Effective LSTM with K-means Clustering Algorithm for Electricity Load Prediction
Authors: 
DaWei Geng, HaiFeng Zhang, Ting XuAuthors Info & Claims
DaWei Geng, HaiFeng Zhang, Ting XuAuthors Info & ClaimsPages 476 - 481
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.
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- Effective LSTM with K-means Clustering Algorithm for Electricity Load Prediction
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Published In
September 2019
803 pages
ISBN:9781450372985
DOI:10.1145/3366194
Copyright © 2019 ACM.
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Association for Computing Machinery
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Published: 20 September 2019
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RICAI 2019
RICAI 2019: 2019 International Conference on Robotics, Intelligent Control and Artificial Intelligence
September 20 - 22, 2019
Shanghai, China
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RICAI '19 Paper Acceptance Rate 140 of 294 submissions, 48%;
Overall Acceptance Rate 140 of 294 submissions, 48%
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