Residential Electrical Load Monitoring and Modeling – State of the Art and Future Trends for Smart Homes and Grids

References

• P. H. Shaikh, N. B. M. Nor, P. Nallagownden, I. Elamvazuthi and T. Ibrahim, “A review on optimized control systems for building energy and comfort management of smart sustainable buildings,” Renew. Sust. Energ. Rev., vol. 34, pp. 409–429, 2014. DOI: 10.1016/j.rser.2014.03.027.
   Web of Science ®Google Scholar
• K. C. Armel, A. Gupta, G. Shrimali and A. Albert, “Is disaggregation the holy grail of energy efficiency? the case of electricity,” Energy Policy, vol. 52, pp. 213–234, 2013. DOI: 10.1016/j.enpol.2012.08.062.
   Web of Science ®Google Scholar
• Y. Wang, Q. Chen, T. Hong and C. Kang, “Review of smart meter data analytics: Applications, methodologies, and challenges,” IEEE Trans. Smart Grid, vol. 10, no. 3, pp. 3125–3148, May 2019. DOI: 10.1109/TSG.2018.2818167.
   Web of Science ®Google Scholar
• R. Alden, P. Han and D. Ionel, “Smart plug and circuit breaker technologies for residential buildings in the US,” 8th International Conference on Renewable Energy Research and Applications (ICRERA), 2019, pp. 1018–1021.
   Google Scholar
• S. Giri, M. Bergés and A. Rowe, “Towards automated appliance recognition using an EMF sensor in NILM platforms,” Adv. Eng. Inform., vol. 27, no. 4, pp. 477–485, 2013. DOI: 10.1016/j.aei.2013.03.004.
   Web of Science ®Google Scholar
• A. Filip, “Blued: A fully labeled public dataset for event-based nonintrusive load monitoring research,” Proc. Workshop Data Mining Appl. Sustain. (SustKDD), 2011, p. 2012.
   Google Scholar
• L. M. L. Oliveira, J. Reis, J. J. P. C. Rodrigues and A. F. de Sousa, “IoT based solution for home power energy monitoring and actuating,” Proc. Int. Conf. Ind. Inform. (INDIN), July 2015, pp. 988–992.
   Google Scholar
• S. Ahmadi-Karvigh, A. Ghahramani, B. Becerik-Gerber and L. Soibelman, “Real-time activity recognition for energy efficiency in buildings,” Appl. Energy, vol. 211, pp. 146–160, 2018. DOI: 10.1016/j.apenergy.2017.11.055.
   Web of Science ®Google Scholar
• J. A. Oliveira-Lima, R. Morais, J. Martins, A. Florea and C. Lima, “Load forecast on intelligent buildings based on temporary occupancy monitoring,” Energ. Buildings, vol. 116, pp. 512–521, 2016. DOI: 10.1016/j.enbuild.2016.01.028.
   Web of Science ®Google Scholar
• R. Brown, N. Ghavami, H.-U.-R. Siddiqui, M. Adjrad, M. Ghavami and S. Dudley, “Occupancy based household energy disaggregation using ultra wideband radar and electrical signature profiles,” Energ. Buildings, vol. 141, pp. 134–141, 2017. DOI: 10.1016/j.enbuild.2017.02.004.
   Web of Science ®Google Scholar
• O. Ardakanian, A. Bhattacharya and D. Culler, “Non-intrusive occupancy monitoring for energy conservation in commercial buildings,” Energ. Buildings, vol. 179, pp. 311–323, 2018. DOI: 10.1016/j.enbuild.2018.09.033.
   Web of Science ®Google Scholar
• H. Zou, H. Jiang, J. Yang, L. Xie and C. Spanos, “Non-intrusive occupancy sensing in commercial buildings,” Energ. Buildings, vol. 154, pp. 633–643, 2017. DOI: 10.1016/j.enbuild.2017.08.045.
   Web of Science ®Google Scholar
• Sense, “Sense Home Energy Monitor.” [Online]. Available: https://sense.com/
   Google Scholar
• D. Balsamo, G. Gallo, D. Brunelli and L. Benini, “Non-intrusive Zigbee power meter for load monitoring in smart buildings,” Proc. IEEE Sens. Appl. Sympo. (SAS), Apr. 2015, pp. 1–6.
   Google Scholar
• 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. ACM Int. Conf. Ubiq. Comput, New York, NY, USA, 2010, pp. 139–148.
   Google Scholar
• K. Basu, V. Debusschere, A. Douzal-Chouakria and S. Bacha, “Time series distance-based methods for non-intrusive load monitoring in residential buildings,” Energ. Buildings, vol. 96, pp. 109–117, 2015. DOI: 10.1016/j.enbuild.2015.03.021.
   Web of Science ®Google Scholar
• E. S. Jones, R. E. Alden, D. C. H. Gong, A. G. Frye and D. M. Ionel, “The effect of high efficiency building technologies and PV generation on the energy profiles for typical US residences,” In 2020 International Conference on Renewable Energy Research and Applications (ICRERA), 2020.
   Google Scholar
• G. M. Gilbert, S. Naiman, H. Kimaro, and B. Bagile, “A critical review of edge and fog computing for smart grid applications,” in Information and Communication Technologies for Development. Strengthening Southern-Driven Cooperation as a Catalyst for ICT4D, P. Nielsen and H. C. Kimaro, Eds. Cham: Springer International Publishing, 2019, pp. 763–775.
   Google Scholar
• L. Morales-Velazquez, R. de Jesus Romero-Troncoso, G. Herrera-Ruiz, D. Morinigo-Sotelo and R. A. Osornio-Rios, “Smart sensor network for power quality monitoring in electrical installations,” Meas., vol. 103, pp. 133–142, 2017. DOI: 10.1016/j.measurement.2017.02.032.
   Web of Science ®Google Scholar
• M. Erol-Kantarci and H. T. Mouftah, “Wireless sensor networks for cost-efficient residential energy management in the smart grid,” IEEE Trans. Smart Grid, vol. 2, no. 2, pp. 314–325, June 2011. DOI: 10.1109/TSG.2011.2114678.
   Web of Science ®Google Scholar
• E. P. R. Institute. “DOE SHINES residential demonstration.” 2020. [Online]. Available: https://dashboards.epri.com/shines-residential/dashboard
   Google Scholar
• D. Dua and C. Graff, “UCI machine learning repository.” 2017. [Online]. Available: http://archive.ics.uci.edu/ml
   Google Scholar
• R. E. Alden, H. Gong, and D. M. Ionel, “LSTM forecasts for smart home electricity usage,” 2020 International Conference on Renewable Energy Research and Applications (ICRERA), Glasgow, UK, 2020.
   Google Scholar
• S. Hosseini, S. Kelouwani, K. Agbossou, A. Cardenas and N. Henao, “A semi-synthetic dataset development tool for household energy consumption analysis,” Proc. IEEE Int. Conf. Ind. Tech. (ICIT), Mar. 2017, pp. 564–569.
   Google Scholar
• J. Kelly and W. Knottenbelt, “Neural NILM: Deep neural networks applied to energy disaggregation,” In Proc. ACM Int. Conf. Embedded Syst. Energy-Efficient Built Environ, New York, NY, USA, 2015, pp. 55–64.
   Google Scholar
• C. Gisler, A. Ridi, D. Zujferey, O. A. Khaled and J. Hennebert, “Appliance consumption signature database and recognition test protocols,” Proc. Int. Workshop Syst. Signal Process. Appl. (WoSSPA), 2013, pp. 336–341.
   Google Scholar
• S. Makonin, F. Popowich, L. Bartram, B. Gill and I. V. Bajic, “AMPds: A public dataset for load disaggregation and eco-feedback research,” Proc. IEEE Electr. Power Energy Conf, 2013, pp. 1–6.
   Google Scholar
• K. D. Anderson, M. E. Bergés, A. Ocneanu, D. Benitez and J. M. F. Moura, “Event detection for non intrusive load monitoring,” Proc. Annu. Conf. IEEE Ind. Electr. Soc, Oct. 2012, pp. 3312–3317.
   Google Scholar
• C. Beckel, W. Kleiminger, R. Cicchetti, T. Staake, and S. Santini, “The ECO data set and the performance of non-intrusive load monitoring algorithms,” Proceedings of the 1st ACM Conference on Embedded Systems for Energy-Efficient Buildings, 2014. DOI: 10.1145/2674061.2674064.
   Google Scholar
• Honda. [Online]. Available: https://www.hondasmarthome.com
   Google Scholar
• IrishCER. [Online]. Available: http://www.ucd.ie/issda/data/commissionforenergyregulationcer/
   Google Scholar
• J. Z. Kolter and M. J. Johnson, “REDD: A public data set for energy disaggregation research,” In Proc. Workshop Data Mining Appl. Sust., San Diego, CA, 2011, pp. 59–62.
   Google Scholar
• D. Murray, et al., “A data management platform for personalised real-time energy feedback,” Proc. Int. Conf. Energ. Effic. Domestic Appliances and Lighting, 2015.
   Google Scholar
• G. C. Koutitas and L. Tassiulas, “Low cost disaggregation of smart meter sensor data,” IEEE Sensors J., vol. 16, no. 6, pp. 1665–1673, Mar. 2016. DOI: 10.1109/JSEN.2015.2501422.
   Web of Science ®Google Scholar
• D. Egarter, V. P. Bhuvana and W. Elmenreich, “PALDi: Online load disaggregation via particle filtering,” IEEE Trans. Instrum. Meas., vol. 64, no. 2, pp. 467–477, Feb. 2015. DOI: 10.1109/TIM.2014.2344373.
   Web of Science ®Google Scholar
• G. Mateos and G. B. Giannakis, “Load curve data cleansing and imputation via sparsity and low rank,” IEEE Trans. Smart Grid, vol. 4, no. 4, pp. 2347–2355, Dec. 2013. DOI: 10.1109/TSG.2013.2259853.
   Web of Science ®Google Scholar
• H. N. Akouemo and R. J. Povinelli, “Data improving in time series using ARX and ANN models,” IEEE Trans. Power Syst., vol. 32, no. 5, pp. 3352–3359, Sep. 2017. DOI: 10.1109/TPWRS.2017.2656939.
   Web of Science ®Google Scholar
• X. Li, C. P. Bowers and T. Schnier, “Classification of energy consumption in buildings with outlier detection,” IEEE Trans. Ind. Electron., vol. 57, no. 11, pp. 3639–3644, Nov. 2010. DOI: 10.1109/TIE.2009.2027926.
   Web of Science ®Google Scholar
• M. Chi, A. Plaza, J. A. Benediktsson, Z. Sun, J. Shen and Y. Zhu, “Big data for remote sensing: Challenges and opportunities,” Proc. IEEE, vol. 104, no. 11, pp. 2207–2219, Nov. 2016. DOI: 10.1109/JPROC.2016.2598228.
   Web of Science ®Google Scholar
• G. W. Hart, “Nonintrusive appliance load monitoring,” Proc. IEEE, vol. 80, no. 12, pp. 1870–1891, Dec. 1992. DOI: 10.1109/5.192069.
   Web of Science ®Google Scholar
• B. Zhao, L. Stankovic and V. Stankovic, “On a training-less solution for non-intrusive appliance load monitoring using graph signal processing,” IEEE Access, vol. 4, pp. 1784–1799, 2016. DOI: 10.1109/ACCESS.2016.2557460.
   Web of Science ®Google Scholar
• A. Zoha, A. Gluhak, M. Imran and S. Rajasegarar, “Non-intrusive load monitoring approaches for disaggregated energy sensing: A survey,” Sens., vol. 12, no. 12, pp. 16838–16866, 2012. DOI: 10.3390/s121216838.
   PubMed Web of Science ®Google Scholar
• J. Liang, S. K. K. Ng, G. Kendall and J. W. M. Cheng, “Load signature study—part i: Basic concept, structure, and methodology,” IEEE Trans. Power Delivery, vol. 25, no. 2, pp. 551–560, Apr. 2010. DOI: 10.1109/TPWRD.2009.2033799.
   Web of Science ®Google Scholar
• N. Sadeghianpourhamami, J. Ruyssinck, D. Deschrijver, T. Dhaene and C. Develder, “Comprehensive feature selection for appliance classification in NILM,” Energ. Buildings, vol. 151, pp. 98–106, 2017. DOI: 10.1016/j.enbuild.2017.06.042.
   Web of Science ®Google Scholar
• 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, Mar. 2012. DOI: 10.1109/TSG.2011.2163950.
   Web of Science ®Google Scholar
• H.-H. Chang, “Non-intrusive demand monitoring and load identification for energy management systems based on transient feature analyses,” Energies, vol. 5, no. 11, pp. 4569–4589, 2012. DOI: 10.3390/en5114569.
   Web of Science ®Google Scholar
• B. Liu, W. Luan and Y. Yu, “Dynamic time warping based non-intrusive load transient identification,” Appl. Energy, vol. 195, pp. 634–645, 2017. DOI: 10.1016/j.apenergy.2017.03.010.
   Web of Science ®Google Scholar
• S. R. Shaw, S. B. Leeb, L. K. Norford and R. W. Cox, “Nonintrusive load monitoring and diagnostics in power systems,” IEEE Trans. Instrum. Meas., vol. 57, no. 7, pp. 1445–1454, July 2008. DOI: 10.1109/TIM.2008.917179.
   Web of Science ®Google Scholar
• H. Kim, M. Marwah, M. Arlitt, G. Lyon and J. Han, “Unsupervised disaggregation of low frequency power measurements,” Proc. SIAM Int. Conf. Data Mining, 2011, pp. 747–758. DOI: 10.1137/1.9781611972818.64.
   Google Scholar
• R. Martinez, D. Pacheco and D. Robergs, “NILM power disaggregation via artificial neural networks,” Proc. Int. workshop on Non-Intrusive Load Monitoring (NILM), 2018.
   Google Scholar
• H. Lange and M. Bergés, “The neural energy decoder: Energy disaggregation by combining binary subcomponents,” Proc. Int. Workshop Non-Intrusive Load Monitoring (NILM), 2016.
   Google Scholar
• C. Deb, M. Frei, J. Hofer and A. Schlueter, “Automated load disaggregation for residences with electrical resistance heating,” Energ. Buildings, vol. 182, pp. 61–74, 2019. DOI: 10.1016/j.enbuild.2018.10.011.
   Web of Science ®Google Scholar
• C. C. Yang, C. S. Soh and V. V. Yap, “A systematic approach in appliance disaggregation using k-nearest neighbours and Naive Bayes classifiers for energy efficiency,” Energ. Effic., vol. 11, no. 1, pp. 239–259, Jan. 2018. DOI: 10.1007/s12053-017-9561-0.
   Web of Science ®Google Scholar
• C. C. Yang, C. S. Soh and V. V. Yap, “A systematic approach to ON-OFF event detection and clustering analysis of non-intrusive appliance load monitoring,” Front. Energy, vol. 9, no. 2, pp. 231–237, June 2015. DOI: 10.1007/s11708-015-0358-6.
   Web of Science ®Google Scholar
• S. K. Barker, “Model-driven analytics of energy meter data in smart homes,” Ph.D. dissertation, University of Massachusetts, Amherst, 2014.
   Google Scholar
• L. R. Morais and A. R. G. Castro, “Competitive autoassociative neural networks for electrical appliance identification for non-intrusive load monitoring,” IEEE Access, vol. 7, pp. 111746–111755, 2019. DOI: 10.1109/ACCESS.2019.2934019.
   Web of Science ®Google Scholar
• C. Liu, A. Akintayo, Z. Jiang, G. P. Henze and S. Sarkar, “Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network,” Appl. Energy, vol. 211, pp. 1106–1122, 2018. DOI: 10.1016/j.apenergy.2017.12.026.
   Web of Science ®Google Scholar
• D. Li and S. Dick, “Whole-house non-intrusive appliance load monitoring via multi-label classification,” Proc. Int. Joint Conf. Neural Networks (IJCNN), July 2016. pp. 2749–2755.
   Google Scholar
• K. He, L. Stankovic, J. Liao and V. Stankovic, “Non-intrusive load disaggregation using graph signal processing,” IEEE Trans. Smart Grid, vol. 9, no. 3, pp. 1739–1747, May 2018. DOI: 10.1109/TSG.2016.2598872.
   Web of Science ®Google Scholar
• A. Huss, “Hybrid model approach to appliance load disaggregation: Expressive appliance modelling by combining convolutional neural networks and hidden semi markov models,” Master’s thesis, KTH, School of Computer Science and Communication (CSC), 2015.
   Google Scholar
• M. Aiad and P. H. Lee, “Unsupervised approach for load disaggregation with devices interactions,” Energ. Buildings, vol. 116, pp. 96–103, 2016. DOI: 10.1016/j.enbuild.2015.12.043.
   Web of Science ®Google Scholar
• F. Paradiso, F. Paganelli, D. Giuli and S. Capobianco, “Context-based energy disaggregation in smart homes,” Future Intern., vol. 8, no. 1, pp. 4, 2016. DOI: 10.3390/fi8010004.
   Web of Science ®Google Scholar
• R. Bonfigli, S. Squartini, M. Fagiani and F. Piazza, “Unsupervised algorithms for non-intrusive load monitoring: An up-to-date overview,” Proc. Int. Conf. Environ. Electr. Eng. (EEEIC), June 2015, pp. 1175–1180.
   Google Scholar
• D. Kelly, “Disaggregation of domestic smart meter energy data,” Ph.D. dissertation, Imperial College, London, 2016.
   Google Scholar
• O. Parson, “Unsupervised training methods for non-intrusive appliance load monitoring from smart meter data,” Ph.D. dissertation, University of Southampton, 2014.
   Google Scholar
• C. Fischer, “Feedback on household electricity consumption: A tool for saving energy?,” Energ. Effic., vol. 1, no. 1, pp. 79–104, Feb. 2008. DOI: 10.1007/s12053-008-9009-7.
   Web of Science ®Google Scholar
• H. Wilhite and R. Ling, “Measured energy savings from a more informative energy bill,” Energ. Buildings, vol. 22, no. 2, pp. 145–155, 1995. DOI: 10.1016/0378-7788(94)00912-4.
   Web of Science ®Google Scholar
• L. Nielsen, “How to get the birds in the bush into your hand: Results from a Danish research project on electricity savings,” Energy Policy, vol. 21, no. 11, pp. 1133–1144, 1993. DOI: 10.1016/0301-4215(93)90263-F.
   Web of Science ®Google Scholar
• T. Ueno, R. Inada, O. Saeki and K. Tsuji, “Effectiveness of an energy-consumption information system for residential buildings,” Appl. Energy, vol. 83, no. 8, pp. 868–883, 2006. DOI: 10.1016/j.apenergy.2005.09.004.
   Web of Science ®Google Scholar
• A. Faruqui, S. Sergici and A. Sharif, “The impact of informational feedback on energy consumption–a survey of the experimental evidence,” Energy, vol. 35, no. 4, pp. 1598–1608, 2010. DOI: 10.1016/j.energy.2009.07.042.
   Web of Science ®Google Scholar
• T. Kane, S. Cockbill, A. May, V. Mitchell, C. Wilson, et al., “Supporting retrofit decisions using smart metering data: A multi-disciplinary approach.” ECEEE, 2015.
   Google Scholar
• P. Jokar, N. Arianpoo and V. C. M. Leung, “Electricity theft detection in AMI using customers’consumption patterns,” IEEE Trans. Smart Grid, vol. 7, no. 1, pp. 216–226, Jan. 2016. DOI: 10.1109/TSG.2015.2425222.
   Web of Science ®Google Scholar
• X. Liu, N. Iftikhar, P. S. Nielsen and A. Heller, “Online anomaly energy consumption detection using Lambda architecture,” Proc. Int. Conf. Big Data Analyt. and Knowledge Discovery, Aug. 2016.
   Google Scholar
• D. Araya, K. Grolinger, H. ElYamany, M. Capretz and G. Bitsuamlak, “An ensemble learning framework for anomaly detection in building energy consumption,” Energ. Buildings, vol. 144, pp. 191–206, 2017. DOI: 10.1016/j.enbuild.2017.02.058.
   Web of Science ®Google Scholar
• C. Fan, F. Xiao, Y. Zhao and J. Wang, “Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data,” Appl. Energy, vol. 211, pp. 1123–1135, 2018. DOI: 10.1016/j.apenergy.2017.12.005.
   Web of Science ®Google Scholar
• P. A. Lindahl, D. H. Green, G. Bredariol, A. Aboulian, J. S. Donnal and S. B. Leeb, “Shipboard fault detection through nonintrusive load monitoring: A case study,” IEEE Sensors J., vol. 18, no. 21, pp. 8986–8995, Nov. 2018. DOI: 10.1109/JSEN.2018.2869115.
   Web of Science ®Google Scholar
• H. Rashid, P. Singh, V. Stankovic and L. Stankovic, “Can non-intrusive load monitoring be used for identifying an appliance’s anomalous behaviour?,” Appl. Energy, vol. 238, pp. 796–805, 2019. DOI: 10.1016/j.apenergy.2019.01.061.
   Web of Science ®Google Scholar
• F. Luo, G. Ranzi, W. Kong, Z. Y. Dong, S. Wang, et al., “Non-intrusive energy saving appliance recommender system for smart grid residential users,” IET Gen. Trans. Dist., vol. 11, no. 7, pp. 1786–1793, 2017. DOI: 10.1049/iet-gtd.2016.1615.
   Web of Science ®Google Scholar
• M. Pipattanasomporn, M. Kuzlu and S. Rahman, “An algorithm for intelligent home energy management and demand response analysis,” IEEE Trans. Smart Grid, vol. 3, no. 4, pp. 2166–2173, Dec. 2012. DOI: 10.1109/TSG.2012.2201182.
   Web of Science ®Google Scholar
• J. Berka and J. Rojicek, “Load forecasting for residential sector demand response,” U.S. Patent US9753477B2, 2017.
   Google Scholar
• D. He, W. Lin, N. Liu, R. G. Harley and T. G. Habetler, “Incorporating non-intrusive load monitoring into building level demand response,” IEEE Trans. Smart Grid, vol. 4, no. 4, pp. 1870–1877, Dec. 2013. DOI: 10.1109/TSG.2013.2258180.
   Web of Science ®Google Scholar
• M. A. F. Ghazvini, J. Soares, O. Abrishambaf, R. Castro and Z. Vale, “Demand response implementation in smart households,” Energ. Buildings, vol. 143, pp. 129–148, 2017. DOI: 10.1016/j.enbuild.2017.03.020.
   Web of Science ®Google Scholar
• B. Celik, R. Roche, D. Bouquain and A. Miraoui, “Decentralized neighborhood energy management with coordinated smart home energy sharing,” IEEE Trans. Smart Grid, vol. 9, no. 6, pp. 6387–6397, Nov. 2018. DOI: 10.1109/TSG.2017.2710358.
   Web of Science ®Google Scholar
• H. Gong, E. Jones, R. Alden, A. Frye, D. Colliver and D. Ionel, “Demand response of HVACs in large residential communities based on experimental developments,” Proceedings, IEEE ECCE 2020, Oct. 2020, p. 4.
   Google Scholar
• H. Gong, E. Jones and D. Ionel, “An aggregated and equivalent home model for power system studies with examples of building insulation and HVAC control improvements,” Proceedings, IEEE PESGM 2020, Aug. 2020, p. 4.
   Google Scholar
• H. Gong, V. Rallabandi, D. M. Ionel, D. Colliver, S. Duerr and C. Ababei, “Dynamic modeling and optimal design for net zero energy houses including hybrid electric and thermal energy storage,” IEEE Trans. Industry Applicat., vol. 56, no. 4, pp. 4102–4113, 2020.
   Web of Science ®Google Scholar
• H. Gong, V. Rallabandi, M. L. McIntyre and D. M. Ionel, “On the optimal energy controls for large scale residential communities including smart homes,” 2019 IEEE Energy Conversion Congress and Exposition (ECCE), 2019, pp. 503–507. DOI: 10.1109/ECCE.2019.8912490.
   Google Scholar
• H. Gong, V. Rallabandi, D. M. Ionel, D. Colliver, S. Duerr and C. Ababei, “Net zero energy houses with dispatchable solar PV power supported by electric water heater and battery energy storage,” 2018 IEEE Energy Conversion Congress and Exposition (ECCE), 2018, pp. 2498–2503. DOI: 10.1109/ECCE.2018.8557720.
   Google Scholar
• S. Duerr, C. Ababei and D. M. Ionel, “Load balancing with energy storage systems based on co-simulation of multiple smart buildings and distribution networks,” 2017 IEEE 6th International Conference on Renewable Energy Research and Applications (ICRERA), 2017, pp. 175–180. DOI: 10.1109/ICRERA.2017.8191262.
   Google Scholar
• S. Duerr, C. Ababei and D. M. Ionel, “Smartbuilds: An energy and power simulation framework for buildings and districts,” IEEE Trans. Ind. Applicat., vol. 53, no. 1, pp. 402–410, 2017. DOI: 10.1109/TIA.2016.2611667.
   Web of Science ®Google Scholar
• H. Gong, O. Akeyo, V. Rallabandi and D. M. Ionel, “Real time operation of smart homes with PV and battery systems under variable electricity rate schedules and transactive power flow,” 2018 7th International Conference on Renewable Energy Research and Applications (ICRERA), 2018, pp. 1392–1395. DOI: 10.1109/ICRERA.2018.8566895.
   Google Scholar
• D. Bergman, D. Jin, J. Juen, N. Tanaka, C. Gunter and A. Wright, “Nonintrusive load-shed verification,” IEEE Pervasive Comput., vol. 10, no. 1, pp. 49–57, Jan. 2011. DOI: 10.1109/MPRV.2010.71.
   Web of Science ®Google Scholar
• Y. Chen, et al., “Short-term electrical load forecasting using the support vector regression (SVR) model to calculate the demand response baseline for office buildings,” Appl. Energy, vol. 195, pp. 659–670, 2017. DOI: 10.1016/j.apenergy.2017.03.034.
   Web of Science ®Google Scholar
• Y. Ozturk, D. Senthilkumar, S. Kumar and G. Lee, “An intelligent home energy management system to improve demand response,” IEEE Trans. Smart Grid, vol. 4, no. 2, pp. 694–701, June 2013. DOI: 10.1109/TSG.2012.2235088.
   Web of Science ®Google Scholar
• N. G. Paterakis, A. Taşc Ikaraoğlu, O. Erdinç, A. G. Bakirtzis and J. P. S. Catalão, “Assessment of demand-response-driven load pattern elasticity using a combined approach for smart households,” IEEE Trans. Ind. Inf., vol. 12, no. 4, pp. 1529–1539, Aug. 2016. DOI: 10.1109/TII.2016.2585122.
   Web of Science ®Google Scholar
• S. Arora and J. W. Taylor, “Rule-based autoregressive moving average models for forecasting load on special days: A case study for france,” Europ. J. Operat. Res., vol. 266, no. 1, pp. 259–268, 2018. DOI: 10.1016/j.ejor.2017.08.056.
   Web of Science ®Google Scholar
• D. Vu, K. Muttaqi, A. Agalgaonkar and A. Bouzerdoum, “Short-term electricity demand forecasting using autoregressive based time varying model incorporating representative data adjustment,” Appl. Energy, vol. 205, pp. 790–801, 2017. DOI: 10.1016/j.apenergy.2017.08.135.
   Web of Science ®Google Scholar
• M. Manivannan, B. Najafi and F. Rinaldi, “Machine learning-based short-term prediction of air-conditioning load through smart meter analytics,” Energies, vol. 10, no. 11, pp. 1905, 2017.
   Web of Science ®Google Scholar
• A. H. Neto and F. A. S. Fiorelli, “Comparison between detailed model simulation and artificial neural network for forecasting building energy consumption,” Energ. Buildings, vol. 40, no. 12, pp. 2169–2176, 2008. DOI: 10.1016/j.enbuild.2008.06.013.
   Web of Science ®Google Scholar
• W. Kong, Z. Y. Dong, D. J. Hill, F. Luo and Y. Xu, “Short-term residential load forecasting based on resident behaviour learning,” IEEE Trans. Power Syst., vol. 33, no. 1, pp. 1087–1088, Jan. 2018. DOI: 10.1109/TPWRS.2017.2688178.
   Web of Science ®Google Scholar
• Y. Wang, D. Gan, M. Sun, N. Zhang, Z. Lu and C. Kang, “Probabilistic individual load forecasting using pinball loss guided lstm,” Appl. Energy, vol. 235, pp. 10–20, 2019. DOI: 10.1016/j.apenergy.2018.10.078.
   Web of Science ®Google Scholar
• X. Kong, C. Li, F. Zheng and C. Wang, “Improved deep belief network for short-term load forecasting considering demand-side management,” IEEE Trans. Power Syst., vol. 35, no. 2, pp. 1531–1538, Mar. 2020. DOI: 10.1109/TPWRS.2019.2943972.
   Web of Science ®Google Scholar
• P. Lusis, K. R. Khalilpour, L. Andrew and A. Liebman, “Short-term residential load forecasting: Impact of calendar effects and forecast granularity,” Appl. Energy, vol. 205, pp. 654–669, 2017. DOI: 10.1016/j.apenergy.2017.07.114.
   Web of Science ®Google Scholar
• C. Kuster, Y. Rezgui and M. Mourshed, “Electrical load forecasting models: A critical systematic review,” Sustain. Cities Soc., vol. 35, pp. 257–270, 2017. DOI: 10.1016/j.scs.2017.08.009.
   Web of Science ®Google Scholar
• B. Yildiz, J. Bilbao and A. Sproul, “A review and analysis of regression and machine learning models on commercial building electricity load forecasting,” Renew. Sust. Energ. Rev., vol. 73, pp. 1104–1122, 2017. DOI: 10.1016/j.rser.2017.02.023.
   Web of Science ®Google Scholar
• Y. Lin and M. Tsai, “An advanced home energy management system facilitated by nonintrusive load monitoring with automated multiobjective power scheduling,” IEEE Trans. Smart Grid, vol. 6, no. 4, pp. 1839–1851, July 2015. DOI: 10.1109/TSG.2015.2388492.
   Web of Science ®Google Scholar
• A. Capasso, W. Grattieri, R. Lamedica and A. Prudenzi, “A bottom-up approach to residential load modeling,” IEEE Trans. Power Syst., vol. 9, no. 2, pp. 957–964, May 1994. DOI: 10.1109/59.317650.
   Web of Science ®Google Scholar
• A. Collin, G. Tsagarakis, A. Kiprakis and S. McLaughlin, “Development of low-voltage load models for the residential load sector,” IEEE Trans. Power Syst., vol. 29, no. 5, pp. 2180–2188, Sep. 2014. DOI: 10.1109/TPWRS.2014.2301949.
   Web of Science ®Google Scholar
• D. Fischer, A. Härtl and B. Wille-Haussmann, “Model for electric load profiles with high time resolution for German households,” Energ. Buildings, vol. 92, pp. 170–179, 2015. DOI: 10.1016/j.enbuild.2015.01.058.
   Web of Science ®Google Scholar
• A. J. Collin, G. Tsagarakis, A. E. Kiprakis and S. McLaughlin, “Multi-scale electrical load modelling for demand-side management,” in Proc. IEEE PES Innovative Smart Grid Tech. Europ. (ISGT Europe), Oct. 2012, pp. 1–8.
   Google Scholar
• I. Richardson, M. Thomson, D. Infield and C. Clifford, “Domestic electricity use: A high-resolution energy demand model,” Energ. Buildings, vol. 42, no. 10, pp. 1878–1887, 2010. DOI: 10.1016/j.enbuild.2010.05.023.
   Web of Science ®Google Scholar
• T. Shoji, W. Hirohashi, Y. Fujimoto, Y. Amano, S. Ichi Tanabe and Y. Hayashi, “Personalized energy management systems for home appliances based on Bayesian networks,” J. Int. Council Electr. Eng., vol. 5, no. 1, pp. 64–69, 2015. DOI: 10.1080/22348972.2015.1115171.
   Google Scholar
• S. Zhai, Z. Wang, X. Yan and G. He, “Appliance flexibility analysis considering user behavior in home energy management system using smart plugs,” IEEE Trans. Ind. Electron., vol. 66, no. 2, pp. 1391–1401, Feb. 2019. DOI: 10.1109/TIE.2018.2815949.
   Web of Science ®Google Scholar
• Z. Guo, Z. J. Wang and A. Kashani, “Home appliance load modeling from aggregated smart meter data,” IEEE Trans. Power Syst., vol. 30, no. 1, pp. 254–262, Jan. 2015. DOI: 10.1109/TPWRS.2014.2327041.
   Web of Science ®Google Scholar
• Eurostat, “Harmonised european time use surveys,” Eurostat Methododologies Working Papers, Office Official Publications European Communities, Tech. Rep., 2000.
   Google Scholar
• J. Gershuny, “United Kingdom Time Use Survey, 2014–2015,” 2017.
   Google Scholar
• J. Gershuny, “Multinational time use study,” 2013.
   Google Scholar
• S. Shao, M. Pipattanasomporn and S. Rahman, “Development of physical-based demand response-enabled residential load models,” IEEE Trans. Power Syst., vol. 28, no. 2, pp. 607–614, May 2013. DOI: 10.1109/TPWRS.2012.2208232.
   Web of Science ®Google Scholar
• M. Alizadeh, A. Scaglione, A. Applebaum, G. Kesidis and K. Levitt, “Reduced-order load models for large populations of flexible appliances,” IEEE Trans. Power Syst., vol. 30, no. 4, pp. 1758–1774, July 2015. DOI: 10.1109/TPWRS.2014.2354345.
   Web of Science ®Google Scholar
• Y. Tang, L. Zhu, J. Ning and Q. Wang, “A data-driven approach for online aggregated load modeling through intelligent terminals,” Int. J. Distrib. Sens. Netw., vol. 15, no. 1, pp. 155014771982599, 2019. DOI: 10.1177/1550147719825996.
   Web of Science ®Google Scholar
• P. Zhang, C. Zhou, B. G. Stewart, D. M. Hepburn, W. Zhou and J. Yu, “An improved non-intrusive load monitoring method for recognition of electric vehicle battery charging load,” Energy Procedia, vol. 12, pp. 104–112, 2011. DOI: 10.1016/j.egypro.2011.10.015.
   Google Scholar
• A. A. Munshi and Y. A. I. Mohamed, “Unsupervised nonintrusive extraction of electrical vehicle charging load patterns,” IEEE Trans. Ind. Inf., vol. 15, no. 1, pp. 266–279, Jan. 2019. DOI: 10.1109/TII.2018.2806936.
   Web of Science ®Google Scholar
• Y. Cao, et al., “An optimized EV charging model considering TOU price and SOC curve,” IEEE Trans. Smart Grid, vol. 3, no. 1, pp. 388–393, Mar. 2012. DOI: 10.1109/TSG.2011.2159630.
   Web of Science ®Google Scholar
• M. Alizadeh, A. Scaglione, J. Davies and K. S. Kurani, “A scalable stochastic model for the electricity demand of electric and plug-in hybrid vehicles,” IEEE Trans. Smart Grid, vol. 5, no. 2, pp. 848–860, Mar. 2014. DOI: 10.1109/TSG.2013.2275988.
   Web of Science ®Google Scholar
• B. Khaki, Y. Chung, C. Chu and R. Gadh, “Probabilistic electric vehicle load management in distribution grids,” Proc. IEEE Transport. Electrif. Conf. Expo (ITEC), June 2019, pp. 1–6.
   Google Scholar
• X. Yuan, J. He, S. Li, L. Li, S. Shi and B. He, “Can electric vehicles meet the requirements of the highway trip?,” IEEE Ind. Electron. Mag., 2020. DOI: 10.1109/MIE.2020.2969848.
   Google Scholar
• M. Li, et al., “GIS-based probabilistic modeling of BEV charging load for Australia,” IEEE Trans. Smart Grid, vol. 10, no. 4, pp. 3525–3534, July 2019. DOI: 10.1109/TSG.2018.2829917.
   Web of Science ®Google Scholar
• H. Gong and D. Ionel, “Optimization of agregated EV power in residential communities with smart homes,” Proceedings, IEEE Transportation Electrification Conference and Expo (ITEC), June 2020, p. 4.
   Google Scholar
• N. K. Thokala, A. Bapna and M. G. Chandra, “A deployable electrical load forecasting solution for commercial buildings,” 2018 IEEE International Conference on Industrial Technology (ICIT), 2018, pp. 1101–1106.
   Google Scholar
• W. Kong, Z. Y. Dong, Y. Jia, D. J. Hill, Y. Xu and Y. Zhang, “Short-term residential load forecasting based on LSTM recurrent neural network,” IEEE Trans. Smart Grid, vol. 10, no. 1, pp. 841–851, Jan. 2019. DOI: 10.1109/TSG.2017.2753802.
   Web of Science ®Google Scholar
• J. D. Kim, “Insights into residential EV charging behavior using energy meter data,” Energy Policy, vol. 129, pp. 610–618, 2019. DOI: 10.1016/j.enpol.2019.02.049.
   Web of Science ®Google Scholar
• D. Yu, W. Choi, M. Kim and L. Liu, “Forecasting day-ahead hourly photovoltaic power generation using convolutional self-attention based long short-term memory,” Energies, vol. 13, no. 15, pp. 4017, 2020. DOI: 10.3390/en13154017.
   Web of Science ®Google Scholar
• Markets and Markets, “Home automation system market by protocol and technology (network and wireless), product (lighting, security and access control, HVAC and entertainment control), software and algorithm (behavioral and proactive), and geography – global forcast to 2020,” Nov. 2019.
   Google Scholar
• H. Jo and Y. I. Yoon, “Intelligent smart home energy efficiency model using artificial TensorFlow engine,” Hum.-Cent. Comput. Inf., vol. 8, no. 9, pp. 1–18, 2018.
   Google Scholar
• T. Hargreaves, et al., “Smart homes, control and energy management: How do smart home technologies influence control over energy use and domestic life?,” in Europ. Council Energy Efficient Economy 2015 Summer Study, June 2015, pp. 1021–1032.
   Google Scholar
• A. S. Shaikat, R. Tasnim, R. Saleheen, M. Rayhan, M. T. Khan and R. Hasan, “A real time electrical load distribution monitoring and controlling system based on PLC and webserver,” Proc. Int. Conf. Energ. Power Eng. (ICEPE), Mar. 2019, pp. 1–6.
   Google Scholar
• S. S. Hosseini, K. Agbossou, S. Kelouwani and A. Cardenas, “Non-intrusive load monitoring through home energy management systems: A comprehensive review,” Renew. Sust. Energ. Rev., vol. 79, pp. 1266–1274, 2017. DOI: 10.1016/j.rser.2017.05.096.
   Web of Science ®Google Scholar
• O. Saberi and R. Menes, “Artificial intelligence and the future for smart homes,” International Finance Corporation, Washington, DC. Available: https://openknowledge.worldbank.org/handle/10986/33615.
   Google Scholar
• X. Guo, Z. Shen, Y. Zhang and T. Wu, “Review on the application of artificial intelligence in smart homes,” Smart Cities, vol. 2, no. 3, pp. 402–420, 2019. DOI: 10.3390/smartcities2030025.
   Google Scholar