Sensitivity of Characterizing the Heat Loss Coefficient through On-Board Monitoring: A Case Study Analysis

Marieline Senave,Staf Roels,Dirk Saelens,Stijn Verbeke,Evi Lambie

doi:10.3390/en12173322

Marieline Senave, Staf Roels + Show 3 more

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https://doi.org/10.3390/en12173322

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Abstract

Recently, there has been an increasing interest in the development of an approach to characterize the as-built heat loss coefficient (HLC) of buildings based on a combination of on-board monitoring (OBM) and data-driven modeling. OBM is hereby defined as the monitoring of the energy consumption and interior climate of in-use buildings via non-intrusive sensors. The main challenge faced by researchers is the identification of the required input data and the appropriate data analysis techniques to assess the HLC of specific building types, with a certain degree of accuracy and/or within a budget constraint. A wide range of characterization techniques can be imagined, going from simplified steady-state models applied to smart energy meter data, to advanced dynamic analysis models identified on full OBM data sets that are further enriched with geometric info, survey results, or on-site inspections. This paper evaluates the extent to which these techniques result in different HLC estimates. To this end, it performs a sensitivity analysis of the characterization outcome for a case study dwelling. Thirty-five unique input data packages are defined using a tree structure. Subsequently, four different data analysis methods are applied on these sets: the steady-state average, Linear Regression and Energy Signature method, and the dynamic AutoRegressive with eXogenous input model (ARX). In addition to the sensitivity analysis, the paper compares the HLC values determined via OBM characterization to the theoretically calculated value, and explores the factors contributing to the observed discrepancies. The results demonstrate that deviations up to 26.9% can occur on the characterized as-built HLC, depending on the amount of monitoring data and prior information used to establish the interior temperature of the dwelling. The approach used to represent the internal and solar heat gains also proves to have a significant influence on the HLC estimate. The impact of the selected input data is higher than that of the applied data analysis method.

Highlights

With a share of 25.7% in the final energy consumption in the European Union [1], the residential sector has an important potential for the application of energy saving strategies such as increasing the energy efficiency, using renewable energy, and exchanging energy between buildings
This paper aims to enhance the understanding of the impact of (1) the data analysis method and (2) the input data on the characterization outcome
The discussion on the sensitivity of the heat loss coefficient (HLC) estimate will be organized around the three topics indicated in Figure 5: the impact of the way (1) the interior temperature is represented, (2) the internal heat gains are approximated, and (3) the solar heat gains are modeled

Summary

Introduction

With a share of 25.7% in the final energy consumption in the European Union [1], the residential sector has an important potential for the application of energy saving strategies such as increasing the energy efficiency, using renewable energy, and exchanging energy between buildings. A key performance indicator to express the performance of the building envelope is the Heat Loss Coefficient or HLC (W/K) This metric describes the heating power (W) needed to sustain a temperature difference of 1K over the building envelope. The Htr , on the one hand, embeds four separate heat transfer coefficients (Equation (2)): the heat transfer coefficient between the conditioned zone and the exterior environment (Htr,e (W/K)), and the heat transfer coefficients to the ground (Htr,g ), to unconditioned spaces (Htr,u ) and to adjacent buildings (Htr,a ) [2]. A temperature ratio bT (-) ensures that all building fabric is evaluated over the temperature difference between the interior and exterior environment (Equation (4))

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