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

TLDR

Smart meters generate vast fine‑grained electricity consumption data, and the global deregulation of power delivery has spurred extensive research into smart‑meter data analytics. This paper reviews current smart‑meter data analytics, identifies future research challenges, and explores how large‑scale data can improve grid efficiency and sustainability. The review follows the three analytics stages—descriptive, predictive, and prescriptive—to examine load analysis, forecasting, and management, and surveys the techniques, methodologies, and emerging trends such as big‑data challenges, novel machine learning, new business models, system transition, and privacy/security.

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.