Research on deep learning-oriented algorithms for power grid investment forecasting

With the development of renewable energy and the access of new types of loads such as electric vehicles, the operational characteristics of the grid have changed, which poses a new challenge to grid investment forecasting. First, the influencing factors of grid investment are deeply analyzed, and a grid investment dataset containing multi-dimensional features such as economy, policy and technology is constructed. Then, a deep learning model integrating convolutional neural network (CNN) and long short-term memory network (LSTM) is designed to realize feature extraction and time series prediction of grid investment data. The experimental results show that the proposed algorithm outperforms the traditional prediction methods in terms of prediction accuracy and stability, and provides powerful support for grid investment decision-making. This study provides new ideas and methods for the application of deep learning in the field of grid investment prediction. In addition, this study provides an exhaustive analysis of the applicability of the model, and by utilizing deep learning technology, this study proposes an innovative deep learning-based grid investment demand forecasting model by taking the grid investment demand forecasting model as an exploratory object. Through empirical research, the effectiveness, applicability and superiority of the model in grid investment demand forecasting are verified, providing a new research method in this field.