Power load prediction method based on improved long-term and short-term memory network

A long-term and short-term memory, power load technology, applied in the direction of load forecasting, AC network circuit, forecasting, etc. in the AC network, can solve the problems of complex structure, considering, unconsidered features and feature effects, etc.

Active Publication Date: 2020-11-24
HUAZHONG UNIV OF SCI & TECH
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Problems solved by technology

[0007] (1) There are many factors that affect the power load, and these factors cannot be reasonably considered in the load forecasting problem
[0008] (2) Existing feature selection for power load forecasting mostly considers the influence of a certain feature on its power load, and does not consider the effect of the combination of features and features...

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  • Power load prediction method based on improved long-term and short-term memory network
  • Power load prediction method based on improved long-term and short-term memory network
  • Power load prediction method based on improved long-term and short-term memory network

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Embodiment Construction

[0075] The present invention will be further described below with reference to the accompanying drawings:

[0076] figure 1 The power load prediction method for the improved long-term memory network of the present invention is the overall flow chart, and specifically includes the following steps:

[0077] (1) Collect electricity load and its related factor data, based on historical power load on its future impact;

[0078] The historical power load sequence and the maximum information factor (MIC) analysis of 168 hours (one time for 1 hour) are used in the history of 168 times (1 hour), and the calculated MIC is greater than 0.6. The historical load makes a pre-selected set, and the remaining load group has become a candidate set.

[0079] (2) Preliminary treatment of power load correlation factors;

[0080] The factors that affect the power load-related factors are collected, including continuous feature and discrete feature, which requires a certain processing method to be appl...

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Abstract

The invention discloses a power load prediction method based on an improved long-term and short-term memory network. The method comprises steps of the characteristic of historical load being preliminarily screened by adopting the maximum information coefficient; the influence caused by load related factors being considered; and further screening the historical load by adopting a maximum correlation minimum redundancy algorithm, taking the screened feature set as the input of the model, carrying out power load prediction by adopting an improved long-short memory network, and verifying the obtained prediction result and the actual power grid load to prove the practicability of the model. According to the forecasting method (H-ILSTM) provided by the invention, the power load and related factors influencing the load are accurately considered, precision of power load forecasting is effectively improved, and safety and economy of power grid operation are improved to a certain extent.

Description

Technical field [0001] The present invention relates to the field of electricity load prediction, and more particularly to a power load prediction method based on an improved long-term memory network. Background technique [0002] Electricity load prediction plays a key role in the operation of the grid, and the accurate short-term load can greatly improve the safety and economy of the grid operation. In addition, it is also important for the optimal combination of the unit, economic scheduling, optimal trend, and electricity market transactions. The higher the accuracy of the electric power load forecast, the better the utilization of the power generation equipment, the better the economic scheduling. However, power load is very sensitive to external factors, such as climate change, date type, and social activities. These uncertainties increase the randomness of the load sequence. Therefore, how to identify the strong correlation factors of extracting loads from many influencing...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/04H02J3/00
CPCG06Q10/04G06Q50/06H02J3/003G06N3/045G06N3/044Y04S10/50
Inventor 覃晖裴少乾卢桂源吕昊谢伟曲昱华付佳龙
Owner HUAZHONG UNIV OF SCI & TECH
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