Principal component analysis of day‐ahead electricity price forecasting in CAISO and its implications for highly integrated renewable energy markets

Joseph Nyangon and Ruth Akintunde

Wiley Interdisciplinary Reviews: Energy and Environment, 2024, vol. 13, issue 1

Abstract: Electricity price forecasting is crucial for grid management, renewable energy integration, power system planning, and price volatility management. However, poor accuracy due to complex generation mix data and heteroskedasticity poses a challenge for utilities and grid operators. This paper evaluates advanced analytics methods that utilize principal component analysis (PCA) to improve forecasting accuracy amidst heteroskedastic noise. Drawing on the experience of the California Independent System Operator (CAISO), a leading producer of renewable electricity, the study analyzes hourly electricity prices and demand data from 2016 to 2021 to assess the impact of day‐ahead forecasting on California's evolving generation mix. To enhance data quality, traditional outlier analysis using the interquartile range (IQR) method is first applied, followed by a novel supervised PCA technique called robust PCA (RPCA) for more effective outlier detection and elimination. The combined approach significantly improves data symmetry and reduces skewness. Multiple linear regression models are then constructed to forecast electricity prices using both raw and transformed features obtained through PCA. Results demonstrate that the model utilizing transformed features, after outlier removal using the traditional method and SAS Sparse Matrix method, achieves the highest forecasting performance. Notably, the SAS Sparse Matrix outlier removal method, implemented via proc RPCA, greatly contributes to improved model accuracy. This study highlights that PCA methods enhance electricity price forecasting accuracy, facilitating the integration of renewables like solar and wind, thereby aiding grid management and promoting renewable growth in day‐ahead markets. This article is categorized under: Energy and Power Systems > Energy Management Energy and Power Systems > Distributed Generation Emerging Technologies > Digitalization

Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://doi.org/10.1002/wene.504

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bla:wireae:v:13:y:2024:i:1:n:e504

Ordering information: This journal article can be ordered from
http://www.blackwell ... bs.asp?ref=2041-8396

Access Statistics for this article

Wiley Interdisciplinary Reviews: Energy and Environment is currently edited by Peter Lund and John Byrne

More articles in Wiley Interdisciplinary Reviews: Energy and Environment from Wiley Blackwell
Bibliographic data for series maintained by Wiley Content Delivery ().