{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T01:00:29Z","timestamp":1775869229028,"version":"3.50.1"},"reference-count":49,"publisher":"MDPI AG","issue":"10","license":[{"start":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T00:00:00Z","timestamp":1760659200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>This paper presents an optimized hybrid deep learning model for power load forecasting\u2014QR-FMD-CNN-BiGRU-Attention\u2014that integrates similar day selection, load decomposition, and deep learning to address the nonlinearity and volatility of power load data. Firstly, the original data are classified using Gaussian Mixture Clustering optimized by ICPO (ICPO-GMM), and similar day samples consistent with the predicted day category are selected. Secondly, the load data are decomposed into multi-scale components (IMFs) using feature mode decomposition optimized by ICPO (ICPO-FMD). Then, with the IMFs as targets, the quantile interval forecasting is trained using the CNN-BiGRU-Attention model optimized by ICPO. Subsequently, the forecasting model is applied to the features of the predicted day to generate interval forecasting results. Finally, the model\u2019s performance is validated through comparative evaluation metrics, sensitivity analysis, and interpretability analysis. The experimental results show that compared with the comparative algorithm presented in this paper, the improved model has improved RMSE by at least 39.84%, MAE by 26.12%, MAPE by 45.28%, PICP and MPIW indicators by at least 3.80% and 2.27%, indicating that the model not only outperforms the comparative model in accuracy, but also exhibits stronger adaptability and robustness in complex load fluctuation scenarios.<\/jats:p>","DOI":"10.3390\/a18100659","type":"journal-article","created":{"date-parts":[[2025,10,17]],"date-time":"2025-10-17T14:29:17Z","timestamp":1760711357000},"page":"659","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Optimized Hybrid Deep Learning Framework for Short-Term Power Load Interval Forecasting via Improved Crowned Crested Porcupine Optimization and Feature Mode Decomposition"],"prefix":"10.3390","volume":"18","author":[{"given":"Shucheng","family":"Luo","sequence":"first","affiliation":[{"name":"Key Laboratory of Energy Saving and Controlling in Power System of Liaoning Province, Shenyang Institute of Engineering, Shenyang 110136, China"}]},{"given":"Xiangbin","family":"Meng","sequence":"additional","affiliation":[{"name":"Key Laboratory of Energy Saving and Controlling in Power System of Liaoning Province, Shenyang Institute of Engineering, Shenyang 110136, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6981-596X","authenticated-orcid":false,"given":"Xinfu","family":"Pang","sequence":"additional","affiliation":[{"name":"Key Laboratory of Energy Saving and Controlling in Power System of Liaoning Province, Shenyang Institute of Engineering, Shenyang 110136, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0951-9947","authenticated-orcid":false,"given":"Haibo","family":"Li","sequence":"additional","affiliation":[{"name":"Key Laboratory of Energy Saving and Controlling in Power System of Liaoning Province, Shenyang Institute of Engineering, Shenyang 110136, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6394-6571","authenticated-orcid":false,"given":"Zedong","family":"Zheng","sequence":"additional","affiliation":[{"name":"School of Computing and Mathematical Sciences, University of Leicester, Leicester LE1 7RH, UK"}]}],"member":"1968","published-online":{"date-parts":[[2025,10,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Tang, J., Saga, R., Cai, H., Ma, Z., and Yu, S. 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