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首页> 外文期刊>Science and Technology for the Built Environment >Artificial neural network prediction models of stratified thermal energy storage system and borehole heat exchanger for model predictive control
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Artificial neural network prediction models of stratified thermal energy storage system and borehole heat exchanger for model predictive control

机译：模型预测控制分层热能存储系统和钻孔换热器的人工神经网络预测模型

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摘要

We present a method for constructing artificial neural network (ANN) models of the stratified chilled water thermal energy storage (TES) system and borehole heat exchanger (BHE) of a ground-source heat pump (GSHP) system to assess the feasibility of using ANNs in model predictive control (MPC) applications. In the MPC technique, prediction models are required to describe the system being studied, and ANNs have been used to emulate the system of late. However, the training dataset and structure for ANNs should be constructed with care since incorrect training may lead to prediction errors. This work involved performing case studies on different combination of input parameters of training dataset and the ANN structure for modeling the stratified TES tank and BHE. The suitability of the ANNs of the TES system trained using the simulation results of a physical model and that of the model of the BHE trained using the results of a numerical simulation were assessed. The trained ANNs were evaluated based on the coefficient of determination (R-2), root mean square error, and coefficient of variation of the root mean square error. Selected ANNs showed a high prediction accuracy for both systems, and the speed of model run was significantly improved.
机译：我们介绍了一种构建地面源热泵（GSHP）系统的分层冷水热能储存（TES）系统和钻孔热交换器（BHE）的人工神经网络（ANN）模型，以评估使用ANN的可行性在模型预测控制（MPC）应用中。在MPC技术中，需要预测模型来描述正在研究的系统，并且ANNS已被用于模拟晚期系统。然而，由于不正确的训练可能导致预测误差，因此应根据护理构建ANNS的训练数据集和结构。这项工作涉及对训练数据集的输入参数的不同组合以及用于建模分层TES罐和BHE的ANN结构进行案例研究。评估了使用物理模型的模拟结果的TES系统的ANN的适用性以及使用数值模拟结果训练的BHE培训的模型的仿真结果。基于培训的ANN基于确定系数（R-2），根均方误差和根均方误差的变化系数。所选的ANNS显示了两个系统的高预测精度，并且模型运行的速度显着提高。

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