Load prediction method and device based on auto-encoder and meta-learning strategy

A self-encoder and load forecasting technology, applied in the energy field, can solve problems such as large forecast deviation, unfavorable scheduling optimization, and undiscovered solutions, and achieve the effect of improving accuracy and reducing forecast deviation

Active 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 auto-encoder and meta-learning strategy
  • Load prediction method and device based on auto-encoder and meta-learning strategy
  • Load prediction method and device based on auto-encoder 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 block diagram of the hardware structure of a load prediction network terminal based on an autoencoder and a meta-learning strategy according to 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 processor 102 (the processor 102 may include, but is 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 aforementioned network terminal is also It may include a transmission device 106 and an input / output device 108 for communication functions. Those of ordinary skill in the art can understand, figure 1 The structure shown is only for illustra...

Embodiment 2

[0074] In this embodiment, a load prediction device based on an autoencoder and a meta-learning strategy is also provided. The device is used to implement the above-mentioned embodiments and preferred implementations, and what has been described will not be repeated. As used below, the term "module" may implement a combination of software and / or hardware with predetermined functions. Although the devices described in the following embodiments are preferably implemented by software, hardware or a combination of software and hardware is also possible and conceived.

[0075] Figure 4 It is a structural block diagram of a load prediction device based on an autoencoder and a meta-learning strategy according to an embodiment of the present invention, such as Figure 4 As shown, the device includes:

[0076] The receiving module 40 is used to receive the target time to be predicted by the target energy system;

[0077] The obtaining module 42 is configured to obtain historical data of t...

Embodiment 3

[0087] An embodiment of the present invention also provides a storage medium in which a computer program is stored, wherein the computer program is configured to execute the steps in any one of the foregoing method embodiments when running.

[0088] Optionally, in this embodiment, the foregoing storage medium may be configured to store a computer program for executing the following steps:

[0089] S1, receiving the target time to be predicted by the target energy system;

[0090] S2: Obtain historical data of the target energy system before the target time, and use an autoencoder to extract time series characteristics of the historical data;

[0091] S3, selecting a load forecasting algorithm matching the time series feature from a plurality of algorithm models according to the classification model;

[0092] S4: Use the load prediction algorithm to output the load value of the target energy system at the target time.

[0093] Optionally, in this embodiment, the foregoing storage medium m...

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PUM

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Abstract

The invention provides a load prediction method and device based on an auto-encoder and a meta-learning strategy, and the method comprises the steps: receiving to-be-predicted target time of a targetenergy system; obtaining historical data of the target energy system before the target time, and extracting time sequence characteristics of the historical data by adopting the auto-encoder; selectinga load prediction algorithm matched with the time sequence characteristics from a plurality of algorithms according to a classification model obtained by meta-learning; 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 attemptingvarious 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 present invention relates to the field of energy technology, in particular to a load prediction method and device based on an autoencoder and a meta-learning strategy. Background technique [0002] In the prior art, energy forecasting in advance can ensure the actual users of the users and reduce energy waste. [0003] 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. Moreover, the weights and thresholds of various neural networks may not be trained to the optimal Therefore, the single load prediction algorithm has poor prediction accuracy and large prediction deviation, which is not conducive to later scheduling optimization. [0004] Aiming at the above-mentioned problems in the prior art, no effective solution has yet been found. Summary of the invention [0005] The embodiment of the present invention provides a load prediction method and device base...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06F16/2458
CPCG06Q10/04G06Q50/06G06F16/2474
Inventor 宋英豪
Owner XINAO SHUNENG TECH CO LTD
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