基于LSTM-Seq2Seq的兔舍环境多参数预测

Multivariable Environmental Prediction Model of Rabbit House Based on LSTM-Seq2Seq

为解决传统兔舍环境参数预测方法忽略环境参数间耦合关系的问题,提出了基于LSTM的Seq2Seq兔舍环境多参数关联序列预测模型。在建模过程中,使用双层LSTM作为Seq2Seq结构的编码器和解码器,以提高环境参数预测模型的表征能力及预测精度,而Seq2Seq结构不仅能够有效提取兔舍环境参数序列自身时间相关性,还能够挖掘参数间的耦合关系。利用该模型对浙江省嵊州市某兔场兔舍环境数据进行实验及预测。结果显示,该兔舍环境多参数预测模型取得了良好的预测性能,分别与标准LSTM、标准SVR模型对比分析,温度预测精度分别提高28.41%和48.60%,相对湿度预测精度分别提高9.84%和56.08%,二氧化碳浓度预测精度分别提高5.39%和11.19%。表明所提出的兔舍环境多参数预测模型能够充分挖掘关联环境参数序列间的耦合关系,满足兔舍环境数据精准预测的需要。

In order to improve the prediction accuracy of the rabbit house environment parameters,solve the coupling relationship between environmental parameters ignored in traditional predict method,and reduce the cost of rabbit house environmental control,a multivariable environmental prediction sequence to sequence model of rabbit house based on Long Short-Term Memory was proposed.Double-layer LSTM was used as the encoder and decoder of the Seq2 Seq structure to improve the characterization ability and prediction accuracy of the environmental parameter prediction model.The Seq2 Seq structure can not only effectively extract the time correlation of the rabbit house environmental parameter sequence itself,but also can mine the coupling relationship between the parameters.The model was used to test and predict the data of temperature,humidity and carbon dioxide concentration in the rabbit house which in a rabbit farm in Shengzhou City,Zhejiang Province.The results showed that the multi-parameter prediction model of the rabbit house environment achieved good prediction performance.Compared with standard LSTM model and standard SVM model,the prediction accuracy of temperature is improved by 28.41%and 48.60%,the prediction accuracy of humidity is improved by 9.84%and 56.08%,and the prediction accuracy of carbon dioxide concentration is improved by 5.39%and 11.19%.The experimental results showed that the proposed multivariable environmental prediction model of rabbit house not only had good forecasting effect,but also can meet the needs of accurate of prediction of rabbit house environmental data.