This article conducts in-depth research on power quality data based on time series analysis algorithms, and comprehensively analyzes renewable energy marketing strategies. The electricity demand for the past five years shows an average annual growth rate of 5.2%. In the power quality indicators, the average voltage deviation rate is 2.3%, and the average frequency deviation rate is 0.12%. In terms of data analysis, this article adopts multiple algorithms for mining. Among them, the prediction method based on ARIMA model has high prediction accuracy, with an average prediction error rate of 3.8%. Meanwhile, the moving average algorithm effectively reduces data noise. Based on the supply of renewable energy, this article explores the optimization of marketing strategies. Simulation analysis shows that comprehensive marketing strategies can increase the penetration rate of renewable energy from 25% to 35%. Power quality data mining based on time series analysis algorithms provides strong support for improving power quality and optimizing renewable energy marketing strategies, and is expected to promote the stability of power supply and the healthy development of the renewable energy industry.
Qinghua Fan (Sun,) studied this question.