[1] Looney, B. (2020). “Statistical Review of World Energy 2020, 69th,” ed: Edition. [2] Faustine, A., Mvungi, N. H., Kaijage, S., & Michael, K. (2017). A survey on non-intrusive load monitoring methodies and techniques for energy disaggregation problem. arXiv preprint arXiv:1703.00785. [3] Dong, K., Dong, X., & Jiang, Q. (2020). How renewable energy consumption lower global CO2 emissions? Evidence from countries with different income levels. The World Economy, 43(6), 1665-1698. [4] Armel, K. C., Gupta, A., Shrimali, G., & Albert, A. (2013). Is disaggregation the holy grail of energy efficiency? The case of electricity. Energy policy, 52, 213-234. [5] Darby, S., Liddell, C., Hills, D., & Drabble, D. (2015). Smart metering early learning project: synthesis report. [6] Wagner, L., Ross, I., Foster, J., & Hankamer, B. (2016). Trading off global fuel supply, CO2 emissions and sustainable development. PloS one, 11(3), e0149406. [7] Yang, Y., Yuan, J., Xiao, Z., Yi, H., Zhang, C., Gang, W., & Hu, H. (2021). Energy consumption characteristics and adaptive electricity pricing strategies for college dormitories based on historical monitored data. Energy and Buildings, 245, 111041. [8] Batra, N., Singh, A., & Whitehouse, K. (2015, November). If you measure it, can you improve it? exploring the value of energy disaggregation. In Proceedings of the 2nd ACM international conference on embedded systems for energy-efficient built environments (pp. 191-200). [9] Hart, G. W. (1992). Nonintrusive appliance load monitoring. Proceedings of the IEEE, 80(12), 1870-1891. [10] Shin, C., Joo, S., Yim, J., Lee, H., Moon, T., & Rhee, W. (2019, July). Subtask gated networks for non-intrusive load monitoring. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 33, No. 01, pp. 1150-1157). [11] Revuelta Herrero, J., Lozano Murciego, Á., López Barriuso, A., Hernández de la Iglesia, D., Villarrubia González, G., Corchado Rodríguez, J. M., & Carreira, R. (2018). Non intrusive load monitoring (nilm): A state of the art. In Trends in Cyber-Physical Multi-Agent Systems. The PAAMS Collection-15th International Conference, PAAMS 2017 15 (pp. 125-138). Springer International Publishing. [12] Bonfigli, R., Squartini, S., Fagiani, M., & Piazza, F. (2015, June). Unsupervised algorithms for non-intrusive load monitoring: An up-to-date overview. In 2015 IEEE 15th international conference on environment and electrical engineering (EEEIC) (pp. 1175-1180). IEEE. [13] Barsim, K. S., & Yang, B. (2015, December). Toward a semi-supervised non-intrusive load monitoring system for event-based energy disaggregation. In 2015 IEEE global conference on signal and information processing (GlobalSIP) (pp. 58-62). IEEE. doi: 10.1109/GlobalSIP.2015.7418156. [14] Li, D., & Dick, S. (2017, July). A graph-based semi-supervised learning approach towards household energy disaggregation. In 2017 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) (pp. 1-7). IEEE. doi: 10.1109/FUZZ-IEEE.2017.8015650 [15] Precioso, D., & Gómez-Ullate, D. (2022). Thresholding Methods in Non-Intrusive Load Monitoring to Estimate Appliance Status. doi: 10.21203/rs.3.rs-1923023/v1 [16] Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural computation, 9(8), 1735-1780. [17] Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271. doi: 10.48550/arXiv.1803.01271. [18] Precioso, D., & Gómez-Ullate, D.: NILM as a regression versus classification problem: the importance of thresholding. arXiv preprint arXiv:2010.16050 (2020). [19] Saraswat, G., Lundstrom, B., & Salapaka, M. V.: Scalable Hybrid Classification-Regression Solution for High-Frequency Nonintrusive Load Monitoring. arXiv preprint arXiv:2208.10638 (2022). [20] Naderian, S.: A Novel Hybrid Deep Learning Approach for Non-Intrusive Load Monitoring of Residential Appliance Based on Long Short Term Memory and Convolutional Neural Networks. arXiv preprint arXiv:2104.07809 (2021). [21] Faustine, A., Pereira, L., Bousbiat, H., & Kulkarni, S. (2020, November). UNet-NILM: A deep neural network for multi-tasks appliances state detection and power estimation in NILM. In Proceedings of the 5th International Workshop on Non-Intrusive Load Monitoring (pp. 84-88). [22] Kim, H., Marwah, M., Arlitt, M., Lyon, G., & Han, J. (2011, April). Unsupervised disaggregation of low frequency power measurements. In Proceedings of the 2011 SIAM international conference on data mining (pp. 747-758). Society for Industrial and Applied Mathematics. [23] Kolter, J. Z., & Johnson, M. J. (2011, August). REDD: A public data set for energy disaggregation research. In Workshop on data mining applications in sustainability (SIGKDD), San Diego, CA (Vol. 25, No. Citeseer, pp. 59-62). [24] Buddhahai, B., & Makonin, S. (2021). A nonintrusive load monitoring based on multi-target regression approach. IEEE Access, 9, 163033-163042. [25] Struyf, J., & Dzeroski, S. (2006). Constraint based induction of multi-objective regression trees. Lecture notes in computer science, 3933, 222-233. [26] Kaselimi, M., Doulamis, N., Doulamis, A., Voulodimos, A., & Protopapadakis, E. (2019, May). Bayesian-optimized bidirectional LSTM regression model for non-intrusive load monitoring. In ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 2747-2751). IEEE. [27] Makonin, S., Popowich, F., Bartram, L., Gill, B., & Bajić, I. V. (2013, August). AMPds: A public dataset for load disaggregation and eco-feedback research. In 2013 IEEE electrical power & energy conference (pp. 1-6). IEEE. [28] Lin, J., Ma, J., Zhu, J., & Liang, H. (2021). Deep domain adaptation for non-intrusive load monitoring based on a knowledge transfer learning network. IEEE Transactions on Smart Grid, 13(1), 280-292. [29] Hadi, M. U., Suhaimi, N. H. N., & Basit, A. (2022). Efficient Supervised Machine Learning Network for Non-Intrusive Load Monitoring. Technologies, 10(4), 85. [30] Timplalexis, C., Krinidis, S., Ioannidis, D., & Tzovaras, D. EMD and Gradient Boosting Regression for NILM at Residential Houses. [31] Schirmer, P. A., Mporas, I., & Paraskevas, M. (2019, July). Evaluation of regression algorithms and features on the energy disaggregation task. In 2019 10th International Conference on Information, Intelligence, Systems and Applications (IISA) (pp. 1-4). IEEE. [32] Piccialli, V., & Sudoso, A. M. (2021). Improving non-intrusive load disaggregation through an attention-based deep neural network. Energies, 14(4), 847. [33] Konstantopoulos, C., Sioutas, S., & Tsichlas, K. (2022, June). Machine Learning Techniques for Regression in Energy Disaggregation. In Artificial Intelligence Applications and Innovations: 18th IFIP WG 12.5 International Conference, AIAI 2022, Hersonissos, Crete, Greece, June 17–20, 2022, Proceedings, Part I (pp. 356-366). Cham: Springer International Publishing. [34] Rao, K. M., Ravichandran, D., & Mahesh, K. (2016). Non-intrusive load monitoring and analytics for device prediction. In Proceedings of the International MultiConference of Engineers and Computer Scientists (Vol. 1, pp. 132-136). [35] Laouali, I., Ruano, A., Ruano, M. D. G., Bennani, S. D., & Fadili, H. E. (2022). Non-intrusive load monitoring of household devices using a hybrid deep learning model through convex hull-based data selection. Energies, 15(3), 1215. [36] Li, C., R. Yang, and H. Wang. (2022). Non-intrusive Load Monitoring in Industry Based on Gradient Boosting Algorithm. In 2022 IEEE 25th International Conference on Computer Supported Cooperative Work in Design (CSCWD) (pp. 1523-1528). IEEE. doi:10.1109/CSCWD54268.2022.9776262. [37] Precioso, D., & Gómez-Ullate, D. (2021, June). Non-Intrusive Load Monitoring using Multi-Output CNNs. In 2021 IEEE Madrid PowerTech (pp. 1-6). IEEE. [38] Chen, K., Zhang, Y., Wang, Q., Hu, J., Fan, H., & He, J. (2019). Scale-and context-aware convolutional non-intrusive load monitoring. IEEE Transactions on Power Systems, 35(3), 2362-2373. [39] Yang, W., Pang, C., Huang, J., & Zeng, X. (2021). Sequence-to-point learning based on temporal convolutional networks for nonintrusive load monitoring. IEEE Transactions on Instrumentation and Measurement, 70, 1-10. [40] Mollel, R. S., Stankovic, L., & Stankovic, V. (2022, November). Using explainability tools to inform NILM algorithm performance: a decision tree approach. In Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (pp. 368-372). [41] Hernandez, A. S., Ballado, A. H., & Heredia, A. P. D. (2021, June). Development of a non-intrusive load monitoring (nilm) with unknown loads using support vector machine. In 2021 IEEE International Conference on Automatic Control & Intelligent Systems (I2CACIS) (pp. 203-207). IEEE. [42] Yang, C. C., Soh, C. S., & Yap, V. V. (2018). A systematic approach in appliance disaggregation using k-nearest neighbours and naive Bayes classifiers for energy efficiency. Energy Efficiency, 11, 239-259. [43] Hock, D., M. Kappes, and B. Ghita. (2018). Non-intrusive appliance load monitoring using genetic algorithms. In IOP Conference Series: Materials Science and Engineering (Vol. 366, No. 1, p. 012003). IOP Publishing. doi:10.1088/1757-899X/366/1/012003. [44] Zhao, B., L. Stankovic, and V. Stankovic. (2016). On a training-less solution for non-intrusive appliance load monitoring using graph signal processing. IEEE Access, 4, 1784-1799. doi:10.1109/ACCESS.2016.2557460. [45] Jia, Z., Yang, L., Zhang, Z., Liu, H., & Kong, F. (2021). Sequence to point learning based on bidirectional dilated residual network for non-intrusive load monitoring. International Journal of Electrical Power & Energy Systems, 129, 106837. doi: 10.1016/j.ijepes.2021.106837. [46] Alami, M., Decock, J., Kaddah, R., & Read, J. (2022, September). Conv-NILM-Net, a causal and multi-appliance model for energy source separation. In European Conference on Machine Learning (ECML), MLBEM Workshop. [47] Qian, Y., Yang, Q., Li, D., An, D., & Zhou, S. (2021, May). An Improved Temporal Convolutional Network for Non-intrusive Load Monitoring. In 2021 33rd Chinese Control and Decision Conference (CCDC) (pp. 2557-2562). IEEE. doi: 10.1109/CCDC52312.2021.9601611. [48] Liu, Y., Qiu, J., Lu, J., Wang, W., & Ma, J. (2021). A Single-to-Multi Network for Latency-Free Non-Intrusive Load Monitoring. IEEE Transactions on Network Science and Engineering, 9(2), 755-768. doi: 10.1109/TNSE.2021.3132309. [49] Zhang, Z., Li, Y., Duan, J., Guo, Y., Hou, Z., Duan, Y., ... & Rehtanz, C. (2022). A Multi-State Load State Identification Model Based on Time Convolutional Networks and Conditional Random Fields. IEEE Transactions on Artificial Intelligence. doi:10.1109/TAI.2022.3203685. [50] Zhou, X., Li, S., Liu, C., Zhu, H., Dong, N., & Xiao, T. (2021). Non-intrusive load monitoring using a cnn-lstm-rf model considering label correlation and class-imbalance. IEEE Access, 9, 84306-84315. doi:10.1109/ACCESS.2021.3087696. [51] Kim, J. G., & Lee, B. (2019). Appliance classification by power signal analysis based on multi-feature combination multi-layer LSTM. Energies, 12(14), 2804. [52] de Diego-Otón, L., Fuentes-Jimenez, D., Hernández, Á., & Nieto, R. (2021, May). Recurrent lstm architecture for appliance identification in non-intrusive load monitoring. In 2021 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) (pp. 1-6). IEEE. doi:10.1109/I2MTC50364.2021.9460046. [53] Li, D., Sawyer, K., & Dick, S. (2015, August). Disaggregating household loads via semi-supervised multi-label classification. In 2015 Annual Conference of the North American Fuzzy Information Processing Society (NAFIPS) held jointly with 2015 5th World Conference on Soft Computing (WConSC) (pp. 1-5). IEEE. [54] Gillis, J. M., & Morsi, W. G. (2016). Non-intrusive load monitoring using semi-supervised machine learning and wavelet design. IEEE Transactions on Smart Grid, 8(6), 2648-2655. doi: 10.1109/TSG.2016.2532885. [55] Iwayemi, A., & Zhou, C. (2015). SARAA: Semi-supervised learning for automated residential appliance annotation. IEEE Transactions on Smart Grid, 8(2), 779-786. doi: 10.1109/TSG.2015.2498642. [56] Yang, Y., Zhong, J., Li, W., Gulliver, T. A., & Li, S. (2019). Semisupervised multilabel deep learning based nonintrusive load monitoring in smart grids. IEEE Transactions on Industrial Informatics, 16(11), 6892-6902. doi: 10.1109/TII.2019.2955470. [57] Hur, C. H., Lee, H. E., Kim, Y. J., & Kang, S. G. (2022). Semi-Supervised Domain Adaptation for Multi-Label Classification on Nonintrusive Load Monitoring. Sensors, 22(15), 5838. doi:10.3390/s22155838. [58] Fatouh, A. M., Nasr, O. A., & Eissa, M. M. (2018, August). New semi-supervised and active learning combination technique for non-intrusive load monitoring. In 2018 IEEE International Conference on Smart Energy Grid Engineering (SEGE) (pp. 181-185). IEEE. doi:10.1109/SEGE.2018.8499498. [59] da Silva Nolasco, L., Lazzaretti, A. E., & Mulinari, B. M. (2021). DeepDFML-NILM: A new CNN-based architecture for detection, feature extraction and multi-label classification in NILM signals. IEEE Sensors Journal, 22(1), 501-509. [60] Devlin, M., & Hayes, B. P. (2019, August). Non-intrusive load monitoring using electricity smart meter data: A deep learning approach. In 2019 IEEE Power & Energy Society General Meeting (PESGM) (pp. 1-5). IEEE. [61] Xiao, P., & Cheng, S. (2019). Neural network for nilm based on operational state change classification. arXiv preprint arXiv:1902.02675. [62] Kim, J., & Kim, H. (2016, July). Classification performance using gated recurrent unit recurrent neural network on energy disaggregation. In 2016 international conference on machine learning and cybernetics (ICMLC) (Vol. 1, pp. 105-110). IEEE. [63] Zhang, C., Zhong, M., Wang, Z., Goddard, N., & Sutton, C. (2018, April). Sequence-to-point learning with neural networks for non-intrusive load monitoring. In Proceedings of the AAAI conference on artificial intelligence (Vol. 32, No. 1). [64] Kelly, J., & Knottenbelt, W. (2015, November). Neural nilm: Deep neural networks applied to energy disaggregation. In Proceedings of the 2nd ACM international conference on embedded systems for energy-efficient built environments (pp. 55-64). [65] Serafini, L., Tanoni, G., Principi, E., Spinsante, S., & Squartini, S. (2022, August). A Multiple Instance Regression Approach to Electrical Load Disaggregation. In 2022 30th European Signal Processing Conference (EUSIPCO) (pp. 1666-1670). IEEE. [66] Lu, Z., Cheng, Y., Zhong, M., Luan, W., Ye, Y., & Wang, G.: LightNILM: lightweight neural network methods for non-intrusive load monitoring. In Proceedings of the 9th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (pp. 383-387) (2022, November). [67] Commercial Buildings Energy Consumption Survey, Consumption and Expenditures Highlights, U.S. Energy Information Administration, December 2022, https://www.eia.gov/cbecs. [68] Delinchant, B., Martin, G., Laranjeira, T., Muhammad, S., & Wurtz, F. (2021, July). Machine Learning on Buildings Data for Future Energy Community Services. In SGE 2021-Symposium de Génie Electrique. [69] Martin Nascimento, G.F. (2022). Optimization of resources and consumption of smart buildings with a view to energy efficiency, (PhD Thesis, UGA/UFSC). https://thares.univ-grenoble-alpes.fr/2022GRALT078.pdf. [70] Martin Nascimento, G. F., Delinchant, B., Wurtz, F., Kuo-Peng, P., Jhoe Batistela, N., & Laranjeira, T. G. E. (2020). Electricity Consumption Data of a Tertiary Building. Mendeley Data, 1. [71] GreEn-ER API, available online: https://mhi-srv.g2elab.grenoble-inp.fr/django/API. [72] Hodencq, S., Delinchant, B., & Wurtz, F. (2021, September). Open and Reproducible Use Cases for Energy (ORUCE) methodology in systems design and operation: a dwelling photovoltaic self-consumption example. In Building Simulation 2021. [73] McKinney, W. (2011). pandas: a foundational Python library for data analysis and statistics. Python for high performance and scientific computing, 14(9), 1-9. [74] Pedregosa, F., Varoquaux, G., Gramfort, A., Michel, V., Thirion, B., Grisel, O., ... & Duchesnay, E. (2011). Scikit-learn: Machine learning in Python. the Journal of machine Learning research, 12, 2825-2830. [75] Breiman, L. (1996). Bagging predictors. Machine learning, 24, 123-140. [76] Bishop, C. M., & Nasrabadi, N. M. (2006). Pattern recognition and machine learning (Vol. 4, No. 4, p. 738). New York: springer. [77] Brownlee, J. (2016). A gentle introduction to the gradient boosting algorithm for machine learning. Machine Learning Mastery, 21. [78] Shi, H. (2007). Best-first decision tree learning (Doctoral dissertation, The University of Waikato). [79] Ahamed, B. S. (2021). Prediction of type-2 diabetes using the LGBM classifier methods and techniques. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(12), 223-231. [80] Bahmani, MJ. (2022). Understanding LightGBM Parameters (and How to Tune Them). The MLOps Blog. https://neptune.ai/blog/lightgbm-parameters-guide. [81] Derpanis, K. G. (2010). Overview of the RANSAC Algorithm. Image Rochester NY, 4(1), 2-3. [82] Brachmann, E., & Rother, C. (2019). Neural-guided RANSAC: Learning where to sample model hypotheses. In Proceedings of the IEEE/CVF International Conference on Computer Vision (pp. 4322-4331). [83] Reinhardt, A., Baumann, P., Burgstahler, D., Hollick, M., Chonov, H., Werner, M., & Steinmetz, R. (2012, October). On the accuracy of appliance identification based on distributed load metering data. In 2012 Sustainable Internet and ICT for Sustainability (SustainIT) (pp. 1-9). IEEE. [84] Maasoumy, M., Sanandaji, B., Poolla, K., & Vincentelli, A. S. (2013, December). Berds-berkeley energy disaggregation data set. In Proceedings of the Workshop on Big Learning at the Conference on Neural Information Processing Systems (NIPS) (Vol. 7). [85] Batra, N., Parson, O., Berges, M., Singh, A., & Rogers, A. (2014). A comparison of non-intrusive load monitoring methods for commercial and residential buildings. arXiv preprint arXiv:1408.6595. [86] MacQueen, I. (1967). Some methods for classifiction and analysis of multivariate observations. In Proceedings 5th Berkeley Symposium on Mathematical Statistics Problems (pp. 281-297). [87] Desai, S., Alhadad, R., Mahmood, A., Chilamkurti, N., & Rho, S. (2019). Multi-state energy classifier to evaluate the performance of the nilm algorithm. Sensors, 19(23), 5236. doi: 10.3390/s19235236. [88] Ioffe, S., & Szegedy, C. (2015, June). Batch normalization: Accelerating deep network training by reducing internal covariate shift. In International conference on machine learning (pp. 448-456). PMLR. [89] Srivastava, S., & Lessmann, S. (2018). A comparative study of LSTM neural networks in forecasting day-ahead global horizontal irradiance with satellite data. Solar Energy, 162, 232-247. doi: 10.1016/j.solener.2018.01.005. [90] Zhang, M., Li, J., Li, Y., & Xu, R. (2021). Deep learning for short-term voltage stability assessment of power systems. IEEE Access, 9, 29711-29718. doi: 10.1109/ACCESS.2021.3057659. [91] Ko, M. S., Lee, K., Kim, J. K., Hong, C. W., Dong, Z. Y., & Hur, K. (2020). Deep concatenated residual network with bidirectional LSTM for one-hour-ahead wind power forecasting. IEEE Transactions on Sustainable Energy, 12(2), 1321-1335. doi: 10.1109/TSTE.2020.3043884. [92] Wang, K., Qi, X., & Liu, H. (2019). Photovoltaic power forecasting based LSTM-Convolutional Network. Energy, 189, 116225. doi: 10.1016/j.energy.2019.116225. [93] Wang, W., Hong, T., Xu, X., Chen, J., Liu, Z., & Xu, N. (2019). Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm. Applied Energy, 248, 217-230. doi: 10.1016/j.apenergy.2019.04.085. [94] Schuster, M., & Paliwal, K. K. (1997). Bidirectional recurrent neural networks. IEEE transactions on Signal Processing, 45(11), 2673-2681. doi: 10.1109/78.650093. [95] Liu, S., Lee, K., & Lee, I. (2020). Document-level multi-topic sentiment classification of email data with bilstm and data augmentation. Knowledge-Based Systems, 197, 105918. doi: 10.1016/j.knosys.2020.105918. [96] Liu, G., & Guo, J. (2019). Bidirectional LSTM with attention mechanism and convolutional layer for text classification. Neurocomputing, 337, 325-338. doi: 10.1016/j.neucom.2019.01.078. [97] Tarvainen, A., & Valpola, H. (2017). Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results. Advances in neural information processing systems, 30. [98] Kingma, D. P., & Ba, J. (2014). Adam: A method for stochastic optimization. arXiv preprint arXiv:1412.6980. [99] Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. MIT press. [100] Kelly, Jason P. MD; James, Michelle A. MD. Radiographic Outcomes of Hemiepiphyseal Stapling for Distal Radius Deformity Due to Multiple Hereditary Exostoses. Journal of Pediatric Orthopaedics 36(1):p 42-47, January 2016. | DOI: 10.1097/BPO.0000000000000394. [101] Murray, D., Liao, J., Stankovic, L., Stankovic, V., Hauxwell-Baldwin, R., Wilson, C., ... & Firth, S. (2015). A data management platform for personalised real-time energy feedback. [102] Lipton, Z. C., Elkan, C., & Narayanaswamy, B. (2014). Thresholding classifiers to maximize F1 score. arXiv preprint arXiv:1402.1892. [103] Harbecke, D., Chen, Y., Hennig, L., & Alt, C. (2022). Why only Micro-F1? Class Weighting of Measures for Relation Classification. arXiv preprint arXiv:2205.09460.