{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T10:06:01Z","timestamp":1771668361688,"version":"3.50.1"},"reference-count":0,"publisher":"Slovenian Association Informatika","issue":"6","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IJCAI"],"abstract":"<jats:p>In the power system, ice-covering on transmission lines in high-altitude areas can easily cause serious problems like increased line weight and wire breakage. To accurately predict the ice-covering and provide timely warnings to ensure the reliable operation, a method for predicting ice-covering in high-altitude areas relying on Long Short-Term Memory (LSTM) and deep learning (DL) methods is proposed. In the feature extraction stage, considering the complexity and diversity of ice-covering data, and combining the advantages of multiple algorithms, a model for extracting ice-covering features is proposed, which integrates multiple algorithms. This model effectively integrates the respective strengths of diverse algorithms, effectively decompose and cluster ice-covering data, and extract more representative features. When constructing the prediction model, the DL method is combined to extract features related to changes in ice-covering thickness as input. A method for predicting ice-covering depending on multivariate deep regression is constructed. Finally, based on this prediction method, a power grid line ice-covering warning platform suitable for high-altitude regions is designed based on big data and big models. The determination coefficient reached 0.9887, the mean absolute error was 0.0130, the root mean square error was 0.0142, and the mean absolute percentage error was 1.2558%. It can predict the thickness of ice-covering. The warning platform based on this method can effectively achieve ice-covering warning, providing decision-making basis for power grid operation and maintenance.<\/jats:p>","DOI":"10.31449\/inf.v50i6.11568","type":"journal-article","created":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T09:24:27Z","timestamp":1771665867000},"source":"Crossref","is-referenced-by-count":0,"title":["Prediction of Ice-covering on Transmission Lines in High-altitude Regions Based on LSTM and Deep Learning Methods"],"prefix":"10.31449","volume":"50","author":[{"given":"Maojie","family":"Tian","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Huamin","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Weidong","family":"Liu","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shirui","family":"Sun","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Chao","family":"Gao","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"16141","published-online":{"date-parts":[[2026,2,21]]},"container-title":["Informatica"],"original-title":[],"link":[{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/11568\/6451","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/download\/11568\/6451","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,2,21]],"date-time":"2026-02-21T09:24:27Z","timestamp":1771665867000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.informatica.si\/index.php\/informatica\/article\/view\/11568"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,2,21]]},"references-count":0,"journal-issue":{"issue":"6","published-online":{"date-parts":[[2026,2,21]]}},"URL":"https:\/\/doi.org\/10.31449\/inf.v50i6.11568","relation":{},"ISSN":["1854-3871","0350-5596"],"issn-type":[{"value":"1854-3871","type":"electronic"},{"value":"0350-5596","type":"print"}],"subject":[],"published":{"date-parts":[[2026,2,21]]}}}