Load forecasting method and device based on neural network

A load forecasting and neural network technology, applied in the field of communication, can solve problems such as undiscovered solutions, poor forecasting accuracy, unfavorable scheduling optimization, etc., and achieve the effect of improving accuracy, high network approximation accuracy, and reducing forecasting deviation

Inactive Publication Date: 2019-01-22
ENNEW DIGITAL TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] There are many methods of load forecasting in the prior art, such as exponential smoothing, Arima, neural network, etc., but a single load forecasting algorithm has poor forecast accuracy and large forecast deviation, 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

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0052] 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 1 It 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. Such as figure 1 As shown, the network terminal 10 may include one or more ( figure 1Only 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 d...

Embodiment 2

[0100] 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.

[0101] 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:

[0102] A receiving module 30, configured to receive a time period to be predicted;

[0103] The input module 32 is used to input the time period into the neural network model for predicting the energy load,...

Embodiment 3

[0108] 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.

[0109] Optionally, in this embodiment, the above-mentioned storage medium may be configured to store a computer program for performing the following steps:

[0110] S1, receiving the time period to be predicted;

[0111] S2, inputting the time period into a neural network model for predicting energy load, wherein the neural network model is a radial basis neural network (RBF) trained based on a hybrid particle swarm optimization algorithm;

[0112] S3. Using the neural network model to predict the energy load value in the time period.

[0113] 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 ...

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Abstract

The invention provides a load forecasting method and a 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 a radial basis function neural network trained based on a hybrid particle swarm optimization algorithm; predicting an energy load value 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 existing technology, energy (such as steam) supply users are divided into industrial, commercial, residential, office, etc., and the steam load, load magnitude, and load characteristics of different users are different. The accuracy of load forecasting is related to optimal scheduling and operating strategy. Carrying out energy forecast in advance can guarantee the actual user of the user, and reduce the waste of energy at the same time. [0003] There are many load forecasting methods in the prior art, such as exponential smoothing, Arima, neural network, etc., but a single load forecasting algorithm has poor forecast accuracy and large forecast deviation, which is not conducive to later scheduling optimization. [0004] For the above-mentioned problems existing in the prior art, ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/00G06N3/02
CPCG06N3/006G06N3/02G06Q10/04G06Q50/06G06N3/086G06N3/048G06N3/04G06N3/08
Inventor 黄信刘胜伟
Owner ENNEW DIGITAL TECH CO LTD
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