A model-based feature selection and load forecasting framework

Accurate power load forecasting plays a crucial role in grid scheduling and stable operation. As power systems develop, load data has become more extensive and detailed. While this provides extensive data support for deep learning models to predict power loads, the challenge lies in identifying the optimal input features within these data. To tackle this problem, this paper presents a model-based feature evaluation and selection method designed to evaluate the importance of input features for deep learning models in predicting power loads, thereby enabling the selection of the best input features for prediction models. Experimental results from the Nordic electricity market and the New England region of the United States demonstrate the superiority of this approach over methods like the Pearson correlation coefficient, the Spearman correlation coefficient, the maximum information coefficient, and gray correlation analysis. It facilitates the selection of necessary input features for deep learning models, thereby enhancing the accuracy of power load forecasting. The proposed method proves beneficial for grid scheduling and provides more reliable load predictions for related power enterprises.