{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T07:45:03Z","timestamp":1768981503042,"version":"3.49.0"},"reference-count":35,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T00:00:00Z","timestamp":1745452800000},"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>Maintaining the equilibrium between electricity supply and demand remains a central concern in power systems. A demand response program can adjust the power load demand from the demand side to promote the balance of supply and demand. Load forecasting can facilitate the implementation of this program. However, as electricity consumption patterns become more diverse, the resulting load data grows increasingly irregular, making precise forecasting more difficult. Therefore, this paper developed a specialized forecasting scheme. First, the parameters of improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN) were optimized using beluga whale optimization (BWO). Then, the nonlinear power load data were decomposed into multiple subsequences using ICEEMDAN. Finally, each subsequence was independently predicted using the iTransformer model, and the overall forecast was derived by integrating these individual predictions. Data from Singapore was selected for validation. The results showed that the BWO\u2013ICEEMDAN\u2013iTransformer model outperformed the other comparison models, with an R2 of 0.9873, RMSE of 48.0014, and MAE of 66.2221.<\/jats:p>","DOI":"10.3390\/a18050243","type":"journal-article","created":{"date-parts":[[2025,4,24]],"date-time":"2025-04-24T08:08:41Z","timestamp":1745482121000},"page":"243","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["BWO\u2013ICEEMDAN\u2013iTransformer: A Short-Term Load Forecasting Model for Power Systems with Parameter Optimization"],"prefix":"10.3390","volume":"18","author":[{"given":"Danqi","family":"Zheng","sequence":"first","affiliation":[{"name":"Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China"}]},{"given":"Jiyun","family":"Qin","sequence":"additional","affiliation":[{"name":"China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China"}]},{"given":"Zhen","family":"Liu","sequence":"additional","affiliation":[{"name":"Shandong Future Network Research Institute, Jinan 250002, China"}]},{"given":"Qinglei","family":"Zhang","sequence":"additional","affiliation":[{"name":"China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China"}]},{"given":"Jianguo","family":"Duan","sequence":"additional","affiliation":[{"name":"China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China"}]},{"given":"Ying","family":"Zhou","sequence":"additional","affiliation":[{"name":"China Institute of FTZ Supply Chain, Shanghai Maritime University, Shanghai 201306, China"}]}],"member":"1968","published-online":{"date-parts":[[2025,4,24]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5902","DOI":"10.1016\/j.eswa.2010.11.033","article-title":"Short-term load forecasting using lifting scheme and ARIMA models","volume":"38","author":"Lee","year":"2011","journal-title":"Expert Syst. 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