{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T17:55:04Z","timestamp":1791914104897,"version":"4.3.4"},"reference-count":39,"publisher":"Public Library of Science (PLoS)","issue":"10","license":[{"start":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T00:00:00Z","timestamp":1791849600000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["www.plosone.org"],"crossmark-restriction":false},"short-container-title":["PLoS One"],"abstract":"<jats:p>Accurate renewable energy power forecasting is of great significance for the safe operation, dispatch optimization, and efficient management of modern power systems. However, wind and photovoltaic power generation series usually exhibit significant non-stationarity, volatility, and multi-scale variation characteristics, posing great challenges to medium- and long-term forecasting. To address these issues, this paper proposes FDMSNet, a frequency-decomposed multi-scale hybrid network for renewable energy power forecasting. The model first divides the input sequence into non-stationary and stationary components through a frequency decomposition module. For the non-stationary component, a multilayer perceptron is adopted to extract global nonlinear variation features. For the stationary component, a multi-scale forecasting module is designed based on multi-branch downsampling and cross-scale prediction fusion. Subsequently, the forecasting results of different components are fused to generate the final power prediction. Experiments are conducted on real-world wind farm and photovoltaic power station datasets under multiple prediction horizons. MAE, MSE, and RSE are used as evaluation metrics, and FDMSNet is compared with different types of forecasting models. The experimental results show that FDMSNet achieves strong forecasting accuracy and stability in both wind and photovoltaic power forecasting tasks. Further average ranking analysis, significance testing, ablation experiments, and parameter sensitivity analysis verify the effectiveness and robustness of the frequency decomposition module, the multi-scale modeling structure, and the key parameter settings.<\/jats:p>","DOI":"10.1371\/journal.pone.0356755","type":"journal-article","created":{"date-parts":[[2026,10,13]],"date-time":"2026-10-13T17:42:16Z","timestamp":1791913336000},"page":"e0356755","update-policy":"https:\/\/doi.org\/10.1371\/journal.pone.corrections_policy","source":"Crossref","is-referenced-by-count":0,"title":["FDMSNet: A frequency-decomposed multi-scale hybrid framework for renewable energy power forecasting"],"prefix":"10.1371","volume":"21","author":[{"given":"Miao","family":"Xiao","sequence":"first","affiliation":[{"name":"School of Intelligent Manufacturing, Zhejiang Dongfang Polytechnic","place":["Wenzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hongli","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Intelligent Manufacturing, Zhejiang Dongfang Polytechnic","place":["Wenzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nan","family":"He","sequence":"additional","affiliation":[{"name":"School of Intelligent Manufacturing, Zhejiang Dongfang Polytechnic","place":["Wenzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0002-0162-1581","authenticated-orcid":true,"given":"Xiaobing","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Intelligent Manufacturing, Zhejiang Dongfang Polytechnic","place":["Wenzhou, China"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"340","published-online":{"date-parts":[[2026,10,13]]},"reference":[{"issue":"14","key":"pone.0356755.ref001","doi-asserted-by":"crossref","first-page":"3480","DOI":"10.3390\/en17143480","article-title":"Advancing Renewable Energy Forecasting: A Comprehensive Review of Renewable Energy Forecasting Methods","volume":"17","author":"R Teixeira","year":"2024","journal-title":"Energies"},{"issue":"10","key":"pone.0356755.ref002","doi-asserted-by":"crossref","first-page":"8331","DOI":"10.1007\/s11042-024-18916-3","article-title":"Advancements in wind power forecasting: A comprehensive review of artificial intelligence-based approaches","volume":"84","author":"K Kumar","year":"2024","journal-title":"Multimed Tools Appl"},{"key":"pone.0356755.ref003","doi-asserted-by":"crossref","first-page":"308","DOI":"10.4018\/979-8-3693-0492-1.ch013","article-title":"Particle Swarm Optimization-Based MPPT for PV Applications and Its Comparison With Incremental Conductance Method","volume-title":"Optimization Techniques for Hybrid Power Systems: Renewable Energy, Electric Vehicles, and Smart Grid","author":"A Ghosh","year":"2024"},{"issue":"4","key":"pone.0356755.ref004","doi-asserted-by":"crossref","first-page":"8842","DOI":"10.1080\/15567036.2022.2126560","article-title":"A new virtual oscillator control for synchronization of single-phase parallel inverters in islanded microgrid","volume":"44","author":"V Gurugubelli","year":"2022","journal-title":"Energy Sources, Part A: Recovery, Utilization, and Environmental Effects"},{"issue":"6","key":"pone.0356755.ref005","doi-asserted-by":"crossref","first-page":"7013","DOI":"10.1109\/TIA.2023.3307078","article-title":"Design and Implementation of Optimized Andronov-Hopf Oscillator Control Method for Parallel Inverters in Standalone Microgrid","volume":"59","author":"V Gurugubelli","year":"2023","journal-title":"IEEE Trans on Ind Applicat"},{"key":"pone.0356755.ref006","doi-asserted-by":"crossref","unstructured":"Vikash G, Ghosh A, Rudra S. 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