Abstract

The widespread popularity of smart meters enables an immense amount of\nfine-grained electricity consumption data to be collected. Meanwhile, the\nderegulation of the power industry, particularly on the delivery side, has\ncontinuously been moving forward worldwide. How to employ massive smart meter\ndata to promote and enhance the efficiency and sustainability of the power grid\nis a pressing issue. To date, substantial works have been conducted on smart\nmeter data analytics. To provide a comprehensive overview of the current\nresearch and to identify challenges for future research, this paper conducts an\napplication-oriented review of smart meter data analytics. Following the three\nstages of analytics, namely, descriptive, predictive and prescriptive\nanalytics, we identify the key application areas as load analysis, load\nforecasting, and load management. We also review the techniques and\nmethodologies adopted or developed to address each application. In addition, we\nalso discuss some research trends, such as big data issues, novel machine\nlearning technologies, new business models, the transition of energy systems,\nand data privacy and security.\n

Keywords

Smart meterBig dataComputer scienceData analysisAnalyticsData scienceMetreSmart gridEngineeringElectrical engineeringData mining

Affiliated Institutions

Related Publications

Advances in Li–S batteries

Rechargeable Li–S batteries have received ever-increasing attention recently due to their high theoretical specific energy density, which is 3 to 5 times higher than that of Li ...

2010 Journal of Materials Chemistry 1902 citations

Publication Info

Year
2018
Type
article
Volume
10
Issue
3
Pages
3125-3148
Citations
1194
Access
Closed

External Links

Social Impact

Social media, news, blog, policy document mentions

Citation Metrics

1194
OpenAlex

Cite This

Yi Wang, Qixin Chen, Tao Hong et al. (2018). Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges. IEEE Transactions on Smart Grid , 10 (3) , 3125-3148. https://doi.org/10.1109/tsg.2018.2818167

Identifiers

DOI
10.1109/tsg.2018.2818167