small-area-level ultra-short-term load prediction and visualization method based on a deep LSTM network

A load forecasting and ultra-short-term technology, applied in forecasting, neural learning methods, biological neural network models, etc., can solve problems such as surges in operating costs

Inactive Publication Date: 2019-05-31
ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY +2
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Problems solved by technology

With the increase of distributed energy sources, the randomness of load changes brings greater challenges to the accuracy of load forecasting. In the competitive environment of the power market, the

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  • small-area-level ultra-short-term load prediction and visualization method based on a deep LSTM network
  • small-area-level ultra-short-term load prediction and visualization method based on a deep LSTM network
  • small-area-level ultra-short-term load prediction and visualization method based on a deep LSTM network

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Embodiment Construction

[0057] Embodiments of the present invention will be described in detail below with reference to the accompanying drawings.

[0058] A small area-level ultra-short-term load forecasting method based on deep LSTM, the flow chart of the method is as follows figure 1 As shown, the method includes the following steps:

[0059] Step 1: Determine the input and output variables of the model.

[0060] The determination of the input and output data sets is the key to determining the performance of the model. The ultra-short-term load forecasting in the power system is generally carried out in a point-by-point manner. The output of the model For the load value to be predicted, the interval of prediction points can be 15min, 30min or 1h. The input data refers to various attributes that affect the load. Before determining the input attributes, it is often necessary to know the approximate composition of the load and demand-side management information, such as whether to use pe...

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Abstract

The invention provides a small area-level ultra-short-term load prediction and visualization method based on a Long Short-Term Memory (LSTM) network, the method comprising: step 1: an input and outputvariable of the model is determined; step 2: The input and output data sets are separately preprocessed; step 3: the depth LSTM load prediction model is constructed and the random search method is used to find the appropriate hyperparameters until the test set prediction error is minimized. step 4: The t-SNE visualization technology is used to visually characterize the network hidden layer vector, and the correlation coefficient heat map is formed according to the hidden layer vector to perform correlation quantitative analysis, thereby reflecting the network's ability to extract feature datafrom the input data. The method aims to utilize the feature extraction capabilities of the deep learning model and the LSTM temporal correlation learning capabilities to achieve higher prediction accuracy than the machine learning model.

Description

technical field [0001] The invention relates to a small area-level ultra-short-term load forecasting method based on a deep long-short-term memory network under the background of big data, and a visual representation of its learning ability. Background technique [0002] Very short term load forecasting (VSTLF) forecasting generally refers to the load forecasting within one hour after the current moment, and is mainly used for making intraday and real-time power generation plans. With the increase of distributed energy sources, the randomness of load changes brings greater challenges to the accuracy of load forecasting. In the competitive environment of the power market, the results of ultra-short-term load forecasting are an important basis for determining the liquidation price in the real-time power market. Excessive forecast errors will cause a surge in operating costs. Therefore, the improvement of the accuracy of ultra-short-term load forecasting is of great significan...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/04G06N3/08
Inventor 魏大钧张宇帆李昭昱郝然艾芊孙树敏程艳管荑于芃李广磊王士柏王玥娇张兴友滕玮王楠赵帅
Owner ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY
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