时间序列深度学习模型的性能分析,用于不同时间间隔的室内水培温室气候预测
1Department of Future Vehicle Engineering, Inha University, 100 Inharo, Mitchuholgu, Incheon 22212, Republic of Korea.
Plants (Basel, Switzerland)
|June 28, 2023
概括
深度学习模型准确地预测了温室气候条件. 长短记忆 (LSTM) 模型在较短的时间间隔中表现出色,用于优化室内水培温室环境.
科学领域:
- 农业工程 农业工程
- 人工智能的人工智能
- 环境科学 环境科学
背景情况:
- 室内水培温室对于可持续的粮食生产至关重要.
- 精确的气候控制对于这些环境中的作物成功至关重要.
- 时间序列深度学习模型对气候预测有希望.
研究的目的:
- 为了比较深度神经网络,长短期记忆 (LSTM) 和1D卷积神经网络模型的气候预测性能.
- 为了评估不同时间间隔 (1,5,10,15分钟) 的模型性能.
- 确定最佳的时间间隔,以便在水培温室中准确地预测气候.
主要方法:
- 使用了三个深度学习模型:深度神经网络,LSTM和1D CNN.
- 收集了一周的数据集,每隔一分钟收集温度,湿度和二氧化碳度的数据.
- 在1,5,10,15分钟间隔进行模型性能比较.
主要成果:
- 所有测试的模型在预测温室气候变量方面表现良好.
- 在较短的时间间隔 (1 分钟) 中,LSTM 模型显示出更优异的性能.
- 随着时间间隔从1分钟增加到15分钟,预测准确性下降.
结论:
- 时间序列深度学习模型对于室内水培温室的气候预测是有效的.
- 选择适当的时间间隔对于实现准确的预测至关重要.
- 这些发现可以为开发可持续农业的智能控制系统提供信息.
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