Typical meteorological year data construction method for building energy consumption evaluation

Abstract

The invention relates to the technical field of meteorological information processing, and particularly discloses a typical meteorological year data construction method for building energy consumption assessment, and the method comprises the steps: data collection, typical meteorological month selection, typical meteorological year data construction, and building energy consumption assessment. According to the scheme, the typical meteorological month is determined by combining the multi-dimensional day-by-day meteorological elements, the weights are given to the different meteorological elements, and the weighted average value is calculated, so that the dependence on a single element is avoided, the change trend of the meteorological elements in time is more comprehensively reflected through the combination of CDF and FS statistical values, the difference between long-term and short-term climate modes is considered, and the calculation accuracy is improved. A visual quantitative index is provided; according to the method, climate zones are divided based on building thermal load demands, missing hourly solar radiation data are complemented by using a mathematical model, so that a calculation result is more complete, the problem of data mutation caused by monthly boundaries is reduced by using smoothing processing, and the continuity and accuracy of data are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of meteorological information processing, and in particular to a method for constructing typical meteorological year data for building energy consumption assessment. Background Art

[0002] Reducing the energy consumption of building operation is an important issue for the sustainable development of buildings. The typical meteorological year data is usually used to simulate the building thermal process, which represents the hourly meteorological data of the whole year based on the climate characteristics of the area where the building is located. The typical meteorological year refers to the average value of each month in a year close to the average value of 10 years selected from the data of the past 10 years based on the monthly average value of the past 10 years. The traditional hourly meteorological data is interpolated by observing data four times a day, and there is a large error with the real meteorological data; general data processing relies on fixed climate zone division standards, lacks dynamic adjustment, cannot well reflect the actual energy consumption demand of different building climate zones, and there are problems of discontinuous and inaccurate data. Summary of the invention

[0003] In view of the above situation, in order to overcome the defects of the prior art, the present invention provides a method for constructing typical meteorological year data for building energy consumption assessment. The traditional hourly meteorological data is interpolated from observation data four times a day, and there is a large error with the real meteorological data. This scheme combines multi-dimensional daily meteorological elements to determine the typical meteorological month, assigns weights to different meteorological elements and calculates the weighted average to avoid dependence on a single element. Through the combination of CDF and FS statistical values, it more comprehensively reflects the temporal change trend of meteorological elements, considers the differences between long-term and short-term climate patterns, and provides intuitive quantitative indicators. In view of the fact that general data processing relies on fixed climate zone division standards, lacks dynamic adjustment, cannot well reflect the actual energy consumption requirements of different building climate zones, and has the problem of discontinuous and inaccurate data, this scheme divides climate zones based on building heat load demand, uses mathematical models to complete the missing hourly solar radiation data, makes the calculation results more complete, uses smoothing processing to reduce data mutation problems caused by monthly boundaries, and improves data continuity and accuracy.

[0004] The technical solution adopted by the present invention is as follows: The present invention provides a method for constructing typical meteorological year data for building energy consumption assessment, the method comprising the following steps:

[0005] Step S1: Data collection, collecting observational meteorological data of the observation area for no less than ten years, and extracting daily meteorological elements, including maximum dry bulb temperature, minimum dry bulb temperature, average dry bulb temperature, maximum wind speed, average wind speed, total solar radiation, maximum dew point temperature, minimum dew point temperature and average dew point temperature;

[0006] Step S2: Select a typical meteorological month, calculate the FS weighted average of each month, and select the month with the smallest FS weighted average as the typical meteorological month;

[0007] Step S3: constructing typical meteorological year data, obtaining hourly meteorological data of each typical meteorological month, including dry-bulb temperature, dew point temperature, air pressure, wind speed, and wind direction, processing the hourly meteorological data, and constructing typical meteorological year data;

[0008] Step S4: Building energy consumption assessment, using the building information model to analyze the energy consumption performance of the building under typical climatic conditions based on typical meteorological year data.

[0009] Furthermore, in step S2, the typical meteorological month selection specifically includes the following steps:

[0010] Step S21: Calculate the daily average value of each daily meteorological element;

[0011] Step S22: sort all data points of each daily meteorological element in ascending order according to data value, calculate the cumulative distribution function value of the data point in the selected time period, and use it to represent the cumulative probability of the data point and all previous data points. The formula used is as follows: ;

[0012] In the formula, Represents the cumulative distribution function value of daily meteorological elements, Indicates the number of days in the selected time period. Represents the ascending rank of a single data point;

[0013] Step S23: Calculate the FS statistical value and FS weighted average value of each daily meteorological element using the long-term cumulative distribution function value and the monthly cumulative distribution function value. The long-term cumulative distribution function value is the cumulative distribution function value of the daily average value in the selected year, and the monthly cumulative distribution function value is the cumulative distribution function value of the data point in the selected month. The formula used is as follows: ; ;

[0014] In the formula, Indicates the FS statistical value of each daily meteorological element in the selected month, Represents the long-term cumulative distribution function value of the daily average of each daily meteorological element, represents the monthly cumulative distribution function value of the data point, Indicates the day of the selected month. Indicates the index of the selected month day. represents the FS weighted average, Represents the index of daily meteorological elements, Indicates The weight coefficient of each daily meteorological element, Indicates FS statistics of daily meteorological elements, It represents the weighted sum calculation of the FS statistical values ​​of the nine daily meteorological elements in the selected month;

[0015] Step S24: Sort the FS weighted average values ​​of each month in previous years from small to large, and select the month with the smallest FS weighted average value as the typical meteorological month.

[0016] Furthermore, in step S3, the typical meteorological year data is constructed, specifically including the following steps:

[0017] Step S31: extracting hourly meteorological data of each typical meteorological month from the observed meteorological data to form preliminary data of a typical meteorological year;

[0018] Step S32: Calculate the number of air-conditioning degree days and heating degree days in the observed area;

[0019] Step S33: Divide the observation area into different building climate zones according to the number of air-conditioning degree days and the number of heating degree days, and select a representative meteorological station in each building climate zone;

[0020] Step S34: For a weather station without hourly solar radiation data, the Collares-Pereira and Rabe model is used to calculate the hourly horizontal surface solar total radiation illumination;

[0021] Step S35: Perform monthly smoothing on the dry-bulb temperature and dew-point temperature in the preliminary data of a typical meteorological year, and smoothly connect the data of the six hours before and after the dry-bulb temperature and dew-point temperature in adjacent months. The formula used is as follows: ;

[0022] In the formula, Indicates the time index, with a value of 0 to 12. The value is 0 at 18:00 of the previous day and 12 at 6:00 of the next day. After smoothing Time data, Indicates the previous day Time data, Indicates the next day Time data;

[0023] Step S36: Replace the original temperature with the smoothed dry-bulb temperature and dew-point temperature to obtain the final typical meteorological year data.

[0024] The beneficial effects achieved by the present invention using the above scheme are as follows:

[0025] (1) In view of the problem that the traditional hourly meteorological data is interpolated from observation data four times a day and has a large error with the real meteorological data, this scheme combines multi-dimensional daily meteorological elements to determine the typical meteorological month, assigns weights to different meteorological elements and calculates the weighted average, thus avoiding dependence on a single element. By combining CDF and FS statistical values, it more comprehensively reflects the temporal trend of meteorological elements, takes into account the differences between long-term and short-term climate patterns, and provides intuitive quantitative indicators.

[0026] (2) General data processing relies on fixed climate zone division standards, lacks dynamic adjustment, cannot well reflect the actual energy consumption demand of different building climate zones, and has problems of discontinuous and inaccurate data. This scheme divides climate zones based on building heat load demand and uses mathematical models to supplement the missing hourly solar radiation data, making the calculation results more complete. The use of smoothing processing reduces the data mutation problem caused by monthly boundaries and improves the continuity and accuracy of the data. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 A schematic diagram of a process for constructing typical meteorological year data for building energy consumption assessment proposed by the present invention;

[0028] Figure 2 This is a schematic diagram of the division results of building climate zones in Example 4.

[0029] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention. DETAILED DESCRIPTION

[0030] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0031] Example 1, see Figure 1 The present invention provides a method for constructing typical meteorological year data for building energy consumption assessment, the method comprising the following steps:

[0032] Step S1: Data collection, collect the old version of typical meteorological year data in Chongqing and the observed meteorological data from 2005 to 2018, including the maximum dry bulb temperature, minimum dry bulb temperature, average dry bulb temperature, maximum wind speed, average wind speed in the "Daily Value Dataset of China's Ground Basic Meteorological Elements (V3.0)", the total solar radiation data in the "Dataset of Estimated Total Solar Radiation of China's National Ground Stations (V1.0)", and the hourly dry bulb temperature, hourly dew point temperature, hourly 2-minute average wind speed and wind direction provided by the National Meteorological Information Center of the China Meteorological Administration; calculate the daily maximum dew point temperature, minimum dew point temperature and average dew point temperature based on the hourly dew point temperature; extract daily meteorological elements, delete the data of February 29 of leap years, a total of 8760 hours;

[0033] Step S2: Select a typical meteorological month, calculate the FS weighted average of each month, and select the month with the smallest FS weighted average as the typical meteorological month;

[0034] Step S3: constructing typical meteorological year data, obtaining hourly meteorological data of each typical meteorological month, including dry-bulb temperature, dew point temperature, air pressure, wind speed, and wind direction, processing the hourly meteorological data, and constructing typical meteorological year data;

[0035] Step S4: Building energy consumption assessment, using the building information model to analyze the energy consumption performance of the building under typical climatic conditions based on typical meteorological year data.

[0036] Example 2, see Figure 1 This embodiment is based on the above embodiment. In step S1, the dew point temperature refers to the temperature when the humid air reaches saturation after constant pressure cooling. The missing dew point temperature in the data set is supplemented by the Magnus empirical formula. The corresponding temperature calculated using the actual water vapor pressure is the dew point temperature. The formula used is as follows: ;

[0037] In the formula, represents the saturated water vapor pressure, Indicates temperature, Represents a natural constant.

[0038] Example 3, see Figure 1 This embodiment is based on the above embodiment. In step S2, the typical meteorological month selection specifically includes the following steps:

[0039] Step S21: Calculate the daily average value of each daily meteorological element;

[0040] Step S22: sort all data points of each daily meteorological element in ascending order according to data value, calculate the cumulative distribution function value of the data point in the selected time period, and use it to represent the cumulative probability of the data point and all previous data points. The formula used is as follows: ;

[0041] In the formula, Represents the cumulative distribution function value of daily meteorological elements, Indicates the number of days in the selected time period. Represents the ascending rank of a single data point;

[0042] Step S23: Calculate the FS statistical value and FS weighted average value of each daily meteorological element using the long-term cumulative distribution function value and the monthly cumulative distribution function value. The long-term cumulative distribution function value is the cumulative distribution function value of the daily average value in the selected year, and the monthly cumulative distribution function value is the cumulative distribution function value of the data point in the selected month. The formula used is as follows: ; ;

[0043] In the formula, Indicates the FS statistical value of each daily meteorological element in the selected month, Represents the long-term cumulative distribution function value of the daily average of each daily meteorological element, represents the monthly cumulative distribution function value of the data point, Indicates the day of the selected month. Indicates the index of the selected month day. represents the FS weighted average, Represents the index of daily meteorological elements, Indicates The weight coefficient of each daily meteorological element, Indicates FS statistics of daily meteorological elements, It represents the weighted sum calculation of the FS statistical values ​​of the nine daily meteorological elements in the selected month;

[0044] Weight coefficients of daily meteorological elements The values ​​are: maximum dry bulb temperature: 1 / 24, minimum dry bulb temperature: 1 / 24, average dry bulb temperature: 2 / 24, maximum dew point temperature: 1 / 24, minimum dew point temperature: 1 / 24, average dew point temperature: 2 / 24, maximum wind speed: 2 / 24, average wind speed: 2 / 24, total solar radiation: 12 / 24;

[0045] Step S24: Sort the FS weighted average values ​​of each month in previous years from small to large, and select the month with the smallest FS weighted average value as the typical meteorological month.

[0046] By performing the above operations, the traditional hourly meteorological data is interpolated from observation data four times a day, which has a large error with the real meteorological data. This scheme combines multi-dimensional daily meteorological elements to determine the typical meteorological month, assigns weights to different meteorological elements and calculates the weighted average, avoiding dependence on a single element. Through the combination of CDF and FS statistical values, it more comprehensively reflects the temporal change trend of meteorological elements, takes into account the differences between long-term and short-term climate patterns, and provides intuitive quantitative indicators.

[0047] Example 4, see Figure 1 and Figure 2 This embodiment is based on the above embodiment. In step S3, typical meteorological year data is constructed, specifically including the following steps:

[0048] Step S31: extracting hourly meteorological data of each typical meteorological month from the observed meteorological data to form preliminary data of a typical meteorological year;

[0049] Step S32: According to the old version of typical meteorological data in Chongqing, the number of air-conditioning degree days and heating degree days of 34 stations in the city are calculated. The formula used is as follows: ; ;

[0050] In the formula, Indicates the number of heating degree days, Indicates the number of air-conditioning days. Indicates The average daily temperature of the day or When the calculation result of is negative, the value is 0. Indicates time weight, the value is 1;

[0051] Step S33: dividing Chongqing area into three building climate zones according to the number of air-conditioning degree days and heating degree days: hot summer and cold winter zone, cold winter and cool summer zone, and cold winter and cool summer zone, and selecting a representative meteorological station in each building climate zone;

[0052] Step S34: For a meteorological station without hourly solar radiation data, the Collares-Pereira and Rabe model is used to calculate the hourly horizontal surface solar total radiation illumination, and the formula used is as follows: ; ; ;

[0053] In the formula, It represents the total hourly solar radiation on the horizontal surface. It represents the total solar daily radiation on the horizontal surface. represents the hour angle, represents the sunset time angle, and is a constant;

[0054] Step S35: Perform monthly smoothing on the dry-bulb temperature and dew-point temperature in the preliminary data of a typical meteorological year, and smoothly connect the data of the six hours before and after the dry-bulb temperature and dew-point temperature in adjacent months. The formula used is as follows: ;

[0055] In the formula, Indicates the time index, with a value of 0 to 12. The value is 0 at 18:00 of the previous day and 12 at 6:00 of the next day. After smoothing Time data, Indicates the previous day Time data, Indicates the next day Time data;

[0056] Step S36: Replace the original temperature with the smoothed dry-bulb temperature and dew-point temperature to obtain the final typical meteorological year data.

[0057] By executing the above operations, the general data processing relies on fixed climate zone division standards, lacks dynamic adjustment, cannot well reflect the actual energy consumption demand of different building climate zones, and has problems of discontinuous and inaccurate data. This scheme divides climate zones based on building heat load demand, and uses mathematical models to complete the missing hourly solar radiation data, making the calculation results more complete. The use of smoothing processing reduces the data mutation problem caused by monthly boundaries and improves the continuity and accuracy of the data.

[0058] Example 5, see Figure 1 This embodiment is based on the above embodiment. In step S4, the building energy consumption assessment specifically includes the following steps:

[0059] Step S41: Determine building parameters and requirements, including basic building information, HVAC system, and building use conditions;

[0060] Step S42: Obtain a building information model, import the building information model into the energy consumption simulation software EnergyPlus, and import typical meteorological year data;

[0061] Step S43: setting simulation parameters and building usage condition parameters, including indoor temperature set point, personnel activity schedule, equipment load, heat load, ventilation rate, and system operation strategy;

[0062] Step S44: running energy consumption simulation software to calculate the energy consumption of heating demand, cooling demand, ventilation and air quality control, lighting and equipment energy consumption of the building hour by hour;

[0063] Step S45: Calculate the energy cost of the building based on the energy consumption data output by the simulation, analyze the energy-saving effect of the building and optimize it.

[0064] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0065] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0066] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A method for constructing typical meteorological year data for building energy consumption assessment, characterized by: The method comprises the following steps: Step S1: Data collection, collecting observational meteorological data of the observation area for no less than ten years, and extracting daily meteorological elements, including maximum dry bulb temperature, minimum dry bulb temperature, average dry bulb temperature, maximum wind speed, average wind speed, total solar radiation, maximum dew point temperature, minimum dew point temperature and average dew point temperature; Step S2: Select a typical meteorological month, calculate the FS weighted average of each month, and select the month with the smallest FS weighted average as the typical meteorological month; Step S3: constructing typical meteorological year data, obtaining hourly meteorological data of each typical meteorological month, including dry-bulb temperature, dew point temperature, air pressure, wind speed, and wind direction, processing the hourly meteorological data, and constructing typical meteorological year data; Step S4: Building energy consumption assessment, using the building information model to analyze the energy consumption performance of the building under typical climatic conditions based on typical meteorological year data.

2. The method for constructing typical meteorological year data for building energy consumption assessment according to claim 1, characterized in that: In step S2, the typical meteorological month selection includes the following steps: Step S21: Calculate the daily average value of each daily meteorological element; Step S22: sort all data points of each daily meteorological element in ascending order according to data value, calculate the cumulative distribution function value of the data point in the selected time period, and use it to represent the cumulative probability of the data point and all previous data points. The formula used is as follows: ； In the formula, Represents the cumulative distribution function value of daily meteorological elements, Indicates the number of days in the selected time period. Represents the ascending rank of a single data point; Step S23: Calculate the FS statistical value and FS weighted average value of each daily meteorological element using the long-term cumulative distribution function value and the monthly cumulative distribution function value. The long-term cumulative distribution function value is the cumulative distribution function value of the daily average value in the selected year, and the monthly cumulative distribution function value is the cumulative distribution function value of the data point in the selected month. The formula used is as follows: ； ； In the formula, Indicates the FS statistical value of each daily meteorological element in the selected month, Represents the long-term cumulative distribution function value of the daily average of each daily meteorological element, represents the monthly cumulative distribution function value of the data point, Indicates the day of the selected month. Indicates the index of the selected month day. represents the FS weighted average, Represents the index of daily meteorological elements, Indicates The weight coefficient of each daily meteorological element, Indicates FS statistics of daily meteorological elements, It represents the weighted sum calculation of the FS statistical values ​​of the nine daily meteorological elements in the selected month; Step S24: Sort the FS weighted average values ​​of each month in previous years from small to large, and select the month with the smallest FS weighted average value as the typical meteorological month.

3. The method for constructing typical meteorological year data for building energy consumption assessment according to claim 1, characterized in that: In step S3, typical meteorological year data is constructed, including the following steps: Step S31: extracting hourly meteorological data of each typical meteorological month from the observed meteorological data to form preliminary data of a typical meteorological year; Step S32: Calculate the number of air-conditioning degree days and heating degree days in the observed area; Step S33: Divide the observation area into different building climate zones according to the number of air-conditioning degree days and the number of heating degree days, and select a representative meteorological station in each building climate zone; Step S34: For a weather station without hourly solar radiation data, the Collares-Pereira and Rabe model is used to calculate the hourly horizontal surface solar total radiation illumination; Step S35: Perform monthly smoothing on the dry-bulb temperature and dew-point temperature in the preliminary data of a typical meteorological year, and smoothly connect the data of the six hours before and after the dry-bulb temperature and dew-point temperature in adjacent months. The formula used is as follows: ； In the formula, Indicates the time index, with a value of 0 to 12. The value is 0 at 18:00 of the previous day and 12 at 6:00 of the next day. After smoothing Time data, Indicates the previous day Time data, Indicates the next day Time data; Step S36: Replace the original temperature with the smoothed dry-bulb temperature and dew-point temperature to obtain the final typical meteorological year data.

4. The method for constructing typical meteorological year data for building energy consumption assessment according to claim 1, characterized in that: In step S4, the building energy consumption assessment includes the following steps: Step S41: Determine building parameters and requirements; Step S42: Obtain a building information model, import the building information model into energy consumption simulation software, and import typical meteorological year data; Step S43: setting simulation parameters and building usage condition parameters; Step S44: running energy consumption simulation software to calculate the energy consumption of heating demand, cooling demand, ventilation and air quality control, lighting and equipment energy consumption of the building hour by hour; Step S45: Calculate the energy cost of the building based on the energy consumption data output by the simulation, analyze the energy-saving effect of the building and optimize it.

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