Aggregation and Data Driven Identification of Building Thermal Dynamic Model and Unmeasured Disturbance

TL;DR

This paper presents a novel method for aggregating multi-zone building models into a single-zone equivalent and accurately identifying model parameters and unknown disturbances from data, improving control strategies for large HVAC systems.

Contribution

It introduces a principled aggregation approach and an identification algorithm for unknown disturbances, with insights on conditions affecting estimation accuracy.

Findings

Aggregation method reveals when disturbance estimation is unreliable.

The identification algorithm performs well on real multi-zone building data.

Heuristic provided to assess the accuracy of disturbance identification.

Abstract

An aggregate model is a single-zone equivalent of a multi-zone building, and is useful for many purposes, including model based control of large heating, ventilation and air conditioning (HVAC) equipment. This paper deals with the problem of simultaneously identifying an aggregate thermal dynamic model and unknown disturbances from input-output data. The unknown disturbance is a key challenge since it is not measurable but non-negligible. We first present a principled method to aggregate a multi-zone building model into a single zone model, and show the aggregation is not as trivial as it has been assumed in the prior art. We then provide a method to identify the parameters of the model and the unknown disturbance for this aggregate (single-zone) model. Finally, we test our proposed identification algorithm to data collected from a multi-zone building testbed in Oak Ridge National Laboratory. A key insight provided by the aggregation method allows us to recognize under what conditions the estimation of the disturbance signal will be necessarily poor and uncertain, even in the case of a specially designed test in which the disturbances affecting each zone are known (as the case of our experimental testbed). This insight is used to provide a heuristic that can be used to assess when the identification results are likely to have high or low accuracy.