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Load prediction method and device based on recurrent neural network and meta-learning strategy

A cyclic neural network and load forecasting technology, applied in forecasting, character and pattern recognition, instruments, etc., can solve problems such as large forecast deviation, unfavorable scheduling optimization, and undiscovered solutions, so as to improve accuracy and reduce forecast deviation Effect

Inactive Publication Date: 2019-08-02
XINAO SHUNENG TECH CO LTD
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] The energy prediction in the prior art, whether it is a linear model or a nonlinear model, cannot be fully adapted due to the irregularity of the basic data, and because the weights and thresholds of various neural networks may not necessarily be trained to the optimal value, resulting in a single load forecasting algorithm with poor forecasting accuracy and large forecast deviations, 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 prediction method and device based on recurrent neural network and meta-learning strategy
  • Load prediction method and device based on recurrent neural network and meta-learning strategy
  • Load prediction method and device based on recurrent neural network and meta-learning strategy

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Embodiment 1

[0029] 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 hardware structural block diagram of a load forecasting network terminal based on a cyclic neural network and a meta-learning strategy in an embodiment of the present invention. Such as 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 on...

Embodiment 2

[0075] In this embodiment, a load forecasting device based on a cyclic neural network and a meta-learning strategy is also provided. The device 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.

[0076] Figure 4 is a structural block diagram of a load forecasting device based on a cyclic neural network and a meta-learning strategy according to an embodiment of the present invention, such as Figure 4 As shown, the device includes:

[0077] The receiving module 40 is used to receive the target time to be predicted by the target energy system;

[007...

Embodiment 3

[0088] 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.

[0089] Optionally, in this embodiment, the above-mentioned storage medium may be configured to store a computer program for performing the following steps:

[0090] S1, receiving the target time to be predicted by the target energy system;

[0091] S2. Obtain historical data of the target energy system before the target time, and extract time series features of the historical data by using a recurrent neural network;

[0092] S3, selecting a load forecasting algorithm matching the time series features from multiple algorithm models according to the classification model;

[0093] S4. Using the load forecasting algorithm to output the load value of the target energy system at the target time.

[0094] Optionally, in this embodiment, the above-menti...

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Abstract

The invention provides a load prediction method and device based on a recurrent neural network and a meta-learning strategy, and the method comprises the steps: receiving to-be-predicted target time of a target energy system; obtaining historical data of the target energy system before the target time, and extracting time sequence characteristics of the historical data by adopting a recurrent neural network; selecting a load prediction algorithm matched with the time sequence characteristics from a plurality of algorithm models according to a classification model; and outputting the load valueof the target energy system at the target time by using the load prediction algorithm. Through the method and the device, the technical problems of high time consumption and low accuracy in attempting various prediction algorithms when the load prediction algorithm is adopted to predict the energy load in the prior art are solved.

Description

technical field [0001] The invention relates to the field of energy technology, in particular to a load forecasting method and device based on a recurrent neural network (RNN) and a meta-learning strategy. 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] The energy prediction in the prior art, whether it is a linear model or a nonlinear model, cannot be fully adapted due to the irregularity of the basic data, and because the weights and thresholds of various neural networks may not necessarily be trained to the optimal As a result, the prediction accuracy of the single load forecasting algorithm is not good, and the forecast deviation is large, 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] Embodime...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06K9/62
CPCG06Q10/04G06F18/241G06F18/214
Inventor 宋英豪
Owner XINAO SHUNENG TECH CO LTD
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