Black-box models for fault detection and performance monitoring of buildings

Abstract

Black-box models such as linear regression have proven to be helpful in ongoing building commissioning in many ways. The aim of this work is to improve linear models with change point for fault detection in buildings. Building simulations revealed poor performance of them (R 2 2. Remark: In the Inverse Modelling Toolkit (IMT) (Kissock et al. 2003) similar models are called CP-MVR or rather 3P-MVR or 4P-MVR. The difference to the CPMLRs used here is that there are different intercept terms for all linear regressions for the CPMLRs instead of one equal intercept term for all linear regression models 3P-MVR. This is introducing discontinuities on continuous independent variables but for the mean daily perspective taken here this is not disadvantageous. < 0.7) for some low energy buildings. The regression models (RMs) can be considerably improved by introducing the rate of change of the indoor air temperature (ΔT ind) as an independent variable. Thus, R 2 values were raised by up to 0.5 (e.g. from 0.2 to 0.7, example with the lowest R 2). A new training and application process for the RMs revealed further improvements by using a hierarchical agglomerative clustering algorithm to determine different day-types as additional (categorical) variables in the RM. The application of these improved RMs for outlier detection is demonstrated in three buildings.