Power load prediction system based on long short term memory neural network

A long- and short-term memory, power load technology, applied in biological neural network models, forecasting, instruments, etc., can solve the problems of inaccurate power supply load forecasting models, less ability to predict power consumption at the same time, etc., to improve the forecasting effect and forecasting. effect precise effect

Inactive Publication Date: 2017-07-14
X TRIP INFORMATION TECH CO LTD
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The existing neural network-based forecasting methods can rarely predict the cross-regional...

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  • Power load prediction system based on long short term memory neural network
  • Power load prediction system based on long short term memory neural network
  • Power load prediction system based on long short term memory neural network

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[0036] In order to further illustrate the technical means and effects of the present invention to achieve the above objectives, the specific implementation, structure, features and effects of the present invention will be described in detail below in conjunction with the accompanying drawings and preferred embodiments. It should be understood that the specific embodiments described here are only used to explain the present invention, not to limit the present invention.

[0037] refer to figure 1 as shown, figure 1 It is a block diagram of a preferred embodiment of the electric load forecasting system based on the long-short-term memory neural network of the present invention. In this embodiment, the long-short-term memory neural network-based power load forecasting system 10 is installed and operated in a computer 1, the computer 1 also includes, but not limited to, an input unit 11, a storage unit 12, a processing unit 13 and output unit 14. The input unit 11 is an input d...

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Abstract

The invention discloses a power load prediction system based on a long short term memory neural (LSTM) network. The LSTM network comprises an input layer, a LSTM network layer and an output layer. The system comprises an information receiving module which is used for transmitting the inputted power load data of historical moments and regional feature factors to the input layer; a model establishing module which is used for performing training and modeling on the power load data of the historical moments and the regional feature factors through the LSTM network layer so as to generate a deep neural network load prediction model; a power prediction module which is used for predicting the power load of the region by using the deep neural network load prediction model and generating the power load prediction result of the region through a regressor connected with the LSTM network layer; and a result output module which is used for outputting the power load prediction result of the region through the output layer. The multitask learning load prediction model is constructed based on the LSTM network so that the power load of multiple regions can be accurately predicted and the prediction effect can be enhanced.

Description

technical field [0001] The invention relates to the technical field of power load forecasting, in particular to a power load forecasting system based on a long-short-term memory neural network. Background technique [0002] The power load forecasting problem aims to predict the power demand of a single or multiple transmission lines in the power grid. According to the forecast time span, it can be divided into: short-term forecasting (a few minutes to a week), medium-term forecasting (a month to a quarter) and long-term forecasting. Forecast (over one year). Under the current technical conditions, it is difficult to effectively store electric energy in large-scale power storage devices. Therefore, under the condition of meeting the power supply demand, reducing the remaining power generation as much as possible is an effective way to reduce costs and improve the efficiency of electric energy use. Therefore, using various forecasting methods to accurately predict the medium ...

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Application Information

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IPC IPC(8): G06Q50/06G06Q10/04G06N3/02
CPCG06N3/02G06Q10/04G06Q50/06
Inventor 杨延东邓力李书芳张贯京葛新科
Owner X TRIP INFORMATION TECH CO LTD
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