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Resources allocation method based on electricity consumption data and radial basis function neural network

A technology based on neural network and resource allocation, applied in the field of resource allocation based on power consumption data and radial basis neural network, can solve problems such as insufficient training speed, and achieve rapid optimization of configuration, rapid increase and decrease, and accelerated computing speed Effect

Pending Publication Date: 2021-12-21
STATE GRID JIANGSU ELECTRIC POWER CO LTD MARKETING SERVICE CENT +1
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Problems solved by technology

The general neural network model has too many hidden layers, and the training speed is not fast enough

Method used

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  • Resources allocation method based on electricity consumption data and radial basis function neural network
  • Resources allocation method based on electricity consumption data and radial basis function neural network
  • Resources allocation method based on electricity consumption data and radial basis function neural network

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Embodiment Construction

[0053] The application will be further described below in conjunction with the accompanying drawings. The following examples are only used to illustrate the technical solutions of the present invention more clearly, but not to limit the protection scope of the present application.

[0054] like figure 1 As shown, the resource optimization configuration method based on electricity consumption data and radial basis neural network of the present invention comprises the following steps:

[0055] Step 1: Obtain the electricity consumption data of enterprises in the region, historical industrial data and regional weather temperature data, clean and integrate the data, and distinguish the training set and the test set.

[0056] Further, step 1 includes the following steps:

[0057] Step 1.1: Obtain electricity consumption data of enterprises in the area, historical industrial data, and regional weather and temperature data.

[0058] Among them, the electricity consumption data of en...

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Abstract

The invention discloses a resources allocation method based on electricity consumption data and a radial basis function neural network, and the method comprises the following steps: obtaining enterprise electricity consumption data, historical industrial data and regional weather and temperature data of a region, cleaning and integrating the data, and distinguishing a training set and a test set; using the training set data as radial basis function neural network model input, carrying out model training, and generating a power-industry prediction model; and taking test set data as model input to obtain a regional industry prediction result, and performing optimal configuration of resources by using a resource optimization algorithm. The industrial development trend is simulated and predicted by relying on the enterprise power data and the regional environment data, and optimal configuration of resources can be quickly and accurately carried out.

Description

technical field [0001] The invention relates to the technical field of electric power big data, in particular to a resource allocation method based on power consumption data and a radial basis neural network. Background technique [0002] In the allocation of resources, due to the hysteresis of regional GDP data statistics, and the fact that enterprises may misreport data, resulting in inaccurate statistics, relying only on traditional financial statistics methods has been unable to support the resources brought about by unbalanced industrial development. Configure countermeasures. In the modern environment, statistics will change from relying only on existing data to relying on intelligent means to make more accurate predictions and make adjustments and optimizations in a timely manner. Due to its timeliness and accuracy, electric power big data can be used to predict the development of various industries in various regions and serve as the basis and basis for resource all...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/06G06Q10/04G06Q50/06
CPCG06Q10/0631G06Q10/04G06Q50/06
Inventor 左强何维民钱旭盛赵双双尹泽然王贺刘颖丁泽诚杨美蓉周家亿陈奕彤
Owner STATE GRID JIANGSU ELECTRIC POWER CO LTD MARKETING SERVICE CENT
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