{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,30]],"date-time":"2026-05-30T02:01:05Z","timestamp":1780106465412,"version":"3.54.0"},"reference-count":55,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2017,1,25]],"date-time":"2017-01-25T00:00:00Z","timestamp":1485302400000},"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":["71671029"],"award-info":[{"award-number":["71671029"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41475013"],"award-info":[{"award-number":["41475013"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Entropy"],"abstract":"<jats:p>Machine learning plays a vital role in several modern economic and industrial fields, and selecting an optimized machine learning method to improve time series\u2019 forecasting accuracy is challenging. Advanced machine learning methods, e.g., the support vector regression (SVR) model, are widely employed in forecasting fields, but the individual SVR pays no attention to the significance of data selection, signal processing and optimization, which cannot always satisfy the requirements of time series forecasting. By preprocessing and analyzing the original time series, in this paper, a hybrid SVR model is developed, considering periodicity, trend and randomness, and combined with data selection, signal processing and an optimization algorithm for short-term load forecasting. Case studies of electricity power data from New South Wales and Singapore are regarded as exemplifications to estimate the performance of the developed novel model. The experimental results demonstrate that the proposed hybrid method is not only robust but also capable of achieving significant improvement compared with the traditional single models and can be an effective and efficient tool for power load forecasting.<\/jats:p>","DOI":"10.3390\/e19020052","type":"journal-article","created":{"date-parts":[[2017,1,25]],"date-time":"2017-01-25T09:50:44Z","timestamp":1485337844000},"page":"52","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":44,"title":["Research and Application of a Novel Hybrid Model Based on Data Selection and Artificial Intelligence Algorithm for Short Term Load Forecasting"],"prefix":"10.3390","volume":"19","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-6378-0732","authenticated-orcid":false,"given":"Wendong","family":"Yang","sequence":"first","affiliation":[{"name":"School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jianzhou","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China"},{"name":"State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Rui","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2017,1,25]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"392","DOI":"10.1109\/TPWRS.2005.860944","article-title":"Short-term load forecasting based on an adaptive hybrid method","volume":"21","author":"Shu","year":"2006","journal-title":"IEEE Trans. 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