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Design and construction of a non-linear model predictive controller for building's cooling system

机译：建筑物冷却系统非线性模型预测控制器的设计与构建

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摘要

This research aims to optimize a multi-zone Air Handling Unit's (AHU) energy consumption by using a Nonlinear Model Predictive Control (NMPC) approach. In this paper, Genetic Algorithm (GA) and Non-linear autoregressive network with exogenous inputs (NARX) have been utilized to design NMPC for a multi-zone AHU. The NMPC problem could be divided into two main sections: internal model and the optimizer. NARX serves as the controller's internal model to predict the building's thermal dynamics. GA is then used to solve the NMPC problem and find the optimal value of the control signals at each time step. The proposed NMPC jointly minimizes energy consumption of the AHU and the deviation from the set-point temperature. Finally, the designed controller was implemented and applied to the mentioned AHU. Also, a data acquisition system has been fabricated to secure training and test data for NARX. Utilizing NARX for modeling system's dynamics resulted in a highly accurate model with an accuracy of 97.71%. The empirical results of the proposed NMPC showed significant reduction in gas and electricity consumption of the AHU. NMPC yielded a 55.1% and 43.7% reduction in electricity and gas consumption of the AHU respectively.
机译：本研究旨在通过使用非线性模型预测控制（NMPC）方法来优化多区域空气处理机组（AHU）的能耗。在本文中，遗传算法（GA）和带有外源输入的非线性自回归网络（NARX）已被用于设计用于多区域AHU的NMPC。 NMPC问题可以分为两个主要部分：内部模型和优化器。 NARX作为控制器的内部模型来预测建筑物的热力学。然后，将GA用于解决NMPC问题并在每个时间步长找到控制信号的最佳值。拟议的NMPC可以将AHU的能耗和与设定温度的偏差最小化。最后，所设计的控制器得以实施并应用于上述AHU。而且，已经制造出一种数据采集系统以确保用于NARX的训练和测试数据的安全。利用NARX对系统动力学进行建模，可以得出精度高达97.71％的高精度模型。拟议的NMPC的经验结果表明，AHU的燃气和电力消耗显着降低。 NMPC分别使AHU的电力和天然气消耗减少了55.1％和43.7％。

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