About 10 years ago, the term "smart grid" was officially defined in the Energy Independence and Security Act of 2007 (EISA-2007). Soon after that, many power companies started their smart meter deployments. As of 2016, more than 70 million smart meters were installed in the united states. The world installed base was projected to reach 780 million by 2020, pushed by the mass roll-outs in China. We are now sitting on a gold mine of data collected by these smart meters. Last year I gave a forecast:
This is the longest paper ever published by the IEEE Transactions on Smart Grid. I'm sure reading this 24-page review article can save the readers significant amount of time from digging thousands of papers in the literature. Load forecasters may find some interesting stuff in Section III, which is dedicated to load forecasting in the smart grid era.
Citation
Yi Wang, Qixin Chen, Tao Hong, and Chongqing Kang, "Review of smart meter data analytics: applications, methodologies, and challenges," IEEE Transactions on Smart Grid, in press. (working paper; IEEE Xplore)
Abstract
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.
The energy companies will be moving more Gigabytes of data than GWh of electricity.The scientific community has been trying to understand the smart meter data and get some actionable insights out of it. Thousands of papers have been published in the recent decade on the various aspects of smart meter data analytics. Last year, I worked with my collaborators in Tsinghua University to complete a review of smart meter data analytics. The paper was just put on the IEEE Xplore yesterday.
This is the longest paper ever published by the IEEE Transactions on Smart Grid. I'm sure reading this 24-page review article can save the readers significant amount of time from digging thousands of papers in the literature. Load forecasters may find some interesting stuff in Section III, which is dedicated to load forecasting in the smart grid era.
Citation
Yi Wang, Qixin Chen, Tao Hong, and Chongqing Kang, "Review of smart meter data analytics: applications, methodologies, and challenges," IEEE Transactions on Smart Grid, in press. (working paper; IEEE Xplore)
Review of Smart Meter Data Analytics: Applications, Methodologies, and Challenges
Yi Wang, Qixin Chen, Tao Hong, and Chongqing Kang
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.
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