Skylight Opening Degree Prediction Method Based on Parallel Clustering

doi:10.16182/j.issn1004731x.joss.201710029

Abstract

Abstract: To process the massive distributed data and control the agricultural facilities intelligently, a parallel Dirichlet Process Mixture Model (DPMM) clustering method was proposed based on Spark. With this method, the prediction model of greenhouse skylight opening degree was obtained by training the agricultural environmental and facilities data. The model was used to predict the greenhouse skylight opening degree. Through several comparison experiments, both the feasibility and the efficiency of the proposed parallel clustering were verified, the prediction accuracy was calculated. The experimental results show that the proposed approach has higher efficiency and accuracy.

Key words: DPMM, agriculture environmental data, skylight opening value prediction, Spark

CLC Number:

TP338.8

Deng Li, Yu Yue, Pang Honglin, Fei Minrui. Skylight Opening Degree Prediction Method Based on Parallel Clustering[J]. Journal of System Simulation, 2017, 29(10): 2459-2467.

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