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Reduced-order residential home modeling for model predictive control

机译：用于模型预测控制的降阶住宅模型

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摘要

Building simulation software packages such as EnergyPlus are useful energy modeling tools. These software packages, however, are often not amenable to model-based control due to model complexity or difficulties connecting control algorithms with the software. We present a method for automatically generating input/output data from an EnergyPlus residential home model using the OpenStudio software suite. These input/output data are used to create a simple reduced-order model that can be evaluated in fractions of a second. The reduced-order model is implemented in a model predictive controller to minimize the home's electricity costs during summer months in Austin, Texas, USA. The controller optimally precools the home in the morning and turns down or off the air conditioning system in the afternoon. For this example, electricity prices were taken from actual market prices in the Austin area. The optimal precooling strategy given by the model predictive controller reduces peak energy consumption from the air conditioning unit by an average of 70% and reduces operating costs by 60%. Precooling, however, consumes more total energy versus not precooling. Reducing peak energy consumption by 1 kWh results, on average, in an increase of 0.63 kWh in overall energy consumption.
机译：诸如EnergyPlus之类的建筑仿真软件包是有用的能源建模工具。但是，由于模型复杂性或控制算法与软件之间的连接困难，这些软件包通常不适合基于模型的控制。我们提出了一种使用OpenStudio软件套件从EnergyPlus住宅模型自动生成输入/输出数据的方法。这些输入/输出数据用于创建简单的降阶模型，可以在几分之一秒内对其进行评估。降阶模型在模型预测控制器中实施，以最大程度地减少美国德克萨斯州奥斯汀市夏季房屋的用电成本。控制器在早上为房屋提供最佳的预冷，并在下午关闭或关闭空调系统。对于此示例，电价取自奥斯丁地区的实际市场价格。由模型预测控制器提供的最佳预冷策略可将空调设备的峰值能耗平均降低70％，并将运营成本降低60％。但是，预冷却比不预冷却消耗更多的总能量。平均将峰值能耗降低1 kWh，可使总体能耗增加0.63 kWh。

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