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Optimal Control of Chilled Water System With Ensemble Learning and Cloud Edge Terminal Implementation

机译:融合水系统与集合学习和云边缘终端实现的最佳控制

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摘要

In modern large buildings, the chilled water system regulates the indoor temperature through a series of heat exchanges. This article studies the optimal control problem of the chilled water system and proposes an ensemble learning method for the cooling load prediction under different operation conditions with imbalance sample distribution. A new control strategy is afterwards developed for optimal selection of the process control inputs that guarantee the demand for cooling load with a lower energy consumption. The optimal control strategy is also learned in real time using a cloud edge terminal form, which can be used for big data modeling and increase the effectiveness of the system response. The proposed method is applied to a real high-rise building, and the results show a significant improvement in the proposed prediction model and the optimal control strategy, compared to the state-of-the-art methods. Regarding manual operation, the control strategy decreased the energy consumption by 5.59%, and, on average, 35 645 kWh of electric energy per month was saved.
机译:在现代大型建筑中,冷却水系统通过一系列热交换器调节室内温度。本文研究了冷却水系统的最佳控制问题,提出了一种在不同操作条件下进行冷却负荷预测的集合学习方法,具有不平衡样本分布。之后开发了一种新的控制策略,可用于最佳选择过程控制输入,以保证能耗较低的冷却负载的需求。使用云边缘终端形式,最佳控制策略也实时学习,可用于大数据建模并提高系统响应的有效性。该方法应用于真正的高层建筑物,结果表明,与最先进的方法相比,该结果显示了所提出的预测模型和最佳控制策略的显着改进。关于手工操作,控制策略将能源消耗降低5.59%,平均每月35 645千瓦时保存。

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