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Design and experimental evaluation of model predictive control vs. intelligent methods for domestic heating systems

机译：家用供暖系统模型预测控制与智能方法的设计和实验评估

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In this paper, it has been attempted to present a temperature control method for the building and, simultaneously, reduce costs of providing energy in hybrid heating systems. In the present work, a building in Tehran city has been investigated as a sample during a single day and applying intelligent control methods in the presence of two gas and solar heat sources. Furthermore, the influence of each of these methods has been studied on reducing costs as well as regulating indoor temperature. In the next step, the utilized controllers have been redesigned for the laboratory model and their capability has been evaluated through experimental tests. Based on the acquired results, it is deduced that, compared to other methods, utilizing a PID controller optimized by genetic algorithm not only reduces 50% of energy providing costs, but also regulates the inner temperature of the Lab model with an error lower than 1%. This method is faster than the others in regulating the model temperature. However, in the real building modeling case, the efficiency of this method has been reduced relative to the laboratory model, mostly because of variable conditions such as variable solar emission intensity during different hours of the day. Yet, acceptable results have been acquired by performing MPC and correct modeling. These results show that, as the variable parameters increase, the MPC presents higher capability compared to other methods In this case, the MPC has similar costs to the genetic algorithm while it regulates the temperature faster and with lower error. (C) 2017 Elsevier B.V. All rights reserved.
机译：在本文中，已经尝试提出一种用于建筑物的温度控制方法，并且同时降低在混合采暖系统中提供能量的成本。在目前的工作中，已经对德黑兰市的一栋建筑物进行了一天的抽样调查，并在存在两个燃气和太阳能热源的情况下应用了智能控制方法。此外，已经研究了每种方法对降低成本以及调节室内温度的影响。下一步，已针对实验室模型重新设计了使用的控制器，并通过实验测试评估了它们的能力。根据获得的结果，推论出，与其他方法相比，利用通过遗传算法优化的PID控制器不仅可以减少50％的能源提供成本，而且还可以将Lab模型的内部温度调节到误差小于1 ％。这种方法在调节模型温度方面比其他方法更快。但是，在实际建筑物建模的情况下，相对于实验室模型，此方法的效率有所降低，这主要是因为在一天中的不同小时内，诸如可变的太阳辐射强度之类的可变条件。但是，通过执行MPC和正确的建模已经获得了可接受的结果。这些结果表明，随着变量参数的增加，MPC与其他方法相比具有更高的性能。在这种情况下，MPC具有与遗传算法相似的成本，同时它可以更快地调节温度并且误差更低。 （C）2017 Elsevier B.V.保留所有权利。

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