Combined use of nonintrusive monitoring techniques and energy recipes to reduce energy hungry behaviours

The fact that occupant behaviour has a large effect on buildings' energy consumption is well accepted. Occupants affect energy consumption by their behaviours: using lighting, appliances, thermostats etc. and interacting with envelope components such as windows and blinds. During last decades, specific ICT solutions have been developed for addressing behavioural changes toward energy saving and consequently leading to energy conscious occupant behaviours. This paper presents a mean of interaction between sensors and users for tackling behavioural changes toward energy saving in homes. This is based on premade rules and instructions (referred to as recipes), meant for tackling energy hungry everyday life actions. Moreover, it investigates the use of nonintrusive appliance load monitoring (NIALM) system. Among all the available off the shelfs sensors, NIALM seems to be very promising, especially if combined with self-learning algorithms, for detecting energy consumption of electrical appliances. This combination can potentially solve existing constrains: detecting and identifying appliances with very small or continuous electricity consumption or that turn on or turn off slowly. Literature information have been cross checked with home energy consumption monitored data in order to create pre-formulated energy recipes for empowering occupants addressing specific energy hungry behaviours. Besides establishing virtuous occupant behaviours, devoted to a rational and judicious use of energy, the adoption of energy recipes aims to generate critical knowledge about the intrinsic meaning of technical variables.

[1]  Dragan Savic,et al.  A Web-Based Platform for Water Efficient Households , 2014 .

[2]  Barbara Schlomann,et al.  Characterization of the household electricity consumption in the EU, potential energy savings and sp , 2011 .

[3]  Alexander Klapproth,et al.  Improving the Recognition Performance of NIALM Algorithms through Technical Labeling , 2014, 2014 12th IEEE International Conference on Embedded and Ubiquitous Computing.

[4]  Corinna Fischer Feedback on household electricity consumption: a tool for saving energy? , 2008 .

[5]  Seppo Junnila,et al.  Occupants have little influence on the overall energy consumption in district heated apartment build , 2011 .

[6]  Peter Morris,et al.  The Effectiveness of Energy Feedback for Conservation and Peak Demand: A Literature Review , 2013 .

[7]  Sarah C. Darby,et al.  Making it Obvious: Designing Feedback into Energy Consumption , 2001 .

[8]  Sanem Sergici,et al.  The Impact of Informational Feedback on Energy Consumption -- A Survey of the Experimental Evidence , 2009 .

[9]  Riccardo Russo,et al.  The question of energy reduction: The problem(s) with feedback , 2015 .

[10]  S. Karjalainen Consumer preferences for feedback on household electricity consumption , 2011 .

[11]  H. Pihala Non Intrusive Appliance Load Monitoring System Based On A , 1998 .

[12]  C. Vlek,et al.  A review of intervention studies aimed at household energy conservation , 2005 .

[13]  A. Nilsson,et al.  Effects of continuous feedback on households’ electricity consumption: Potentials and barriers , 2014 .

[14]  Muhd Zaimi Abd Majid,et al.  A global review of energy consumption, CO2 emissions and policy in the residential sector (with an overview of the top ten CO2 emitting countries) , 2015 .

[15]  Michael Zeifman,et al.  Disaggregation of home energy display data using probabilistic approach , 2012, IEEE Transactions on Consumer Electronics.

[16]  Jack Kelly,et al.  Neural NILM: Deep Neural Networks Applied to Energy Disaggregation , 2015, BuildSys@SenSys.

[17]  Radu Zmeureanu,et al.  Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses , 1999 .

[18]  Giulio Jacucci,et al.  Supporting the Serendipitous Use of Domestic Technologies , 2016, IEEE Pervasive Computing.

[19]  Milan Z. Bjelica,et al.  Set-top box-based home controller , 2010, IEEE International Symposium on Consumer Electronics (ISCE 2010).

[20]  Tarja Häkkinen,et al.  User engaging practices for energy saving in buildings: Critical review and new enhanced procedure , 2017 .

[21]  Gabrielle Wong-Parodi,et al.  Creating an in-home display: Experimental evidence and guidelines for design , 2013 .

[22]  G.W. Hart,et al.  Residential energy monitoring and computerized surveillance via utility power flows , 1989, IEEE Technology and Society Magazine.

[23]  Xiaowei Feng,et al.  Nonintrusive appliance load monitoring for smart homes: recent advances and future issues , 2016, IEEE Instrumentation & Measurement Magazine.