论文标题
在废物到燃料植物中预测传感器值：案例研究
Forecasting Sensor Values in Waste-To-Fuel Plants: a Case Study
论文作者
论文摘要
在这项研究中，我们开发了机器学习模型，以预测废物到燃料工厂的未来传感器读数，这将积极控制工厂的运营。我们开发了可预测传感器读数30和60分钟的模型。使用历史数据对模型进行了训练，并根据在特定时间进行的传感器读数进行预测。我们比较了三种类型的模型：（a）仅考虑最后一个预测值的näive预测，（b）基于过去传感器数据进行预测的神经网络（我们考虑用于做出预测的不同时间窗口大小），以及（c）梯度增强的树回归器，创建了我们开发的一组功能。我们在加拿大的一家废物对燃料工厂的现实世界用例中开发并测试了我们的模型。我们发现方法（C）提供了最佳结果，而方法（b）提供了不同的结果，并且无法始终如一地超越Näive。
In this research, we develop machine learning models to predict future sensor readings of a waste-to-fuel plant, which would enable proactive control of the plant's operations. We developed models that predict sensor readings for 30 and 60 minutes into the future. The models were trained using historical data, and predictions were made based on sensor readings taken at a specific time. We compare three types of models: (a) a näive prediction that considers only the last predicted value, (b) neural networks that make predictions based on past sensor data (we consider different time window sizes for making a prediction), and (c) a gradient boosted tree regressor created with a set of features that we developed. We developed and tested our models on a real-world use case at a waste-to-fuel plant in Canada. We found that approach (c) provided the best results, while approach (b) provided mixed results and was not able to outperform the näive consistently.
