Generalized load space prediction method and system

Abstract

The invention provides a generalized load space prediction method and system, and the method comprises the steps: analyzing historical data of land type conversion, and obtaining the area change dataof each type of land in a prediction time period; performing prediction based on historical data of a distributed power supply, an electric vehicle and an energy storage device to obtain data of the distributed power supply, the electric vehicle and the energy storage device in a prediction time period; and based on the area change data of each type of land in the prediction time period and the data of the distributed power supply, the electric vehicle and the energy storage device, predicting by adopting a pre-established generalized load space prediction model to obtain a generalized load prediction value of each type of land. The influence of the distributed power supply, the electric vehicle and the energy storage in the urban intelligent power grid on the load density increase condition is considered, the accuracy of load space distribution prediction is improved, and effective data is provided for generalized load prediction of the intelligent power grid.

Description

technical field [0001] The invention belongs to the technical field of electric power, and in particular relates to a generalized load space prediction method and system. Background technique [0002] In terms of power load forecasting, traditional load forecasting methods include trend extrapolation method, time series method, regression model forecasting method and intelligent forecasting methods such as gray model forecasting method and artificial neural network forecasting method. These methods belong to the forecasting of the total power load in the region. On the one hand, it is powerless to predict the spatial distribution of the power load containing geographic spatial information such as the time and location of the load; Little of the load is taken into account in load forecasting. [0003] Among the current studies on the spatial distribution prediction of power load based on land function conversion, the existing spatial load prediction is mainly based on the di...

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Problems solved by technology

[0004] Moreover, among the existing spatial load forecasting methods that combine land function conversion and load density, the main focus of prediction is on land function conversion, and the future load density changes are not fully considered, especially in intelligent applications. In the electrical environment, loads with power source attributes appearing in a wide range enable frequent migration of electrical loads in different regions, which can change the existing law of load density changes. The existing spatial load forecasting method does not consider the flexible load access. However, the applicability of this method in the future intelligent power consumption environment will face serious challenges.

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