Identifying Main Factors of Wind Power Generation Based on Principal Component Regression: A Case Study of Xiamen

Abstract

To realize the goals of carbon reduction, it is important for understanding the driving force of the wind power industry. In this study, a principal component regression (PCR) model is employed to identify the main factors of wind power generation in the City of Xiamen. Results disclose that two principal components have a cumulative contribution rate about 95%. The economic component (contributing 81.9%) is dominated by the proportion of secondary industry (SI) and gross domestic product (GDP). The energy component (contributing 12.9%) is dominated by annual wind speed (WS) and the number of patents (NP). Results can provide desired decision support for clean energy utilization and environmental emission reduction.