A quantitative prediction method for latent heat load of air conditioner

Abstract

The present invention provides a quantitative prediction method for the latent heat load of an air conditioner, the prediction method is Q=a*ITMRH+b*RH+c*T+d, wherein: a, b, c, d are determination coefficients; Q is the latent heat load, T is the temperature; RH is the relative humidity; ITMRH is the temperature-humidity product index; the calculation formula of the temperature-humidity product index is: wherein i represents the sampling period, T is the temperature, and RH is the relative humidity. In practice, multiple sampling periods can be set as needed within a predetermined number of days, months, or years, and then the latent heat load value corresponding to the corresponding sampling period can be obtained. The present invention can predict the air conditioning latent heat load of the climate zone based on only a small amount of climate data in the climate zone, uniformly control the air conditioning system of the office building in the climate zone, and reduce the energy consumption of the building.

Description

Technical Field

[0001] The invention relates to the technical field of air-conditioning latent heat load prediction, and in particular to a method for quantitatively predicting the latent heat load of an air-conditioning. Background Art

[0002] With the continuous improvement of air-conditioning energy saving and indoor thermal comfort requirements, new technologies of air-conditioning systems are constantly innovating, especially temperature and humidity independent control air-conditioning systems. Temperature and humidity independent control air-conditioning systems decouple temperature and humidity control processing, and through reasonable allocation and regulation of the module capacity of sensible heat load and latent heat load, improve the operating efficiency of the air-conditioning system while ensuring that the indoor temperature and humidity meet the requirements of human thermal comfort, and achieve the effect of air-conditioning energy saving. The patent number of the prior art is: CN105371423A, and the patent name is: Design method of temperature and humidity independent control air-conditioning system based on wet days, which controls the operation of the air-conditioning according to the "wet days" and "dry high temperature days".

[0003] Compared with traditional air-conditioning systems, independent temperature and humidity control air-conditioning systems can effectively reduce energy consumption, especially when the outdoor humidity is high. Through scientific dehumidification, the latent heat load can be reduced, resulting in energy savings of up to 20%. However, the precise design of the capacity of the independent temperature and humidity control system and its scientific operation after completion require the establishment of indicators that can quantitatively evaluate sensible heat, especially latent heat load. Summary of the invention

[0004] In view of this, the problem to be solved by the present invention is to provide a quantitative prediction method for the latent heat load of air conditioning, which can predict the latent heat load of air conditioning in a set climate zone or a set city based on only a small amount of climate data in the climate zone or the city.

[0005] In order to solve the above technical problems, the technical solution adopted by the present invention is:

[0006] A quantitative prediction method for latent heat load of air conditioner includes the following formula:

[0007] Q=a*ITMRH+b*RH+c*T+d,

[0008] Among them: a, b, c, d are determination coefficients; Q is latent heat load, T is temperature; RH is relative humidity; ITMRH is temperature and humidity index;

[0009] The calculation formula of the temperature and humidity index is: ITMRH = T i ×RH i , where i represents the sampling period, T i is the temperature of sampling period i, RH i is the relative humidity during sampling period i.

[0010] Furthermore, the applicable climate zones are severely cold areas, cold areas, mild areas, hot summer and cold winter areas, or hot summer and warm winter areas.

[0011] Furthermore, the determination process of the coefficient of determination includes:

[0012] Obtaining the temperature and relative humidity of a predetermined area during a predetermined historical period, wherein the predetermined area is a predetermined climate zone or a predetermined city in the predetermined climate zone;

[0013] Obtaining software simulation values ​​or actual monitoring values ​​of air conditioning latent heat loads of a predetermined office building in a predetermined climate zone or a predetermined office building in a predetermined city in a predetermined climate zone during a predetermined historical period;

[0014] Taking temperature, relative humidity and temperature-humidity integrated index as factor set, multiple linear stepwise regression analysis was used to screen and obtain the determination coefficient of the predetermined area.

[0015] Furthermore, the software simulation value of the air conditioning latent heat load is obtained through TRNSYS software simulation.

[0016] Furthermore, the predetermined historical period includes several years, the time when the air conditioner is used each year is the air-conditioning period, and the air-conditioning period includes several sampling periods.

[0017] Further, determining the determination coefficient includes calculating a relative deviation, and judging whether the determination coefficient is accurate based on the relative deviation;

[0018] The formula for the relative deviation is: Where: RE is the relative deviation, is the software simulation value or the actual monitoring value, and Y is the predicted value of the latent heat load.

[0019] The advantages and positive effects of the present invention are:

[0020] By setting the temperature and humidity accumulation index, a latent heat prediction model with temperature, relative humidity and temperature and humidity accumulation index as factors is constructed for each climate zone. By obtaining the temperature, relative humidity and temperature and humidity accumulation index of the climate zone during the sampling period through the meteorological station, the air conditioning latent heat load of the climate zone can be accurately calculated and predicted.

[0021] The latent heat prediction model can be used to predict the air conditioning latent heat load of all cities in the climate zone, so as to assist in controlling the temperature and humidity independently controlling the air conditioning system, improve the energy efficiency of the air conditioning system, and reduce building energy consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The accompanying drawings are used to provide a further understanding of the present invention and constitute a part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0023] Figure 1 It is a calculation flow chart of the determination coefficient in a quantitative prediction method of latent heat load of an air conditioner of the present invention;

[0024] Figure 2 It is a latent heat load comparison diagram in a method for quantitatively predicting latent heat load of an air conditioner of the present invention;

[0025] Figure 3 It is a relative deviation distribution diagram in a quantitative prediction method for latent heat load of an air conditioner according to the present invention. DETAILED DESCRIPTION

[0026] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0027] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as those commonly understood by those skilled in the art of the present invention. The terms used herein in the specification of the present invention are only for the purpose of describing specific embodiments and are not intended to limit the present invention. The term "and / or" used herein includes any and all combinations of one or more related listed items.

[0028] The present invention provides a quantitative prediction method for the latent heat load of air conditioners, which can be used to predict the latent heat load of air conditioners in different climate zones. The mathematical formula of the model is: Q = a*ITMRH + b*RH + c*T + d,

[0029] Among them: a, b, c, d are determination coefficients, Q is latent heat load, T is temperature, RH is relative humidity, and ITMRH is temperature-humidity product index. Because the calculation unit of latent heat load is kW·h·m-2, the unit of temperature is ℃, the unit of relative humidity is %RH, and the unit of temperature-humidity product index is ℃·h, in order to meet the mathematical formula of the above model, the latent heat load, relative humidity, temperature and temperature-humidity index can be dimensionless. If the unit problem of the data is not processed directly, of course, the four determination coefficients a, b, c, d can also be assigned dimensions to meet the dimensional calculation requirements.

[0030] The calculation formula of temperature and humidity index is: ITMRH = T i ×RH i , where i represents the sampling period, T i is the temperature of sampling period i, RH iis the relative humidity of the i sampling period. In practice, multiple sampling periods can be planned within a time span of a predetermined number of days, months or years as needed, and then the latent heat load value corresponding to the corresponding sampling period can be obtained.

[0031] One embodiment of the present invention is: the sampling period is two hours, such as from 12:00-14:00, and the average temperature data and average relative humidity of a future sampling period (12:00-14:00) of a city in the climate zone are obtained through a meteorological station, and the temperature and humidity integrated index of the sampling period is calculated from the average temperature data and the average relative humidity.

[0032] That is, the average temperature data, average relative humidity and temperature-humidity integrated index from 12:00 to 14:00 are brought into the latent heat prediction model to predict the predicted value of the air conditioning latent heat load in the future sampling period (12:00-14:00). In the actual period from 12:00 to 14:00, the temperature and humidity independent control air conditioning system combines the predicted value of the latent heat load to uniformly control the air conditioning systems of all office buildings in the preset city, uniformly regulate the module capacity of the sensible heat load and latent heat load of the air conditioning system, ensure the performance of the air conditioning system, save air conditioning electricity, and thus reduce building energy consumption.

[0033] There are climate differences in different climate zones. In order to improve the prediction accuracy of the latent heat prediction model for different climate zones, the determination coefficients of different climate zones are calculated separately. The determination process of the determination coefficient includes:

[0034] Obtain the temperature, relative humidity, and temperature-humidity integrated index of a predetermined historical period of a predetermined office building in a predetermined climate zone or a predetermined city in a predetermined climate zone;

[0035] Obtain software simulation values ​​or actual monitoring values ​​of air conditioning latent heat loads of a predetermined office building in a predetermined climate zone, a predetermined office building in a predetermined city in a predetermined climate zone, or a predetermined office building in a predetermined city during a predetermined historical period;

[0036] Taking temperature, relative humidity and ITMRH as the factor set, multivariate linear stepwise regression analysis was used to screen the optimal factor set to calculate the determination coefficient of the climate zone or the predetermined city in this climate zone.

[0037] By comparing the TRNSYS software simulation value with the actual monitoring value, the effect of the software in simulating the building load was evaluated, and it was found that the simulation result error was controlled within 10%, which could well represent the actual value. Therefore, the software simulation value of the air conditioning latent heat load can be obtained through TRNSYS software simulation.

[0038] Air conditioning is only used in summer and autumn (air conditioning period), and the air conditioning period varies in different cities or climate zones. Therefore, the scheduled historical period includes several air conditioning periods, and the air conditioning period includes several sampling periods. The actual sampling period is defined by the technicians. For example, the sampling period is defined as 1 day.

[0039] Obtain the climate data and air conditioning usage data of the predetermined climate zone, the predetermined city in the predetermined climate zone or the predetermined city where the predetermined office building is located within the predetermined historical period, and obtain the design parameters of the office building. The design parameters of the office building include building height, wall thickness, window thickness, and building orientation; the climate data of the location of the office building includes wind speed, wind direction, temperature, cloud cover, relative humidity, and precipitation, which are obtained through meteorological base stations; the air conditioning usage data of the office building includes air conditioning operation parameters, daily operation time, and annual usage months. Bring the above data into the TRNSYS software to output the software simulation value of the air conditioning latent heat load of the office building.

[0040] Then, the software simulation value, temperature, relative humidity and temperature-humidity accumulation index of the TRNSYS software in the predetermined historical period of the office building are obtained. The temperature, relative humidity and temperature-humidity accumulation index of each sampling period are introduced into the latent heat prediction model in turn through multivariate linear stepwise regression to obtain the determination coefficient of each factor.

[0041] Among them, the multivariate linear stepwise regression is to introduce factors in sequence (factors include temperature, relative humidity and temperature-humidity index). After introducing a factor, analyze whether it causes significant changes in the latent heat prediction model (F test). If significant changes occur, perform t tests on all factors. When the original introduced factor no longer changes significantly due to the introduction of the later added factor, remove this factor to ensure that only significant variables are included in the regression equation before each introduction of a new factor, until no significant explanatory factors are selected into the regression equation and no insignificant explanatory factors are removed from the regression equation, and finally obtain an optimal factor set. Based on the factor set, calculate the values ​​of the determination coefficients a, b, c, and d to generate a latent heat prediction model for the city or climate zone where the office building is located.

[0042] like Figure 2 and Figure 3 As shown, it includes calculating the relative deviation of the latent heat prediction model to determine whether the determination coefficient of the latent heat prediction model is effective. The formula for the relative deviation is: Where: RE is the relative deviation, is the software simulation value or the actual monitoring value, and Y is the predicted value.

[0043] An embodiment of the present invention is as shown in Table 1, the representative cities in the hot summer and cold winter area include Shanghai and Chengdu, and the representative cities in the hot summer and warm winter area include Guangzhou and Nanning. Substitute the temperature, relative humidity, temperature and humidity accumulation index and other factors from 2011 to 2017 into the latent heat prediction model of each city to obtain the predicted value of the latent heat load, and obtain the software simulation value of each city (corresponding to Figure 2 The prediction effect of the prediction model in different climate zones is evaluated by comparing the relative deviation RE calculated by comparing the latent heat load.

[0044] Table 1 shows the climate zone prediction model for coupled temperature and humidity climate conditions in each city

[0045]

[0046]

[0047] like Figure 2 The figure shows the simulated and predicted values ​​of latent heat load in May and June for representative cities in the hot summer and cold winter regions and hot summer and warm winter regions from 2011 to 2017. In the three cities of Chengdu, Guangzhou and Nanning, where the relative humidity is relatively high, the predicted values ​​for each month are close to the measured values. In general, the months with larger latent heat load in each city have better prediction results.

[0048] like Figure 3 As shown in the figure, RE is generally small in the hot summer and cold winter area and the hot summer and warm winter area, which is basically less than 15%, and can better represent the actual value. The determination coefficients of the hot summer and cold winter area and the hot summer and warm winter area are effective. In the hot summer and cold winter area, when the latent heat load is greater than 7kW·h·m-2, RE decreases significantly, and when it reaches 10kW·h·m-2, it is less than 5%; in the hot summer and warm winter area, when the latent heat load is greater than 6kW·h·m-2, RE decreases significantly, less than 5%, and when the latent heat load is 11kW·h·m-2, RE is only 1.9%.

[0049] An air conditioning control method based on latent heat load: The present invention can construct a latent heat prediction model for different climate zones or different cities in a climate zone through a quantitative prediction method for the latent heat load of air conditioners. The latent heat load prediction values ​​of air conditioners in different climate zones or different cities in a climate zone are predicted based on the latent heat prediction model. The temperature and humidity independently control air conditioning system combines the latent heat load prediction value to control the air conditioning systems of all office buildings in the climate zone or city, thereby improving the energy efficiency of the air conditioning system and reducing building energy consumption.

[0050] The embodiments of the present invention are described in detail above, but the contents described are only preferred embodiments of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of this patent.

Claims

1. A method for quantitatively predicting the latent heat load of an air conditioner, characterized in that: The calculation formula for latent heat load is: Q=a*ITMRH + b*RH +c*T + d, Among them: a, b, c, d are determination coefficients, Q is latent heat load, T is temperature, RH is relative humidity, ITMRH is temperature and humidity index; The calculation formula of the temperature and humidity index is: , where i represents the sampling period, is the temperature of sampling period i, is the relative humidity during sampling period i; The determination process of the coefficient of determination includes: Obtaining the temperature and relative humidity of a predetermined area during a predetermined historical period, wherein the predetermined area is a predetermined climate zone or a predetermined city within the predetermined climate zone; Obtaining software simulation values ​​or actual monitoring values ​​of air conditioning latent heat loads of a predetermined office building in a predetermined climate zone or a predetermined office building in a predetermined city during a predetermined historical period; Taking temperature, relative humidity and temperature-humidity integrated index as factor set, multiple linear stepwise regression analysis was used to screen and obtain the coefficient of determination of the predetermined area.

2. The method for quantitatively predicting the latent heat load of an air conditioner according to claim 1, characterized in that: Applicable climate zones are extremely cold areas, cold areas, mild areas, hot summer and cold winter areas, or hot summer and warm winter areas.

3. The method for quantitatively predicting the latent heat load of an air conditioner according to claim 1, characterized in that: The software simulation value of the air conditioning latent heat load is obtained by simulation with TRNSYS software.

4. The method for quantitatively predicting the latent heat load of an air conditioner according to claim 1, characterized in that: The predetermined historical period includes several years, the time when the air conditioner is used each year is the air conditioning period, and the air conditioning period includes several sampling periods.

5. The method for quantitatively predicting the latent heat load of an air conditioner according to claim 1, characterized in that: It also includes the steps of calculating the relative deviation and judging whether the coefficient of determination is accurate based on the relative deviation; The formula for the relative deviation is: ,in: is the relative deviation, is the software simulation value or the actual monitoring value, and Y is the predicted value of the latent heat load.

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