{"title":"基于k均值聚类的有效LSTM电力负荷预测算法","authors":"Dawei Geng, Haifeng Zhang, Ting Xu","doi":"10.1145/3366194.3366279","DOIUrl":null,"url":null,"abstract":"Short-term electricity load has the characteristics of timing and non-linearity, which is affected by many factors such as temperature, humidity. This paper proposes a hybrid prediction algorithm based on K-means clustering and long short-term memory (LSTM). K-means, which clusters the highest temperature, the lowest temperature, humidity and other characteristics of the electricity load, divides the data set into K classes. LSTM is utilized to solve nonlinear regressive and time series problem. Simulation results based on real load data show its priority to LSTM without clustering.","PeriodicalId":105852,"journal":{"name":"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2019-09-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"2","resultStr":"{\"title\":\"Effective LSTM with K-means Clustering Algorithm for Electricity Load Prediction\",\"authors\":\"Dawei Geng, Haifeng Zhang, Ting Xu\",\"doi\":\"10.1145/3366194.3366279\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Short-term electricity load has the characteristics of timing and non-linearity, which is affected by many factors such as temperature, humidity. This paper proposes a hybrid prediction algorithm based on K-means clustering and long short-term memory (LSTM). K-means, which clusters the highest temperature, the lowest temperature, humidity and other characteristics of the electricity load, divides the data set into K classes. LSTM is utilized to solve nonlinear regressive and time series problem. Simulation results based on real load data show its priority to LSTM without clustering.\",\"PeriodicalId\":105852,\"journal\":{\"name\":\"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2019-09-20\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"2\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1145/3366194.3366279\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Proceedings of the 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3366194.3366279","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Effective LSTM with K-means Clustering Algorithm for Electricity Load Prediction
Short-term electricity load has the characteristics of timing and non-linearity, which is affected by many factors such as temperature, humidity. This paper proposes a hybrid prediction algorithm based on K-means clustering and long short-term memory (LSTM). K-means, which clusters the highest temperature, the lowest temperature, humidity and other characteristics of the electricity load, divides the data set into K classes. LSTM is utilized to solve nonlinear regressive and time series problem. Simulation results based on real load data show its priority to LSTM without clustering.