{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,8]],"date-time":"2026-01-08T07:41:03Z","timestamp":1767858063817,"version":"3.49.0"},"reference-count":25,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T00:00:00Z","timestamp":1754611200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Symmetry"],"abstract":"<jats:p>The core structure of modern power systems reflects a fundamental symmetry between electricity supply and demand, and accurate load forecasting is essential for maintaining this dynamic balance. To improve the accuracy of short-term load forecasting in power systems, this paper proposes a novel model that combines a Multi-Strategy Improved Sand Cat Swarm Optimization algorithm (MSCSO) with a Self-Attention Temporal Convolutional Network (SA TCN). The model constructs efficient input features through data denoising, correlation filtering, and dimensionality reduction using UMAP. MSCSO integrates Uniform Tent Chaos Mapping, a sensitivity enhancement mechanism, and L\u00e9vy flight to optimize key parameters of the SA TCN, ensuring symmetrical exploration and stable convergence in the solution space. The self-attention mechanism exhibits structural symmetry when processing each position in the input sequence and does not rely on fixed positional order, enabling the model to more effectively capture long-term dependencies and preserve the symmetry of the sequence structure\u2014demonstrating its advantage in symmetry-based modeling. Experimental results on historical load data from Panama show that the proposed model achieves excellent forecasting accuracy (RMSE = 24.7072, MAE = 17.5225, R2 = 0.9830), highlighting its innovation and applicability in symmetrical system environments.<\/jats:p>","DOI":"10.3390\/sym17081270","type":"journal-article","created":{"date-parts":[[2025,8,8]],"date-time":"2025-08-08T08:09:35Z","timestamp":1754640575000},"page":"1270","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":4,"title":["Research on Load Forecasting Prediction Model Based on Modified Sand Cat Swarm Optimization and SelfAttention TCN"],"prefix":"10.3390","volume":"17","author":[{"given":"Haotong","family":"Han","sequence":"first","affiliation":[{"name":"Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jishen","family":"Peng","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0009-6208-8187","authenticated-orcid":false,"given":"Jun","family":"Ma","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Hao","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Shanglin","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Electrical and Control Engineering, Liaoning Technical University, Huludao 125000, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,8]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5986","DOI":"10.1109\/TSG.2017.2700436","article-title":"Load modeling\u2014A review","volume":"9","author":"Arif","year":"2017","journal-title":"IEEE Trans. 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