Open Source Building Science Sensors (OSBSS): A low-cost Arduino-based platform for long-term indoor environmental data collection

Abstract Accurate characterization of parameters that influence indoor environments is often limited to the use of proprietary hardware and software, which can adversely affect costs, flexibility, and data integration. Here we describe the Open Source Building Science Sensors (OSBSS) project, which we created to design and develop a suite of inexpensive, open source devices based on the Arduino platform for measuring and recording long-term indoor environmental and building operational data. The goal of OSBSS is to allow for more flexibility in synchronizing a large number of measurements with high spatial and temporal resolution in a cost effective manner for use in research projects and, eventually, in building automation and control. Detailed tutorials with instructions for constructing the data loggers using off-the-shelf electronic components are made available freely online. The project currently includes a variety of sensors and data loggers designed to measure a number of important parameters in buildings, including air and surface temperatures, air relative humidity, human occupancy, light intensity, CO2 concentrations, and a generic voltage data logger that can log data from a variety of other sensors such as differential pressure sensors. We also describe results from co-location tests with each data logger installed for one week in an educational building alongside their commercial counterparts, which demonstrate excellent performance at substantially lower costs.

[1]  Jaeshin Yi,et al.  Modeling the temperature dependence of the discharge behavior of a lithium-ion battery in low environmental temperature , 2013 .

[2]  Edward Arens,et al.  Indoor environmental quality assessment models: A literature review and a proposed weighting and classification scheme , 2013 .

[3]  Wai Lok Chan,et al.  A distributed sensor network for measurement of human thermal comfort feelings , 2008 .

[4]  Norberto Barroca,et al.  Wireless sensor networks for temperature and humidity monitoring within concrete structures , 2013 .

[5]  Weidong He,et al.  Materials insights into low-temperature performances of lithium-ion batteries , 2015 .

[6]  Olivia Guerra-Santin,et al.  In-use monitoring of buildings: An overview of data collection methods , 2015 .

[7]  Zoltán Nagy,et al.  Balancing envelope and heating system parameters for zero emissions retrofit using building sensor data , 2014 .

[8]  Jack Allan Barnes.  The measurement of linear frequency drift in oscillators , 1985 .

[9]  Manuel Jimenez,et al.  Introduction to Embedded Systems: Using Microcontrollers and the MSP430 , 2013 .

[10]  M. Broussely,et al.  Main aging mechanisms in Li ion batteries , 2005 .

[11]  Damir Ilić,et al.  Temperature measurements by means of NTC resistors and a two-parameter approximation curve , 2008 .

[12]  Brent Stephens,et al.  Spatial and Temporal Variations in Indoor Environmental Conditions, Human Occupancy, and Operational Characteristics in a New Hospital Building , 2015, PloS one.

[13]  Jin Wen,et al.  Simulating the human-building interaction: Development and validation of an agent-based model of office occupant behaviors , 2015 .

[14]  Akram Syed Ali.  Open source building science sensors (OSBSS): A low-cost arduino-based platform for long-term data collection in indoor environments , 2015 .

[15]  Brent Stephens,et al.  Tools to improve built environment data collection for indoor microbial ecology investigations , 2014 .

[16]  Arne Martin Holberg.  Innovative Techniques for Extremely Low Power Consumption with 8-bit Microcontrollers , 2006 .

[17]  Miroslaw J. Skibniewski,et al.  Wireless sensor networks as part of a web-based building environmental monitoring system , 2008 .

[18]  Chee Peng Lim,et al.  A study and a directory of energy consumption data sets of buildings , 2015 .

[19]  Fu Xiao,et al.  Data mining in building automation system for improving building operational performance , 2014 .

[20]  Shengwei Wang,et al.  Experimental Validation of CO2-Based Occupancy Detection for Demand-Controlled Ventilation , 1999 .

[21]  Wen-Tsai Sung,et al.  Intelligent multi-sensor control system based on innovative technology integration via ZigBee and Wi-Fi networks , 2013, J. Netw. Comput. Appl..