Power load forecasting and interpretable models based on GS_XGBoost and SHAP

At present, the prediction accuracy of the power load prediction model is not high and the black box model prediction results are not explanatory, so this paper proposes an improved XGBoost based grid search for power load forecasting model (GS-XGBoost), and uses SHAP for model analysis and interpretation to show the impact of the feature set on the model in a visual way according to the marginal contribution of power load feature samples The impact of the prediction results is shown in a visual way to improve the interpretability of the model, and the key factors affecting the electricity load are identified based on the ranking results of the feature importance to provide decision support for the power system equipment maintenance plan, guide the formulation of strategies related to electricity production, resource procurement and pricing, and avoid market risks. In this paper, the electricity consumption dataset of an enterprise in Jiangsu is used as the research object, and the dataset is pre-processed with unique thermal coding, outlier analysis, null filling, and standardization. Through experimental comparison, the GS-XGBoost load forecasting model has the best prediction accuracy compared to SVR, random forest, decision tree, and XGBoost machine learning models, and then SHAP is applied to interpret the model and improve the model interpretability.