# Time Series Anomaly Detection for Smart Grids: A Survey

arXiv: https://arxiv.org/abs/2107.08835 · HTML: https://mlsanity.com/paper/2107.08835/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 68 references that are in the map.
- First posted on arXiv 16 Jul 2021.

## Builds on (most cited first, 6 of 6)

- [A Unifying Review of Deep and Shallow Anomaly Detection](https://mlsanity.com/paper/2009.11732/) (2020)
- [Deep Learning for Anomaly Detection: A Survey](https://mlsanity.com/paper/1901.03407/) (2019)
- [Real-valued (Medical) Time Series Generation with Recurrent Conditional GANs](https://mlsanity.com/paper/1706.02633/) (2017)
- [Transfer learning for time series classification](https://mlsanity.com/paper/1811.01533/) (2018)
- [Smart Anomaly Detection in Sensor Systems: A Multi-Perspective Review](https://mlsanity.com/paper/2010.14946/) (2020)
- [Regression-based Online Anomaly Detection for Smart Grid Data](https://mlsanity.com/paper/1606.05781/) (2016)

## Built on by (most cited first, 12 of 12)

- [Navigating the Metric Maze: A Taxonomy of Evaluation Metrics for Anomaly Detection in Time Series](https://mlsanity.com/paper/2303.01272/) (2023)
- [Open Challenges in Time Series Anomaly Detection: An Industry Perspective](https://mlsanity.com/paper/2502.05392/) (2025)
- [Time Series Anomaly Detection via Reinforcement Learning-Based Model Selection](https://mlsanity.com/paper/2205.09884/) (2022)
- [Anomaly Detection Techniques in Smart Grid Systems: A Review](https://mlsanity.com/paper/2306.02473/) (2023)
- [Impact of Deep Learning Libraries on Online Adaptive Lightweight Time Series Anomaly Detection](https://mlsanity.com/paper/2305.00595/) (2023)
- [Impact of Recurrent Neural Networks and Deep Learning Frameworks on Real-time Lightweight Time Series Anomaly Detection](https://mlsanity.com/paper/2407.18439/) (2024)
- [A Problem-Oriented Taxonomy of Evaluation Metrics for Time Series Anomaly Detection](https://mlsanity.com/paper/2511.18739/) (2025)
- [Anomaly Detection with Ensemble of Encoder and Decoder](https://mlsanity.com/paper/2303.06431/) (2023)
- [Acquiring Better Load Estimates by Combining Anomaly and Change Point Detection in Power Grid Time-series Measurements](https://mlsanity.com/paper/2405.16164/) (2024)
- [Anomaly Detection for IoT Global Connectivity](https://mlsanity.com/paper/2508.09660/) (2025)
- [Anomaly Detection with Machine Learning Algorithms in Large-Scale Power Grids](https://mlsanity.com/paper/2602.10888/) (2026)
- [Contrast to Detect: Dynamic Graph Contrastive Regularization for Unsupervised Anomaly Detection in Multivariate Time Series](https://mlsanity.com/paper/2605.23744/) (2026)

## Abstract (arXiv)

With the rapid increase in the integration of renewable energy generation and the wide adoption of various electric appliances, power grids are now faced with more and more challenges. One prominent challenge is to implement efficient anomaly detection for different types of anomalous behaviors within power grids. These anomalous behaviors might be induced by unusual consumption patterns of the users, faulty grid infrastructures, outages, external cyberattacks, or energy fraud. Identifying such anomalies is of critical importance for the reliable and efficient operation of modern power grids. Various methods have been proposed for anomaly detection on power grid time-series data. This paper presents a short survey of the recent advances in anomaly detection for power grid time-series data. Specifically, we first outline current research challenges in the power grid anomaly detection domain and further review the major anomaly detection approaches. Finally, we conclude the survey by identifying the potential directions for future research.