基于物理约束宽度学习的冷水机组外推模型
Extrapolation model for chillers based on physics-constrained broad learning
摘要：
为实现冷水机组的节能控制优化,在宽度学习的基础上引入误差反向传播、Adam优化器、自定义物理约束损失函数,开发了基于物理约束宽度学习的冷水机组外推模型,有助于根据现有工况的实测数据,准确得出冷水机组在未知工况下的运行状态。与多层感知器、随机森林、卷积神经网络、支持向量回归的比较表明,相较于其他非外推模型,该外推模型的平均绝对误差(MAE)降低约22.35%,均方根误差(RMSE)降低约25.36%,决定系数(R2)提高约19.22%,能量损失降低约99.45%,且具有较短的训练时间,可以作为一种兼顾模型结构复杂度、模型精度和模型训练时间复杂度的方法,用于冷水机组的外推场景建模。
Abstract：
In order to realize energy-saving control optimization of the chillers, an extrapolation model for chillers based on physics-constrained broad learning is developed by introducing error backpropagation, Adam optimizer, and custom physics-constrained loss function on the basis of broad learning, which is helpful to accurately obtain the operating state of the chiller under unknown working conditions according to the measured data of the existing working conditions. Comparisons with multilayer perceptron, random forest, convolutional neural network, support vector regression show that, compared with other non-extrapolation models, this extrapolation model reduces MAE by about 22.35% and RMSE by about 25.36%, increases R2by about 19.22%, and reduces energy loss by about 99.45%, and shorts the training time, making it a method that balances model structure complexity, accuracy and training time, suitable for extrapolation scenario modeling of chillers.
Keywords:chiller; extrapolation scenario; broad learning; online model; physical constraint