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Machine learning vs. hybrid machine learning model for optimal operation of a chiller

机译：机器学习与混合机学习模型，用于冷冻机的最佳运行

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摘要

This article compares two modeling approaches for optimal operation of a turbo chiller installed in an office building: (1) a machine learning model developed with artificial neural network (ANN) and (2) a hybrid machine learning model developed with the ANN model and available physical knowledge of the chiller. Before developing the ANN model of the chiller, the authors used Gaussian mixture model in order to check the validity of measured data. Then, the hybrid model was developed by combining the ANN model and physics-based regression equations from the EnergyPlus engineering reference. It was found that both the ANN and hybrid ANN model are satisfactory to predict the chiller's power consumption: mean bias error (MBE) = -2.63%, coefficient of variation of the root mean square error (CVRMSE) = 8.05% by the ANN model; MBE = -3.99%, CVRMSE = 11.98% by the hybrid ANN model. However, the hybrid model requires fewer inputs (four inputs) than the ANN model (eight inputs). The energy savings of both models are similar coefficient of performance (COP) = 4.32 by the optimal operation of the ANN model; COP = 4.44 by the optimal operation of the hybrid ANN model. In addition, the hybrid ANN model can be applied where the ANN model is unable to provide accurate predictions.
机译：本文将安装在办公楼中的涡轮式冷却器的最佳运行（1）用人工神经网络（ANN）开发的机器学习模型和（2）与ANN模型开发的混合机床学习模型开发的机器学习模型冷却器的身体知识。在开发冷却器的ANN模型之前，作者使用高斯混合模型以检查测量数据的有效性。然后，通过将ANN模型和基于物理的回归方程组合从能量级工程参考来开发混合动力模型。发现ANN和Hybrid Ann模型既令人满意，以预测冷却器的功耗：平均偏置误差（MBE）= -2.63％，根均线误差（CVRMSE）的变化系数（CVRMSE）= 8.05％，ANN模型; MBE = -3.99％，CVRMSE = 11.98％由混合ANN模型。但是，混合模型需要比ANN模型（八个输入）更少的输入（四个输入）。通过ANN模型的最佳操作，两种模型的节能是类似的性能系数（COP）= 4.32; COP = 4.44通过混合ANN模型的最佳运行。此外，可以应用Hybrid Ann模型，其中ANN模型无法提供准确的预测。

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