Load Monitoring Solutions for the Smart Buildings – An Overview

Argatu, F.C.; Argatu, Violeta; Enache, B.A.; Cepisca, C.; Seritan, G.C.; Andrei, P.C.

doi:10.1515/sbeef-2017-0026

Abstract

The development of intelligent smart grids requires new electrical monitoring solutions in order to optimize power consumption. The implementation of intelligent energy-monitors leads to new technical developments to optimize consumption using intrusive and non-intrusive techniques. This paper makes a review of the main methods used for load monitoring systems, indicating their advantages and disadvantages. The emergence of high-harmonic consumer electronics in electrical networks such as LED lighting sources implies the choice of the appropriate method and equipment for optimizing energy consumption based on energy signatures.

References

[1] C. Fischer.Feedback on household electricity consumption: a tool for saving energy. Energy Effic: vol. 1, no. 1, pp. 79–104, 2008.
Search in Google Scholar Back to article
[2] S. Barker, S. Kalra, and D. Irwin. PowerPlay : Creating Virtual Power Meters through Online Load Tracking. 1st ACM Int. Conf. Embed. Syst. Energy-Efficient Build., pp. 60–69, 2014.10.1145/2674061.2674068
Search in Google Scholar Back to article
[3] M. Temneanu and A.S. Ardeleanu. Hardware and software architecture of a smart meter based on electrical signature analysis, 8th International Symposium on Advanced Topics in Electrical Engineering (ATEE), pp.1-6, 2013.10.1109/ATEE.2013.6563499
Search in Google Scholar Back to article
[4] M. Temneanu and A. Ardeleanu. Non-intrusive Hybrid Energy Monitoring System. Advanced Materials Research, vol. 837, pp. 495-499, 2014.10.4028/www.scientific.net/AMR.837.495
Search in Google Scholar Back to article
[5] G. Seritan, I.Triştiu, O.Ceaki and T.Boboc. Power quality assessment for microgrid scenarios. Electrical and Power Engineering (EPE), 2016 International Conference and Exposition, pp. 723-727, 2016.10.1109/ICEPE.2016.7781434
Search in Google Scholar Back to article
[6] S. Darby. The Effectiveness of Feedback on Energy Consumption a Review for Defra of the Literature on Metering, Billing. Environ. Chang. Inst. Univ. Oxford, vol. 22, no. April, pp. 1–21, 2006.
Search in Google Scholar Back to article
[7] A. K. Ehrhardt-Martinez and K. Donnelly. Advanced Metering Initiatives and Residential Feedback Programs : A Meta-Review for Household Electricity-Saving Opportunities. Energy, vol. 123, no. 6, p. 128, 2010.
Search in Google Scholar Back to article
[8] K. D. Lee, S. B. Leeb, L. K. Norford, P. R. Armstrong, J. Holloway, and S. R. Shaw. Estimation of variable-speed-drive power consumption from harmonic content. IEEE Trans. Energy Convers., vol. 20, no. 3, pp. 566–574, 2005.10.1109/TEC.2005.852963
Search in Google Scholar Back to article
[9] W. Wichakool, A. T. Avestruz, R. W. Cox, and S. B. Leeb. Modelling and estimating current harmonics of variable electronic loads. IEEE Trans. Power Electron., vol. 24, no. 12, pp. 2803–2811, 2009.
Search in Google Scholar Back to article
[10] Z. Wang and G. Zheng. Residential appliances identification and monitoring by a nonintrusive method. IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 80–92, 2012.10.1109/TSG.2011.2163950
Search in Google Scholar Back to article
[11] Y. K. Cho and M. Gai. Projection-Recognition-Projection Method for Automatic Object Recognition and Registration for Dynamic Heavy Equipment Operations. J. Comput. Civ. Eng., vol. 28, no. 5, pp. 1–9, 2013.10.1061/(ASCE)CP.1943-5487.0000332
Search in Google Scholar Back to article
[12] A. Zoha, A. Gluhak, M. A. Imran, and S. Rajasegarar. Non-Intrusive Load Monitoring Approaches for Disaggregated Energy Sensing: A Survey. no. 11, pp. 16838–16866, 2012.
Search in Google Scholar Back to article
[13] M. Zeifman and K. Roth. Nonintrusive appliance load monitoring: Review and outlook. IEEE Trans. Consum. Electron., vol. 57, no. 1, pp. 76–84, 2011.10.1109/TCE.2011.5735484
Search in Google Scholar Back to article
[14] M. Baranski and J. Voss. Nonintrusive appliance load monitoring based on an optical sensor. in 2003 IEEE Bologna Power Tech Conference Proceedings, 2003, vol. 4, p. 8 pp. Vol.4.
Search in Google Scholar Back to article
[15] G. W. Hart. Nonintrusive appliance load monitoring, Proc. IEEE, vol. 80, no. 12, pp. 1870–1891, 1992.10.1109/5.192069
Search in Google Scholar Back to article
[16] C. Klemenjak and P. Goldsborough. Non-Intrusive Load Monitoring: A Review and Outlook. 2016.
Search in Google Scholar Back to article
[17] W. K. Lee, G. S. K. Fung, H. Y. Lam, F. H. Y. Chan, and M. Lucente. Exploration on Load Signatures. Electr. Eng., no. 725, p. 5, 2004.
Search in Google Scholar Back to article
[18] F. Sultanem. Using appliance signatures for monitoring residential loads at meter panel level. IEEE Trans. Power Deliv., vol. 6, no. 4, pp. 1380–1385, 1991.10.1109/61.97667
Search in Google Scholar Back to article
[19] J. Liang, S. K. K. Ng, G. Kendall, and J. W. M. Cheng. Load Signature Study — Part I : Basic Concept, Structure, and Methodology. vol. 25, no. 2, pp. 551–560, 2010.10.1109/TPWRD.2009.2033799
Search in Google Scholar Back to article
[20] P. Electronics and S. Conference. A Taxonomy of Load Signatures for Single-Phase Electric Appliances. System, vol. 1, no. July 2004, pp. 24–29, 2005.
Search in Google Scholar Back to article
[21] H. Lam, K. Ting, and W. Lee. Building a vector-based load taxonomy using electrical load signatures. Conf. Electr., no. June, 2005.
Search in Google Scholar Back to article
[22] H. Lam, G. Fung, and W. Lee. A Novel Method to Construct Taxonomy Electrical Appliances Based on Load Signatures. IEEE Trans. Consum. Electron., vol. 53, no. 2, pp. 653–660, 2007.10.1109/TCE.2007.381742
Search in Google Scholar Back to article
[23] H. Najmeddine et al. State of art on load monitoring methods, Proc. 2nd IEEE Int. Conf. Power Energy (PECon 2008), no. PECon 08, pp. 1256–1258, 2008.10.1109/PECON.2008.4762669
Search in Google Scholar Back to article
[24] J. Paris. A Framework for Non-Intrusive Load Monitoring and Diagnostics. Archives, p. 260, 2006.
Search in Google Scholar Back to article
[25] M. Baranski and J. Voss. Detecting patterns of appliances from total load data using a dynamic programming approach, Proc. - Fourth IEEE Int. Conf. Data Mining, ICDM 2004, pp. 327–330, 2004.
Search in Google Scholar Back to article
[26] A. Schoofs, A. Guerrieri, D. T. Delaney, G. M. P. O’Hare, and A. G. Ruzzelli. ANNOT: Automated Electricity Data Annotation Using Wireless Sensor Networks, in 2010 7th Annual IEEE Communications Society Conference on Sensor, Mesh and Ad Hoc Communications and Networks (SECON), 2010, pp. 1–9.10.1109/SECON.2010.5508248
Search in Google Scholar Back to article
[27] S. Park, H. Kim, H. Moon, J. Heo, and S. Yoon. Concurrent simulation platform for energy-aware smart metering systems. IEEE Trans. Consum. Electron., vol. 56, no. 3, pp. 1918–1926, 2010.
Search in Google Scholar Back to article
[28] T. Kato, H. S. Cho, D. Lee, T. Toyomura, and T. Yamazaki. Appliance recognition from electric current signals for information-energy integrated network in home environments. Lect. Notes Comput. Sci., vol. 5597 LNCS, pp. 150–157, 2009.
Search in Google Scholar Back to article
[29] M. E. Berges, E. Goldman, H. S. Matthews, and L. Soibelman. Enhancing electricity audits in residential buildings with nonintrusive load monitoring. J. Ind. Ecol., vol. 14, no. 5, pp. 844–858, 2010.10.1111/j.1530-9290.2010.00280.x
Search in Google Scholar Back to article
[30] L. Farinaccio and R. Zmeureanu. Using a pattern recognition approach to disaggregate the total electricity consumption in a house into the major end-uses. Energy Build., vol. 30, no. 3, pp. 245–259, Aug. 1999.10.1016/S0378-7788(99)00007-9
Search in Google Scholar Back to article
[31] M. T. Fitta. Load Classification and Appliance Fingerprinting for Residential Load Monitoring System. English, p. 100, 2010.
Search in Google Scholar Back to article
[32] C. Cepisca, S. Ganatsios, H.Andrei, S.D. Grigorescu, and N. Taousanidis, Methods for power measurements in energy meters, The Scientific Bulletin of Electrical Engineering Faculty, 2008, year 8, no. 2, pp. 7-13.
Search in Google Scholar Back to article
[33] C. Laughman et al. Power signature analysis. IEEE Power Energy Mag., vol. 1, no. 2, pp. 56–63, 2003.10.1109/MPAE.2003.1192027
Search in Google Scholar Back to article
[34] M. Berges, E. Goldman, H. S. Matthews, and L. Soibelman. Training Load Monitoring Algorithms on Highly Sub-Metered Home Electricity Consumption Data. Tsinghua Sci. Technol., vol. 13, no. SUPPL. 1, pp. 406–411, 2008.10.1016/S1007-0214(08)70182-2
Search in Google Scholar Back to article
[35] M. Hazas, A. Friday, and J. Scott. Look back before leaping forward: Four decades of domestic energy inquiry. IEEE Pervasive Comput., vol. 10, no. 1, pp. 13–19, 2011.10.1109/MPRV.2010.89
Search in Google Scholar Back to article
[36] D. D. Sabin, D. L. Brooks, and A. Sundaram. Indices for assessing harmonic distortion from power quality measurements: definitions and benchmark data. IEEE Trans. Power Deliv., vol. 14, no. 2, pp. 489–496, 1999.10.1109/61.754093
Search in Google Scholar Back to article
[37] D. Srinivasan, W. S. Ng, and A. C. Liew. Neural-network-based signature recognition for harmonic source identification. IEEE Trans. Power Deliv., vol. 21, no. 1, pp. 398–405, 2006.10.1109/TPWRD.2005.852370
Search in Google Scholar Back to article
[38] J. Z. Kolter and M. J. Johnson. REDD : A Public Data Set for Energy Disaggregation Research. SustKDD Work., vol. xxxxx, no. 1, pp. 1–6, 2011.
Search in Google Scholar Back to article
[39] S. Gupta, M. S. Reynolds, and S. N. Patel. ElectriSense: single-point sensing using EMI for electrical event detection and classification in the home. Proc. 12th ACM Int. Conf. Ubiquitous Comput., pp. 139–148, 2010.10.1145/1864349.1864375
Search in Google Scholar Back to article
[40] J. Z. Kolter, S. Batra, and A. Y. Ng. Energy disaggregation via discriminative sparse coding. Adv. Neural Inf. Process. Syst., pp. 1153–1161, 2010.
Search in Google Scholar Back to article
[41] O.M. Ghita, S.D. Grigorescu, H. Andrei, and N. Calin, Solution for inspection of Power Energy Equipment Using Augmented Reality, The Scientific Bulletin of Electrical Engineering Faculty, 2017, no. 1, https://doi.org/10.1515/sbeef-2016-0007.10.1515/sbeef-2016-0007
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Load Monitoring Solutions for the Smart Buildings – An Overview

Abstract

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