# Regression-based Online Anomaly Detection for Smart Grid Data

arXiv: https://arxiv.org/abs/1606.05781 · HTML: https://mlsanity.com/paper/1606.05781/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Jun 2016.

## Built on by (most cited first, 5 of 5)

- [Time Series Anomaly Detection for Smart Grids: A Survey](https://mlsanity.com/paper/2107.08835/) (2021)
- [Anomaly Detection Techniques in Smart Grid Systems: A Review](https://mlsanity.com/paper/2306.02473/) (2023)
- [A new Video Synopsis Based Approach Using Stereo Camera](https://mlsanity.com/paper/2106.12362/) (2021)
- [Artificial Intelligence-Based Smart Grid Vulnerabilities and Potential Solutions for Fake-Normal Attacks: A Short Review](https://mlsanity.com/paper/2202.07050/) (2022)
- [Imbalanced Aircraft Data Anomaly Detection](https://mlsanity.com/paper/2305.10082/) (2023)

## Abstract (arXiv)

With the widely used smart meters in the energy sector, anomaly detection becomes a crucial mean to study the unusual consumption behaviors of customers, and to discover unexpected events of using energy promptly. Detecting consumption anomalies is, essentially, a real-time big data analytics problem, which does data mining on a large amount of parallel data streams from smart meters. In this paper, we propose a supervised learning and statistical-based anomaly detection method, and implement a Lambda system using the in-memory distributed computing framework, Spark and its extension Spark Streaming. The system supports not only iterative detection model refreshment from scalable data sets, but also real-time detection on scalable live data streams. This paper empirically evaluates the system and the detection algorithm, and the results show the effectiveness and the scalability of the proposed lambda detection system.