering","volume":"115","author":"Li","year":"2024","journal-title":"Comput. Electr. Eng."},{"key":"10.1016\/j.asoc.2026.115007_bib0100","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2025.112768","article-title":"A double-layer forecasting model for pv power forecasting based on gru-informer-svr and blending ensemble learning framework","volume":"172","author":"Xu","year":"2025","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115007_bib0105","doi-asserted-by":"crossref","DOI":"10.1016\/j.engappai.2023.107691","article-title":"Photovoltaic power forecasting: a dual-attention gated recurrent unit framework incorporating weather clustering and transfer learning strategy","volume":"130","author":"Tang","year":"2024","journal-title":"Eng. Appl. Artif. Intell."},{"key":"10.1016\/j.asoc.2026.115007_bib0110","doi-asserted-by":"crossref","first-page":"8062","DOI":"10.3390\/en15218062","article-title":"Multi-task autoencoders and transfer learning for day-ahead wind and photovoltaic power forecasts","volume":"15","author":"Schreiber","year":"2022","journal-title":"Energies"},{"key":"10.1016\/j.asoc.2026.115007_bib0115","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1038\/s41598-022-18516-x","article-title":"Transfer learning strategies for solar power forecasting under data scarcity","volume":"12","author":"Sarmas","year":"2022","journal-title":"Sci. Rep."},{"key":"10.1016\/j.asoc.2026.115007_bib0120","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2023.110988","article-title":"Effectiveness of neural networks and transfer learning to forecast photovoltaic power production","volume":"149","author":"Bellagarda","year":"2023","journal-title":"Appl. Soft Comput."},{"key":"10.1016\/j.asoc.2026.115007_bib0125","doi-asserted-by":"crossref","DOI":"10.1016\/j.rser.2022.112473","article-title":"Photovoltaic power forecasting: a hybrid deep learning model incorporating transfer learning strategy","volume":"162","author":"Tang","year":"2022","journal-title":"Renew. Sustain. Energy Rev."},{"key":"10.1016\/j.asoc.2026.115007_bib0130","doi-asserted-by":"crossref","DOI":"10.1016\/j.jfranklin.2025.108125","article-title":"Fast online transfer learning for photovoltaic power prediction","author":"Wang","year":"2025","journal-title":"J. Frankl. Inst."},{"key":"10.1016\/j.asoc.2026.115007_bib0135","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2024.122685","article-title":"Adversarial discriminative domain adaptation for solar radiation prediction: a cross-regional study for zero-label transfer learning in japan","volume":"359","author":"Gao","year":"2024","journal-title":"Appl. Energy."},{"issue":"4","key":"10.1016\/j.asoc.2026.115007_bib0140","doi-asserted-by":"crossref","first-page":"474","DOI":"10.1140\/epjp\/s13360-022-02666-y","article-title":"Unleashing deep neural network full potential for solar radiation forecasting in a new geographic location with historical data scarcity: a transfer learning approach","volume":"137","author":"Abubakr","year":"2022","journal-title":"Eur. Phys. J. Plus"},{"key":"10.1016\/j.asoc.2026.115007_bib0145","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2024.123215","article-title":"Solar irradiance time series forecasting using auto-regressive and extreme learning methods: Influence of transfer learning and clustering","volume":"365","author":"Despotovic","year":"2024","journal-title":"Appl. Energy."},{"key":"10.1016\/j.asoc.2026.115007_bib0150","doi-asserted-by":"crossref","DOI":"10.1109\/TII.2025.3578119","article-title":"A transferable framework of pv power forecasting for cross-regional distributed pv systems using domain adversarial temporal network","volume":"21","author":"Qu","year":"2025","journal-title":"IEEE Trans. Ind. Informat."},{"key":"10.1016\/j.asoc.2026.115007_bib0155","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2024.125058","article-title":"Distributed-regional photovoltaic power generation prediction with limited data: a robust autoregressive transfer learning method","volume":"380","author":"Zheng","year":"2025","journal-title":"Appl. Energy"},{"issue":"2","key":"10.1016\/j.asoc.2026.115007_bib0160","doi-asserted-by":"crossref","first-page":"305","DOI":"10.1109\/JAS.2022.106004","article-title":"A survey on negative transfer","volume":"10","author":"Zhang","year":"2022","journal-title":"IEEE\/CAA J. Autom. Sin."},{"key":"10.1016\/j.asoc.2026.115007_bib0165","doi-asserted-by":"crossref","DOI":"10.1016\/j.egyai.2023.100249","article-title":"Model selection, adaptation, and combination for transfer learning in wind and photovoltaic power forecasts","volume":"14","author":"Schreiber","year":"2023","journal-title":"Energy and AI"},{"key":"10.1016\/j.asoc.2026.115007_bib0170","series-title":"International conference on machine learning","first-page":"1243","article-title":"Convolutional sequence to sequence learning","author":"Gehring","year":"2017"},{"key":"10.1016\/j.asoc.2026.115007_bib0175","author":"Sai Madiraju"},{"key":"10.1016\/j.asoc.2026.115007_bib0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2025.125319","article-title":"Edformer family: End-to-end multi-task load forecasting frameworks for day-ahead economic dispatch","volume":"383","author":"Tian","year":"2025","journal-title":"Appl. Energy"},{"key":"10.1016\/j.asoc.2026.115007_bib0185","doi-asserted-by":"crossref","DOI":"10.1016\/j.renene.2025.123021","article-title":"Day ahead solar forecast using long short term memory network augmented with fast fourier transform-assisted decomposition technique","volume":"247","author":"Rathore","year":"2025","journal-title":"Renewable Energy"},{"key":"10.1016\/j.asoc.2026.115007_bib0190","doi-asserted-by":"crossref","first-page":"1235","DOI":"10.1162\/neco_a_01199","article-title":"A review of recurrent neural networks: Lstm cells and network architectures","volume":"31","author":"Yu","year":"2019","journal-title":"Neural Comput."},{"key":"10.1016\/j.asoc.2026.115007_bib0195","article-title":"Short-term pv power prediction based on meteorological similarity days and ssa-bilstm","volume":"6","author":"Li","year":"2024","journal-title":"Syst. Soft Comput."},{"issue":"15","key":"10.1016\/j.asoc.2026.115007_bib0200","doi-asserted-by":"crossref","DOI":"10.1142\/S0218126621502790","article-title":"User-level ultra-short-term load forecasting model based on optimal feature selection and bahdanau attention mechanism","volume":"30","author":"Wang","year":"2021","journal-title":"J. Circ. Syst. Comput."},{"key":"10.1016\/j.asoc.2026.115007_bib0205","series-title":"Proceedings of the AAAI conference on artificial intelligence","first-page":"11121","article-title":"Are transformers effective for time series forecasting","volume":"vol. 37","author":"Zeng","year":"2023"},{"key":"10.1016\/j.asoc.2026.115007_bib0210","doi-asserted-by":"crossref","first-page":"592","DOI":"10.1016\/j.ins.2020.08.089","article-title":"Time works well: Dynamic time warping based on time weighting for time series data mining","volume":"547","author":"Li","year":"2021","journal-title":"Inf. Sci."},{"key":"10.1016\/j.asoc.2026.115007_bib0215","doi-asserted-by":"crossref","DOI":"10.1016\/j.apenergy.2024.124717","article-title":"Solving few-shot problem in wind speed prediction: a novel transfer strategy based on decomposition and learning ensemble","volume":"377","author":"Sun","year":"2025","journal-title":"Appl. Energy."},{"key":"10.1016\/j.asoc.2026.115007_bib0220","doi-asserted-by":"crossref","first-page":"122","DOI":"10.1016\/j.solener.2021.09.050","article-title":"A photovoltaic power output dataset: Multi-source photovoltaic power output dataset with python toolkit","volume":"230","author":"Yao","year":"2021","journal-title":"Solar Energy"},{"key":"10.1016\/j.asoc.2026.115007_bib0225","doi-asserted-by":"crossref","DOI":"10.1016\/j.measurement.2023.112462","article-title":"Decomposition integration and error correction method for photovoltaic power forecasting","volume":"208","author":"Li","year":"2023","journal-title":"Measurement"},{"key":"10.1016\/j.asoc.2026.115007_bib0230","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","volume":"vol. 35","author":"Zhou","year":"2021"},{"key":"10.1016\/j.asoc.2026.115007_bib0235","author":"Liu"},{"key":"10.1016\/j.asoc.2026.115007_bib0240","doi-asserted-by":"crossref","first-page":"178","DOI":"10.1016\/j.neucom.2023.01.043","article-title":"Pdbi: a partitioning davies-bouldin index for clustering evaluation","volume":"528","author":"Ros","year":"2023","journal-title":"Neurocomputing"},{"issue":"5","key":"10.1016\/j.asoc.2026.115007_bib0245","doi-asserted-by":"crossref","first-page":"3061","DOI":"10.1007\/s00521-024-10706-0","article-title":"Silhouette coefficient-based weighting k-means algorithm","volume":"37","author":"Lai","year":"2025","journal-title":"Neural Comput. Appl."},{"key":"10.1016\/j.asoc.2026.115007_bib0250","doi-asserted-by":"crossref","DOI":"10.1016\/j.energy.2024.132456","article-title":"A novel time-series probabilistic forecasting method for multi-energy loads","volume":"306","author":"Xie","year":"2024","journal-title":"Energy"}],"container-title":["Applied Soft Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626004552?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S1568494626004552?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,3,23]],"date-time":"2026-03-23T08:56:15Z","timestamp":1774256175000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S1568494626004552"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,6]]},"references-count":50,"alternative-id":["S1568494626004552"],"URL":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115007","relation":{},"ISSN":["1568-4946"],"issn-type":[{"value":"1568-4946","type":"print"}],"subject":[],"published":{"date-parts":[[2026,6]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"A robust framework for long-term photovoltaic power prediction through dual-mode modeling with transfer learning","name":"articletitle","label":"Article Title"},{"value":"Applied Soft Computing","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.asoc.2026.115007","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.","name":"copyright","label":"Copyright"}],"article-number":"115007"}}