Modeling of Spatial Distribution Characteristics of High Proportion Renewable Energy Based on Complex Principal Component Analysis

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

“…Additionally, PCA is generally beneficial for studies requiring the discovery of latent common structures of factors and the interpretation of the structural meaning of each PC. Numerous studies have applied PCA to research questions in electricity markets and relevant areas (Hopwood et al, 2020;Ji et al, 2017;Li et al, 2018;Liu et al, 2020;K. Wang et al, 2019).…”

“…Additionally, PCA is generally beneficial for studies requiring the discovery of latent common structures of factors and the interpretation of the structural meaning of each PC. Numerous studies have applied PCA to research questions in electricity markets and relevant areas (Hopwood et al, 2020; Ji et al, 2017; Li et al, 2018; Liu et al, 2020; K. Wang et al, 2019). For example, PCA has been employed to investigate the dynamics of implied price volatility and capture long‐ and short‐term fluctuations of the volatility term structure for the stock index.…”

“…Their studies project a potential enhancement of up to 60% by 2030, potentially translating to capital savings of roughly US $20 billion in stationary storage technology investments. Liu et al (2020) employed sophisticated PCA techniques to extract spatio-temporal features of renewable energy, laying the foundation for a predictive model for renewable energy generation. Sreedharan et al (2016) highlighted the importance of innovative incentive mechanisms to spur DERs in bolstering renewable integration services, particularly for commercial and industrial sectors across California.…”

Extensions of Some Statistical Concepts to the Complex Domain

“…Most of the papers in these applied areas, concentrate on developing algorithms for computing eigenvalues and eigenvectors, which are useful and relevant in principal component analysis, independent component analysis, factor analysis, and so on. Statistical analysis in the complex domain is widely used in the analysis of multi-look return signals in radar [1], in multi-task learning in artificial intelligence and machine learning [2], in problems such as signal processing [3], in principal component analysis and independent component analysis in analyzing meteorological data in the complex domain [4], in optimal allocation of resources, especially energy resources [5], in holography, microscopy and optical metrology [6], in delayed mixing in speech processing, in biomedical signal analysis, and in financial data modeling, etc. [7].…”