A Novel Hybrid Modeling Method for Predicting Energy Use of Hydronic Radiant Slab Systems
A Novel Hybrid Modeling Method for Predicting Energy Use of Hydronic Radiant Slab Systems
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发表时间：
2022
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通讯作者：
Liping Wang;Lichen Wu;James Braun
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作者：
Liping Wang;Lichen Wu;James Braun
由于辐射板的大热容和室温分层，准确预测辐射板系统的性能可能具有挑战性。用于预测液体循环辐射板的加热和冷却能量消耗的当前方法包括详细的第一原理（例如，有限差分）和降阶（例如，热敏电阻-电容（RC）网络）模型。创建和校准详细的第一性原理模型，以及详细的RC网络模型，用于预测辐射板的性能需要大量的工作。为了开发改进的控制，监测和诊断方法，需要更简单的模型，可以很容易地使用现场测量进行训练。在这项研究中，我们探索了一种新的混合建模方法，它集成了一个简单的RC网络模型与一个不断发展的基于学习的算法称为增长高斯混合回归（GGMR）建模方法来预测的加热和冷却速率的辐射板系统的生活实验室办公空间。RC网络模型预测作为GGMR模型的输入提供的辐射板系统的加热或冷却负荷。在这项研究中考虑了三种建模方法：1）RC网络模型; 2）GGMR模型; 3）建议的RC和GGMR之间的混合建模。使用2022年1月15日至3月7日的测量数据，对三种建模方法进行了比较，以预测Living Laboratory办公空间的辐射板系统的能源使用。前两周的数据用于训练，而剩余的数据用于测试所有三种建模方法。混合方法的归一化均方根误差（NRMSE）为15.46%（比单独的RC模型3低8.62%，比单独的GGMR低19.36%），RMSE变异系数（CVRMSE）为6.43%（比RC-3型低3.59%，比GGMR低8.05%），平均绝对误差（MAE）为3.61 kW（分别比RC-Model 3和GGMR低2.13 kW和3.87 kW），平均绝对百分比误差（MAPE）为5.28%（分别比RC-Model 3和GGMR低3.85%和3.92%）。
Accurately predicting the performance of radiant slab systems can be challenging due to the large thermal capacitance of the radiant slab and room temperature stratification. Current methods for predicting heating and cooling energy consumption of hydronic radiant slabs include detailed first-principles (e.g., finite difference) and reduced-order (e.g., thermal Resistor-Capacitor (RC) network) models. Creating and calibrating detailed firstprinciples models, as well as detailed RC network models for predicting the performance of radiant slabs require substantial effort. To develop improved control, monitoring, and diagnostic methods, there is a need for simpler models that can be readily trained using in-situ measurements. In this study, we explored a novel hybrid modeling method that integrates a simple RC network model with an evolving learning-based algorithm termed the Growing Gaussian Mixture Regression (GGMR) modeling approach to predict the heating and cooling rates of a radiant slab system for a Living Laboratory office space. The RC network model predicts heating or cooling load of the radiant slab system that is provided as an input to the GGMR model. Three modeling approaches were considered in this study: 1) an RC network model; 2) a GGMR model, and 3) the proposed hybrid modeling between RC and GGMR. The three modeling methods have been compared for predicting the energy use of a radiant slab system of a Living Laboratory office space using measurement data from January 15th to March 7th, 2022. The first two weeks of data were used for training, while the remaining data was used for testing of all three modeling methods. The hybrid approach had a Normalized Root Mean Square Error (NRMSE) of 15.46 percent (8.62 percent less than the RC-Model 3 alone and 19.36 percent less than the GGMR alone), a Coefficient of Variation of RMSE (CVRMSE) of 6.43 percent (3.59 percent less than the RC-Model 3 and 8.05 percent less than the GGMR), a Mean Absolute Error (MAE) of 3.61 kW (2.13 kW and 3.87 kW less than the RC-Model 3 and GGMR, respectively), and a Mean Absolute Percentage Error (MAPE) of 5.28 percent (3.85 percent and 3.92 percent lower than the RC-Model 3 and GGMR, respectively).