Adverse condition and critical event prediction in commercial buildings: Danish case study

Søren Egedorf,Hamid Reza Shaker,Bo Nørregaard Jørgensen,Rodney A Martin

doi:10.1186/s42162-018-0015-5

Søren Egedorf, Hamid Reza Shaker + Show 2 more

Open Access

https://doi.org/10.1186/s42162-018-0015-5

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Abstract

Over the last two decades, there has been a growing realization that the actual energy performances of many buildings fail to meet the original intent of building design. Faults in systems and equipment, incorrectly configured control systems and inappropriate operating procedures increase the energy consumption about 20% and therefore compromise the building energy performance. To improve the energy performance of buildings and to prevent occupant discomfort, adverse condition and critical event prediction plays an important role. The Adverse Condition and Critical Event Prediction Toolbox (ACCEPT) is a generic framework to compare and contrast methods that enable prediction of an adverse event, with low false alarm and missed detection rates. In this paper, ACCEPT is used for fault detection and prediction in a real building at the University of Southern Denmark. To make fault detection and prediction possible, machine learning methods such as Kernel Density Estimation (KDE), and Principal Component Analysis (PCA) are used. A new PCA–based method is developed for artificial fault generation. While the proposed method finds applications in different areas, it has been used primarily for analysis purposes in this work. The results are evaluated, discussed and compared with results from Canonical Variate Analysis (CVA) with KDE. The results show that ACCEPT is more powerful than CVA with KDE which is known to be one of the best multivariate data-driven techniques in particular, under dynamically changing operational conditions.

Highlights

Over the last decade, the contribution of buildings energy consumption to total energy consumption has been between 20% – 40% in developed countries (Lombard et al 2007; Shaker and Lazarova-Molnar 2017)
ACCEPT tests and compares these algorithms according to their ability to predict adverse events in arbitrary time-series data from systems or processes
Adverse condition and critical event prediction is an important subject in a variety of applications and it is very closely related to the area of fault detection

Summary

Introduction

The contribution of buildings energy consumption to total energy consumption has been between 20% – 40% in developed countries (Lombard et al 2007; Shaker and Lazarova-Molnar 2017). Buildings account for approximately 20% of total CO2 emissions (Lazarova-Molnar et al 2016). There is an excellent opportunity for reducing energy consumption and CO2 emissions if the general performance of energy-consuming equipment in buildings could be improved. A traditional, and more passive measure for improving energy performance of buildings is to implement energy conservation measures such as more insulation to exterior. The Danish government aims at a reduction in energy consumption in new buildings in 2020 by 75% relative to 2006 levels. By 2050 the energy consumption should be reduced by 50% in existing buildings (Government 2009). There is a need to detect those faults early so their impact on energy consumption will be minimized

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Conclusion