{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2024,12,24]],"date-time":"2024-12-24T05:06:55Z","timestamp":1735016815753,"version":"3.32.0"},"reference-count":0,"publisher":"IOS Press","isbn-type":[{"value":"9781643685694","type":"electronic"}],"license":[{"start":{"date-parts":[[2024,12,20]],"date-time":"2024-12-20T00:00:00Z","timestamp":1734652800000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,20]]},"abstract":"<jats:p>Wind power prediction is essential for the optimal operation of wind farms. Therefore, a novel combined model is proposed to improve the prediction performance for the short-term wind power forecasting. First, the wavelet threshold denoising (WTD) technique is used to preprocess the original wind power data. Second, the NeuralProphet (NP) model is used to predict the preprocessed wind power data. Then, the error sequence predicted by the NP model is subjected to ICEEMDAN-VMD quadratic decomposition to reduce its complexity. The sparrow search algorithm (SSA) optimization algorithm is used to optimize the library for support vector machines (LIBSVM) hyperparameters to predict the decomposed error subsequences. Finally, the predicted error sequence results and the NP prediction results are combined to obtain the final prediction results. A hybrid WTD-NP-ICEEMDAN-VMD-SSA-LIBSVM model is proposed for wind power prediction. To evaluate the model\u2019s performance and reliability, we compared the prediction effects of 6 single models and 6 hybrid models through two sets of experiments. Four evaluation indices were used to assess the model. It is evident that the model demonstrates high accuracy and efficiency in wind power prediction.<\/jats:p>","DOI":"10.3233\/faia241438","type":"book-chapter","created":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:48:52Z","timestamp":1734947332000},"source":"Crossref","is-referenced-by-count":0,"title":["Application of Hybrid Model Based on NeuralProphet Model and Error Correction in Wind Power Prediction"],"prefix":"10.3233","author":[{"given":"Zai-hong","family":"Hou","sequence":"first","affiliation":[{"name":"College of Physics and Electrical Engineering, Northwest Normal University, Lanzhou, Gansu, 730070, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3323-0211","authenticated-orcid":false,"given":"Yu-long","family":"Bai","sequence":"additional","affiliation":[{"name":"College of Physics and Electrical Engineering, Northwest Normal University, Lanzhou, Gansu, 730070, China"}]}],"member":"7437","container-title":["Frontiers in Artificial Intelligence and Applications","Fuzzy Systems and Data Mining X"],"original-title":[],"link":[{"URL":"https:\/\/ebooks.iospress.nl\/pdf\/doi\/10.3233\/FAIA241438","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2024,12,23]],"date-time":"2024-12-23T09:48:53Z","timestamp":1734947333000},"score":1,"resource":{"primary":{"URL":"https:\/\/ebooks.iospress.nl\/doi\/10.3233\/FAIA241438"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2024,12,20]]},"ISBN":["9781643685694"],"references-count":0,"URL":"https:\/\/doi.org\/10.3233\/faia241438","relation":{},"ISSN":["0922-6389","1879-8314"],"issn-type":[{"value":"0922-6389","type":"print"},{"value":"1879-8314","type":"electronic"}],"subject":[],"published":{"date-parts":[[2024,12,20]]}}}