Optimal Design of Power Marketing Decision Support System Based on Power Big Data

As a new technical means, electric power big data analysis technology provides new ideas and methods for marketing decision-making of electric power enterprises. In this article, the problem of consumer propensity prediction in power marketing is studied, aiming at discussing the optimal design of power marketing decision support system (DSS) based on power big data, providing better marketing decision support for power enterprises and realizing sustainable development. The results show that the accuracy of this algorithm is over 94%, which is more than 10% higher than ID3 algorithm. And the recall has reached more than 85%, which is more than 8% higher than ID3 algorithm. The algorithm in this article has obvious advantages in forecasting the consumption tendency of consumers in power marketing. These advantages mainly come from the fact that the algorithm in this article considers more features, adopts more flexible and efficient methods for prediction, and uses regularization technology. These advantages enable this algorithm to predict consumers' consumption tendency more accurately and cover more potential consumers. Therefore, this method can provide more accurate and reliable basis for the marketing decision of power enterprises.