Non-intrusive estimation and prediction of residential AC energy consumption

Residential buildings account for a significant proportion of overall energy consumption across the world. Decentralized room level Air Conditioners (ACs) are a commonplace in developing countries such as India, contributing a major share (34% in India) of the total residential energy consumption. Option to independently control each AC presents a prime opportunity for an energy saving system. Thus, we propose PACMAN to non-intrusively (using only the temperature information) predict AC energy consumption prior to usage and estimate energy consumption post-usage. We discuss various possible applications and use cases of such feedback for the occupants. To empirically validate the performance of PACMAN, we conducted an in-situ study across seven homes in Delhi (India). We collected around 2200 hours of usage data from the different ACs, room types, and thermostat temperatures. We achieved an average accuracy of 85.3% and 83.7% with the best accuracy of 97.0% and 93.3% for the estimation and prediction of AC energy consumption respectively, across all homes. Towards the end, we discuss various outlier scenarios, opening up multiple directions for further research in this domain.

[1]  Nicholas R. Jennings,et al.  A Scalable Low-Cost Solution to Provide Personalised Home Heating Advice to Households , 2013, BuildSys@SenSys.

[2]  G. W. Hart,et al.  Nonintrusive appliance load monitoring , 1992, Proc. IEEE.

[3]  Nirmalya Roy,et al.  Acoustic based appliance state identifications for fine-grained energy analytics , 2015, 2015 IEEE International Conference on Pervasive Computing and Communications (PerCom).

[4]  Pushpendra Singh,et al.  Experiences with Occupancy based Building Management Systems , 2013, 2013 IEEE Eighth International Conference on Intelligent Sensors, Sensor Networks and Information Processing.

[5]  Thomas Weng,et al.  Managing plug-loads for demand response within buildings , 2011, BuildSys '11.

[6]  Haimonti Dutta,et al.  NILMTK v0.2: a non-intrusive load monitoring toolkit for large scale data sets: demo abstract , 2014, BuildSys@SenSys.

[7]  Haimonti Dutta,et al.  NILMTK: an open source toolkit for non-intrusive load monitoring , 2014, e-Energy.

[8]  Amarjeet Singh,et al.  Energy Usage Attitudes of Urban India , 2014, ICT4S.

[9]  Gaël Varoquaux,et al.  Scikit-learn: Machine Learning in Python , 2011, J. Mach. Learn. Res..

[10]  Mark W. Newman,et al.  Learning from a learning thermostat: lessons for intelligent systems for the home , 2013, UbiComp.

[11]  Kamin Whitehouse,et al.  If You Measure It, Can You Improve It? Exploring The Value of Energy Disaggregation , 2015, BuildSys@SenSys.

[12]  Mark W. Newman,et al.  The potential and challenges of inferring thermal comfort at home using commodity sensors , 2015, UbiComp.

[13]  Mani B. Srivastava,et al.  It's Different: Insights into home energy consumption in India , 2013, BuildSys@SenSys.

[14]  R. N. Elliott,et al.  American Council for an Energy-Efficient Economy , 2002 .

[15]  Anind K. Dey,et al.  TherML: occupancy prediction for thermostat control , 2013, UbiComp.

[16]  Kamin Whitehouse,et al.  The smart thermostat: using occupancy sensors to save energy in homes , 2010, SenSys '10.

[17]  Amarjeet Singh,et al.  An in depth study into using EMI signatures for appliance identification , 2014, BuildSys@SenSys.

[18]  Srinivasan Keshav,et al.  Optimal Personal Comfort Management Using SPOT+ , 2013, BuildSys@SenSys.

[19]  Nicholas R. Jennings,et al.  Adaptive home heating control through Gaussian process prediction and mathematical programming , 2011 .

[20]  John Krumm,et al.  PreHeat: controlling home heating using occupancy prediction , 2011, UbiComp '11.

[21]  P. O. Fanger,et al.  Thermal comfort: analysis and applications in environmental engineering, , 1972 .

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

[23]  Nicholas R. Jennings,et al.  A scalable low-cost solution to provide personalized home heating advice to households , 2012, BuildSys '12.