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Self-learning model predictive control for dynamic activation of structural thermal mass in residential buildings

机译:住宅楼宇动态激活动态激活的自学习模型预测控制

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Changes in the electricity supply system induce the challenge of matching the highly fluctuating and unpredictable renewable energy generation with the yet inflexible electricity demand. This leads to an increasing demand for energy storage and demand-flexibility. Electrification of residential heating systems in combination with advanced controls utilizing dynamically the structural thermal mass (STM) of buildings as thermal storage could provide some of the required demand flexibility.In this work, a model predictive control (MPC) algorithm is developed and applied within a simulation framework to control dynamic heating operation as a measure of STM based residential load shifting (LS). The self-learning algorithm is functional without extensive measurement data or expert knowledge for parametrization. It optimizes heating operations required for LS according to a dynamic primary energy factor signal, while observing transient thermal comfort constraints. The implemented auto-regressive black-box model with explanatory variables predicts thermal conditions within the observed thermal zone with sufficient quality to support MPC. Based on that model, the control algorithm successfully activates STM as a measure of LS according to the given primary energy (PE) oriented utility function. For the observed system, the PE demand can be reduced by 3-7% while maintaining or even improving the thermal comfort. (C) 2019 Elsevier B.V. All rights reserved.
机译:电力供应系统的变化促使匹配高度波动和不可预测的可再生能源发电的挑战,即令人信心的电力需求。这导致越来越多的能量存储和需求灵活性。住宅加热系统的电气化与动态控制的先进控制相结合,建筑物的结构热质量(STM)作为热存储器可以提供一些所需的需求灵活性。在这项工作中,开发和应用了模型预测控制(MPC)算法一种控制动态加热操作的仿真框架,作为STM基于STM的住宅载荷移位(LS)。自学习算法是功能的,没有广泛的测量数据或参数化专家知识。它根据动态初级能量因子信号优化LS所需的加热操作,同时观察瞬态热舒适约束。具有解释性变量的实现的自动回归黑匣子型号预测观察到的热区内的热条件,具有足够的质量来支持MPC。基于该模型,控制算法根据给定的主要能量(PE)面向实用程序功能成功激活STM作为LS的量度。对于观察到的系统,PE需求可以减少3-7%,同时保持甚至改善热舒适度。 (c)2019 Elsevier B.v.保留所有权利。

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