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Short-term residence load prediction method based on Attention-GRU

A load forecasting and residential technology, applied in forecasting, instrumentation, character and pattern recognition, etc., can solve the problems that RNN cannot guarantee the forecasting effect and is not suitable for dealing with long-term data dependence

Active Publication Date: 2019-12-27
GUANGDONG UNIV OF TECH
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Problems solved by technology

Some researchers have found that the use of RNNs does not ensure good forecasting performance due to the fact that RNNs are not well suited to handle the long-term dependencies of the data

Method used

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  • Short-term residence load prediction method based on Attention-GRU
  • Short-term residence load prediction method based on Attention-GRU
  • Short-term residence load prediction method based on Attention-GRU

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Embodiment Construction

[0036] The present invention will be further described in detail below in conjunction with the embodiments and the accompanying drawings, but the embodiments of the present invention are not limited thereto.

[0037] Such as Figure 1~2 As shown, a short-term residential load forecasting method based on Attention-GRU includes the following steps:

[0038] Step 1, data preprocessing;

[0039] The first step in load forecasting using a deep learning model is to use the appropriate format, e.g. LSTM and GRU models expect input 3D data, prepare the data with [samples, timesteps, features], and apply spatial clustering using density-based noise (DBSCAN ) technology to evaluate the consistency of daily power distribution; in the second step, construct training set and test set;

[0040]For each element of the data set and test set, there are two parts, namely matrix X and matrix Y; matrix X is the input of the gated recurrent neural network, and matrix Y is the output of the gated...

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Abstract

The invention discloses a short-term residence load prediction method based on Attention-GRU. The method comprises the following steps: data preprocessing; the first step of carrying out load prediction by using a deep learning model being to prepare data in a proper format and evaluate the consistency of daily power distribution by using a density-based noise application space clustering (DBSCAN)technology; in the secondary step, constructing a training set and a test set. According to the method, two algorithms of artificial intelligence in natural language processing are combined to construct a short-term residence load prediction model, and the model uses a GRU algorithm to not only overcome the defects of a recurrent neural network of a traditional intelligent prediction algorithm, but also solve the problems of gradient explosion and gradient disappearance of RNN. The Attention layer is used for endowing the input vector in the next time step with the feature weight learned by the model, and highlighting the influence of the key feature on the predicted load.

Description

technical field [0001] The invention relates to the technical field of power load forecasting, in particular to an Attention-GRU-based short-term residential load forecasting method. Background technique [0002] Load forecasting is the first stage of power system planning and control. Accurate load forecasting is very important for utilities to ensure the reliability and stability of the grid to meet load demands. According to the forecast time horizon, electricity demand forecasting can be roughly divided into three categories, namely, short-term electricity demand forecasting, medium-term electricity demand forecasting and long-term electricity demand forecasting. It is useful for efficiently handling day-to-day operations, generation capacity scheduling, procurement planning and evaluation. Residential Daily Load Forecasting is a type of short-term load forecasting. It is an important basis for estimating the standby capacity of the power system, daily load rate, and ...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06K9/62
CPCG06Q10/04G06Q50/06G06F18/2321G06F18/241
Inventor 张少峰刘义杨超蒋丽谢胜利
Owner GUANGDONG UNIV OF TECH
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