.png){kind=icon}
.png){kind=icon}
Return
Wireless sensor network for estimating building performance
.png){kind=icon}
PRE
AI
DOI：10.1016/j.autcon.2019.103043.png)
.png){kind=icon}
Original request for help.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
Accurate building energy assessments are often limited by inaccurate assumptions about the buildings' properties and occupant behavior. Many assessment methods use assumptions instead of measured data because it is cumbersome, time-intensive, and cost-intensive to acquire up-to-date, on-site measured data. This work bridges this gap by presenting the design of an open-source, wireless sensor network (WSN) that is easy to program, manufacture, modify, and deploy. The version of the WSN presented here measures air temperature, relative humidity, supply and return temperature of hot water from the heating system and the radiators, heat flux through the walls and the windows, luminosity, oil flow, electricity, window opening times, and CO2-concentration. The modularity of the WSN allows it to be extended with the addition of new types of sensors to the existing hardware framework. The data is streamed to an online database, allowing monitoring of the measurements in near real-time. The cost and performance of the sensor network are intended to be as good as or better than the existing solutions. The current cost for one temperature sensor node starts at 116 USD. The observed battery lifetime reaches up to a year for a five-minute sampling interval. The average installation time for a sensor node is 7 min. We describe the hardware architecture, software architecture, cost of the WSN, along with results from co-location tests, and a deployment in an occupied building.
Keywords:
Wireless sensor networks
Remote monitoring
Open-source hardware
Sensors
Building performance
Indoor environment
In-situ measurements
Performance gap
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
11.5
Papers:
6.2K
Citations:
4.2W