Abstract
Finding solutions for the current period of climate change or “global warming” is possibly the most serious and pressing challenge faced by scientists and the wider community today. Although governments are beginning to act, a community wide approach is needed with a large proportion of individuals engaging to reduce energy consumption that depends on fossil fuels. The Passive House (or Passivhaus) standard is an ultra-low energy standard for building construction and design that aims at dramatically reducing energy consumption in the home. While appropriate for new builds, this standard may be difficult to achieve with existing buildings. In this work, Wireless Sensor Network (WSN) technology is examined as an enabling tool to support rapid progression to improved energy efficiency and increased comfort for existing buildings. As with participatory urban sensing, the home occupant could, in the future, take on the role of scientist; developing an awareness of trouble spots in the house would allow them to target problems thus reducing the need for heating and improving comfort. The paper reports on experiences and findings from several residential and commercial environmental monitoring WSN deployments using a WSN developed from off the shelf components. The sensors deployed measure temperature, relative humidity, CO2 concentration and light. Depending on the size and layout of the space to be monitored, added to the scope of deployment, between 12 and 20 nodes were deployed and the monitoring period was 7-14 days per location. The paper illustrates the value of using WSN technologies as enablers for the amateur eco-home scientist on the path towards reduced energy consumption and increased comfort. It evaluates the suitability of the system for both commercial and residential deployments and shows how large quantities of data can be reduced to meaningful high level information delivered to the user.
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Daniel, T., Gaura, E., Brusey, J. (2009). Wireless Sensor Networks to Enable the Passive House - Deployment Experiences. In: Barnaghi, P., Moessner, K., Presser, M., Meissner, S. (eds) Smart Sensing and Context. EuroSSC 2009. Lecture Notes in Computer Science, vol 5741. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-04471-7_14
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DOI: https://doi.org/10.1007/978-3-642-04471-7_14
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