Intelligent power grid park terminal user energy demand condition dynamic prediction system and method

Abstract

The invention discloses an intelligent power grid park terminal user energy demand condition dynamic prediction system and a method. The method includes: conducting a main constituent analysis of meteorological factors for influencing the cooling and heating load demand of intelligent power grid park terminal users; converting related variables to a few of linear independent random variables; quantifying the weather factor and the day type, conducting an analysis with historical load data by employing a fuzzy clustering method, and forming a sample; representing load characteristics of various types of loads and various types of distributed energy supply systems in the intelligent power grid park in load curves; and finally solving a model according to the process of a BP neural network algorithm, and obtaining a cooling and heating load prediction result. The system comprises a main constituent analysis module, an analysis sample formation module, a load characteristic curve module, and a load prediction module. According to the method and the system, the network size is reduced, the prediction precision is improved, and advantages of a BP neural network for large-scale parallel processing and adaptive learning ability are fully developed.

Description

technical field [0001] The invention belongs to the technical field of power systems, and in particular relates to a system and method for dynamically predicting energy demand status of terminal users in a smart grid park. Background technique [0002] As an important part of the energy management of smart grid parks, load forecasting provides important decision support for planning, construction, operation optimization and management of smart grid parks. However, there are a large number of user terminals in the smart grid park, and the types are complex. There are differences in the energy consumption curves of industrial users, data centers, and public institutions. [0003] Traditional load forecasting methods mainly include time series method and regression analysis method. Time series method does not consider the influence of weather on load, while regression analysis method is difficult to solve the dynamic and nonlinear relationship between load and weather and other...

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Problems solved by technology

[0004] The purpose of the present invention is to provide a system and method for dynamic forecasting of end-user energy demand in smart grid parks, aiming to solve the unsatisfactory forecasting accuracy of traditional load forecasting methods, and also have deficiencies in massive data processing and mining The problem

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