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LSTM-RNN-based combined cooling heating and power system load prediction method and system

A technology of combined cooling, heating and power supply and system load, applied in forecasting, neural learning methods, data processing applications, etc., can solve the problems of rarely considering the coupling relationship and inapplicability, and achieve the effect of improving prediction accuracy and accuracy

Pending Publication Date: 2019-12-27
SHANDONG UNIV
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

However, the inventors found that most of the forecasts of cooling, heating and power loads in the literature are mostly univariate forecasts, and the coupling relationship between them is rarely considered
And the load sequence is periodic, some forecasting methods are not suitable for long-term periodic load sequence forecasting

Method used

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  • LSTM-RNN-based combined cooling heating and power system load prediction method and system
  • LSTM-RNN-based combined cooling heating and power system load prediction method and system
  • LSTM-RNN-based combined cooling heating and power system load prediction method and system

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Embodiment 1

[0052] This embodiment discloses a LSTM-RNN-based load forecasting method for combined cooling, heating and power generation systems based on long-short-term memory recurrent neural networks, such as figure 2 shown, including the following steps:

[0053] Step 1: Calculate the correlation of historical data and determine the input and output of the model.

[0054]Specifically, in the step 1, the autocorrelation coefficient, partial correlation coefficient and Pearson correlation coefficient are respectively used to analyze the correlation between historical data, so as to select appropriate data as input and output variables.

[0055] The autocorrelation coefficient is used to measure the degree of correlation between the same event in two different periods. Specifically, with a certain moment as a reference, the autocorrelation coefficient of each moment in the moment and a certain period of time before it is calculated respectively. Its formula can be expressed as:

[005...

Embodiment 2

[0100] The purpose of this embodiment is to provide a load forecasting system for a combined cooling, heating and power system.

[0101] In order to achieve the above purpose, this embodiment provides a load forecasting system for a combined cooling, heating and power system, including:

[0102] The data acquisition module receives the historical data of heating load, cooling load and electric load;

[0103] The correlation analysis module determines the time period of the input data according to the correlation between a certain moment of historical data and multiple moments before it; determines the input and output according to the correlation coefficient between the historical data of heating load, cooling load and electric load data;

[0104] The model training module inputs the training set into the long-short-term memory recurrent neural network, and trains the load prediction network model;

[0105] A load forecasting module performs load forecasting based on the loa...

Embodiment 3

[0107]The purpose of this embodiment is to provide an electronic device.

[0108] An electronic device, comprising a memory, a processor, and a computer program stored on the memory and operable on the processor, when the processor executes the program, the following steps are implemented, including:

[0109] Receive historical data of heating load, cooling load and electrical load;

[0110] Determine the time period for input data based on the correlation between a certain moment of historical data and multiple moments before it;

[0111] Determine the input and output data according to the correlation coefficient between the historical data of heating load, cooling load and electric load;

[0112] Input the training set into the long short-term memory recurrent neural network to train the load prediction network model;

[0113] Load forecasting is performed based on the load forecasting network model.

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Abstract

The invention discloses an LSTM-RNN-based combined cooling heating and power supply system load prediction method and system, and the method comprises the following steps: receiving historical data ofa thermal load, a cold load and an electrical load, and determining input and output data; taking part of the input and output data as training data, and training a load prediction network model based on a long-term and short-term memory recurrent neural network; and performing load prediction based on the load prediction network model. According to the method, the coupling relationship among thecooling, heating and power loads is mined by adopting the long-term and short-term memory recurrent neural network model, so that the load prediction precision of the LSTM-RNN-based cooling, heatingand power combined supply system is improved.

Description

technical field [0001] The invention belongs to the technical field of load forecasting of renewable energy systems, and in particular relates to a method and system for load forecasting of Combined Cooling Heating and Power (CCHP) systems based on Long Short-Term Memory Recurrent Neural Network (LSTM-RNN). Background technique [0002] The statements in this section merely provide background information related to the present disclosure and do not necessarily constitute prior art. [0003] The combined cooling, heating and power system is a comprehensive energy production and utilization system based on energy cascade utilization. In the traditional energy supply system, the power generation efficiency is only about 40%, and the remaining 60% of the energy is wasted. The cooling, heating and power cogeneration system can effectively reduce the emission of pollutants by recovering the waste heat of power generation, and significantly increase the energy utilization rate to ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06N3/04G06N3/08G06Q10/04G06Q50/06
CPCG06N3/08G06Q10/04G06Q50/06G06N3/044G06N3/045
Inventor 李珂李淑珍张承慧孙芸馨严毅
Owner SHANDONG UNIV
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