Series-wound long short-term memory recurrent neural network-based heating load prediction method

A cyclic neural network, long-term and short-term memory technology, applied in neural learning methods, biological neural network models, prediction and other directions, can solve problems such as low and difficult data prediction accuracy, improve prediction accuracy, promote convergence, and avoid gradient explosions Effect

Active Publication Date: 2017-10-10
ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY +2
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[0006] In order to solve the above-mentioned problems, the present invention proposes a heating load forec

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[0033] The present invention will be further described below in conjunction with the accompanying drawings and embodiments.

[0034] It should be pointed out that the following detailed description is exemplary and intended to provide further explanation to the present application. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs.

[0035] It should be noted that the terminology used here is only for describing specific implementations, and is not intended to limit the exemplary implementations according to the present application. As used herein, unless the context clearly dictates otherwise, the singular is intended to include the plural, and it should also be understood that when the terms "comprising" and / or "comprising" are used in this specification, they mean There are features, steps, operations, means, components and / or combinatio...

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Abstract

The present invention discloses a series-wound long short-term memory recurrent neural network-based heating load prediction method. The method comprises the steps of constructing a sample data set based on temperature, climate and heat supply data during a given period of time, and respectively subjecting the input data and the output data of the sample data set to standardized treatment; dividing the input data into two portions, respectively inputting the two portions into two independent long short-term memory recurrent neural networks to merge the two portions of the input data, inputting the output data to a long short-term memory recurrent neural network at a next layer, and finally inputting the data into two full connection layers; training a constructed series-wound long short-term memory recurrent neural network, and optimizing the network by adopting the parameter optimization-based adaptive torque estimation algorithm; inputting to-be-predicted data into the series-wound long short-term memory recurrent neural network, calculating and obtaining a heating load prediction result. The method of the invention can effectively discriminate input data, and accelerate the learning speed. Therefore, the learning efficiency is improved and the prediction accuracy is improved.

Description

technical field [0001] The invention belongs to the field of heating load forecasting, and in particular relates to a heating load forecasting method based on a serial long-short-term memory cycle neural network. Background technique [0002] The main feature of the coal-fired cogeneration unit is that the generator set not only produces electric energy, but also uses steam generated by the steam turbine generator to provide heat to the user. Therefore, during the heating season in the north, coal-fired cogeneration units are mainly responsible for the central heating of residents in specific areas. At present, the operation principle of cogeneration power plants stipulated by the state is "heat-based power generation", that is, thermal power plants should determine the best operation plan according to the needs of heat load, and the main goal is to meet the needs of heat load. Regional power management departments must fully consider the heat supply load curve and energy-s...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/08
CPCG06N3/08G06Q10/04G06Q50/06
Inventor 路宽苏建军赵岩毕贞福郎澄宇孟祥荣麻常辉王文宽孙雯雪韩英昆庞向坤李广磊张用王世柏
Owner ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY
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