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

arXiv: https://arxiv.org/abs/1802.04117 · HTML: https://mlsanity.com/paper/1802.04117/

## Key facts

- Cited by 47 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Feb 2018.

## Built on by (most cited first, 30 of 47)

- [Review of Low Voltage Load Forecasting: Methods, Applications, and Recommendations](https://mlsanity.com/paper/2106.00006/) (2021)
- [Short-Term Electricity Load Forecasting Using the Temporal Fusion Transformer: Effect of Grid Hierarchies and Data Sources](https://mlsanity.com/paper/2305.10559/) (2023)
- [Customized Load Profiles Synthesis for Electricity Customers Based on Conditional Diffusion Models](https://mlsanity.com/paper/2304.12076/) (2023)
- [Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows](https://mlsanity.com/paper/2204.13939/) (2022)
- [A Machine Learning-Based Framework for Clustering Residential Electricity Load Profiles to Enhance Demand Response Programs](https://mlsanity.com/paper/2310.20367/) (2023)
- [PowerGrow: Feasible Co-Growth of Structures and Dynamics for Power Grid Synthesis](https://mlsanity.com/paper/2509.12212/) (2025)
- [Data-Driven Copy-Paste Imputation for Energy Time Series](https://mlsanity.com/paper/2101.01423/) (2021)
- [Machine Learning Approach to Uncovering Residential Energy Consumption Patterns Based on Socioeconomic and Smart Meter Data](https://mlsanity.com/paper/2104.05154/) (2021)
- [Hierarchical transfer learning with applications for electricity load forecasting](https://mlsanity.com/paper/2111.08512/) (2021)
- [Privacy-preserving Federated Learning for Residential Short Term Load Forecasting](https://mlsanity.com/paper/2111.09248/) (2021)
- [CommonPower: A Framework for Safe Data-Driven Smart Grid Control](https://mlsanity.com/paper/2406.03231/) (2024)
- [Temporal Encoding Strategies for Energy Time Series Prediction](https://mlsanity.com/paper/2503.15456/) (2025)
- [PowerGNN: A Topology-Aware Graph Neural Network for Electricity Grids](https://mlsanity.com/paper/2503.22721/) (2025)
- [External Data-Enhanced Meta-Representation for Adaptive Probabilistic Load Forecasting](https://mlsanity.com/paper/2506.23201/) (2025)
- [AMI-FML: A Privacy-Preserving Federated Machine Learning Framework for AMI](https://mlsanity.com/paper/2109.05666/) (2021)
- [Bridging Smart Meter Gaps: A Benchmark of Statistical, Machine Learning and Time Series Foundation Models for Data Imputation](https://mlsanity.com/paper/2501.07276/) (2025)
- [Electricity Demand Forecasting in Future Grid States: A Digital Twin-Based Simulation Study](https://mlsanity.com/paper/2503.04757/) (2025)
- [SEA-TS: Self-Evolving Agent for Autonomous Code Generation of Time Series Forecasting Algorithms](https://mlsanity.com/paper/2603.04873/) (2026)
- [A Bayesian Deep Learning Technique for Multi-Step Ahead Solar Generation Forecasting](https://mlsanity.com/paper/2203.11379/) (2022)
- [Agglomerative Hierarchical Clustering with Dynamic Time Warping for Household Load Curve Clustering](https://mlsanity.com/paper/2210.09523/) (2022)
- [Meta-Regression Analysis of Errors in Short-Term Electricity Load Forecasting](https://mlsanity.com/paper/2305.18550/) (2023)
- [From Dense to Sparse: Event Response for Enhanced Residential Load Forecasting](https://mlsanity.com/paper/2501.02781/) (2025)
- [Multi-task Online Learning for Probabilistic Load Forecasting](https://mlsanity.com/paper/2502.04163/) (2025)
- [Leveraging External Factors in Household-Level Electrical Consumption Forecasting using Hypernetworks](https://mlsanity.com/paper/2506.14472/) (2025)
- [Scalable Cloud-Native Architectures for Intelligent PMU Data Processing](https://mlsanity.com/paper/2512.22231/) (2025)
- [Appliance Operation Modes Identification Using Cycles Clustering](https://mlsanity.com/paper/2101.10472/) (2021)
- [Investigating Underlying Drivers of Variability in Residential Energy Usage Patterns with Daily Load Shape Clustering of Smart Meter Data](https://mlsanity.com/paper/2102.11027/) (2021)
- [Prediction of Energy Consumption for Variable Customer Portfolios Including Aleatoric Uncertainty Estimation](https://mlsanity.com/paper/2110.02166/) (2021)
- [On Designing Data Models for Energy Feature Stores](https://mlsanity.com/paper/2205.04267/) (2022)
- [Cybersecurity Challenges of Power Transformers](https://mlsanity.com/paper/2302.13161/) (2023)

## Abstract (arXiv)

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.