{"title":"基于并行预测方法的混合小波神经网络电力负荷预测","authors":"N. Sovann, P. Nallagownden, Z. Baharudin","doi":"10.1109/ICIAS.2016.7824088","DOIUrl":null,"url":null,"abstract":"This paper presents a new hybrid load forecast model to improve the accuracy and robustness of load profile forecasting (1–24 hours ahead). It comprises of Wavelet transform and Neural network based on parallel prediction method, which is called \"PWNN\". Wavelet transform is used to decompose the original load series into multiple load sub-series with different frequencies. Then, neural network is used to predict each load sub-series using parallel prediction method. The load forecast can be obtained by inverse wavelet transform. The results indicate that PWNN has a significant improvement of accuracy and robustness in load forecasting over other models used for comparison in this study.","PeriodicalId":247287,"journal":{"name":"2016 6th International Conference on Intelligent and Advanced Systems (ICIAS)","volume":"25 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2016-08-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"3","resultStr":"{\"title\":\"Electricity load forecasting using hybrid wavelet neural network based on parallel prediction method\",\"authors\":\"N. Sovann, P. Nallagownden, Z. Baharudin\",\"doi\":\"10.1109/ICIAS.2016.7824088\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper presents a new hybrid load forecast model to improve the accuracy and robustness of load profile forecasting (1–24 hours ahead). It comprises of Wavelet transform and Neural network based on parallel prediction method, which is called \\\"PWNN\\\". Wavelet transform is used to decompose the original load series into multiple load sub-series with different frequencies. Then, neural network is used to predict each load sub-series using parallel prediction method. The load forecast can be obtained by inverse wavelet transform. The results indicate that PWNN has a significant improvement of accuracy and robustness in load forecasting over other models used for comparison in this study.\",\"PeriodicalId\":247287,\"journal\":{\"name\":\"2016 6th International Conference on Intelligent and Advanced Systems (ICIAS)\",\"volume\":\"25 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2016-08-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"3\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2016 6th International Conference on Intelligent and Advanced Systems (ICIAS)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/ICIAS.2016.7824088\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2016 6th International Conference on Intelligent and Advanced Systems (ICIAS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/ICIAS.2016.7824088","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Electricity load forecasting using hybrid wavelet neural network based on parallel prediction method
This paper presents a new hybrid load forecast model to improve the accuracy and robustness of load profile forecasting (1–24 hours ahead). It comprises of Wavelet transform and Neural network based on parallel prediction method, which is called "PWNN". Wavelet transform is used to decompose the original load series into multiple load sub-series with different frequencies. Then, neural network is used to predict each load sub-series using parallel prediction method. The load forecast can be obtained by inverse wavelet transform. The results indicate that PWNN has a significant improvement of accuracy and robustness in load forecasting over other models used for comparison in this study.