A review of electric load classification in smart grid environment

Zhou, Kai-le; Yang, Shan-lin; Shen, Chao

doi:10.1016/j.rser.2013.03.023

Cited by 254Smart Grid Energy ManagementEnergy Load and Power ForecastingSmart Grid and Power Systems

A review of electric load classification in smart grid environment

Zhou, Kai-le, Yang, Shan-lin, Shen, Chao·2013·Renewable and Sustainable Energy Reviews

DOI 10.1016/j.rser.2013.03.023

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Using ML, we can model and cluster individuals based on their climate knowledge, preferences, demographics, and consumption characteristics, and thus predict who will be most amenable to new technologies and sustainable behavior change.
“Using ML, we can model and cluster individuals based on their climate knowledge, preferences, demographics, and consumption characteristics (e.g., [66,116,166,261,861]), and thus predict who will be most amenable to new technologies and sustainable behavior change.”
From Tackling climate change with machine learning (2023) · cites this paper as [861]

Untitled work
(1967) · Cited by 23,719 · see citing works
The self-organizing map
Kohonen, T. (1990) · Proceedings of the IEEE · doi:10.1109/5.58325 · Cited by 8,414 · see citing works
Algorithms for Clustering Data
Sarle, W. S., Jain, A. K., & Dubes, R. C. (1990) · Technometrics · doi:10.2307/1268876 · Cited by 7,804 · see citing works
FCM: The fuzzy c-means clustering algorithm
Bezdek, J. C., Ehrlich, R., & Full, W. E. (1984) · Computers & Geosciences · doi:10.1016/0098-3004(84)90020-7 · Cited by 6,706 · see citing works
A Fuzzy Relative of the ISODATA Process and Its Use in Detecting Compact Well-Separated Clusters
Dunn, J. C. (1973) · Journal of Cybernetics · doi:10.1080/01969727308546046 · Cited by 6,682 · see citing works
Survey of Clustering Algorithms
Xu, R., & Wunsch, D. C. (2005) · IEEE Transactions on Neural Networks · doi:10.1109/tnn.2005.845141 · Cited by 6,223 · see citing works
Hierarchical Clustering Schemes
Johnson, S. C. (1967) · Psychometrika · doi:10.1007/bf02289588 · Cited by 4,948 · see citing works
An Examination of Procedures for Determining the Number of Clusters in a Data Set
Milligan, G. W., & Cooper, M. C. (1985) · Psychometrika · doi:10.1007/bf02294245 · Cited by 3,891 · see citing works
A validity measure for fuzzy clustering
Xie, X., & Beni, G. (1991) · IEEE Transactions on Pattern Analysis and Machine Intelligence · doi:10.1109/34.85677 · Cited by 3,421 · see citing works
On cluster validity for the fuzzy c-means model
Pal, N. R., & Bezdek, J. C. (1995) · IEEE Transactions on Fuzzy Systems · doi:10.1109/91.413225 · Cited by 1,859 · see citing works
Some new indexes of cluster validity
Bezdek, J. C., & Pal, N. R. (1998) · IEEE Transactions on Systems Man and Cybernetics Part B (Cybernetics) · doi:10.1109/3477.678624 · Cited by 1,147 · see citing works
QUADRATIC ASSIGNMENT AS A GENERAL DATA ANALYSIS STRATEGY
Hubert, L. J., & Schultz, J. V. (1976) · British Journal of Mathematical and Statistical Psychology · doi:10.1111/j.2044-8317.1976.tb00714.x · Cited by 835 · see citing works
Numerical taxonomy with fuzzy sets
Bezdek, J. C. (1974) · Journal of Mathematical Biology · doi:10.1007/bf02339490 · Cited by 591 · see citing works
On fuzzy cluster validity indices
Wang, W., & Zhang, Y. (2007) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2007.03.004 · Cited by 531 · see citing works
A cluster validity index for fuzzy clustering
Wu, K.-L., & Yang, M. (2004) · Pattern Recognition Letters · doi:10.1016/j.patrec.2004.11.022 · Cited by 445 · see citing works
Validity-guided (re)clustering with applications to image segmentation
Bensaid, A. M., Hall, L., Bezdek, J. C., Clarke, L. P., Silbiger, M. L., Arrington, J. A., & Murtagh, R. F. (1996) · IEEE Transactions on Fuzzy Systems · doi:10.1109/91.493905 · Cited by 428 · see citing works
A new cluster validity measure and its application to image compression
Chou, C.-H., Su, M., & Lai, E. (2004) · Pattern Analysis and Applications · doi:10.1007/s10044-004-0218-1 · Cited by 328 · see citing works
Clustering analysis of residential electricity demand profiles
Rhodes, J. D., Cole, W., Upshaw, C. R., Edgar, T. F., & Webber, M. E. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.08.111 · Cited by 292 · see citing works
Customer characterization options for improving the tariff offer
Chicco, G., Napoli, R., Postolache, P., Scutariu, M., & Toader, C. (2003) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2002.807085 · Cited by 292 · see citing works
New indices for cluster validity assessment
Kim, M., & Ramakrishna, R. S. (2005) · Pattern Recognition Letters · doi:10.1016/j.patrec.2005.04.007 · Cited by 271 · see citing works
A review and an analysis of the residential electric load curve models
Grandjean, A., Adnot, J., & Binet, G. (2012) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2012.08.013 · Cited by 270 · see citing works
Nonparametric genetic clustering: comparison of validity indices
Bandyopadhyay, S., & Maulik, U. (2001) · IEEE Transactions on Systems Man and Cybernetics Part C (Applications and Reviews) · doi:10.1109/5326.923275 · Cited by 266 · see citing works
Measuring the Power of Hierarchical Cluster Analysis
Baker, F. B., & Hubert, L. J. (1975) · Journal of the American Statistical Association · doi:10.1080/01621459.1975.10480256 · Cited by 253 · see citing works
A study of some fuzzy cluster validity indices, genetic clustering and application to pixel classification
Pakhira, M. K., Bandyopadhyay, S., & Maulik, U. (2005) · Fuzzy Sets and Systems · doi:10.1016/j.fss.2005.04.009 · Cited by 216 · see citing works
Data-based method for creating electricity use load profiles using large amount of customer-specific hourly measured electricity use data
Räsänen, T., Voukantsis, D., Niska, H., Karatzas, K. D., & Kolehmainen, M. (2010) · Applied Energy · doi:10.1016/j.apenergy.2010.05.015 · Cited by 210 · see citing works
A review of Integration, Control, Communication and Metering (ICCM) of renewable energy based smart grid
Reddy, K. S., Kumar, M., Mallick, T. K., Sharon, H., & Lokeswaran, S. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.05.049 · Cited by 187 · see citing works
Load Pattern-Based Classification of Electricity Customers
Chicco, G., Napoli, R., Piglione, F., Postolache, P., Scutariu, M., & Toader, C. (2004) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2004.826810 · Cited by 182 · see citing works
Customer Classification and Load Profiling Method for Distribution Systems
Mutanen, A., Ruska, M., Repo, S., & Järventausta, P. (2011) · IEEE Transactions on Power Delivery · doi:10.1109/tpwrd.2011.2142198 · Cited by 181 · see citing works
Support Vector Clustering of Electrical Load Pattern Data
Chicco, G., & Ilie, I.-S. (2009) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2009.2023009 · Cited by 142 · see citing works
Short-term prediction of household electricity consumption: Assessing weather sensitivity in a Mediterranean area
Beccali, M., Cellura, M., Brano, V. L., & Marvuglia, A. (2007) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2007.04.010 · Cited by 141 · see citing works
Methods for customer and demand response policies selection in new electricity markets
Valero, S., Ortíz, M., Senabre, C., Álvarez-Bel, C., Franco, F. J. G., & Gabaldón, A. (2007) · IET Generation Transmission & Distribution · doi:10.1049/iet-gtd:20060183 · Cited by 139 · see citing works
A Bounded Index for Cluster Validity
Saitta, S., Raphael, B., & Smith, I. F. C. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-73499-4_14 · Cited by 127 · see citing works
2003 IEEE Bologna PowerTech - Conference Proceedings
(2003) · doi:10.1109/ptc.2003.1304691 · Cited by 112 · see citing works
Feed-in tariff outlook in Malaysia
Chua, S. C., Oh, T. H., & Goh, W. W. (2010) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2010.09.009 · Cited by 107 · see citing works
SEP/COP: An efficient method to find the best partition in hierarchical clustering based on a new cluster validity index
Gurrutxaga, I., Albisua, I., Arbelaitz, O., Martin, J. I., Muguerza, J., Pérez, J. M., & Perona, I. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.04.021 · Cited by 99 · see citing works
Building load management using cluster and statistical analyses
Jota, P. R. S., Silva, V. R. B., & Jota, F. G. (2011) · International Journal of Electrical Power & Energy Systems · doi:10.1016/j.ijepes.2011.06.034 · Cited by 87 · see citing works
Hopfield–K-Means clustering algorithm: A proposal for the segmentation of electricity customers
López, J. J., Aguado, J. A., Martín, F., Muñoz, F., Rodríguez, A., & Ruiz, J. E. (2010) · Electric Power Systems Research · doi:10.1016/j.epsr.2010.10.036 · Cited by 86 · see citing works
An annual framework for clustering-based pricing for an electricity retailer
Mahmoudi-Kohan, N., Moghaddam, M. P., & Sheikh‐El‐Eslami, M. K. (2010) · Electric Power Systems Research · doi:10.1016/j.epsr.2010.01.010 · Cited by 76 · see citing works
Methods for generating TLPs (typical load profiles) for smart grid-based energy programs
Kim, Y., Ko, J.-M., & Choi, S.-H. (2011) · doi:10.1109/ciasg.2011.5953331 · Cited by 58 · see citing works
Cluster validity index for estimation of fuzzy clusters of different sizes and densities
Žalik, K. R. (2010) · Pattern Recognition · doi:10.1016/j.patcog.2010.04.025 · Cited by 57 · see citing works
Characterization and identification of electrical customers through the use of self-organizing maps and daily load parameters
Verdu, S. V., García, M. O., Franco, F. J. G., Encinas, N., Marin, A. G., Molina, A., & Lazaro, E. G. (2005) · IEEE PES Power Systems Conference and Exposition · doi:10.1109/psce.2004.1397641 · Cited by 53 · see citing works
Determination and allocation of typical load profiles to the eligible consumers
Gerbec, D., Gašperič, S., & Gubina, F. (2004) · doi:10.1109/ptc.2003.1304159 · Cited by 46 · see citing works
Construction of China's smart grid information system analysis
Wu, Y., Chen, J., & Liu, L. (2011) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2011.07.129 · Cited by 41 · see citing works
Optimized Clusters for Disaggregated Electricity Load Forecasting
Misiti, M., Misiti, Y., Oppenheim, G., & Poggi, J. (2022) · doi:10.57805/revstat.v8i2.92 · Cited by 36 · see citing works
Two-stage fuzzy clustering approach for load profiling
Zakaria, Z., & Lo, K. L. (2009) · Cited by 28 · see citing works
Current situation and development of the methods on bad-data detection and identification of power system
Xinli, J. (2010) · Power System Protection and Control · Cited by 19 · see citing works
Study of power load classification based on adaptive fuzzy C means
Niu, Q. (2010) · Power System Protection and Control · Cited by 17 · see citing works
Comparison of several clustering methods in the case of electrical load curves classification
Bidoki, S. M., Mahmoudi-Kohan, N., & Gerami, S. (2011) · Cited by 16 · see citing works
Fuzzy classification of electrical load demand profiles - a case study
Özveren, C. S. (2002) · doi:10.1049/cp:20020061 · Cited by 16 · see citing works
An improved fuzzy C-means algorithm for power load characteristics classification
Kai-le, Z. (2012) · Power System Protection and Control · Cited by 16 · see citing works
Electricity demand supply analysis: Current status and future prospects for Maharashtra, India
Kale, R., & Pohekar, S. D. (2012) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2012.03.008 · Cited by 14 · see citing works
THE CHARACTERISTICS CLUSTERING AND SYNTHESIS OF ELECTRIC DYNAMIC LOADS BASED ON KOHONEN NEURAL NETWORK
Liu, Y.-M. (2003) · Proceedings of the CSEE · Cited by 14 · see citing works
Customer clustering and TDLP (typical daily load profile) generation using the clustering algorithm
Kim, Y., Shin, J., Song, J.-J., & Yang, I.-K. (2009) · Transmission & Distribution Conference & Exposition: Asia and Pacific · doi:10.1109/td-asia.2009.5356926 · Cited by 13 · see citing works
Efficient iterative refinement clustering for electricity customer classification
Batrinu, F., Chicco, G., Napoli, R., Piglione, F., Postolache, P., Scutariu, M., & Toader, C. (2005) · doi:10.1109/ptc.2005.4524366 · Cited by 13 · see citing works
Application of Honey Bee Mating Optimization algorithm to load profile clustering
Gavrilaș, M., Gavrilas, G., & Sfintes, C. V. (2010) · IEEE International Conference on Computational Intelligence for Measurement Systems and Applications (CIMSA) · doi:10.1109/cimsa.2010.5611759 · Cited by 13 · see citing works
Daily typical load clustering of residential customers
Sathiracheewin, S., & Surapatana, V. (2011) · doi:10.1109/ecticon.2011.5947960 · Cited by 12 · see citing works
Short-Term Power Load Forecasting Using Improved Ant Colony Clustering
Li, W., & Han, Z.-H. (2008) · doi:10.1109/wkdd.2008.30 · Cited by 12 · see citing works
The application of artificial immune network in load classification
Gu, D., Ai, Q., & Chen, C. (2008) · IEEE International Conference on Electric Utility Deregulation, Restructuring and Power Technologies · doi:10.1109/drpt.2008.4523624 · Cited by 12 · see citing works
Clustering method of fuzzy equivalence matrix to bad-data detection and identification
Wang, X. (2011) · Power System Protection and Control · Cited by 11 · see citing works
Dynamic Intelligent Cleaning for Dirty Electric Load Data Based on Data Mining
Zhang, X. (2005) · Dianli xitong zidonghua · Cited by 8 · see citing works
A New Classification Method for Aggregate Load Dynamic Characteristics Based on Field Measured Response
Wai-wen, T. (2006) · Proceedings of the CSEE · Cited by 8 · see citing works
A functional model of the rat kidney
Kainer, R. (1979) · Journal of Mathematical Biology · doi:10.1007/bf00276414 · Cited by 6 · see citing works
Study on consumer classification differentiated pricing of electricity
He, N. (2004) · Electric Power · Cited by 6 · see citing works
A Classification Method for Aggregated Load Dynamic Characteristics
Wai-wen, T. (2005) · Dianli xitong zidonghua · Cited by 6 · see citing works
Load profiling with fuzzy self-organizing algorithms
Gavrilaș, M., Ivanov, O., & Gavrilas, G. (2008) · Symposium on Neural Network Applications in Electrical Engineering · doi:10.1109/neurel.2008.4685564 · Cited by 5 · see citing works
CLUSTERING ANALYSIS OF POWER SYSTEM LOAD SERIES BASED ON ANT COLONY OPTIMIZATION ALGORITHM
Liu, S. (2005) · Proceedings of the CSEE · Cited by 5 · see citing works
Application of k-means Algorithm Based on Effective Index to Identifying Bad Data of the Power System
Deng-hu, Z. (2010) · Northeast Electric Power Technology · Cited by 3 · see citing works

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Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
Wang, Y., Chen, Q., Hong, T., & Kang, C. (2018) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2018.2818167 · Cited by 1,384 · see citing works
Big data driven smart energy management: from big data to big insights
Zhou, K., Fu, C., & Yang, S. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.11.050 · Cited by 841 · see citing works
Self-citation
Electricity load forecasting: a systematic review
Nti, I. K., Teimeh, M., Nyarko‐Boateng, O., & Adekoya, A. F. (2020) · Journal of Electrical Systems and Information Technology · doi:10.1186/s43067-020-00021-8 · Cited by 400 · see citing works
Understanding household energy consumption behavior: The contribution of energy big data analytics
Zhou, K., & Yang, S. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.12.001 · Cited by 406 · see citing works
Self-citation
A comprehensive overview on the data driven and large scale based approaches for forecasting of building energy demand: A review
Ahmad, T., Chen, H., Guo, Y., & Wang, J. (2018) · Energy and Buildings · doi:10.1016/j.enbuild.2018.01.017 · Cited by 384 · see citing works
A review of wireless communications for smart grid
Mahmood, A., Javaid, N., & Razzaq, S. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.08.036 · Cited by 359 · see citing works
Big data analytics for dynamic energy management in smart grids
Diamantoulakis, P. D., Kapinas, V. M., & Karagiannidis, G. K. (2015) · Big Data Research · doi:10.1016/j.bdr.2015.03.003 · Cited by 349 · see citing works
Clustering of Electricity Consumption Behavior Dynamics Toward Big Data Applications
Wang, Y., Chen, Q., Kang, C., & Xia, Q. L. (2016) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2016.2548565 · Cited by 343 · see citing works
Energy Internet: The business perspective
Zhou, K., Yang, S., & Shao, Z. (2016) · Applied Energy · doi:10.1016/j.apenergy.2016.06.052 · Cited by 347 · see citing works
Self-citation
Clustering analysis of residential electricity demand profiles
Rhodes, J. D., Cole, W., Upshaw, C. R., Edgar, T. F., & Webber, M. E. (2014) · Applied Energy · doi:10.1016/j.apenergy.2014.08.111 · Cited by 292 · see citing works
Big data analytics in smart grids: state‐of‐the‐art, challenges, opportunities, and future directions
Bhattarai, B. P., Paudyal, S., Luo, Y., Mohanpurkar, M. U., Cheung, K. W., Tonkoski, R., Hovsapian, R. O., Myers, K. S., Zhang, R., Zhao, P. Q., et al. (2019) · IET Smart Grid · doi:10.1049/iet-stg.2018.0261 · Cited by 292 · see citing works
Data science for building energy management: A review
Molina-Solana, M., Ros, M., Ruiz, M. D., Gómez‐Romero, J., & Martín-Bautista, M. J. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.11.132 · Cited by 286 · see citing works
Load profiling and its application to demand response: A review
Wang, Y., Chen, Q., Kang, C., Zhang, M., Wang, K., & Zhao, Y. T. (2015) · Tsinghua Science & Technology · doi:10.1109/tst.2015.7085625 · Cited by 268 · see citing works
A comparative study of clustering techniques for electrical load pattern segmentation
Rajabi, A., Eskandari, M., Ghadi, M. J., Li, L., Zhang, J.-F., & Siano, P. (2020) · Renewable & Sustainable Energy Reviews · doi:10.1016/j.rser.2019.109628 · Cited by 192 · see citing works
Demand side management of electric vehicles in smart grids: A survey on strategies, challenges, modeling, and optimization
Mohanty, S., Panda, S., Parida, S. M., Rout, P. K., Sahu, B. K., Bajaj, M., Zawbaa, H. M., Kumar, N. M., & Kamel, S. M. (2022) · Energy Reports · doi:10.1016/j.egyr.2022.09.023 · Cited by 195 · see citing works
Tackling climate change with machine learning
Rolnick, D., Donti, P. L., Kaack, L. H., Kochanski, K., Lacoste, A., Sankaran, K., Ross, A. S., Milojevic-Dupont, N., Jaques, N., Waldman-Brown, A., et al. (2023) · ACM Computing Surveys · doi:10.1145/3485128 · Cited by 336 · see citing works
A review of Integration, Control, Communication and Metering (ICCM) of renewable energy based smart grid
Reddy, K. S., Kumar, M., Mallick, T. K., Sharon, H., & Lokeswaran, S. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.05.049 · Cited by 187 · see citing works
Smart Grid Big Data Analytics: Survey of Technologies, Techniques, and Applications
Syed, D., Zainab, A., Ghrayeb, A., Refaat, S. S., Abu‐Rub, H., & Bouhali, O. (2020) · IEEE Access · doi:10.1109/access.2020.3041178 · Cited by 173 · see citing works
Blockchain and AI amalgamation for energy cloud management: Challenges, solutions, and future directions
Kumari, A., Gupta, R., Tanwar, S., & Kumar, N. (2020) · Journal of Parallel and Distributed Computing · doi:10.1016/j.jpdc.2020.05.004 · Cited by 172 · see citing works
State-of-the-art review on advancements of data mining in structural health monitoring
Gordan, M., Sabbagh-Yazdi, S.-R., Ismail, Z., Ghaedi, K., Carroll, P., McCrum, D. P., & Samali, B. (2022) · Measurement · doi:10.1016/j.measurement.2022.110939 · Cited by 155 · see citing works
Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings
Capozzoli, A., Piscitelli, M. S., Brandi, S., Grassi, D., & Chicco, G. (2018) · Energy · doi:10.1016/j.energy.2018.05.127 · Cited by 154 · see citing works
A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings
Miller, C. C., Nagy, Z., & Schlueter, A. (2017) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2017.05.124 · Cited by 153 · see citing works
A Model of Customizing Electricity Retail Prices Based on Load Profile Clustering Analysis
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Dong, K. Y., Sun, R., Li, H., & Jiang, H.-D. (2016) · Petroleum Science · doi:10.1007/s12182-016-0136-z · Cited by 127 · see citing works
Big Data Issues in Smart Grids: A Survey
Ghorbanian, M., Dolatabadi, S. H., & Siano, P. (2019) · IEEE Systems Journal · doi:10.1109/jsyst.2019.2931879 · Cited by 123 · see citing works
Household monthly electricity consumption pattern mining: A fuzzy clustering-based model and a case study
Zhou, K., Yang, S., & Shao, Z. (2016) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2016.09.165 · Cited by 121 · see citing works
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Data quality of electricity consumption data in a smart grid environment
Chen, W., Zhou, K., Yang, S., & Wu, C. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.10.054 · Cited by 105 · see citing works
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Zhou, K., Yang, S., Chen, Z., & Ding, S. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.04.028 · Cited by 96 · see citing works
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Potential of unsubsidized distributed solar PV to replace coal-fired power plants, and profits classification in Chinese cities
Yang, Y., Campana, P. E., & Yan, J. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109967 · Cited by 82 · see citing works
Building power demand response methods toward smart grid
Wang, S., Xue, X., & Yan, C. (2014) · Science and Technology for the Built Environment · doi:10.1080/10789669.2014.929887 · Cited by 81 · see citing works
Federated Clustering for Electricity Consumption Pattern Extraction
Wang, Y., Jia, M., Gao, N., Krannichfeldt, L. V., Sun, M., & Hug, G. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3146489 · Cited by 80 · see citing works
Exploring the uniform effect of FCM clustering: A data distribution perspective
Zhou, K., & Yang, S. (2016) · Knowledge-Based Systems · doi:10.1016/j.knosys.2016.01.001 · Cited by 77 · see citing works
Self-citation
An Insight into the Integration of Distributed Energy Resources and Energy Storage Systems with Smart Distribution Networks Using Demand-Side Management
Panda, S., Mohanty, S., Rout, P. K., Sahu, B. K., Parida, S. M., Kotb, H., Flah, A., Tostado‐Véliz, M., Samad, B. A., & Shouran, M. (2022) · Applied Sciences · doi:10.3390/app12178914 · Cited by 72 · see citing works
Smart meter data analytics applications for secure, reliable and robust grid system: Survey and future directions
Mitra, S., Chakraborty, B., & Mitra, P. (2023) · Energy · doi:10.1016/j.energy.2023.129920 · Cited by 72 · see citing works
Secure data analytics for smart grid systems in a sustainable smart city: Challenges, solutions, and future directions
Kumari, A., & Tanwar, S. (2020) · Sustainable Computing Informatics and Systems · doi:10.1016/j.suscom.2020.100427 · Cited by 69 · see citing works
A review of distribution network applications based on smart meter data analytics
Athanasiadis, C. L., Papadopoulos, T. A., Kryonidis, G. C., & Doukas, D. I. (2023) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2023.114151 · Cited by 69 · see citing works
Opportunities and challenges in control of smart grids – Pakistani perspective
Irfan, M., Iqbal, J., Iqbal, A., Iqbal, Z., Riaz, R. A., & Mehmood, A. (2016) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2016.12.095 · Cited by 67 · see citing works
Characterizing patterns and variability of building electric load profiles in time and frequency domains
Li, H., Wang, Z., Hong, T., Parker, A., & Neukomm, M. (2021) · Applied Energy · doi:10.1016/j.apenergy.2021.116721 · Cited by 61 · see citing works
An Embedded Deep-Clustering-Based Load Profiling Framework
Eskandarnia, E., Al-Ammal, H., & Ksantini, R. (2021) · Sustainable cities and society · doi:10.1016/j.scs.2021.103618 · Cited by 59 · see citing works
Smart Grid and its future perspectives in Australia
Haidar, A. M. A., Muttaqi, K. M., & Sutanto, D. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.07.040 · Cited by 60 · see citing works
Efficient methods for approximating the Shapley value for asset sharing in energy communities
Cremers, S., Robu, V., Zhang, P., Andoni, M., Norbu, S., & Flynn, D. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.120328 · Cited by 59 · see citing works
Identifying household electricity consumption patterns: A case study of Kunshan, China
Yang, T., Ren, M., & Zhou, K. (2018) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2018.04.037 · Cited by 58 · see citing works
Self-citation
An overview of load management techniques in smart grid
Mahmood, A., Javaid, N., Khan, M. A., & Razzaq, S. (2015) · International Journal of Energy Research · doi:10.1002/er.3350 · Cited by 57 · see citing works
Mining typical load profiles in buildings to support energy management in the smart city context
Capozzoli, A., Piscitelli, M. S., & Brandi, S. (2017) · Energy Procedia · doi:10.1016/j.egypro.2017.09.545 · Cited by 55 · see citing works
Online adaptive clustering algorithm for load profiling
Ray, G. L., & Pinson, P. (2018) · Sustainable Energy Grids and Networks · doi:10.1016/j.segan.2018.100181 · Cited by 50 · see citing works
Control of a boiler-turbine unit using MPC-based reference governors
Klaučo, M., & Kvasnica, M. (2016) · Applied Thermal Engineering · doi:10.1016/j.applthermaleng.2016.09.041 · Cited by 49 · see citing works
A Hybrid Machine Learning Model for Electricity Consumer Categorization Using Smart Meter Data
Jiang, Z., Lin, R., & Yang, F. (2018) · Energies · doi:10.3390/en11092235 · Cited by 49 · see citing works
Demand side management in hybrid rooftop photovoltaic integrated smart nano grid
Kalair, A. R., Abas, N., Hasan, Q. U., Seyedmahmoudian, M., & Khan, N. (2020) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2020.120747 · Cited by 49 · see citing works
Electricity clustering framework for automatic classification of customer loads
Biscarri, F., Monedero, Í., García, A., Guerrero, J. I., & León, C. (2017) · Expert Systems with Applications · doi:10.1016/j.eswa.2017.05.049 · Cited by 45 · see citing works
Machine Learning for Identifying Demand Patterns of Home Energy Management Systems with Dynamic Electricity Pricing
Koolen, D., Sadat-Razavi, N., & Ketter, W. (2017) · Applied Sciences · doi:10.3390/app7111160 · Cited by 45 · see citing works
Predicting Consumer Load ProfilesUsing Commercial and Open Data
Vercamer, D., Steurtewagen, B., den Poel, D. V., & Vermeulen, F. (2015) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2015.2493083 · Cited by 43 · see citing works
Multi-Layered Clustering for Power Consumption Profiling in Smart Grids
Al-Jarrah, O. Y., Al-Hammadi, Y., Yoo, P. D., & Muhaidat, S. (2017) · IEEE Access · doi:10.1109/access.2017.2712258 · Cited by 41 · see citing works
Real Time Energy Performance Control for Industrial Compressed Air Systems: Methodology and Applications
Benedetti, M., Bonfà, F., Introna, V., Santolamazza, A., & Ubertini, S. (2019) · Energies · doi:10.3390/en12203935 · Cited by 41 · see citing works
Urban power load profiles under ageing transition integrated with future EVs charging
Zhang, H., Chen, J., Yan, J., Song, X., Shibasaki, R., & Yan, J. (2021) · Advances in Applied Energy · doi:10.1016/j.adapen.2020.100007 · Cited by 40 · see citing works
How Smart Meter Data Analysis Can Support Understanding the Impact of Occupant Behavior on Building Energy Performance: A Comprehensive Review
Adams, J. N., Bélafi, Z. D., Horváth, M., Kocsis, J. B., & Csoknyai, T. (2021) · Energies · doi:10.3390/en14092502 · Cited by 39 · see citing works
An adaptive hybrid ensemble with pattern similarity analysis and error correction for short-term load forecasting
Laouafi, A., Laouafi, F., & Boukelia, T. E. (2022) · Applied Energy · doi:10.1016/j.apenergy.2022.119525 · Cited by 38 · see citing works
A smart monitoring infrastructure design for distributed renewable energy systems
Kabalcı, E. (2014) · Energy Conversion and Management · doi:10.1016/j.enconman.2014.10.062 · Cited by 36 · see citing works
A review on clustering of residential electricity customers and its applications
Rajabi, A., Li, L., Zhang, J., Zhu, J. G., Ghavidel, S., & Ghadi, M. J. (2017) · International Conference on Electrical Machines and Systems (ICEMS) · doi:10.1109/icems.2017.8056062 · Cited by 35 · see citing works
A bibliometric survey on incremental clustering algorithm for electricity smart meter data analysis
Chaudhari, A. Y., & Mulay, P. (2019) · Iran Journal of Computer Science · doi:10.1007/s42044-019-00043-0 · Cited by 35 · see citing works
Smart grid public datasets: Characteristics and associated applications
Altamimi, E., Al-Ali, A. K., Malluhi, Q. M., & Al-Ali, A. K. (2024) · IET Smart Grid · doi:10.1049/stg2.12161 · Cited by 34 · see citing works
A Mixed Strategy Based on Self-Organizing Map for Water Demand Pattern Profiling of Large-Size Smart Water Grid Data
Padulano, R., & Giudice, G. D. (2018) · Water Resources Management · doi:10.1007/s11269-018-2012-7 · Cited by 32 · see citing works
Machine learning based energy management system for grid disaster mitigation
Maharjan, L., Ditsworth, M., Niraula, M., Caicedo-Narvaez, C., & Fahimi, B. (2018) · IET Smart Grid · doi:10.1049/iet-stg.2018.0043 · Cited by 32 · see citing works
Optimization and loss estimation in energy-deficient polygeneration systems: A case study of Pakistan's utilities with integrated renewable energy
Saleem, M. S., & Abas, N. (2025) · Results in Engineering · doi:10.1016/j.rineng.2025.104001 · Cited by 31 · see citing works
Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting
Auder, B., Cugliari, J., Goude, Y., & Poggi, J. (2018) · Energies · doi:10.3390/en11071893 · Cited by 31 · see citing works
Analytics framework for optimal smart meters data processing
Alquthami, T., AlAmoudi, A., Alsubaie, A. M., Jaber, A. B., Alshlwan, N., Anwar, M., & Husaien, S. A. (2020) · Electrical Engineering · doi:10.1007/s00202-020-00949-0 · Cited by 31 · see citing works
Classification and characterization of intra-day load curves of PV and non-PV households using interpretable feature extraction and feature-based clustering
Hu, M., Ge, D., Telford, R., Stephen, B., & Wallom, D. C. H. (2021) · Sustainable Cities and Society · doi:10.1016/j.scs.2021.103380 · Cited by 31 · see citing works
Sustainable energy technologies and environmental impacts of energy systems
Vujanović, M., Wang, Q., Mohsen, M. S., Duić, N., & Yan, J. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.113919 · Cited by 29 · see citing works
Clustering of Load Profiles of Residential Customers Using Extreme Points and Demographic Characteristics
Jeong, H. C., Jang, M., Kim, T., & Joo, S. (2021) · Electronics · doi:10.3390/electronics10030290 · Cited by 28 · see citing works
Adversarial Attack and Defence Strategies for Deep-Learning-Based IoT Device Classification Techniques
Singh, A., & Sikdar, B. (2021) · IEEE Internet of Things Journal · doi:10.1109/jiot.2021.3138541 · Cited by 31 · see citing works
A Data Analytics/Big Data Framework for Advanced Metering Infrastructure Data
Guerrero-Prado, J. S., Alfonso-Morales, W., & Caicedo, E. F. (2021) · Sensors · doi:10.3390/s21165650 · Cited by 27 · see citing works
Fuzzy clustering and prediction of electricity demand based on household characteristics
Viegas, J. L., Vieira, S. M., & Sousa, J. M. C. (2015) · Advances in intelligent systems research/Advances in Intelligent Systems Research · doi:10.2991/ifsa-eusflat-15.2015.147 · Cited by 27 · see citing works
Knowledge-Mining the Australian Smart Grid Smart City Data: A Statistical-Neural Approach to Demand-Response Analysis
Motlagh, O., Foliente, G., & Grozev, G. (2015) · Lecture notes in geoinformation and cartography · doi:10.1007/978-3-319-18368-8_10 · Cited by 27 · see citing works
Integration Challenges and Solutions for Renewable Energy Sources, Electric Vehicles and Demand-Side Initiatives in Smart Grids
Yeşilbudak, M., & Colak, A. (2018) · International Conference on Renewable Energy Research and Applications (ICRERA) · doi:10.1109/icrera.2018.8567004 · Cited by 25 · see citing works
Combined Cluster Analysis and Global Power Quality Indices for the Qualitative Assessment of the Time-Varying Condition of Power Quality in an Electrical Power Network with Distributed Generation
Jasiński, M., Sikorski, T., Kostyła, P., Leonowicz, Z. M., & Borkowski, K. (2020) · Energies · doi:10.3390/en13082050 · Cited by 25 · see citing works
Differentially Private K-Means Clustering Applied to Meter Data Analysis and Synthesis
Ravi, N., Scaglione, A., Kadam, S., Gentz, R., Peisert, S., Lunghino, B., Levijarvi, E., & Shumavon, A. (2022) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2022.3184252 · Cited by 25 · see citing works
Feature extraction and classification of time-varying power load characteristics based on PCANet and CNN+Bi-LSTM algorithms
Bian, S., Wang, Z., Song, W., & Zhou, X. (2023) · Electric Power Systems Research · doi:10.1016/j.epsr.2023.109149 · Cited by 25 · see citing works
A sub-sequence clustering method for identifying daily indoor environmental patterns from massive time-series data
Yang, G., Ji, W., Xie, Y., Lin, B., & Zhuang, W. (2022) · Automation in Construction · doi:10.1016/j.autcon.2022.104303 · Cited by 24 · see citing works
A Data-Driven Approach to Predict Hourly Load Profiles From Time-of-Use Electricity Bills
Lazzeroni, P., Lorenti, G., & Repetto, M. (2023) · IEEE Access · doi:10.1109/access.2023.3286020 · Cited by 24 · see citing works
Data-driven classification of residential energy consumption patterns by means of functional connectivity networks
Markovič, R., Gosak, M., Grubelnik, V., Marhl, M., & Virtič, P. (2019) · Applied Energy · doi:10.1016/j.apenergy.2019.03.134 · Cited by 24 · see citing works
Big Data Analysis and Visualization for the Smart Grid
Sanchez, A., & Rivera, W. (2017) · IEEE International Congress on Big Data (BigData Congress) · doi:10.1109/bigdatacongress.2017.59 · Cited by 22 · see citing works
Household Electricity Consumer Classification Using Novel Clustering Approach, Review, and Case Study
Ramnath, G. S., Harikrishnan, R., Muyeen, S. M., & Kotecha, K. V. (2022) · Electronics · doi:10.3390/electronics11152302 · Cited by 21 · see citing works
Gap imputation in related multivariate time series through recurrent neural network-based denoising autoencoder
Alonso, S., Morán, A., Pérez, D., Prada, M. A., Fuertes, J. J., & Domínguez, M. (2023) · Integrated Computer-Aided Engineering · doi:10.3233/ica-230728 · Cited by 21 · see citing works
A Novel Load Image Profile-Based Electricity Load Clustering Methodology
Park, K.-J., & Son, S.-Y. (2019) · IEEE Access · doi:10.1109/access.2019.2914216 · Cited by 20 · see citing works
A framework of service-oriented operation model of China׳s power system
Zhou, K., & Yang, S. (2015) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2015.05.041 · Cited by 19 · see citing works
Self-citation
k-ShapeStream: Probabilistic Streaming Clustering for Electric Grid Events
Bariya, M., von Meier, A., Paparrizos, J., & Franklin, M. J. M. (2021) · IEEE PowerTech Conference · doi:10.1109/powertech46648.2021.9494830 · Cited by 19 · see citing works
Power utilization strategy in smart residential community using non-cooperative game considering customer satisfaction and interaction
Li, C., Cai, W., Luo, H., & Zhang, Q. (2018) · Electric Power Systems Research · doi:10.1016/j.epsr.2018.10.006 · Cited by 19 · see citing works
Clustering-based novelty detection to uncover electricity theft
Viegas, J. L., & Vieira, S. M. (2017) · IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) · doi:10.1109/fuzz-ieee.2017.8015546 · Cited by 18 · see citing works
Disaggregated electricity forecasting using wavelet-based clustering of individual consumers
Cugliari, J., Goude, Y., & Poggi, J. (2016) · IEEE International Energy Conference (ENERGYCON) · doi:10.1109/energycon.2016.7514087 · Cited by 18 · see citing works

Dehydrogenation catalyst
(2013) · Focus on Catalysts · doi:10.1016/s1351-4180(13)70430-6 · Cited by 3 · see citing works
Fuzzy clustering algorithm-based classification of daily electrical load patterns
Sun, Y., Gu, W., Lu, J., & -, Z. Y. (2015) · International Conference on Fuzzy Systems and Knowledge Discovery (FSKD) · doi:10.1109/fskd.2015.7381913 · Cited by 11 · see citing works
Electric Load Clustering in Smart Grid: Methodologies, Applications, and Future Trends
Si, C., Xu, S., Wan, C., Chen, D., Cui, W., & Zhao, J. (2021) · Journal of Modern Power Systems and Clean Energy · doi:10.35833/mpce.2020.000472 · Cited by 121 · see citing works
Typical load profiles in the smart grid context — A clustering methods comparison
Ramos, S., Duarte, J., Soares, J., Vale, Z., & Duarte, F. J. F. (2012) · IEEE Power Engineering Society General Meeting · doi:10.1109/pesgm.2012.6345565 · Cited by 35 · see citing works
Improved Kernel Fuzzy C-Means Clustering Method Based on Smart Grid
Niu, S., Zhang, Z., Tian, X., & Zhao, X. (2022) · World Conference on Mechanical Engineering and Intelligent Manufacturing (WCMEIM) · doi:10.1109/wcmeim56910.2022.10021356

Authors: Zhou, K.-L., Yang, S.-L., & Shen, C.
Affiliations: Hefei University of Technology (CN)
ORCID: Shen, Chao
Venue: Renewable and Sustainable Energy Reviews
Publisher: Elsevier BV
Published: 2013-08
Volume: 24
Pages: 103-110
ISSN: 1364-0321 (ISSN-L)
DOI: 10.1016/j.rser.2013.03.023
OpenAlex: W2171208762
Cited by: 254
References: 72
Citations by year: 2026: 5 · 2025: 9 · 2024: 13 · 2023: 19 · 2022: 28 · 2021: 30 · 2020: 28 · 2019: 31 · 2018: 28 · 2017: 18 · 2016: 18 · 2015: 18 · 2014: 8 · 2013: 1
FWCI: 9.00
Type: journal article
Language: en
Open access: closed
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
Locations: Renewable and Sustainable Energy Reviews (publishedVersion)
RePEc: Research Papers in Economics (submittedVersion)
Topics: Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid Energy Management
Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
Physical Sciences › Engineering › Electrical and Electronic Engineering › Smart Grid and Power Systems
Keywords: smart grid, clustering algorithms, bad data detection, load forecasting, tariff setting, fuzzy c-means clustering

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