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With the growth of the used car market and the development of e-commerce platforms, the need for accurate valuation of used car prices is becoming more urgent. Accuracy of price evaluation is the key to the success of used car transactions. At present, the common methods are manual experience method, Monte Carlo method, etc. Among them, manual experience method and multi-attribute decision method are more mature and widely used in traditional pricing method, but they have some disadvantages such as large computation amount and low accuracy. Aiming at the above problems, a BP neural network model based on mean encoding is designed in this paper. After extracting the features of the model, BP neural network is used to study the pre-processing data to predict the price for the network output. In this paper, a real used car trading dataset was used to test the model. The 2 R error is 0.976. Compared with the SVM and the decision tree model, this model is more accurate.

With the rapid development of renewable energy sources such as wind energy and solar energy in China, structural problems such as wind and light abandonment, system operation imbalance, and insufficient energy supply have arisen in the process of accelerating the energy transformation process. Given the above problems, this paper uses the system dynamics method for modeling. First, the key variables are selected from the perspective of influencing the economic reliability of the power system. Second, the energy storage operation model of the power supply side under the high proportion of wind power access is established, and the impact of new energy access on the system balance and energy storage configuration is explored. Finally, the cost connection of each module body is used to optimize the allocation of energy storage resources, thereby improving the efficiency of the enterprise itself and providing a new idea for the power generation enterprise to participate in the operation and scheduling of the system.

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