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

TL;DR

This paper provides a comprehensive review of smart meter data analytics, covering applications, methodologies, challenges, and future research directions in enhancing power grid efficiency and sustainability.

Contribution

It offers an application-oriented overview of current research, categorizing analytics into descriptive, predictive, and prescriptive, and discusses emerging trends and challenges.

Findings

Key application areas include load analysis, forecasting, and management.

Various techniques and methodologies are reviewed for each application.

Highlights emerging trends like big data, machine learning, and data privacy issues.

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.