Study on heating load calculation model using map data

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Article

Study on heating load calculation model using map data

Muramatsu Takaki, HU, Sapporo, Japan

Abstract

This article deals with devising a method of calculating heating load based on information obtained from map data. Author calculated 5 types of heating loads and clarified problems at the present stage.

Key words: heating load calculation, map data, model.

First of all

Introduction

In recent years, it has become easier to obtain map data and at the same time the precision is advanced. The performance of the personal computer has also improved, enabling collective processing of large volumes of data. The use of map data is spreading in various fields. If heating load calculation using elements obtained from map data becomes possible, it becomes easy to examine regional heat supply and energy management of the entire region. In addition, it becomes possible to calculate the heating load of home at each, and energy management at each housing becomes possible. I regarded this research as a research showing the path in this research, and aimed to increase the accuracy of heating load calculation by map data.

Building Information

In the actual map data obtained from the urban planning basic survey, building information is included for each building, and buildings are distinguished by ID. In this paper I extracted the coordinates of each vertex of the building, the total floor area, the length of the building in each direction, and the number of stories of the building from the urban planning basic survey.

Main material

Target building

I focused on residential areas in Sapporo, Hokkaido. I picked up four adjacent sections with varying heights and densities of houses and adjacent equally spaced intervals. I extracted 57 detached houses existing in the southern 20th west 12, 13 chome and south 21 st west 12, 13 chome from the urban planning basic survey and made it subject to this research. I divided all 57 houses in order of the total floor area into six. I extracted two houses located in the middle and used 12 houses in this paper. Figure 1 shows the location of the target building in Sapporo City, and Figure 2 shows the target site expanded.

Fig 1 - Target site in Sapporo city

Fig 2 - Enlarged view of target site

Calculation condition of heating load calculation and simulation

Calculation conditions of heating load calculation in this study are shown in Table 1.

Table 1 - Summary of calculation conditions

Evaluation of heating load calculation method

Outline

I list the heat sources, heating devices and their efficiencies according to the grade of houses in Table 2. There are four classes of houses, top runners (tr), high level (hl), standard level (sl), and basic level (bl). Houses with the largest number of heat sources and heating equipments were extracted from the database (DB) for each class of housing, and the average value of DB (Eave) and its standard deviation were calculated. Table 3 shows the methods and conditions necessary for calculating Es. The abbreviations in Table 3 are the annual heating load [MWh / year] in each calculation method. Equations (1) - (6) show the calculation formula dealt with in this paper. The U value of Es1 and Es3 is calculated from the specification, Q value of Es2 was input the value of the old standard of Sapporo version of the next-generation housing subsidies system, which was shown in Table 4 for each grade of the housing. The number of times of ventilation was determined for each class of DB housing, and tr = 0.13, hl = 0.17, sl = 0.26, bl = 0.5 [times / h].

Table 2 - Heat source, and, efficiency by class ofhousing

Table 3 - Heat source and efficiency by class of housing

calculation heating surrounding environment

Table 4 - Reference value by class of housing

Results and analysis

The value of the heating load is shown in Fig.3 for each grade of housing. When the total floor area is small, the heating load per floor area is large at Es3 and Es4. Es3 is not influenced by solar radiation, so it is influenced by heat insulation performance. Particularly in a 121 m2 building with a complicated shape, the wall area in contact with the out-side air is large in proportion to the total floor area. Therefore, the amount of heat flow increased and Es increased. For Es4 which takes solar radiation into account, when the to-tal floor area is small, the window area also becomes small. As a result, the solar radiation did not enter and the heating load increased. In addition, Es3 and Es4 are factors due to the increased heating load per unit area as the total floor area becomes smaller.

Also, it can be seen that there is a difference in Es depending on the calculation method. Especially Es1 is significantly smaller than other methods. When considering the difference between Es1 and Es2, Es3, it is only the difference whether to consider solar radiation in DD, so it can be said that Es1 has a strong effect of solar radiation.

Fig 3 - Heating load by housing class

Evaluation of heating load calculation considering the surrounding environment

Overview

In consideration of the surrounding environment, Es used floor area and floor area obtained from map data in the same way as building alone calculation. I calculated the Es1 by calculating the winter southern solar radiation amount using weather data considering the surrounding environment. Fig.4 shows Es (Esn) calculated from the building alone and Es (Ess) considering the surrounding environment in Es1.

Results and analysis

From Fig.4, although there is almost no difference between Esn and Ess, it can be said that the heating load has increased due to the effect of solar radiation shielding since Ess is slightly larger. On the other hand, I considered the factor that the difference between Esn and Ess did not come out. It is conceivable that the value was similar because the building which would be solar radiation shield on the south side affected by the most solar radiation did not exist from the beginning. Also, the designer intentionally designed the building facing the north side towards the north side, and devised to be the building spacing that reduces the influence of the solar radiation shielding on the south side. In any case, as mentioned in Section 2, Es 1 was too small and it was necessary to consider the influence of solar radiation.

Fig 4 - Comparison of heating load considering the surrounding environment by calculation

Evaluation of heating environment simulation considering the surrounding environment

Overview

EnergyPlus (EP) was used as heating environment sim. We extracted each vertex coordinate of the building from the map data, set up the plane created by sketchup according to the calculation conditions, then set the window. In sim in the building alone, we used the extended AMeDAS meteorological data in epw format. I used the meteorological data used in section 3 in the simulation considering the surrounding environment. Building IDs 10, 15, 18, 36, 42 do not have high buildings in the surroundings, but buildings exist. ID 63 has a large building on the south, ID 61 has a large building in the east and west. I classified it into two types according to the height of the surrounding buildings and created Figure 5. Since IDs 10, 15, 18, 36, 42 showed similar values, I ex-tracted 2 houses and they are shown in Fig.5.

Results and analysis

When considering the surrounding environment, heating load slightly increased in any buildings and grades. ES of building IDs 15 and 18 was small because the neighboring building height was a residential area with uniformity. It is thought that the effect of solar radiation shielding was small. However, in the ID 63 where there is a building larger than the target building on the south side and ID 61 with the big building in the east and west, the Ess is larger than the Esn and the influence of the surrounding buildings is stronger.

In the building shown in Fig. 5, Es of ID 15, ID 63, ID 61 excluding ID 18 is tr=hl. As Esn and Ess show similar fluctuations, it is understood that they are not influenced by surrounding buildings. In the EP of this study, the difference between the two in the setting of tr and hl is set as the window area by direction, the outer skin specification, and the number of ventilation only. I think about the difference between 18tr, 18hl and 61tr, 61hl. The outer skin specification and the number of times of ventilation are the same, the difference is only the area by the direction of the window. Therefore, it is considered that the area by direction of the window affected the heating load.

When comparing (a) and (b) in Fig.5, the heating load in (b) is larger. The total floor area of the house of ID 63 is 79m2, ID 61 is 132 m2, ID 15 is 224 m2, ID 18 is 186 m2.(a) has a larger total floor area, the window area of (a) becomes larger, so it is thought that the heating load is decreased because it is strongly influenced by solar radiation. (a), since there was a noticeable fall of Es, it was necessary to review the window size and solar radiation acquisition rate. Window area was calculated based on DB. However, in real buildings the Es may be greater than this model because the window area is much smaller or the solar penetration rate is small.

Fig 5 - Comparison of heating load of surrounding environment by simulation

Overall

I gave the following findings obtained in this study.

We proposed a method to calculate Es from map information.

We grasp the difference by calculation method.

Calculation method of northern type housing and calculation method of EP simulation are strongly influenced by solar radiation even considering the surrounding environment.

Future prospects

I did not consider the solar radiation penetration rate actually decrease due to windows dirt, sash, race etc in the calculation condition of this research. So the influence of solar radiation became too strong and it got smaller by Es calculation. In the future, it is necessary to reconstruct the calculation condition faithfully reproducing the conditions of the window and to improve the accuracy of the calculation obtained from the map data.

References

1. Judgment program on energy conservation performance of housing dwelling, http: /house.app.lowenergy.jp, see reference 2017.1.16

2. Abbreviations table

3. Ki: The heat transmission coefficient of the “i”th part (window and outer wall) of the room [W / m2 K]

4. Si: Area of “i”th part of room [m2] Cp: Specific heat of constant air pressure [J / kgK]

5. p: Air density [kg / m3] n ': Number of ventilation per second [times / s]

6. V: Void [m3] DD: Degredee [K Day]

7. Hm: Indoor heat generated [W] Hsr: Solar radiation acquisition heat [W]

8. Q: heat loss coefficient [W / m2K] S: Total floor area [m2]

9. 0 n: natural temperature difference [K] Qall: Total heat loss coefficient [W / K]

Мурамацу Такаки

УХ, Саппоро, Япония

ИССЛЕДОВАНИЕ МОДЕЛИ РАСЧЕТА ТЕПЛОВОЙ НАГРУЗКИ С ИСПОЛЬЗОВАНИЕМ ДАННЫХ КАРТЫ

Абстракт. Данная статья посвящена разработке способа расчета тепловой нагрузки, основанного на информации, полученной из данных карт.Автор подсчитал 5 типов тепловых нагрузок и выяснил проблемы на нынешнем этапе.

Ключевые слова: расчет тепловой нагрузки, данные карт, модель.

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