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Experimental application of classification learning to generate simplified model predictive controls for a shared office heating system

机译:分类学习的实验应用为共用办公加热系统产生简化模型预测控制

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摘要

The application of model-based predictive control (MPC) to commercial buildings has been a topic of increasing interest in the literature. Despite significant energy saving potential, commercial adoption has been limited. The literature suggests that the limited proliferation of MPC in buildings is due to a mismatch between the mathematical complexity of MPC and the expertise of practitioners. Accordingly, simplified MPC (sMPC) approaches may improve uptake. This paper applies an approach to MPC where rules are extracted from a detailed MPC algorithm to produce an algebraic model of fewer than 50 lines of code. The goal is to produce an sMPC that will be more accepted by practitioners. Testing in a shared office was executed over a 5-week period of the heating season from March 11 to April 14. The detailed MPC algorithm presented in this paper reduced heating energy consumption by 23% compared with conventional reactive control when tested under similar weather conditions. The sMPC algorithm achieved 95% of the heating energy savings of the detailed MPC algorithm with significantly less complexity. Based on this favorable result for a single case, further work should be conducted with larger sample sizes to conclusively determine if rule extraction can produce an effective sMPC.
机译:基于模型的预测控制(MPC)对商业建筑的应用一直是对文献兴趣的主题。尽管节能潜力显着,但商业采用受到限制。该文献表明,建筑物中MPC的扩散有限的是由于MPC的数学复杂性与从业者的专业知识之间存在不匹配。因此,简化的MPC(SMPC)方法可以改善摄取。本文适用于MPC的方法,其中从详细的MPC算法中提取规则以产生少于50行代码的代数模型。目标是产生一个将被从业者接受的SMPC。共享办公室的测试在11月11日至4月14日的加热季节上执行了5周的时间。本文中提出的详细MPC算法将加热能耗降低23%,而在类似的天气条件下进行测试时,与传统的反应控制相比。 SMPC算法达到了95%的加热节能节省了详细的MPC算法,其复杂性明显不那么复杂。基于对单一案例的这种有利结果,应采用更大的样本尺寸进行进一步的工作,以确定规则提取是否可以产生有效的SMPC。

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