{"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}
引用次数: 2
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.