This paper presents a control-oriented building thermal model and optimization framework. Space heating is used as an illustrative example of a flexible building load, with temperature setpoints as a control input. The presented framework is applicable to practical building systems where measurements are limited by cost and installation burden. An Unscented Kalman Filter estimates parameters and disturbance inputs of a multi-zone thermal circuit. Forecast models of multiple exogenous input sources are created from disturbance proxies and estimated disturbance inputs. Zone-level controllers in the thermal circuit simulation estimate the heating system response based on forecasted exogenous thermal inputs and proposed temperature setpoint profiles. Genetic algorithm-based operations are used to find an approximate Pareto set, i.e., the best trade-offs in the objective space. The focus of this work is reducing energy usage from space heating, while maintaining or improving thermal comfort. The full framework is demonstrated using data collected from a university building. Results predict that the proposed method provides a lower energy consumption than the baseline strategy. The framework is implemented in practice in a model predictive control scheme.

中文翻译：

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使用有限传感器测量对多区域建筑进行帕累托优化热控制


本文提出了一种面向控制的建筑热模型和优化框架。空间供暖被用作灵活建筑负载的说明性示例，以温度设定点作为控制输入。所提出的框架适用于实际的建筑系统，其中测量受到成本和安装负担的限制。无迹卡尔曼滤波器估计多区域热回路的参数和扰动输入。多个外源输入源的预测模型是根据扰动代理和估计的扰动输入创建的。热回路仿真中的区域级控制器根据预测的外源热输入和建议的温度设定点曲线来估计加热系统响应。基于遗传算法的操作用于找到近似帕累托集，即目标空间中的最佳权衡。这项工作的重点是减少空间供暖的能源使用，同时保持或提高热舒适度。使用从大学大楼收集的数据演示了完整的框架。结果预测，所提出的方法比基准策略提供更低的能耗。该框架在模型预测控制方案中实际实施。