Time Series Analysis of Electricity Price and Demand to Find Cyber-attacks using Stationary Analysis
It addresses anomaly detection for electricity grid operators, but it is incremental as it applies standard time-series techniques without introducing new methods.
This paper tackles the problem of detecting anomalies in electricity price and demand data by applying stationary analysis to time-series data from New England's operational zones, using methods like moving average, moving standard deviation, and the augmented Dickey-Fuller test to prepare the data for further analysis.
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.