{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T01:37:00Z","timestamp":1760060220920,"version":"build-2065373602"},"reference-count":33,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T00:00:00Z","timestamp":1755216000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["62272239","[2023] General 272"],"award-info":[{"award-number":["62272239","[2023] General 272"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Guizhou Provincial Science and Technology Support Project","award":["62272239","[2023] General 272"],"award-info":[{"award-number":["62272239","[2023] General 272"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>Reliable short-term electricity usage prediction is essential for preserving the stability of topologically symmetric power networks and their dynamic supply\u2013demand equilibrium. To tackle this challenge, this paper proposes a novel approach derived from the standard Extreme Learning Machine (ELM) by integrating an enhanced Crayfish Optimization Algorithm (DSYCOA). This algorithm combines Logistic chaotic mapping, local precise search, and dynamic parameter adjustment strategies designed to achieve a dynamic balance between exploration and exploitation, thereby optimizing the initial thresholds and weights of the ELM. Consequently, a new short-term power load forecasting model, namely the DSYCOA-ELM model, is developed. Experimental validation demonstrates that the improved DSYCOA exhibits fast convergence speed and high convergence accuracy, and successfully harmonizes global exploration and local exploitation capabilities while maintaining an empirical balance between exploration and exploitation. To additionally verify the effectiveness of DSYCOA in improving ELM, this paper conducts simulation comparison experiments among six models, including DSYCOA-ELM, ELM, and ELM improved by BWO (BWO-ELM). The findings demonstrate that the DSYCOA-ELM model outperforms the other five forecasting models in terms of Mean Absolute Percentage Error (MAPE), Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), and other indicators. Specifically, in terms of MAPE, DSYCOA-ELM reduces the error by 96.9% compared to ELM. This model demonstrates feasibility and effectiveness in solving the problem of short-term power load prediction, providing critical support for maintaining the stability of grid topological symmetry and supply\u2013demand balance.<\/jats:p>","DOI":"10.3390\/sym17081335","type":"journal-article","created":{"date-parts":[[2025,8,15]],"date-time":"2025-08-15T10:33:42Z","timestamp":1755254022000},"page":"1335","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Symmetry-Enhanced Locally Adaptive COA-ELM for Short-Term Load Forecasting"],"prefix":"10.3390","volume":"17","author":[{"given":"Shiyu","family":"Dai","sequence":"first","affiliation":[{"name":"Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province, Research and Development Center of Post Industry Technology of the State Posts Bureau (Internet of Things Technology), Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6694-2568","authenticated-orcid":false,"given":"Zhe","family":"Sun","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province, Research and Development Center of Post Industry Technology of the State Posts Bureau (Internet of Things Technology), Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhixin","family":"Sun","sequence":"additional","affiliation":[{"name":"Engineering Research Center of Post Big Data Technology and Application of Jiangsu Province, Research and Development Center of Post Industry Technology of the State Posts Bureau (Internet of Things Technology), Nanjing University of Posts and Telecommunications, Nanjing 210003, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,15]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"112445","DOI":"10.1016\/j.asoc.2024.112445","article-title":"Multiscale spatio-temporal feature fusion based non-intrusive appliance load monitoring for multiple industrial industries","volume":"167","author":"Lin","year":"2024","journal-title":"Appl. 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