TY  - JOUR
AB  - Purpose The purpose of this paper is to propose a simple, fast, and effective method for detecting measurement errors in data collected with low-cost environmental sensors typically used in building monitoring, evaluation, and automation applications.Design/methodology/approach The method combines two unsupervised learning techniques: a distance-based anomaly detection algorithm analyzing temporal patterns in data, and a density-based algorithm comparing data across different spatially related sensors.Findings Results of tests using 60,000 observations of temperature and humidity collected from 20 sensors during three weeks show that the method effectively identified measurement errors and was not affected by valid unusual events. Precision, recall, and accuracy were 0.999 or higher for all cases tested.Originality/value The method is simple to implement, computationally inexpensive, and fast enough to be used in real-time with modest open-source microprocessors and a wide variety of environmental sensors. It is a robust and convenient approach for overcoming the hardware constraints of low-cost sensors, allowing users to improve the quality of collected data at almost no additional cost and effort.
VL  - 8
IS  - 4
SN  - 2046-6099
DO  - 10.1108/SASBE-10-2018-0051
UR  - https://doi.org/10.1108/SASBE-10-2018-0051
AU  - Loyola Mauricio
PY  - 2019
Y1  - 2019/01/01
TI  - A method for real-time error detection in low-cost environmental sensors data
T2  - Smart and Sustainable Built Environment
PB  - Emerald Publishing Limited
SP  - 338
EP  - 350
Y2  - 2024/04/19
ER  -