Building energy consumption prediction method based on deep cascade generative adversarial network and related products

A network and building technology, applied in the computer field, can solve the problems of high energy consumption and carbon emissions, and achieve the effect of recognition, robustness, and accurate energy consumption data

Active Publication Date: 2021-01-05
HUBEI UNIV
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Problems solved by technology

Building energy consumption is the largest energy consumer today. For example, it accounts for about 35% of the total energy consumption in Europe and the United States, and about 30% of the total energy consumption in China. However, most building HVAC system energy consumption and carbon high emissions

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  • Building energy consumption prediction method based on deep cascade generative adversarial network and related products
  • Building energy consumption prediction method based on deep cascade generative adversarial network and related products
  • Building energy consumption prediction method based on deep cascade generative adversarial network and related products

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[0053] The following will clearly and completely describe the technical solutions in the embodiments of the present application with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are part of the embodiments of the present application, not all of them. Based on the embodiments in this application, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the scope of protection of this application.

[0054] It should be understood that when used in this specification and the appended claims, the terms "comprising" and "comprises" indicate the presence of described features, integers, steps, operations, elements and / or components, but do not exclude one or Presence or addition of multiple other features, integers, steps, operations, elements, components and / or collections thereof.

[0055] It should also be understood that the terminology used in the specificati...

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Abstract

The embodiment of the invention discloses a building energy consumption prediction method based on a deep cascade generative adversarial network and a related product, and the method comprises the steps of inputting spatial factors of a building into a symmetric residual network, and extracting the spatial features of the building through the symmetric residual network; performing local feature quantification on the spatial features through a spatial attention mechanism to obtain quantified spatial features; inputting the quantized spatial features and the time sequence factor of the buildinginto a bidirectional long-short-term memory network to obtain time sequence features of the building; performing local feature quantification on the time sequence features through a time sequence attention mechanism to obtain space-time factor joint features; and predicting according to the space-time joint features to obtain energy consumption data of the building.

Description

technical field [0001] The present application relates to the field of computer technology, in particular to a building energy consumption prediction method and related products based on deep cascaded generative confrontation networks. Background technique [0002] Building energy consumption (that is, the operating energy consumption of buildings) is the sum of the energy consumption of various types of equipment (such as HVAC, lighting, and office equipment) during building operation. Building energy consumption is the largest energy consumer today. For example, it accounts for about 35% of the total energy consumption in Europe and the United States, and about 30% of the total energy consumption in China. However, most building HVAC system energy consumption and carbon Emissions are high. China's total building energy consumption in 2015 was as high as 964 million tons of standard coal, and the International Energy Agency predicts that China's total building energy consu...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06N3/04G06K9/62G06F30/13G06F119/06
CPCG06Q10/04G06F30/13G06F2119/06G06N3/044G06N3/045G06F18/214
Inventor 胡书山王鹏余日季
Owner HUBEI UNIV
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