论文标题
数据驱动的热模型与ARMAX，在智能环境中，基于归一化的共同信息
Data-driven Thermal Model Inference with ARMAX, in Smart Environments, based on Normalized Mutual Information
论文作者
论文摘要
了解智能建筑中表征热动态的模型对于乘员的舒适性和能量优化至关重要。大量的研究试图利用热力学（物理）模型来进行智能建筑物控制，但是由于间歇性环境干扰的随机性，这些方法仍然具有挑战性。本文介绍了一种新型的室内热模型推断的数据驱动方法，该方法将自回归的移动平均值与外在输入模型（ARMAX）与归一化互信息方案（NMI）结合在一起。基于此信息理论方法，NMI，室内温度和外源性输入之间的因果关系依赖性是明确获得的，作为ARMAX模型的指南，以找到主导的输入。为了进行验证，我们使用基于构建能量系统 - 重新构建的三个数据集，我们将方法与带有外源输入（ARX）的自回归模型（正规化ARMAX模型和状态空间模型）进行了比较。
Understanding the models that characterize the thermal dynamics in a smart building is important for the comfort of its occupants and for its energy optimization. A significant amount of research has attempted to utilize thermodynamics (physical) models for smart building control, but these approaches remain challenging due to the stochastic nature of the intermittent environmental disturbances. This paper presents a novel data-driven approach for indoor thermal model inference, which combines an Autoregressive Moving Average with eXogenous inputs model (ARMAX) with a Normalized Mutual Information scheme (NMI). Based on this information-theoretic method, NMI, causal dependencies between the indoor temperature and exogenous inputs are explicitly obtained as a guideline for the ARMAX model to find the dominating inputs. For validation, we use three datasets based on building energy systems-against which we compare our method to an autoregressive model with exogenous inputs (ARX), a regularized ARMAX model, and state-space models.
