User side load feature clustering evaluation method based on morphological analysis

A technology of load characteristics and cluster evaluation, applied in data processing applications, instruments, computing, etc., can solve the problem of inability to accurately analyze the load data, the inability to make the best use of the massive user-side data, and the inability to evaluate the merits of energy behaviors. And other issues

Active Publication Date: 2018-11-27
STATE GRID ENERGY RES INST +1
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Problems solved by technology

[0004] The main purpose of the present invention is to provide a user-side load feature clustering evaluation method based on morphological analysis to solve the problem that massive user-side data cannot be fully utilized, load data cannot be accurately analyzed, and user-side energy usage behavior cannot be optimized. bad evaluation problem

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  • User side load feature clustering evaluation method based on morphological analysis
  • User side load feature clustering evaluation method based on morphological analysis
  • User side load feature clustering evaluation method based on morphological analysis

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Embodiment 1

[0075] Such as figure 1 As shown, the program of a user-side load feature clustering evaluation method based on morphological analysis described in the present invention is installed in the computer, and can be realized by writing software such as Matlab and Python. The computer installed in the program includes a data input module 1.1, a load characteristic clustering module 1.2, a load characteristic evaluation module 1.3, and a result output module 1.4. The data input module 1.1 is a computer input device, including but not limited to a keyboard, mouse, etc.; the result output module 1.4 includes but not limited to a monitor.

[0076] Such as figure 1 Shown, described load feature clustering module 1.2 includes but not limited to: single user load feature acquisition unit 1.2.1, user group load feature clustering unit 1.2.2. Each unit of this module refers to the computer program instruction segment that can be executed and run by the processor of the computer to achieve ...

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Abstract

The invention discloses a user side load feature clustering evaluation method based on morphological analysis. The method comprises the following steps: using the data input module of the computer toinput daily load data of various types of users in a region every year; representing the typical energy consumption behavior of single user in one year; the morphological features of each curve are extracted firstly based on the typical daily load curve of each single user obtained in step S2 and the single users are classified according to the morphological features of the typical daily load curve so as to obtain the load feature curves of different types of users; the final evaluation result is generated by using the "cloud center" generator of the "cloud center" evaluation unit; and the computer result output module outputs the visual user side load feature clustering result and the "cloud center" evaluation result. The invention has the advantages of making full use of massive user side data, accurately analyzing the load data, mining the user side load features and evaluating the energy consumption behavior of the user side.

Description

technical field [0001] The invention relates to the technical field of user-side load feature clustering, in particular to a method for evaluating user-side load feature clustering based on morphological analysis. Background technique [0002] In the field of electric power, it is specifically manifested in the popularization and application of distributed energy, energy storage, and electric vehicles, which makes the user-side load structure increasingly complex. At the same time, smart measurement devices on the user side are widely used, and the power department can obtain detailed user-side load data. It is an important issue facing the current power system to evaluate the advantages and disadvantages of energy consumption behavior on the user side, and then formulate an operation strategy with user interaction. [0003] In recent years, the rapid development of big data disciplines and data mining technology has provided technical support for the analysis and research ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q50/06
CPCG06Q10/06393G06Q50/06Y02P90/82
Inventor 张全韩新阳王阳柴玉凤张钰张岩代贤忠靳晓凌白翠粉张晨张钧吴丹雷珽
Owner STATE GRID ENERGY RES INST
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