The DOI matches its Crossref record; 40 of the 40 references it cites were found in a registry or an index.
Load profiling and its application to demand response: A review
Open accessAbstract
The smart grid has been revolutionizing electrical generation and consumption through a two-way flow of power and information. As an important information source from the demand side, Advanced Metering Infrastructure (AMI) has gained increasing popularity all over the world. By making full use of the data gathered by AMI, stakeholders of the electrical industry can have a better understanding of electrical consumption behavior. This is a significant strategy to improve operation efficiency and enhance power grid reliability. To implement this strategy, researchers have explored many data mining techniques for load profiling. This paper performs a state-of-the-art, comprehensive review of these data mining techniques from the perspectives of different technical approaches including direct clustering, indirect clustering, clustering evaluation criteria, and customer segmentation. On this basis, the prospects for implementing load profiling to demand response applications, price-based and incentive-based, are further summarized. Finally, challenges and opportunities of load profiling techniques in future power industry, especially in a demand response world, are discussed.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Reducing the dimensionality of data with neural networks
Hinton, G. E., & Salakhutdinov, R. R. (2006) · Science · doi:10.1126/science.1127647 · Cited by 20,928
A symbolic representation of time series, with implications for streaming algorithms
Lin, J., Keogh, E., Lonardi, S., & Chiu, B. (2003) · Proceedings of the 8th ACM SIGMOD workshop on Research issues in data mining and knowledge discovery · doi:10.1145/882082.882086 · Cited by 1,799
Rifkin, J. (2011) · Cited by 1,321
Real-Time Demand Response Model
Conejo, A. J., Morales, J. M., & Baringo, L. (2010) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2010.2078843 · Cited by 966
Demand Response as a Market Resource Under the Smart Grid Paradigm
Rahimi, F., & Ipakchi, A. (2010) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2010.2045906 · Cited by 848
Benefits and challenges of electrical demand response: a critical review
O׳Connell, N., Pinson, P., Madsen, H., & O׳Malley, M. (2014) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2014.07.098 · Cited by 636
Comparisons Among Clustering Techniques for Electricity Customer Classification
Chicco, G., Napoli, R., & Piglione, F. (2006) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2006.873122 · Cited by 513
Household Energy Consumption Segmentation Using Hourly Data
Kwac, J., Flora, J., & Rajagopal, R. (2014) · IEEE Transactions on Smart Grid · doi:10.1109/tsg.2013.2278477 · Cited by 498
Overview and performance assessment of the clustering methods for electrical load pattern grouping
Chicco, G. (2012) · Energy · doi:10.1016/j.energy.2011.12.031 · Cited by 484
An Electric Energy Consumer Characterization Framework Based on Data Mining Techniques
Figueiredo, V., Rodrigues, F., Vale, Z., & Gouveia, J. B. (2005) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2005.846234 · Cited by 437
Cited by
Antonopoulos, I., Robu, V., Couraud, B., Kirli, D., Norbu, S., Kiprakis, A., Flynn, D., Elizondo-Gonzalez, S., & Wattam, S. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109899 · Cited by 656
From demand response to integrated demand response: review and prospect of research and application
Huang, W., Zhang, N., Kang, C., Li, M., & Huo, M. (2019) · Protection and Control of Modern Power Systems · doi:10.1186/s41601-019-0126-4 · Cited by 312
Self-citationData-Driven Probabilistic Net Load Forecasting With High Penetration of Behind-the-Meter PV
Wang, Y., Zhang, N., Chen, Q., Kirschen, D. S., Li, P., & Xia, Q. (2018) · IEEE Transactions on Power Systems · doi:10.1109/tpwrs.2017.2762599 · Cited by 292
Self-citationArtificial Intelligence Enabled Demand Response: Prospects and Challenges in Smart Grid Environment
Khan, M. A., Saleh, A. M., Waseem, M., & Sajjad, I. A. (2023) · IEEE Access · doi:10.1109/access.2022.3231444 · Cited by 179
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
Metadata
- Authors
- Wang, Y., Chen, Q., Kang, C., Zhang, M., Wang, K., & Zhao, Y.
- Venue
- Tsinghua Science and Technology
- Publisher
- Tsinghua University Press
- Published
- 2015-04
- Volume
- 20
- Issue
- 2
- Pages
- 117-129
- ISSN
- 1007-0214 (ISSN-L)
- OpenAlex
- W1543646709
- Cited by
- 268
- References
- 40
- FWCI
- 9.46
- Type
- journal article
- Language
- en
- Open access
- diamond
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Load profiling and its application to demand response: A review. https://app.cochat.ai/papers/10.1109/tst.2015.7085625
- The paper
- Wang, Y., Chen, Q., Kang, C., Zhang, M., Wang, K., & Zhao, Y. (2015). Load profiling and its application to demand response: A review. Tsinghua Science and Technology. https://doi.org/10.1109/tst.2015.7085625
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
