A performance evaluation framework for building fault detection and diagnosis algorithms

A performance evaluation framework for building fault detection and diagnosis algorithms

TitleA performance evaluation framework for building fault detection and diagnosis algorithms
Publication TypeJournal Article
Year of Publication2019
AuthorsStephen Frank, Guanjing Lin, Xin Jin, Rupam Singla, Amanda Farthing, Jessica Granderson
JournalEnergy and Buildings
Volume192
Pagination84 - 92
Date Published06/2019
ISSN03787788
Keywordsalgorithm testing, benchmarking, building energy performance, building systems, fault detection and diagnosis, Performance Evaluation
Abstract

Fault detection and diagnosis (FDD) algorithms for building systems and equipment represent one of the most active areas of research and commercial product development in the buildings industry. However, far more e↵ort has gone into developing these algorithms than into assessing their performance. As a result, considerable uncertainties remain regarding the accuracy and e↵ectiveness of both research-grade FDD algorithms and commercial products—a state of a↵airs that has hindered the broad adoption of FDD tools. This article presents a general, systematic framework for evaluating the performance of FDD algorithms. The article focuses on understanding the possible answers to two key questions: in the context of FDD algorithm evaluation, what defines a fault and what defines an evaluation input sample? The answers to these questions, together with appropriate performance metrics, may be used to fully specify evaluation procedures for FDD algorithms.

URLhttps://linkinghub.elsevier.com/retrieve/pii/S0378778818335680
DOI10.1016/j.enbuild.2019.03.024
Short TitleEnergy and Buildings