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A saturated load forecasting method based on long-term and short-term memory neural network

A technology of long-term short-term memory and neural network, applied in the field of saturated power load forecasting based on long-term short-term memory neural network, can solve the problem of not considering the load timing delay characteristics, etc., to meet various forecasting needs, improve forecasting effectiveness, and correlate The effect of low degree requirements

Active Publication Date: 2019-01-15
STATE GRID JIANGSU ECONOMIC RES INST +2
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Problems solved by technology

These studies did not take into account the delay characteristics of load timing continuity and the influence of correlation factors

Method used

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  • A saturated load forecasting method based on long-term and short-term memory neural network
  • A saturated load forecasting method based on long-term and short-term memory neural network
  • A saturated load forecasting method based on long-term and short-term memory neural network

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Embodiment Construction

[0035] The present invention will be described in detail below in conjunction with the accompanying drawings and specific embodiments. This embodiment is carried out on the premise of the technical solution of the present invention, and detailed implementation and specific operation process are given, but the protection scope of the present invention is not limited to the following embodiments.

[0036] The present invention provides a saturated electric load forecasting method based on long-short-term memory neural network, such as figure 1 shown, including the following steps:

[0037] Step 1: Select influencing factors and set up forecasting scenarios. The values ​​of influencing factors in each scenario are different. In this step, population, GDP, per capita GDP, proportion of secondary industry, proportion of tertiary industry, and urbanization rate are selected as influencing factors, and the population, urban population, and GDP are extrapolated and predicted by the L...

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Abstract

The invention relates to a saturated power load forecasting method based on a long-term and short-term memory neural network. The method comprises 1. setting satured load influencing factor and forecasting scne, 2. constructing a long-term and short-term memory neural network forecasting model, performing saturation load forecasting on a region to be forecasted by the trained and optimize long-term and short-term memory neural network forecasting model, and obtaining saturation time and saturation scale of the region to be forecasted under different forecasting scenarios. Compared with the prior art, the invention has the advantages of satisfying the requirements of the continuity of the load sequence and the time ductility of the influence factors on the load and the like.

Description

technical field [0001] The invention relates to a power system load forecasting technology, in particular to a saturated power load forecasting method based on a long-short-term memory neural network. Background technique [0002] Saturated power load forecasting refers to the forecast of the time when regional power load enters saturation and the scale of saturated power consumption. The power load saturation scale and saturation time are affected by many factors, including regional population and economic characteristics. The saturated load scale and saturation time are the basis for the long-term planning goals of the power grid, and are of great significance for coordinating the construction of the power grid in the near future. [0003] The traditional saturated power load forecasting methods include: using the improved K-means clustering algorithm to classify the load and using the Logistic curve to predict the saturated load; using the improved gray Verhulst model to...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06Y04S10/50
Inventor 黄俊辉谈健史静姚颖蓓张建平马则良李琥刘国静李冰洁
Owner STATE GRID JIANGSU ECONOMIC RES INST
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