A Data Governance Method for Power Marketing based on Neural Network Algorithm

Now a days, Power marketing count on data analysis to optimize various operations and decision-makings. However, data quality has various issues, inconsistent formats, and regulatory compliance challenges hinder efficient data management. A data governance method for power marketing ensures data and compliance enabling reliable insights and informed decision-making. To overcome those drawbacks this paper, proposed a method of Artificial Neural Networks (ANN) classifier follows a systematic approach to power marketing data governance. Initially, collected and verify input data from the Smart Grid Stability dataset, ensuring a strong foundation for our method. Next, performed feature extraction using SHAP, a technique that identifies the most relevant features in the dataset. Moreover, developed an ANN based classification model that leverages the strengths of both SHAP and ANN to accurately classify and predict outcomes in power marketing data governance. This proposed approach enables reliable insights and informed decision-making, driving efficiency and effectiveness in power marketing strategies. The performance of proposed model evaluated with the various metrics such as ${\mathrm {{R}}}^{2}$ value is of (0.156) Mean Absolute Error (MAE) value (0.121), Mean Squared Error (MSE) error value (${(0. 1 8 9)}$ and Root Mean Squared Error (RMSE) (0.125) metrics on Smart Grid Stability dataset.