Feature Identification for Non-Intrusively Extracting Occupant Energy-Use Information in Office Buildings

Hamed Nabizadeh Rafsanjani (School of Environmental, Civil, Agricultural and Mechanical Engineering, University of Georgia, Athens, GA 30602, USA.)

Abstract


Detailed energy-use information of office buildings’ occupants is necessary to implement proper simulation/intervention techniques. However, acquiring accurate occupant-specific energy consumption in office buildings at low cost is currently a challenging task since existing intrusive load monitoring (ILM) technologies require a large capital investment to provide high-resolution electricity usage data for individual occupants. On the other hand, non-intrusive load monitoring (NILM) approaches have been proven as more cost effective and flexible approaches to provide energy-use information of individual appliances. Therefore, extending the concept of NILM to individual occupants would be beneficial. This paper proposes two occupancy-related energy-consuming features, delay interval and magnitude of power changes and evaluates their significances for extracting occupant-specific power changes in a non-intrusive manner. The proposed features were examined through implementing a logistic regression model as a predictor on aggregate energy load data collected from an office building. Hypotheses tests also confirmed that both features are statistically significant to non-intrusively derive individual occupants’ energy-use information. As the main contribution of this study, these features could be utilized in developing sophisticated NILM-based approaches to monitor individual occupant energy-consuming behavior.  


Keywords


Occupant Energy Consumption; Occupant Energy-Use Features; Feature Extraction; Non-Intrusive Load Monitoring; Office Buildings

Full Text:

PDF

References


[1] Godfried Augenbroe, Daniel Castro, Karthik Ramkrishnan, Decision model for energy performance improvements in existing buildings, J. Eng. Des. Technol. 7 (2009) 21–36. doi:10.1108/17260530910947240.

[2] J. Chen, C. Ahn, Assessing occupants’ energy load variation through existing wireless network infrastructure in commercial and educational buildings, Energy Build. 82 (2014) 540–549. doi:10.1016/j.enbuild.2014.07.053.

[3] A. Ghahramani, C. Tang, B. Becerik-Gerber, An online learning approach for quantifying personalized thermal comfort via adaptive stochastic modeling, Build. Environ. 92 (2015) 86–96. doi:10.1016/j.buildenv.2015.04.017.

[4] H.N. Rafsanjani, Factors Influencing the Energy Consumption of Residential Buildings: A Review, in: Constr. Res. Congr. 2016, American Society of Civil Engineers, 2016: pp. 1133–1142. http://ascelibrary.org/doi/abs/10.1061/9780784479827.114 (accessed May 25, 2016).

[5] C. Clevenger, J. Haymaker, M. Jalili, Demonstrating the Impact of the Occupant on Building Performance, J. Comput. Civ. Eng. 28 (2014) 99–102. doi:10.1061/(ASCE)CP.1943-5487.0000323.

[6] K. Anderson, S. Lee, C. Menassa, Impact of Social Network Type and Structure on Modeling Normative Energy Use Behavior Interventions, J. Comput. Civ. Eng. 28 (2014) 30–39. doi:10.1061/(ASCE)CP.1943-5487.0000314.

[7] A. Ghahramani, F. Jazizadeh, B. Becerik-Gerber, A knowledge based approach for selecting energy-aware and comfort-driven HVAC temperature set points, Energy Build. 85 (2014) 536–548. doi:10.1016/j.enbuild.2014.09.055.

[8] E. Azar, C. Menassa, Framework to Evaluate Energy-Saving Potential from Occupancy Interventions in Typical Commercial Buildings in the United States, J. Comput. Civ. Eng. 28 (2014) 63–78. doi:10.1061/(ASCE)CP.1943-5487.0000318.

[9] H.N. Rafsanjani, C.R. Ahn, M. Alahmad, A Review of Approaches for Sensing, Understanding, and Improving Occupancy-Related Energy-Use Behaviors in Commercial Buildings, Energies. 8 (2015) 10996–11029. doi:10.3390/en81010996.

[10] N. Murtagh, M. Nati, W.R. Headley, B. Gatersleben, A. Gluhak, M.A. Imran, D. Uzzell, Individual energy use and feedback in an office setting: A field trial, Energy Policy. 62 (2013) 717–728. doi:10.1016/j.enpol.2013.07.090.

[11] H. Staats, E. van Leeuwen, A. Wit, A longitudinal study of informational interventions to save energy in an office building., J. Appl. Behav. Anal. 33 (2000) 101–104. doi:10.1901/jaba.2000.33-101.

[12] A. Khosrowpour, R. Gulbinas, J.E. Taylor, Occupant Workstation Level Energy-use Prediction in Commercial Buildings: Developing and Assessing a New Method to Enable Targeted Energy Efficiency Programs, Energy Build. (n.d.). doi:10.1016/j.enbuild.2016.05.071.

[13] A. Zoha, A. Gluhak, M.A. Imran, S. Rajasegarar, Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey, Sensors. 12 (2012) 16838–16866. doi:10.3390/s121216838.

[14] R. Gulbinas, J.E. Taylor, Effects of real-time eco-feedback and organizational network dynamics on energy efficient behavior in commercial buildings, Energy Build. 84 (2014) 493–500. doi:10.1016/j.enbuild.2014.08.017.

[15] M. Zeifman, K. Roth, Nonintrusive appliance load monitoring: Review and outlook, IEEE Trans. Consum. Electron. 57 (2011) 76–84. doi:10.1109/TCE.2011.5735484.

[16] G.W. Hart, Nonintrusive appliance load monitoring, Proc. IEEE. 80 (1992) 1870–1891. doi:10.1109/5.192069.

[17] L.K. Norford, S.B. Leeb, Non-intrusive electrical load monitoring in commercial buildings based on steady-state and transient load-detection algorithms, Energy Build. 24 (1996) 51–64. doi:10.1016/0378-7788(95)00958-2.

[18] N. Batra, O. Parson, M. Berges, A. Singh, A. Rogers, A comparison of non-intrusive load monitoring methods for commercial and residential buildings, in: 2014.

[19] H.N. Rafsanjani, C. Ahn, M. Alahmad, Development of Non-Intrusive Occupant Load Monitoring (NIOLM) in Commercial Buildings: Assessing Occupants’ Energy-Use Behavior at Entry and Departure Events, in: First Int. Symp. Sustain. Hum.-Build. Ecosyst. ISSHBE, American Society of Civil Engineers, Pittsburgh, PA, 2015: pp. 44–53.

[20] R. Gulbinas, A. Khosrowpour, J. Taylor, Segmentation and Classification of Commercial Building Occupants by Energy-Use Efficiency and Predictability, IEEE Trans. Smart Grid. PP (2015) 1–1. doi:10.1109/TSG.2014.2384997.

[21] H.N. Rafsanjani, C. Ahn, Linking Building Energy-Load Variations with Occupants’ Energy-Use Behaviors in Commercial Buildings: Non-Intrusive Occupant Load Monitoring (NIOLM), Procedia Eng. 145 (2016) 532–539. doi:10.1016/j.proeng.2016.04.041.

[22] Measurement science roadmap for net-zero energy buildings workshop summary report, (2010).

[23] H.N. Rafsanjani, C.R. Ahn, J. Chen, Linking Building Energy Consumption with Occupants’ Energy-Consuming Behaviors in Commercial Buildings: Non-Intrusive Occupant Load Monitoring (NIOLM), Energy Build. 172 (2018) 317–327. doi:10.1016/j.enbuild.2018.05.007.

[24] D. Chen, S. Barker, A. Subbaswamy, D. Irwin, P. Shenoy, Non-Intrusive Occupancy Monitoring Using Smart Meters, in: Proc. 5th ACM Workshop Embed. Syst. Energy-Effic. Build., ACM, New York, NY, USA, 2013: pp. 9:1–9:8. doi:10.1145/2528282.2528294.

[25] W. Kleiminger, C. Beckel, T. Staake, S. Santini, Occupancy Detection from Electricity Consumption Data, in: Proc. 5th ACM Workshop Embed. Syst. Energy-Effic. Build., ACM, New York, NY, USA, 2013: pp. 10:1–10:8. doi:10.1145/2528282.2528295.

[26] W. Kleiminger, C. Beckel, S. Santini, Household Occupancy Monitoring Using Electricity Meters, in: Proc. 2015 ACM Int. Jt. Conf. Pervasive Ubiquitous Comput., ACM, New York, NY, USA, 2015: pp. 975–986. doi:10.1145/2750858.2807538.

[27] O. Ardakanian, A. Bhattacharya, D. Culler, Non-Intrusive Techniques for Establishing Occupancy Related Energy Savings in Commercial Buildings, in: Proc. 3rd ACM Int. Conf. Syst. Energy-Effic. Built Environ., ACM, New York, NY, USA, 2016: pp. 21–30. doi:10.1145/2993422.2993574.

[28] Y.F. Wong, Y.A. Şekercioğlu, T. Drummond, V.S. Wong, Recent approaches to non-intrusive load monitoring techniques in residential settings, in: 2013 IEEE Comput. Intell. Appl. Smart Grid CIASG, 2013: pp. 73–79. doi:10.1109/CIASG.2013.6611501.

[29] Characteristics and Performance of Existing Load Disaggregation Technologies, United States. Dept. of Energy. ;, Washington, D.C. :, 2015.

[30] R. Bonfigli, S. Squartini, M. Fagiani, F. Piazza, Unsupervised algorithms for non-intrusive load monitoring: An up-to-date overview, in: 2015 IEEE 15th Int. Conf. Environ. Electr. Eng. EEEIC, 2015: pp. 1175–1180. doi:10.1109/EEEIC.2015.7165334.

[31] M.B. Figueiredo, A. de Almeida, B. Ribeiro, An Experimental Study on Electrical Signature Identification of Non-Intrusive Load Monitoring (NILM) Systems, in: Adapt. Nat. Comput. Algorithms, Springer, Berlin, Heidelberg, 2011: pp. 31–40. doi:10.1007/978-3-642-20267-4_4.

[32] H.-H. Chang, C.-L. Lin, H.-T. Yang, Load recognition for different loads with the same real power and reactive power in a non-intrusive load-monitoring system, in: 2008 12th Int. Conf. Comput. Support. Coop. Work Des., 2008: pp. 1122–1127. doi:10.1109/CSCWD.2008.4537137.

[33] A. Shrestha, E.L. Foulks, R.W. Cox, Dynamic load shedding for shipboard power systems using the non-intrusive load monitor, in: 2009 IEEE Electr. Ship Technol. Symp., 2009: pp. 412–419. doi:10.1109/ESTS.2009.4906545.

[34] L. Farinaccio, R. Zmeureanu, Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses, Energy Build. 30 (1999) 245–259. doi:10.1016/S0378-7788(99)00007-9.

[35] M.L. Marceau, R. Zmeureanu, Nonintrusive load disaggregation computer program to estimate the energy consumption of major end uses in residential buildings, Energy Convers. Manag. 41 (2000) 1389–1403. doi:10.1016/S0196-8904(99)00173-9.

[36] A.J. Bijker, X. Xia, J. Zhang, Active power residential non-intrusive appliance load monitoring system, in: AFRICON 2009, 2009: pp. 1–6. doi:10.1109/AFRCON.2009.5308244.

[37] H.H. Chang, K.L. Chen, Y.P. Tsai, W.J. Lee, A New Measurement Method for Power Signatures of Nonintrusive Demand Monitoring and Load Identification, IEEE Trans. Ind. Appl. 48 (2012) 764–771. doi:10.1109/TIA.2011.2180497.

[38] H.H. Chang, C.L. Lin, J.K. Lee, Load identification in nonintrusive load monitoring using steady-state and turn-on transient energy algorithms, in: 2010 14th Int. Conf. Comput. Support. Coop. Work Des., 2010: pp. 27–32. doi:10.1109/CSCWD.2010.5472008.

[39] H.H. Chang, L.S. Lin, N. Chen, W.J. Lee, Particle Swarm Optimization based non-intrusive demand monitoring and load identification in smart meters, in: 2012 IEEE Ind. Appl. Soc. Annu. Meet., 2012: pp. 1–8. doi:10.1109/IAS.2012.6373990.

[40] S. Drenker, A. Kader, Nonintrusive monitoring of electric loads, IEEE Comput. Appl. Power. 12 (1999) 47–51. doi:10.1109/67.795138.

[41] A. Cole, A. Albicki, Nonintrusive identification of electrical loads in a three-phase environment based on harmonic content, in: IEEE, 2000.

[42] J. Liang, S.K.K. Ng, G. Kendall, J.W.M. Cheng, Load Signature Study #x2014;Part I: Basic Concept, Structure, and Methodology, IEEE Trans. Power Deliv. 25 (2010) 551–560. doi:10.1109/TPWRD.2009.2033799.

[43] A.G. Ruzzelli, C. Nicolas, A. Schoofs, G.M.P. O’Hare, Real-Time Recognition and Profiling of Appliances through a Single Electricity Sensor, in: 2010 7th Annu. IEEE Commun. Soc. Conf. Sens. Mesh Ad Hoc Commun. Netw. SECON, 2010: pp. 1–9. doi:10.1109/SECON.2010.5508244.

[44] W.K. Lee, G.S.K. Fung, H.Y. Lam, F.H.Y. Chan, M. Lucente, Exploration on Load Signatures Abstract, (n.d.). http://citeseerx.ist.psu.edu/viewdoc/citations;jsessionid=93C520073BC7C992B8FF8B8A41880153?doi=10.1.1.120.5328 (accessed February 20, 2017).

[45] H.Y. Lam, G.S.K. Fung, W.K. Lee, A Novel Method to Construct Taxonomy Electrical Appliances Based on Load Signaturesof, IEEE Trans. Consum. Electron. 53 (2007) 653–660. doi:10.1109/TCE.2007.381742.

[46] S. Gupta, ElectriSense: Single-Point Sensing Using EMI for Electrical Event Detection and Classification in the Home, Thesis, 2015. https://digital.lib.washington.edu:443/researchworks/handle/1773/26334 (accessed February 20, 2017).

[47] S.N. Patel, T. Robertson, J.A. Kientz, M.S. Reynolds, G.D. Abowd, At the Flick of a Switch: Detecting and Classifying Unique Electrical Events on the Residential Power Line (Nominated for the Best Paper Award), in: UbiComp 2007 Ubiquitous Comput., Springer, Berlin, Heidelberg, 2007: pp. 271–288. doi:10.1007/978-3-540-74853-3_16.

[48] Y.C. Su, K.L. Lian, H.H. Chang, Feature Selection of Non-intrusive Load Monitoring System Using STFT and Wavelet Transform, in: 2011 IEEE 8th Int. Conf. E-Bus. Eng., 2011: pp. 293–298. doi:10.1109/ICEBE.2011.49.

[49] H.-H. Chang, Non-Intrusive Demand Monitoring and Load Identification for Energy Management Systems Based on Transient Feature Analyses, Energies. 5 (2012) 4569–4589. doi:10.3390/en5114569.

[50] C. Laughman, K. Lee, R. Cox, S. Shaw, S. Leeb, L. Norford, P. Armstrong, Power signature analysis, IEEE Power Energy Mag. 1 (2003) 56–63. doi:10.1109/MPAE.2003.1192027.

[51] H.-H. Chang, H.-T. Yang, C.-L. Lin, Load Identification in Neural Networks for a Non-intrusive Monitoring of Industrial Electrical Loads, in: Comput. Support. Coop. Work Des. IV, Springer, Berlin, Heidelberg, 2007: pp. 664–674. doi:10.1007/978-3-540-92719-8_60.

[52] S.R. Shaw, S.B. Leeb, L.K. Norford, R.W. Cox, Nonintrusive Load Monitoring and Diagnostics in Power Systems, IEEE Trans. Instrum. Meas. 57 (2008) 1445–1454. doi:10.1109/TIM.2008.917179.

[53] H. Kim, M. Marwah, M. Arlitt, G. Lyon, J. Han, Unsupervised Disaggregation of Low Frequency Power Measurements, in: Proc. 2011 SIAM Int. Conf. Data Min., Society for Industrial and Applied Mathematics, 2011: pp. 747–758. doi:10.1137/1.9781611972818.64.

[54] J.T. Powers, B. Margossian, B.A. Smith, Using a rule-based algorithm to disaggregate end-use load profiles from premise-level data, IEEE Comput. Appl. Power. 4 (1991) 42–47. doi:10.1109/67.75875.

[55] A. Albert, R. Rajagopal, Smart Meter Driven Segmentation: What Your Consumption Says About You, IEEE Trans. Power Syst. 28 (2013) 4019–4030. doi:10.1109/TPWRS.2013.2266122.

[56] H.N. Rafsanjani, C. Ahn, K. Eskridge, Understanding the Recurring Patterns of Occupants’ Energy-Use Behaviors at Entry and Departure Events in Office Buildings, Build. Environ. 136 (2018) 77–87.

[57] J.M. Hilbe, Logistic Regression Models, CRC Press, 2009.

[58] J.M. Hilbe, Practical Guide to Logistic Regression, CRC Press, 2016.

[59] D.M. Powers, Evaluation: from Precision, Recall and F-measure to ROC, Informedness, Markedness and Correlation, (2011). http://dspace.flinders.edu.au/xmlui/handle/2328/27165 (accessed September 26, 2016).

[60] Signal Detection Theory and ROC Analysis in Psychology and Diagnostics: Collected Papers - 1996, Page iii by John A. Swets. | Online Research Library: Questia, (n.d.). https://www.questia.com/read/91082370/signal-detection-theory-and-roc-analysis-in-psychology (accessed September 26, 2016).

[61] R.F. Engle, Wald, likelihood ratio, and Lagrange multiplier tests in econometrics, Elsevier, 1984. https://ideas.repec.org/h/eee/ecochp/2-13.html (accessed September 26, 2016).

[62] Z. Yang, N. Li, B. Becerik-Gerber, M. Orosz, A Non-Intrusive Occupancy Monitoring System for Demand Driven HVAC Operations, in: Constr. Res. Congr. 2012, American Society of Civil Engineers, 2012: pp. 828–837.



DOI: https://doi.org/10.30564/jaeser.v1i1.189

Refbacks

  • There are currently no refbacks.
Copyright © 2019 Hamed Nabizadeh Rafsanjani


Creative Commons License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.