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

A smart grid and end-user technology, applied in the field of power system, can solve the problems of unsatisfactory prediction accuracy, insufficient processing and mining of massive data, etc., and achieve the effect of reducing scale, reducing network scale, and improving prediction accuracy.

Inactive Publication Date: 2015-11-18
STATE GRID TIANJIN ELECTRIC POWER +2
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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...

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

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[0053] In order to make the object, technical solution and advantages of the present invention more clear, the present invention will be further described in detail below in conjunction with the examples. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0054] The method for dynamically predicting the energy demand status of end users in smart grid parks based on data mining technology and BP neural network of the present invention is used to solve the problems of insufficient consideration of factors, insufficient depth of data mining and forecasting in the process of cold, heating and power load forecasting in existing smart grid parks Technical issues such as low precision; combine data mining technology with intelligent algorithms, and improve the accuracy of cold, heating and power load forecasting through a combined forecasting method.

[0055] The application princi...

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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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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
CPCY04S10/50
Inventor 蒋菱王旭东于建成李国栋霍现旭王凯徐青山曾艾东纪明苏靖宇
Owner STATE GRID TIANJIN ELECTRIC POWER
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