Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges

By Yi Wang, Qixin Chen, Tao Hong, Chongqing Kang. IEEE Transactions on Smart Grid, 2018

1,141 citations recorded by the source corpus.

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

Subfields: Electrical and Electronic Engineering

View Paper: https://www.semanticscholar.org/paper/4ffbd6860cc2cada1f4c6c77ad3f9b6535c4b3cf

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In Electrical and Electronic Engineering

Citations in the corpus

Short-Term Load Forecasting With Deep Residual Networks (2018, neutral): “2844307 methods can be found in [7]–[10].”
Review of low voltage load forecasting: Methods, applications, and recommendations (2021, methodology): “As with most reviews in other areas, both [102] and [221] use a Scopus search to identify the number of published papers and major journals that publish forecasting and smart meter research.”
Artificial intelligence and machine learning approaches to energy demand-side response: A systematic review (2020, background): “[20] reviewed the application of multi-agent systems (MAS) in smart grid technologies, including DR, and there has also been work which examines smart meter data analytics in applications for DR programmes [21].”
Energy Forecasting: A Review and Outlook (2020, background): “A recently published review of smart meter data analytics has covered these datasets, as well as several other useful datasets [87], where the reader can find a more comprehensive list.”