TY - JOUR KW - Benchmarking KW - Building energy performance KW - Performance Evaluation KW - Fault detection and diagnosis KW - Algorithm testing KW - Building systems AU - Stephen Frank AU - Guanjing Lin AU - Xin Jin AU - Rupam Singla AU - Amanda Farthing AU - Jessica Granderson AB -

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.

BT - Energy and Buildings DA - 06/2019 DO - 10.1016/j.enbuild.2019.03.024 LA - eng N2 -

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.

PY - 2019 SP - 84 EP - 92 ST - Energy and Buildings T2 - Energy and Buildings TI - A performance evaluation framework for building fault detection and diagnosis algorithms UR - https://linkinghub.elsevier.com/retrieve/pii/S0378778818335680 VL - 192 SN - 03787788 ER -