Research on the Mechanism of Power Quality in Renewable Energy Marketing Based on BMA Algorithm
DOI:
https://doi.org/10.52152/4192Keywords:
BMA, Power quality, Renewable energy marketing, Customer satisfaction, Sales growthAbstract
This paper investigates the mechanism of power quality in renewable energy marketing using the Bayesian Model Average algorithm. By analyzing renewable energy market data, including sales, user feedback, and power quality monitoring data, the paper explores the relationship between power quality and renewable energy marketing. The study quantifies power quality through metrics such as voltage stability, frequency deviation, and harmonic distortion, and examines their impact on sales and customer satisfaction. The analysis reveals that a 10% improvement in power quality leads to a 6% increase in sales and a 7% increase in customer satisfaction. Regression models, validated through hypothesis testing (p < 0.05), highlight the significant role of power quality in driving sales and customer loyalty. The study also performs sensitivity analysis to assess the robustness of these findings across varying market conditions. Data collected over 12 months from three renewable energy companies forms the basis of these conclusions. This research demonstrates that improving power quality can enhance both the market competitiveness of renewable energy products and customer loyalty. By prioritizing power quality, renewable energy companies can effectively improve sales, customer satisfaction, and product performance, thus optimizing their marketing strategies.
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