Time Series Analysis of Electricity Price and Demand to Find Cyber-attacks using Stationary Analysis

TL;DR

This paper investigates stationary analysis methods on New England electricity data to detect anomalies potentially indicating cyber-attacks, using techniques like moving averages, standard deviation, and the Augmented Dickey-Fuller test.

Contribution

It introduces a stationary analysis framework for electricity time series data to identify anomalies, focusing on data preparation for cyber-attack detection.

Findings

Stationary criteria effectively prepare data for anomaly detection.

Moving average and standard deviation reveal abnormal patterns.

Augmented Dickey-Fuller test assesses stationarity of electricity data.

Abstract

With developing of computation tools in the last years, data analysis methods to find insightful information are becoming more common among industries and researchers. This paper is the first part of the times series analysis of New England electricity price and demand to find anomaly in the data. In this paper time-series stationary criteria to prepare data for further times-series related analysis is investigated. Three main analysis are conducted in this paper, including moving average, moving standard deviation and augmented Dickey-Fuller test. The data used in this paper is New England big data from 9 different operational zones. For each zone, 4 different variables including day-ahead (DA) electricity demand, price and real-time (RT) electricity demand price are considered.