## 1 Introduction
[43] proposed a hybrid optimization method using differential evolution and grey wolf optimization. Through three simulation experiments, it was proven that the hybrid optimization algorithm had improved the solving precision, convergence speed, and search ability compared with a single algorithm. [PERSON] and [PERSON] [44] proposed a short-term cloud computing resource load forecasting model using grey wolf optimization and support vector machine, which could accurately describe the complex trend of the short-term cloud computing resource loads and effectively improve the prediction accuracy of the short-term cloud computing resource loads. In this article, the three algorithms of differential evolution algorithm, grey wolf optimization algorithm, and support vector machine are combined for power grid investment forecasting. The improved combination prediction model will greatly improve the global search capability for avoiding falling into the local optimum. In this article, for forecasting the power grid investment of China accurately, based on the construction of influencing factors system for power grid investment forecasting, a novel power grid investment prediction model based on DE-GWO-SVM algorithm is proposed in this article. The innovations of this article are as follows:  
1. Power grid investment forecasting in China is affected by many factors. In order to realize the accurate forecasting of China's power grid investment, relevant factors of power grid investment forecasting are preliminarily selected based on studying a large number of literatures in this article. Through the Delphi method, multiple rounds of anonymous consultations and feedbacks are carried out on experts' opinions, and the influencing factors system for China's power grid investment forecasting is finally determined. From the four dimensions of economic development, electricity demand, power grid scale, and power grid benefit, the influencing factors system for power grid investment forecasting is constructed. Then, the method of grey relational analysis is used for screening the main influencing factors of power grid investment as the prediction model input. 2. In this article, the model of DE-GWO-SVM is proposed to predict power grid investment in China. Three algorithms--differential evolution algorithm, grey wolf optimization algorithm, and support vector machine--are combined for power grid investment forecasting. The improved combination prediction model will greatly improve the global search capability for avoiding being lost into the local optimum, which can predict the power grid investment accurately. Through empirical analysis, it is proven that the DE-GWO-SVM model has strong generalization ability and robustness in power grid investment prediction, whose prediction accuracy is better than that of GWO-SVM, SVM, and back propagation (BP) neural network. The main structure of this article is arranged as follows: Section 2 introduces the methodology, including establishing the influencing factors system for power grid investment forecasting, introducing grey relational analysis which is used for the main influencing factors screening,and proposing the DE-GWO-SVM forecasting model. Section 3 carries out empirical analysis to verify the validity of the proposed model for the power grid investment prediction in China, and forecasts the power grid investment in China from 2018 to 2022. Section 4 summarizes the whole article.