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Anomaly detection based on machine learning in IoT-based vertical plant wall for indoor climate control

机译：基于机器学习的异常检测在基于机器的垂直植物墙室室内气候控制

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摘要

Indoor climate is closely related to human health, comfort and productivity. Vertical plant wall systems, embedded with sensors and actuators, have become a promising application for indoor climate control. In this study, we explore the possibility of applying machine learning based anomaly detection methods to vertical plant wall systems so as to enhance the automation and improve the intelligence to realize predictive maintenance of the indoor climate. Two categories of anomalies, namely point anomalies and contextual anomalies are researched. Prediction-based and pattern recognition-based methods are investigated and applied to indoor climate anomaly detection. The results show that neural network-based models, specifically the autoencoder (AE) and the long short-term memory encoder decoder (LSTM-ED) model surpass the others in terms of detecting point anomalies and contextual anomalies, respectively, therefore can be deployed into vertical plant walls systems in industrial practice. Based on the results, a new data cleaning method is proposed and a prediction-based method is deployed to the cloud in practice as a proof-of-concept. This study showcases the advancements in machine learning and Internet of things can be fully utilized by researches on building environment to accelerate the solution development.
机译：室内气候与人类健康，舒适性和生产力密切相关。嵌入式植物墙体系统，嵌入传感器和执行器，已成为室内气候控制的有希望的应用。在这项研究中，我们探讨了将基于机器学习的异常检测方法应用于垂直植物墙体系统的可能性，以提高自动化，提高智能，实现室内气候预测维护。研究了两类异常，即点异常和语境异常。研究了预测和基于模式识别的方法，并应用于室内气候异常检测。结果表明，基于神经网络的模型，特别是AutoEncoder（AE）和长短期存储器编码器解码器（LSTM-ED）模型分别在检测点异常和上下文异常方面超越他人，因此可以部署进入工业实践中的垂直植物墙体系统。基于结果，提出了一种新的数据清洁方法，并将预测的方法作为概念验证部署到云中的云。本研究展示了机器学习的进步，可以通过对建筑环境的研究充分利用物联网来加速解决方案开发。

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