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Basic arithmetic unit containing solar radiation and building energy consumption rapid simulation method applying basic arithmetic unit

A basic computing unit, solar radiation technology, applied in neural learning methods, special data processing applications, calculations, etc., can solve problems such as low accuracy

Pending Publication Date: 2020-08-28
马辰龙
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] In order to overcome the problem of low accuracy caused by the failure of external environmental radiation in the building energy consumption prediction model of the above-mentioned prior art, the present invention provides a solar The basic computing unit of radiation

Method used

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  • Basic arithmetic unit containing solar radiation and building energy consumption rapid simulation method applying basic arithmetic unit
  • Basic arithmetic unit containing solar radiation and building energy consumption rapid simulation method applying basic arithmetic unit
  • Basic arithmetic unit containing solar radiation and building energy consumption rapid simulation method applying basic arithmetic unit

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Experimental program
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Effect test

Embodiment 1

[0062] Such as image 3 , Figure 4 , Figure 5 As shown, a basic operation unit including solar radiation, the basic operation unit is one of the five basic types, and the five basic types are Unit ground-ceiling 、Unit ground-roof 、Unit floor-ceiling 、Unit floor-roof and Unit exposed-ceiling ; where Unit ground-ceiling Indicates that the lower surface of the basic computing unit is the floor surface for heat exchange with the ground, and the upper surface is the ceiling between floors; Unit ground-roof Indicates that the lower surface of the basic computing unit is the floor surface for heat exchange with the ground, and the upper surface is the roof surface exposed to the external environment; Unit floor-ceiling Indicates that the lower surface of the basic computing unit is the floor surface between floors, and the upper surface is the ceiling surface between floors; Unit floor-roof Indicates that the lower surface of the basic computing unit is the floor surface be...

Embodiment 2

[0068] Such as figure 1 As shown, a fast simulation method of building energy consumption, including the parameters of the above basic computing unit for machine learning algorithm generation and fast simulation feedback of building energy consumption.

[0069] Such as figure 2 As shown, machine learning algorithm generation includes training sample generation, machine learning algorithm training and optimal machine learning algorithm screening;

[0070] The training samples are generated by combining the parameters of the basic computing units and random hypercube sampling, using the experimental design to batch generate the basic computing units with different geometric parameters and solar radiation parameters as training samples, and importing the basic computing units into the energy consumption simulation software Conduct accurate physical simulations to obtain energy consumption data;

[0071] Machine learning algorithm training consists of two steps:

[0072] a: Si...

Embodiment 3

[0096] Such as Figure 7 As shown, the energy consumption prediction is carried out for an urban block with a side length of about 140*95 meters. The total construction area of ​​the office building is 33,500 square meters, and the buildings are all facing north and south. , the north-facing window-to-wall ratio of all buildings is 0.4, the south-facing window-to-wall ratio is 0.6, and the east-west window-to-wall ratio is 0.2. The building is composed of a number of high-rise and multi-storey buildings. Each building group has a minimum of 3 floors and a maximum of 15 floors. It has complex forms such as block connections, ground floor overhead, and roof staggered floors. Compared with single-family buildings, it has a more complex structure. Energy consumption simulation environment. Assuming that all the internal building spaces are open office functions and have the same building physical properties (surface thermal coefficient), the model is built on the Rhinoceros3D pla...

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Abstract

The invention relates to the technical field of energy consumption prediction, in particular to a basic arithmetic unit containing solar radiation and a building energy consumption rapid simulation method applying the basic arithmetic unit. The invention discloses a basic arithmetic unit containing solar radiation. The basic arithmetic unit is one of five basic types, wherein the five basic typesare unitground-ceiling, unitground-roof, unitfloor-ceiling, unitfloor-roof and unitexposed-ceiling. The basic arithmetic unit comprises geometrical parameters and solar radiation quantity parameters.The building energy consumption rapid simulation method comprises the steps that the parameters of the basic arithmetic unit are used for machine learning algorithm generation and building energy consumption rapid simulation feedback. According to the method, annual radiation calculation is brought into machine learning training parameters for the first time, and the defect that building externalenvironment influence factors cannot be considered when a current machine learning algorithm simulates building energy consumption is overcome; by solving the defect, the accuracy of the machine learning algorithm is greatly improved, and the practicability of the machine learning algorithm is improved.

Description

technical field [0001] The invention relates to the technical field of energy consumption prediction, and more specifically, to a basic calculation unit including solar radiation and a fast building energy consumption simulation method using the unit. Background technique [0002] Building energy consumption accounts for an important proportion of my country's energy consumption. Under the background of smart cities, energy conservation and emission reduction, optimizing building energy consumption in the architectural design stage is of great significance to the sustainable development of cities. However, the current building energy consumption simulation accuracy and computing speed cannot be balanced at the same time: [0003] On the one hand, the traditional physical simulation method is extremely time-consuming. For a single building or building group with complex shapes, a more accurate energy consumption prediction has to take hours or even days; Combined calculation...

Claims

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Application Information

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IPC IPC(8): G06F30/13G06F30/27G06F30/20G06N3/08G06F119/06
CPCG06F30/13G06F30/27G06N3/08G06F30/20G06F2119/06
Inventor 马辰龙朱姝妍
Owner 马辰龙
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