Method, apparatus, or program

Abstract

Calculate the building's thermal load appropriately. [Solution] A method comprising: a setting process for setting the average value and standard deviation of the heat load of a building at different times; a determination process for determining the probability distribution of the heat load at different times according to the average value and standard deviation; a calculation process for calculating the heat load at different times such that the probability of heat load occurrence at each time time follows the probability distribution; and a process for calculating the heat load in the building for a predetermined period based on the calculation process.

Description

Technical Field

[0005]

[0001] The present invention relates to a method, an apparatus, or a program for executing a heat load calculation method.

Background Art

[0002] As a conventional technique, an apparatus, a program, etc. for estimating the internal heat generation amount of a building and performing heat load calculation are known (Patent Document 1).

Prior Art Documents

Patent Documents

[0003]

Patent Document 1

Non-Patent Documents

[0004]

Non-Patent Document 1

Summary of the Invention

Problems to be Solved by the Invention

[0005] When performing heat load calculation, the internal heat generation amount in a building is estimated. Conventionally, the heat generation amount was uniformly set for each season, for example, 100% in summer, 50% in winter, and 75% in the intermediate period. However, with this method, it was difficult to simulate the actual fluctuations in internal heat generation and obtain accurate heat load calculation results.

Means for Solving the Problems

[0006] In view of the above problems, the present invention provides, in one aspect, a method including: a setting process for setting the average value and standard deviation of the heat load of a building at different times; a determination process for determining the probability distribution of the heat load at different times according to the average value and standard deviation; a calculation process for calculating the heat load at different times such that the probability of heat load occurrence at each time time follows the probability distribution; and a process for calculating the heat load in the building for a predetermined period based on the calculation process.

[0007] Furthermore, in one aspect of the present invention, the present invention provides a processing device for carrying out this method and a program to be executed by the processing device. [Effects of the Invention]

[0008] With the above configuration, the building's thermal load can be calculated appropriately. [Brief explanation of the drawing]

[0009] [Figure 1] This figure shows (a) the hardware configuration and (b) the functional configuration (software configuration) of the information processing device in the embodiment. [Figure 2] This flowchart shows the processing according to the embodiment. [Figure 3] This shows an example of setting the (a) average value for weekdays, (b) standard deviation for weekdays, (c) average value for holidays, and (d) standard deviation for holidays for the design internal heat generation (SC value) in the embodiment. The internal heat generation is expressed as a percentage after normalizing the annual average maximum value to 100%. [Figure 4] This figure shows an example of the probability distribution of the internal heat generation at a given time. [Figure 5] This is an example of setting the design average value and design probability distribution of internal heat generation by time of day on weekdays in the embodiment. [Figure 6] This graph shows the average annual internal heat generation values ​​by time of day for four floors (14th, 19th, 26th, and 27th floors) in a real office building, with (a) weekdays and (b) holidays shown. The internal heat generation values ​​are expressed as a percentage, normalized to the annual maximum value of 100%. [Figure 7] This graph shows the annual average value of internal heat generation by time of day (weekdays) on the 27th floor of an office building, with the frequency distribution of each time of day overlaid on top. [Figure 8] These figures represent (a) the average value of internal heat generation on weekdays, (b) the standard deviation of internal heat generation on weekdays, (c) the average value on holidays, and (d) the standard deviation of internal heat generation on holidays for each floor of the office building. Each figure represents the average value over one year. Internal heat generation is expressed as a percentage after normalizing the maximum value of the annual average to 100%. [Figure 9] This figure shows the annual average of σ / (μ+2σ) on weekdays in an office building, broken down by time on each floor, with the design value (solid line) superimposed on it. [Figure 10] This graph compares the relationship between the maximum and minimum internal heat generation values ​​on each floor of an office building and the values ​​obtained by adding ±σ, ±2σ, and ±3σ to the average value μ. [Modes for carrying out the invention]

[0010] 〔composition〕 Figure 1(a) shows the configuration of an information processing device 100 according to one embodiment of the present invention. The information processing device 100 is used for calculating the dynamic heat load of a building.

[0011] As shown in Figure 1(a), the information processing device 100 comprises a processor 101, a main memory 102, an auxiliary memory 103, an input device 104, an output device 105, and a communication device 106. These are connected to each other via communication means such as a bus (not shown).

[0012] Furthermore, the information processing device 100 does not necessarily have to be entirely implemented in hardware; all or part of its configuration may be implemented using virtual resources, such as a cloud server in a cloud system. Also, the information processing device 100 does not necessarily have to consist of a single device.

[0013] The processor 101 is composed of a CPU (Central Processing Unit), an MPU (Micro Processing Unit), etc. By reading and executing a program stored in either or both of the main memory device 102 and the auxiliary storage device 103, the functions of the server 10 and the terminal 20 are realized.

[0014] The main memory device 102 is a device that stores programs and data, such as a ROM (Read Only Memory), a RAM (Random Access Memory), a non-volatile semiconductor memory (NVRAM (Non Volatile RAM)), etc. The auxiliary storage device 103 is various non-volatile memories (NVRAM: Non-volatile memory) such as an SSD (Solid State Drive), an SD memory card, a hard disk drive, an optical storage device (CD (Compact Disc), DVD (Digital Versatile Disc), etc.), a storage area of a cloud server, etc.

[0015] The input device 104 is an interface that receives input of information. For example, it is a keyboard, a mouse, a touch panel, a card reader, a voice input device (microphone, etc.), a voice recognition device, etc. The information processing device 100 may be configured to receive input of information from other devices via the communication device 106.

[0016] The output device 105 is an interface that outputs various types of information. For example, it is a screen display device (liquid crystal monitor, LCD (Liquid Crystal Display), graphics card, etc.), a printing device, etc.), a voice output device (speaker, etc.), a voice synthesis device, etc. The information processing device 100 may be configured to output information to other devices via the communication device 106. The output device 105 corresponds to the display unit in the present invention.

[0017] The communication device 106 is a wired or wireless communication interface that enables communication with other devices via the network 5, and is, for example, a NIC (Network Interface Card), a wireless communication module, a USB (Universal Serial Interface) module, a serial communication module, etc.

[0018] [Functional Configuration] Figure 1(b) shows the main functional configuration of the information processing device 100. As shown in the figure, the information processing device 100 includes a storage unit 110 and a management unit 120.

[0019] The management unit 120 is a functional unit that is activated by one or more programs.

[0020] Specific examples of programs that start the management unit 120 include HASP and New HASP, which are programs provided by the Japan Association of Building Equipment Engineers (Non-Patent Literature 1). In this embodiment, the management unit 120 calculates the heat acquisition of each element constituting the building and calculates the building's annual heat load.

[0021] The control unit 120 is also a functional unit for performing detailed calculations of the elements used in the heat load calculation. Specifically, the control unit 120 calculates the amount of heat generated inside the building. In addition, the control unit 120 is used for analysis and calculations such as considering the nonlinearity of the thermal properties of the elements, detailed physical properties, and / or thermal equilibrium equations.

[0022] The memory unit 110 has the function of saving calculation results and correction values ​​obtained by the management unit 120. The management unit 120 can also save calculation results and correction values ​​via data files stored in the memory unit 110. The information processing device 100 with the above configuration is mainly used for calculating the dynamic heat load of buildings that have heat sources such as lighting and office automation equipment.

[0023] [Calculation process] An example of a thermal load calculation is explained below using the flowchart in Figure 2.

[0024] The management unit 120 sets the design average value (μ) and the design standard deviation (σ, also called SD value) for each time period regarding the internal heat generation of the building (also referred to as the schedule value or SC value) (S1). The design average value and design standard deviation are values ​​used in building analysis and simulation, and depending on the building's use and actual conditions, two types may be provided: one for holidays and one for weekdays. The range for setting the design average value and standard deviation is set appropriately according to the conditions. Therefore, they may be set for each floor or area of ​​the building, or they may be set for the entire building.

[0025] Figure 3 shows, as an example, the design average value and standard deviation of the internal heat generation for each time on weekdays in a certain building, as set in step S1 (Figures 3(a), (b)), and the design average value and standard deviation for each time on holidays (Figures 3(c), (d)).

[0026] Next, the control unit 120 sets the probability distribution of internal heat generation in the building (S2). More specifically, it sets a normal distribution curve that is distributed around the design mean set in step S1 and according to the design standard deviation set in step S1 (Figure 4). The control unit 120 sets this normal distribution curve as the probability distribution of internal heat generation in the building.

[0027] Figure 4 shows the probability distribution of internal heat generation at a given time. The vertical axis represents the probability density, and the horizontal axis represents the internal heat generation, which is a random variable. The internal heat generation on the horizontal axis is normalized to 100% of the annual average maximum and expressed as a percentage. Figure 4 shows the case where the average internal heat generation is 80% and the standard deviation is 5%. In the case of Figure 4, as defined by the standard deviation, the probability of an internal heat generation occurring within the range of 80±5% is approximately 68%.

[0028] Figure 5 shows an example of setting the design average value and probability distribution of internal heat generation for each time of day on weekdays, all in a single graph. Similar to Figure 3, the internal heat generation is normalized with the maximum value over one year set to 100%. As shown in the figure, a design average value and design standard deviation are set for each time (24-hour display), and the probability of internal heat generation occurring at each time exhibits the shape of a normal distribution.

[0029] In the next step, S3, the control unit 120 calculates the amount of internal heat generated in the building at each time of day. Specifically, the control unit 120 calculates the amount of internal heat generated at each time according to the probability distribution set in step S2. In other words, the amount of internal heat generated at a certain time on a given day is calculated using random numbers, but these random numbers are set to be distributed according to the probability distribution set in step S2. To put it another way, if the number of calculations is increased sufficiently, the frequency distribution of the calculated amount of internal heat will be a normal distribution with the same shape as the probability distribution set in step S2.

[0030] As described above, the amount of internal heat generated at each time of day is calculated sequentially, and by repeating this for a year, the total amount of internal heat generated for the year is obtained. The obtained amount of internal heat generated is stored in the memory unit 110.

[0031] In the next process S4, the management unit 120 calculates the building's heat load using input values ​​necessary for heat load calculation, such as HASP-type weather data, as well as calculated values ​​such as the acquired internal heat generation amount.

[0032] Furthermore, the mean radiant temperature and air temperature considered in the heat load calculation are affected by the heat previously generated by building components and equipment, so iterative calculations may be necessary. In this case, the above heat load calculation is repeated until convergence occurs at each time point.

[0033] After performing the calculations described above, the management unit 120 calculates the annual hourly heat load in the building under consideration and outputs the obtained calculation results.

[0034] (Specific example 1: Mean and frequency distribution) The method described above is based on specific verification data. Details are explained below.

[0035] Figure 6 shows a graph illustrating the annual average internal heat generation (also called the hourly annual average) for four floors of a real office building. The internal heat generation (SC value) shown on the vertical axis is normalized to the maximum value over one year (100%) and then expressed as a percentage. Note that there is a significant difference in the shape of the graph between weekdays (Figure 6(a)) and holidays (Figure 6(b)).

[0036] Figure 7 shows a graph that further overlays the frequency distribution of the annual distribution of internal heat generation, representing the time-based annual average values ​​for the 27th floor (weekdays) in this office building. Thus, it can be seen that the frequency distribution of internal heat generation in the building follows an almost normal distribution throughout the year.

[0037] Therefore, when calculating the heat load of a building over a certain period, it is sufficient to set the probability distribution of internal heat generation to follow a normal distribution centered on the design average value.

[0038] (Specific example 2: Design methodology) Furthermore, specific examples of setting the design mean and design standard deviation are explained below.

[0039] Figure 8 shows the annual average and standard deviation of internal heat generation for each floor at each time point, measured in the same office building as in Specific Example 1. The solid line represents the average of the four floors.

[0040] As shown in Figure 8, the same trend is observed on each floor on both weekdays and weekends. Based on this, the internal heat generation amount for calculating the heat load in a given building can be set.

[0041] In detail, in the example settings shown in Figure 3, based on the characteristics of internal heat generation shown in Figure 8, the design average values ​​for internal heat generation on holidays are set to 5% at night, 10% during working hours, and 7.5% during overtime. Furthermore, the design standard deviations for both weekdays and holidays are set to 5% at night, 10% during working hours, and 7.5% during overtime.

[0042] As an alternative setting example, you can set σ / (μ+2σ) specifically for weekdays as shown by the solid line in Figure 9, and then set the design standard deviation for weekdays based on this (" / " indicates division). In this case, you can calculate σ based on the design average value of internal heat generation at each time (Figure 3) and the set value of σ / (μ+2σ).

[0043] This basis can be explained by a graph (Figure 10) comparing the relationship between the annual maximum and minimum values ​​of internal heat generation on each floor, measured in the same office building as in Specific Example 1, and the values ​​obtained by adding ±σ, ±2σ, and ±3σ to the average value μ.

[0044] As shown in Figure 10, the annual maximum and minimum values ​​of internal heat generation on weekdays on each floor are generally close to μ±2σ at all times. Therefore, the values ​​of σ / (μ+2σ) at each time on weekdays show a similar trend on each floor, as shown in Figure 9.

[0045] Therefore, the design value σ / (μ+2σ) can be set as described above.

[0046] 〔effect〕 (Aspect 1) The above embodiment provides a method that includes a setting process for setting the average value and standard deviation of the heat load of a building at different times of day; a determination process for determining the probability distribution of the heat load at different times of day according to the average value and standard deviation; a calculation process for calculating the heat load at different times of day such that the probability of heat load occurrence at each time of day follows the probability distribution; and a process for calculating the heat load in the building for a predetermined period of time based on the calculation process.

[0047] Traditionally, heat generation was set uniformly for each season, for example, 100% in summer, 50% in winter, and 75% in the transitional seasons. However, it is unrealistic to expect 100% internal heat generation to occur continuously every day. On the other hand, with the above configuration, internal heat generation occurs with variation, which allows for a good simulation of the actual conditions of a building. Therefore, accurate heat load calculation results can be obtained.

[0048] (Aspect 2) In the method of Aspect 1, two types of probability distributions are provided: one for holidays and one for weekdays.

[0049] The above configuration allows for consideration of the different internal heat generation amounts between holidays and weekdays.

[0050] (Aspect 3) The method of any of aspects 1 to 2 further includes a process of setting σ / (μ+2σ) at each time, wherein σ is the standard deviation and μ is the mean, and in the setting process, the standard deviation is set based on the set mean and the value of σ / (μ+2σ).

[0051] The above configuration allows for accurate simulation of the probability of internal heat generation, including the magnitude of the distribution of internal heat generation.

[0052] (Aspect 4) In any of the methods from aspects 1 to 3, the standard deviation and the mean value are set for each of the following time periods: unmanned nighttime, working hours, and overtime.

[0053] The above configuration allows for accurate simulation, taking into account the varying internal heat generation rates at different times of day.

[0054] (Aspect 5) In any of the methods from aspects 1 to 4, the probability distribution is a normal distribution.

[0055] The above configuration takes into account that the frequency distribution of internal heat generation in actual buildings follows a normal distribution. Therefore, the above configuration can accurately simulate the probability of internal heat generation occurring.

[0056] (Aspects 6 and 7) The above embodiments present an information processing device 100 that implements any of the methods described in aspects 1 to 5, and a program to be executed by the information processing device 100.

[0057] [Variation] In the above embodiment, an example of performing an annual heat load calculation was described, but the period for performing the heat load calculation is not limited to one year, but can be any period. Also, the time for setting the average value and standard deviation for design purposes is not limited to every hour, but can be set at any interval, such as every 30 minutes.

[0058] Furthermore, the internal heat generation can also be calculated at each step of the heat load calculation. In the above embodiment, the calculation process for internal heat generation (S3) is shown collectively for ease of understanding.

[0059] The probability distribution of internal heat generation is not limited to a normal distribution. Depending on the use, characteristics, design conditions, and actual conditions of the building in question, the probability distribution can be of various shapes, such as a gamma distribution or a beta distribution. [Explanation of Symbols]

[0060] Information processing device 100, storage unit 110, management unit 120

Claims

1. A setting process to set the average value and standard deviation of the building's heat load at different times of the day, A decision process for determining the probability distribution of the heat load at each time point according to the mean value and standard deviation, The aforementioned time-dependent heat load is calculated such that the probability of heat load occurrence at each time point follows the aforementioned probability distribution, The process includes calculating the heat load in the building for a predetermined period based on the calculation process described above, method.

2. Two types of probability distributions are provided: one for holidays and one for weekdays. The method according to claim 1.

3. The process further includes setting σ / (μ+2σ) at each time point, where σ is the standard deviation and μ is the mean value. In the setting process described above, the standard deviation is set based on the set mean value and the value of σ / (μ+2σ). The method according to claim 1.

4. The aforementioned standard deviation and mean value are set for each of the following time periods: unmanned nighttime, working hours, and overtime hours. The method according to claim 1.

5. The aforementioned probability distribution is a normal distribution. The method according to claim 1.

6. A program that causes a computer to perform the method described in any one of claims 1 to 5.

7. A processing apparatus that performs the method according to any one of claims 1 to 5.

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