Load forecasting method and device based on neural network

A technology of load forecasting and neural network, applied in the field of communication, can solve problems such as large forecast deviation, non-stationary sequence cannot be found, unfavorable scheduling optimization, etc., and achieve the effect of reducing forecast deviation and improving accuracy

Inactive Publication Date: 2019-03-29
ENNEW DIGITAL TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] Energy forecasting in the prior art uses the Arima model. Arima enjoys a high reputation in stationary time series, but it still cannot find the best p, d, and q values ​​for non-stationary series. Arima fits the linear structure of the data is better, but it is not good for nonlinear structures, which leads to poor prediction accuracy and large prediction deviation of a single load forecasting algorithm, which is not conducive to later scheduling optimization
[0004] Aiming at the above problems existing in the prior art, no effective solution has been found yet

Method used

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  • Load forecasting method and device based on neural network
  • Load forecasting method and device based on neural network
  • Load forecasting method and device based on neural network

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Embodiment 1

[0061] The method embodiment provided in Embodiment 1 of the present application may be executed in a server, a network terminal, a computer terminal or a similar computing device. Take running on a network terminal as an example, figure 1It is a block diagram of the hardware structure of a neural network-based load forecasting network terminal according to an embodiment of the present invention. like figure 1 As shown, the network terminal 10 may include one or more ( figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the above-mentioned network terminal also A transmission device 106 for communication functions as well as input and output devices 108 may be included. Those of ordinary skill in the art can understand that, figure 1 The shown structure is only for illustration, and does...

Embodiment 2

[0119] In this embodiment, a neural network-based load forecasting device is also provided, which is used to implement the above-mentioned embodiments and preferred implementation modes, and those that have already been described will not be repeated. As used below, the term "module" may be a combination of software and / or hardware that realizes a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, implementations in hardware, or a combination of software and hardware are also possible and contemplated.

[0120] image 3 is a structural block diagram of a neural network-based load forecasting device according to an embodiment of the present invention, such as image 3 As shown, the device includes:

[0121] A receiving module 30, configured to receive a time period to be predicted;

[0122] The input module 32 is used to input the time period into a neural network model for predicting energy load, where...

Embodiment 3

[0127] An embodiment of the present invention also provides a storage medium, in which a computer program is stored, wherein the computer program is set to execute the steps in any one of the above method embodiments when running.

[0128] Optionally, in this embodiment, the above-mentioned storage medium may be configured to store a computer program for performing the following steps:

[0129] S1, receiving the time period to be predicted;

[0130] S2, inputting the time period into a neural network model for predicting energy load, wherein the neural network model is composed of a linear prediction model and a nonlinear prediction model;

[0131] S3. Using the neural network model to predict the energy load value in the time period.

[0132] Optionally, in this embodiment, the above-mentioned storage medium may include but not limited to: U disk, read-only memory (Read-Only Memory, ROM for short), random access memory (Random Access Memory, RAM for short), Various media th...

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Abstract

The invention provides a load forecasting method and device based on a neural network, wherein, the method comprises the following steps of: receiving a time period to be forecasted; Inputting the time period to a neural network model for predicting an energy load, wherein the neural network model is composed of a linear prediction model and a non-linear prediction model; Predicting an energy loadvalue at the time period using the neural network model. The invention solves the technical problem in the prior art that the accuracy rate is low when the single load forecasting algorithm is used for forecasting the energy load.

Description

technical field [0001] The present invention relates to the communication field, in particular, to a neural network-based load forecasting method and device. Background technique [0002] In the prior art, energy forecasting in advance can ensure the actual users of users and reduce energy waste at the same time. [0003] Energy forecasting in the prior art uses the Arima model. Arima enjoys a high reputation in stationary time series, but it still cannot find the best p, d, and q values ​​for non-stationary series. Arima fits the linear structure of the data It is better, but it is not good for the nonlinear structure, which leads to the poor prediction accuracy of the single load forecasting algorithm and the large prediction deviation, which is not conducive to the later scheduling optimization. [0004] For the above-mentioned problems existing in the prior art, no effective solution has been found yet. Contents of the invention [0005] The embodiment of the present...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06N3/04G06N3/08
CPCG06N3/049G06N3/08G06Q10/04
Inventor 黄信刘胜伟
Owner ENNEW DIGITAL TECH CO LTD
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