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Model-predictive control of mixed-mode buildings with rule extraction

机译：基于规则提取的混合模式建筑物的模型预测控制

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摘要

A series of model-predictive control (MPC) techniques have been explored for optimizing control sequences for window operation in mixed-mode (MM) buildings using EnergyPlus, and results for a simplified MM office building have been presented. Initial results for a small office in Boulder, Colorado show the ability to save upwards of 40% of cooling energy through near-optimal night cooling strategies, even in existing facilities. Strategies can be tuned to avoid overcooling the space by introducing heating energy into the objective function used in the MPC process. A complementary statistical technique has been introduced that allows for the "extraction" of logistic decision models from the optimal control results. The process works best when some time-lagged information is present as a predictor variable to ensure that some process memory is preserved. A generalized linear model (CLM) in the form of a multi-logistic regression was able to mimic the general characteristics of the optimizer results, achieving 70-90% of optimizer energy savings, but at a small fraction of the computational expense. Given the simple mathematical formulation of the logistic regression, it would be possible to implement this sort of decision model into modern direct digital control systems to control MM buildings in a near-optimal manner in real time.
机译：已经探索了一系列模型预测控制（MPC）技术，以使用EnergyPlus优化混合模式（MM）建筑物中窗户操作的控制顺序，并提出了简化的MM办公大楼的结果。科罗拉多州博尔德市一家小型办公室的初步结果表明，即使在现有设施中，也可以通过近乎最佳的夜间制冷策略节省多达40％的制冷能源。通过将加热能量引入MPC过程中使用的目标函数中，可以调整策略以避免空间过冷。已经引入了一种补充统计技术，该技术允许从最佳控制结果中“提取”逻辑决策模型。当某些时滞信息作为预测变量出现时，该过程将发挥最佳效果，以确保保留某些过程内存。采用多逻辑回归的形式的广义线性模型（CLM）能够模仿优化程序结果的一般特征，实现了70-90％的优化程序节能量，但计算费用却很小。给定逻辑回归的简单数学公式，就可以将这种决策模型实施到现代直接数字控制系统中，以近乎最佳的方式实时控制MM建筑物。

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