Anomaly Detection in California Electricity Price Forecasting: Enhancing Accuracy and Reliability Using Principal Component Analysis

TL;DR

This paper improves California electricity price forecasting accuracy by applying principal component analysis and robust outlier removal techniques, demonstrating significant enhancements in model performance and reliability.

Contribution

It introduces a PCA-based approach combined with robust outlier elimination methods to enhance the accuracy of electricity price forecasts in complex, heteroskedastic data environments.

Findings

PCA reduces data skewness and improves model symmetry.

Robust outlier removal enhances forecast accuracy.

SAS Sparse Matrix method significantly outperforms traditional methods.

Abstract

Accurate and reliable electricity price forecasting has significant practical implications for grid management, renewable energy integration, power system planning, and price volatility management. This study focuses on enhancing electricity price forecasting in California's grid, addressing challenges from complex generation data and heteroskedasticity. Utilizing principal component analysis (PCA), we analyze CAISO's hourly electricity prices and demand from 2016-2021 to improve day-ahead forecasting accuracy. Initially, we apply traditional outlier analysis with the interquartile range method, followed by robust PCA (RPCA) for more effective outlier elimination. This approach improves data symmetry and reduces skewness. We then construct multiple linear regression models using both raw and PCA-transformed features. The model with transformed features, refined through traditional and SAS Sparse Matrix outlier removal methods, shows superior forecasting performance. The SAS Sparse Matrix method, in particular, significantly enhances model accuracy. Our findings demonstrate that PCA-based methods are key in advancing electricity price forecasting, supporting renewable integration and grid management in day-ahead markets. Keywords: Electricity price forecasting, principal component analysis (PCA), power system planning, heteroskedasticity, renewable energy integration.