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Commercial building hourly personnel density prediction method

A forecasting method and personnel technology, applied in forecasting, instruments, data processing applications, etc., can solve the problem of low accuracy of energy consumption simulation, and achieve the effect of ensuring dynamics, improving accuracy, and ensuring differentiation

Inactive Publication Date: 2019-09-10
SOUTHEAST UNIV
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  • Summary
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0006] The problem to be solved by the present invention is: the accuracy of the existing energy consumption simulation based on the fixed personnel density change table is not high, and it cannot be better applied to the optimization design of buildings and the formulation of energy-saving policies, and a more accurate dynamic personnel density is needed Density model, providing better technical support for energy consumption simulation research

Method used

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  • Commercial building hourly personnel density prediction method
  • Commercial building hourly personnel density prediction method
  • Commercial building hourly personnel density prediction method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment 1

[0056] like figure 1 A method for predicting the hourly occupancy density of a commercial building as shown includes the following steps:

[0057] Step 1: Determine the specific type of commercial building;

[0058] Step 2: Obtain the actual population density of typical commercial buildings;

[0059] Step 3: Build a population density model.

[0060] Among them, step 1: determine the specific type of commercial building;

[0061] According to the Code for Design of Store Buildings (JGJ48-2014), commercial buildings are divided into shopping malls, supermarkets, specialty stores, etc. (see Table 1 for specific explanations). Since the population density models of specific types of commercial buildings are different, it is not possible to establish a general population density model for commercial buildings, but to establish a population density model for each specific type of commercial buildings.

[0062] Table 1. Description of Commercial Building Classification

[0063...

Embodiment 2

[0084] In order to make the method of Embodiment 1 more intuitive and easy to understand, the establishment of a population density model in a shopping center is taken as an example for related explanations:

[0085] 1. Determine the specific type of commercial building. A shopping center is taken as a specific description object of the present invention.

[0086]2. Obtain the actual population density of typical commercial buildings. In order to ensure the representativeness of the research samples, four shopping centers scattered and not located in the same administrative area were selected for field research, including Dayang Department Store (Hongyang Plaza), Jingfeng (Jiangning), Forest Moore, and Deji Plaza. Each building is selected on weekdays and weekends, and investigators are arranged at each entrance and exit of each sample building to record the number of people entering and exiting hourly. Finally, calculate the hourly personnel density based on the statistical...

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Abstract

The invention discloses a commercial building hourly personnel density prediction method. Three commercial building types are determined, and the actual hourly personnel density is obtained through investigation. And establishing a personnel density model by superposition of normal distribution functions. Wherein the number of the personnel density models of the shopping center and the supermarketis two, and the number of the personnel density models of the supermarket is three; and the model of the specialized store is a superposition of two or three normal distribution functions. Wherein the established model comprises parameters a1, a2, a3, b1, b2, b3, c1, c2 and c3, and replacing the parameters with urban factor traffic accessibility Tr and population level Po so as to simplify the model. Finally, a1, b1 and c1 are found to have strong correlation with Tr or Po and can be represented by a linear function. A2, b2 and c2 are not strongly correlated with Tr and Po, and are expressedby an average value; and a3 and a1, b3 and b1, and c3 and c1 are respectively in a quadratic function relationship. The commercial building hourly personnel density prediction method provided by the invention can be used for city scale and monomer level energy consumption simulation and safe evacuation system design.

Description

technical field [0001] The invention belongs to the technical field of new energy and energy saving, and is a method for predicting the hourly population density of commercial buildings, which is suitable for specific aspects such as building energy saving at city scale and single level, building fire protection design and the like. Background technique [0002] In order to cope with the current energy shortage and environmental deterioration, governments in many countries have successively established the macro target of energy conservation and emission reduction. In order to achieve the ambitious goal of energy conservation and emission reduction, relevant scholars need to further develop energy consumption simulation and prediction technology, so as to provide a scientific basis for the government to formulate efficient energy conservation policies. [0003] Urban building energy consumption is the main component of urban energy consumption, which can account for 30-40% o...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q10/06G06Q50/26
CPCG06Q10/04G06Q10/067G06Q50/26
Inventor 王超石邢武玥
Owner SOUTHEAST UNIV
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