Comparing statistical modeling techniques for heat loss coefficient estimation using in-situ data

Abstract

Abstract The combination of in-situ collected data and statistical modelling techniques proved to be a promising approach in actual building energy performance assessments, such as heat loss coefficient (HLC) evaluation. In this study, based on datasets of co-heating and pseudo-random binary sequence heating tests on a portable site office, the performance of three types of statistical models (i.e. multiple linear regression (MLR), autoregressive with exogenous terms (ARX), and grey-box models) on HLC-determination are examined. It is revealed that a similar HLC estimation outcome (about 115 W/K) is offered by the aforesaid three types of statistical models, but with different confidence intervals (CIs), where the 95% CIs of MLR (±3.1%) and ARX (±2.4%) are relatively narrow and the ones of grey box models are somewhat wider (around ± 9%). Moreover, for the current case study building, with evenly orientation-wise distributed glazed envelope, integrating B-splines into the grey-box model, to characterize the solar aperture (gA) and solar gain dynamics more precisely, imposed insignificant effects on the HLC estimation and corresponding 95% CIs, compared to the grey-box model with a constant gA assumption.