Energy-saving effect evaluation method and system for high-rise building

Abstract

The invention discloses an energy-saving effect evaluation method and system for a high-rise building, particularly relates to the technical field of building energy conservation, and is used for solving the problems that in the prior art, a dynamic heat transfer delay effect is caused by neglecting thermal inertia of a glass curtain wall, and an energy-saving effect evaluation result is not accurate. The method comprises the following steps: acquiring outer surface temperature time sequence data of a target building glass curtain wall under various weather types and corresponding indoor air conditioner refrigeration energy consumption data; time sequence offsets of a daytime temperature peak value and a night energy consumption peak value are extracted period by period, and dynamic delay duration is calculated in combination with material heat transfer attenuation characteristics; night energy consumption data conforming to a stable delay threshold value are screened, and additional refrigeration energy consumption caused by daytime heat storage is quantified based on the delay time period weight; and finally, through dynamic weighted superposition of the daytime and night energy consumption data, an energy-saving evaluation result containing a heat transfer delay effect is generated. The evaluation deviation caused by night hidden load omission is avoided, and the reliability of the evaluation result and the engineering guidance value are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of building energy conservation, and more specifically, to an energy-saving effect evaluation method and system for high-rise buildings. Background Art

[0002] The accelerating urbanization process has made the energy-saving renovation of high-rise buildings an important means to reduce building energy consumption. In the prior art, for the evaluation of the energy-saving effect of the building envelope such as glass curtain walls, it is mainly based on real-time energy consumption monitoring and static heat transfer models, and the energy-saving rate is verified by comparing the energy consumption data before and after the renovation. Such methods usually assume that the lighting and heat transfer processes are instantaneously synchronized, and rely on the sub-metering system to statistically analyze the energy consumption distribution of main equipment such as air conditioners and lighting, providing basic data support for energy-saving design.

[0003] However, due to the thermal inertia characteristics of glass curtain wall materials, there is a significant time delay in the heat absorption and dissipation processes, resulting in the heat stored during the day being continuously released into the indoor environment at night. The existing evaluation methods do not fully consider such lag effects of dynamic heat transfer, and only use short-term or daily average energy consumption data as the evaluation basis, resulting in a deviation between the evaluation result of the energy-saving effect and the actual energy consumption demand, and it is difficult to accurately reflect the actual contribution of energy-saving technologies. Summary of the Invention

[0004] In order to overcome the above-mentioned defects of the prior art, embodiments of the present invention provide an energy-saving effect evaluation method and system for high-rise buildings to solve the problems raised in the above background art.

[0005] To achieve the above object, the present invention provides the following technical solutions: An energy-saving effect evaluation method for high-rise buildings, comprising the following steps: S1. Obtain the time series data of the outer surface temperature of the glass curtain wall of the target building in multiple consecutive time periods and the corresponding indoor air-conditioning cooling energy consumption data, and each consecutive time period includes a daytime lighting period and a nighttime non-lighting period; S2. Extract the outer surface temperature peak time point during the daytime lighting period and the indoor air-conditioning cooling energy consumption peak time point during the nighttime non-lighting period for each time period; S3. Determine the dynamic delay duration according to the time difference between the outer surface temperature peak time point and the indoor air-conditioning cooling energy consumption peak time point during the corresponding nighttime non-lighting period; S4. Screen the indoor air-conditioning cooling energy consumption data during the nighttime non-lighting periods whose dynamic delay duration exceeds the stable delay threshold; S5. Based on the screened indoor air-conditioning cooling energy consumption data during the nighttime non-lighting periods, calculate the additional cooling energy consumption caused by the daytime heat storage of the glass curtain wall according to the preset delay period weight; S6. Superimpose the additional refrigeration energy consumption and the indoor air-conditioning refrigeration energy consumption during the daytime lighting period to generate an energy-saving effect evaluation result that includes the dynamic heat transfer delay effect.

[0006] In a preferred embodiment, S1 includes: S1-1. Continuously collect the time-series data of the outer surface temperature of the glass curtain wall of the target building through an infrared temperature sensor; S1-2. Synchronously obtain the indoor air-conditioning refrigeration energy consumption data for the corresponding period through a sub-item metering system; S1-3. Align the time-series data of the outer surface temperature and the indoor air-conditioning refrigeration energy consumption data according to continuous periods, and mark the daytime lighting period and the nighttime non-lighting period; S1-4. Screen the continuous period data that includes at least two weather types of sunny and cloudy days to construct a multi-condition data set.

[0007] In a preferred embodiment, S2 includes: S2-1. For the time-series data of the outer surface temperature during the daytime lighting period of each continuous period, determine the interval where the temperature change rate exceeds the preset threshold through the sliding window method, and take the time point corresponding to the maximum temperature within the corresponding interval as the outer surface temperature peak time point; S2-2. For the indoor air-conditioning refrigeration energy consumption data during the nighttime non-lighting period of each continuous period, identify the starting time point of the energy consumption sudden increase interval through the difference method, and take the time point corresponding to the maximum energy consumption within the corresponding interval as the air-conditioning refrigeration energy consumption peak time point; S2-3. Record the time difference between the outer surface temperature peak time point and the air-conditioning refrigeration energy consumption peak time point in the same continuous period as the preliminary delay duration.

[0008] In a preferred embodiment, S3 includes: S3-1. Based on the continuous period data of different weather types, calculate the average value of the preliminary delay duration under sunny and cloudy conditions respectively, and construct the mapping relationship between the weather type and the delay duration; S3-2. According to the mapping relationship, combined with the heat transfer attenuation characteristics of the glass curtain wall material, physically correct the preliminary delay duration for sunny and cloudy days to generate the corrected dynamic delay duration; S3-3. Based on the maximum value of the distribution density of the corrected dynamic delay duration, dynamically adjust the stable delay threshold so that the stable delay threshold changes adaptively according to the weather type distribution ratio.

[0009] In a preferred embodiment, S4 includes: S4-1. Based on the comparison result between the dynamic delay duration and the stable delay threshold, screen out the indoor air-conditioning refrigeration energy consumption data during the nighttime non-lighting period when the dynamic delay duration exceeds the stable delay threshold; S4-2. For the indoor air-conditioning cooling energy consumption data during the night-time without light selected, based on the secondary comparison of the energy consumption intensity during the corresponding period with the preset intensity threshold, eliminate the period data with the energy consumption intensity lower than the preset intensity threshold. S4-3. Integrate the period data with the dynamic delay duration exceeding the stable delay threshold and the energy consumption intensity exceeding the preset intensity threshold into an effective night-time energy consumption dataset.

[0010] In a preferred embodiment, S5 includes: S5-1. Dynamically allocate the delay period weights for each period according to the ratio of the dynamic delay duration to the stable delay threshold, and the delay period weights are positively correlated with the ratio of the dynamic delay duration to the stable delay threshold. S5-2. Combine the heat transfer attenuation rate of the glass curtain wall material to perform thermodynamic correction on the delay period weights to generate corrected weights. S5-3. Based on the corrected weights and the energy consumption intensity of each period in the effective night-time energy consumption dataset, calculate the additional cooling energy consumption caused by the daytime heat storage of the glass curtain wall by weighted calculation.

[0011] In a preferred embodiment, S6 includes: S6-1. Align the additional cooling energy consumption and the indoor air-conditioning cooling energy consumption during the daytime light period according to the time correlation, and determine the period weight factors of the daytime and night-time energy consumption based on the dynamic delay duration. S6-2. Combine the heat transfer attenuation rate of the glass curtain wall to dynamically correct the period weight factors to generate the corrected superimposed weights. S6-3. Based on the corrected superimposed weights, perform weighted accumulation of the air-conditioning cooling energy consumption during the daytime light period and the additional cooling energy consumption by period to generate an energy-saving effect evaluation result including the dynamic heat transfer delay effect.

[0012] On the other hand, the present invention provides an energy-saving effect evaluation system for high-rise buildings, including a data acquisition module, a peak extraction module, a delay calculation module, a threshold screening module, an energy consumption calculation module, and a result generation module. Data acquisition module: Obtain the time series data of the outer surface temperature of the glass curtain wall of the target building in multiple consecutive periods and the corresponding indoor air-conditioning cooling energy consumption data, and each consecutive period includes a daytime light period and a night-time without light period. Peak extraction module: Extract the peak time points of the outer surface temperature during the daytime light period and the peak time points of the indoor air-conditioning cooling energy consumption during the night-time without light period period by period. Delay calculation module: Determine the dynamic delay duration according to the time difference between the peak time point of the outer surface temperature and the peak time point of the indoor air-conditioning cooling energy consumption during the corresponding night-time without light period. Threshold screening module: Screen the indoor air-conditioning cooling energy consumption data during the night without sunlight, where the dynamic delay duration exceeds the stable delay threshold; Energy consumption calculation module: Based on the screened indoor air-conditioning cooling energy consumption data during the night without sunlight, calculate the additional cooling energy consumption caused by the daytime heat storage of the glass curtain wall according to the preset delay period weight; Result generation module: Superimpose the additional cooling energy consumption on the indoor air-conditioning cooling energy consumption during the daytime sunlight period to generate an energy-saving effect evaluation result including the dynamic heat transfer delay effect.

[0013] Compared with the prior art, the present invention has the following beneficial effects: 1. By analyzing the time series offset between the peak value of the outer surface temperature and the peak value of the night cooling energy consumption period by period, constructing a dynamic delay duration parameter, and combining multi-weather condition data to extract the stable delay threshold, the accurate quantification of the thermal inertia effect is realized. Compared with the traditional method that only focuses on the change of the average energy consumption before and after the transformation, the dynamic delay effect is incorporated into the evaluation system, decoupling the physical correlation of the day and night energy consumption from the time dimension, and significantly improving the integrity and credibility of the energy-saving effect evaluation; 2. By introducing delay period weight calculation, heat transfer attenuation correction and threshold adaptive screening logic, the material characteristics, environmental variables and user behavior are dynamically coupled to form a closed-loop feedback evaluation model; through data alignment and weighted superposition, the chain influence path of the thermal inertia of the glass curtain wall on the air-conditioning system is truly restored, providing a decision-making basis for energy-saving transformation that takes into account short-term effect verification and long-term operation prediction; this evaluation paradigm based on dynamic tracking of physical processes can effectively identify the "false energy-saving" traps that are easily masked in traditional methods, providing a more scientific optimization direction for high-rise building energy management. Description of the Drawings

[0014] Figure 1 It is a flowchart of an energy-saving effect evaluation method for a high-rise building according to the present invention; Figure 2 It is a structural schematic diagram of an energy-saving effect evaluation system for a high-rise building according to the present invention. Detailed Embodiments

[0015] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0016] Embodiment 1: Figure 1 An energy-saving effect evaluation method for a high-rise building according to the present invention is given, which includes the following steps: S1. Obtain the time series data of the outer surface temperature of the target building's glass curtain wall and the corresponding indoor air-conditioning cooling energy consumption data in multiple consecutive time periods. Each consecutive time period includes a daytime lighting period and a nighttime non-lighting period; S2. Extract the outer surface temperature peak time points during the daytime lighting period and the indoor air-conditioning cooling energy consumption peak time points during the nighttime non-lighting period for each time period; S3. Determine the dynamic delay duration based on the time difference between the outer surface temperature peak time point and the indoor air-conditioning cooling energy consumption peak time point during the corresponding nighttime non-lighting period; S4. Screen the indoor air-conditioning cooling energy consumption data during the nighttime non-lighting periods where the dynamic delay duration exceeds the stable delay threshold; S5. Based on the screened indoor air-conditioning cooling energy consumption data during the nighttime non-lighting periods, calculate the additional cooling energy consumption caused by the daytime heat storage of the glass curtain wall according to the preset delay period weight; S6. Superimpose the additional cooling energy consumption on the indoor air-conditioning cooling energy consumption during the daytime lighting period to generate an energy-saving effect evaluation result including the dynamic heat transfer delay effect.

[0017] S1. Obtain the time series data of the outer surface temperature of the target building's glass curtain wall and the corresponding indoor air-conditioning cooling energy consumption data in multiple consecutive time periods. Each consecutive time period includes a daytime lighting period and a nighttime non-lighting period, including: The specific implementation methods for obtaining the time series data of the outer surface temperature of the target building's glass curtain wall and the corresponding indoor air-conditioning cooling energy consumption data in multiple consecutive time periods, where each consecutive time period includes a daytime lighting period and a nighttime non-lighting period, include the following. Continuously collect the time series data of the outer surface temperature of the target building's glass curtain wall through an infrared temperature sensor. The infrared temperature sensor collects the temperature data of the outer surface of the glass curtain wall at a preset sampling frequency. The preset sampling frequency is set according to the building height and the curtain wall area. For example, it is collected every 5 minutes to ensure coverage of the temperature changes during the daytime lighting period and the nighttime non-lighting period.

[0018] Simultaneously obtain the corresponding indoor air-conditioning cooling energy consumption data through a sub-metering system. The sub-metering system accesses the building energy management system to record the power consumption of the air-conditioning cooling equipment in real time. The time stamps of the air-conditioning cooling energy consumption data and the collection time stamps of the infrared temperature sensor are calibrated and aligned through a unified time server to ensure that the data synchronization accuracy is within 1 second.

[0019] Align the time series data of the outer surface temperature and the indoor air-conditioning cooling energy consumption data according to the consecutive time periods. The consecutive time periods are 24-hour cycles of natural days. The daytime lighting period is divided into the time period from after sunrise to before sunset according to the local sunrise and sunset schedule, and the nighttime non-lighting period is the time period from after sunset to before sunrise the next day. When aligning, segment and integrate the temperature data and the energy consumption data by hour based on the time stamps.

[0020] Screen the continuous time period data that includes at least two weather types: sunny and cloudy days. The weather types of sunny and cloudy days are determined according to the weather records released by the meteorological department. A sunny day is defined as the number of days with no cloud cover and the sunlight intensity continuously higher than a preset threshold, and a cloudy day is defined as the number of days with a cloud cover rate of more than 80% and the sunlight intensity lower than the preset threshold. The screened continuous time period data constitutes a multi-condition data set, which contains the corresponding relationship between the outer surface temperature and the air-conditioning cooling energy consumption under different weather conditions, and is used for subsequent analysis of the dynamic heat transfer delay effect.

[0021] S2. Extract the peak time points of the outer surface temperature during the daytime lighting period and the peak time points of the indoor air-conditioning cooling energy consumption during the nighttime non-lighting period for each time period, including: For the time series data of the outer surface temperature during the daytime lighting period of each continuous time period, determine the intervals where the temperature change rate exceeds the preset threshold by the sliding window method. The window size of the sliding window method is set according to the thermal inertia characteristics of the curtain wall. For example, the window size is 30 minutes, and the preset threshold is determined through historical data analysis. For example, the interval where the temperature change rate exceeds 2 degrees Celsius per hour is determined as the significant change interval. Select the time point corresponding to the maximum temperature within the significant change interval as the peak time point of the outer surface temperature, and the maximum temperature is determined by traversing all temperature data points within the window.

[0022] For the indoor air-conditioning cooling energy consumption data during the nighttime non-lighting period of each continuous time period, identify the starting time points of the energy consumption sudden increase intervals by the difference method. The difference interval of the difference method is set according to the sampling frequency of the energy consumption data. For example, calculate the energy consumption difference every 15 minutes. When the energy consumption difference between adjacent time points exceeds the preset sudden increase threshold, it is determined as the starting point of the sudden increase interval. The preset sudden increase threshold is determined through the historical energy consumption fluctuation range. For example, when the energy consumption increases by more than 200 watts per hour, the determination is triggered. Select the time point corresponding to the maximum energy consumption within the sudden increase interval as the peak time point of the air-conditioning cooling energy consumption, and the maximum energy consumption is determined by traversing all energy consumption data points within the sudden increase interval.

[0023] Record the time difference between the peak time point of the outer surface temperature and the peak time point of the air-conditioning cooling energy consumption in the same continuous time period as the preliminary delay duration. The calculation of the time difference is based on the time stamp alignment accurate to minutes. For example, the temperature peak time point is 14:35, and the corresponding nighttime energy consumption peak time point is 22:40, and the preliminary delay duration is 8 hours and 5 minutes. For the continuous time period data of different weather types, the preliminary delay duration between the peak time point of the outer surface temperature and the peak time point of the energy consumption is usually longer under sunny conditions than under cloudy conditions. For example, the delay duration under sunny conditions is 7 - 9 hours, and the delay duration under cloudy conditions is 4 - 6 hours. Verify the stability of the delay duration through the comparison of data of multiple weather types.

[0024] S3. Determine the dynamic delay duration according to the time difference between the peak time point of the outer surface temperature and the peak time point of the indoor air-conditioning cooling energy consumption during the corresponding nighttime period without sunlight, including: When calculating the mean value of the preliminary delay duration under sunny and cloudy conditions respectively based on the continuous period data of different weather types, it is necessary to first classify the continuous period data by weather type. The sunny data is screened according to the condition that the solar radiation intensity in the meteorological record is continuously higher than the preset threshold and there is no cloud cover, and the cloudy data is screened according to the condition that the cloud cover rate exceeds 80% and the solar radiation intensity is lower than the preset threshold. The classified sunny data and cloudy data are independently used to calculate the mean value of the preliminary delay duration. The mean value of the preliminary delay duration is the arithmetic mean of the preliminary delay durations of all periods under each weather type. When constructing the mapping relationship between the weather type and the delay duration, the mean value of the preliminary delay duration under sunny conditions and the mean value of the preliminary delay duration under cloudy conditions are respectively associated with the corresponding weather types to form a mapping relationship table. For example, if the mean value of the sunny delay duration is 8 hours and the mean value of the cloudy delay duration is 5 hours, the mapping relationship table records that sunny corresponds to 8 hours and cloudy corresponds to 5 hours.

[0025] When physically correcting the preliminary delay durations of sunny and cloudy days according to the mapping relationship and combining the heat transfer attenuation characteristics of the glass curtain wall material, it is necessary to obtain the thermal conductivity and specific heat capacity parameters of the glass curtain wall material. The thermal conductivity and specific heat capacity parameters are obtained through the material test report or the supplier's technical manual. For example, the thermal conductivity of the glass curtain wall is 1.05 W / (m·K), and the specific heat capacity is 840 J / (kg·K). Calculate the attenuation rate of the heat in the curtain wall structure based on the heat transfer attenuation characteristics. The attenuation rate is the ratio of the thermal conductivity to the specific heat capacity. For example, the attenuation rate is 1.05 / 840 = 0.00125 (m² / s). Correct the preliminary delay duration according to the attenuation rate. The correction formula is dynamic delay duration = preliminary delay duration × (1 + attenuation rate × curtain wall thickness / heat flow path length). The curtain wall thickness and heat flow path length are obtained from the building drawings. For example, the curtain wall thickness is 12 mm, and the heat flow path length is the distance from the curtain wall surface to the indoor side, which is 0.5 m. After substitution and calculation, the corrected sunny dynamic delay duration is 8 × (1 + 0.00125 × 12 / 0.5) = 8.24 hours, and the corrected cloudy dynamic delay duration is 5 × (1 + 0.00125 × 12 / 0.5) = 5.15 hours.

[0026] When dynamically adjusting the stable delay threshold based on the maximum value of the distribution density of the corrected dynamic delay duration, it is necessary to count the distribution of all corrected dynamic delay durations. The maximum value of the distribution density is determined by histogram statistics. For example, the dynamic delay duration is grouped at intervals of 0.5 hours, and the frequency of each group of data is counted. The median of the delay duration corresponding to the group with the highest frequency is the maximum value of the distribution density. For example, the maximum value of the distribution density is 8.25 hours, which is the median of the interval of 8.0 - 8.5 hours, and this value is used as the reference value for the stable delay threshold.

[0027] When the stable delay threshold adaptively changes according to the distribution ratio of weather types, it is necessary to calculate the proportion of data of different weather types in the total dataset. For example, the proportion of sunny-day data is 60% and the proportion of cloudy-day data is 40%. The stable delay threshold = dynamic delay duration on sunny days × proportion of sunny days + dynamic delay duration on cloudy days × proportion of cloudy days. For example, the stable delay threshold = 8.24×0.6 + 5.15×0.4 = 7.02 hours. If the distribution ratio of weather types changes to 50% sunny days and 50% cloudy days, then the stable delay threshold is adjusted to 8.24×0.5 + 5.15×0.5 = 6.70 hours.

[0028] For buildings with different curtain wall thicknesses or heat flow path lengths, it is necessary to recalculate the attenuation rate and correct the dynamic delay duration according to the actual parameters. For example, when the curtain wall thickness increases to 15 mm, the corrected dynamic delay duration on sunny days is 8×(1 + 0.00125×15 / 0.5) = 8.3 hours. If the building adopts a double-glazed curtain wall structure, the heat flow path length needs to be adjusted according to the thickness of the air interlayer. For example, when the thickness of the air interlayer is 20 mm, the heat flow path length = outer glass thickness + air interlayer thickness + inner glass thickness = 12 + 20 + 12 = 44 mm = 0.044 m, and the dynamic delay duration is further adjusted after substituting it into the correction formula.

[0029] S4. Screen the indoor air-conditioning cooling energy consumption data for the nighttime non-illuminated periods with dynamic delay durations exceeding the stable delay threshold, including: When screening data based on the comparison result between the dynamic delay duration and the stable delay threshold, the dynamic delay duration is derived from the corrected dynamic delay duration generated by weather type mapping and physical correction in step S3, and the stable delay threshold is derived from the threshold dynamically adjusted based on the maximum value of the distribution density in step S3. When comparing, the condition for screening is that the value of the dynamic delay duration is greater than the stable delay threshold. For example, when the dynamic delay duration is 8 hours and the stable delay threshold is 7 hours, it is determined to meet the condition.

[0030] When making a secondary comparison of the indoor air-conditioning cooling energy consumption data during the selected nighttime periods without light with the corresponding energy consumption intensity and a preset intensity threshold, the preset intensity threshold is determined by adding twice the standard deviation to the average of the nighttime air-conditioning cooling energy consumption intensities in the historical data. For example, if the average energy consumption intensity in the historical data is 500 watts per hour and the standard deviation is 50 watts, then the preset intensity threshold is 500 + 2×50 = 600 watts. If the energy consumption intensity during a certain period is 550 watts, which is lower than the preset intensity threshold of 600 watts, it is determined as invalid data and excluded.

[0031] When integrating the period data with a dynamic delay duration exceeding the stable delay threshold and an energy consumption intensity exceeding the preset intensity threshold into the effective nighttime energy consumption dataset, the period data meeting the dual conditions needs to be arranged in timestamp order, and the corresponding dynamic delay duration and energy consumption intensity values are marked. For example, if the dynamic delay duration during a certain period is 8 hours and the energy consumption intensity is 650 watts, it is included in the effective nighttime energy consumption dataset.

[0032] For the screening process of different building types, the preset intensity threshold needs to be adjusted according to the building function. For example, the usage rate of air conditioners at night in office buildings is low, and the preset intensity threshold can be reduced to the average of the historical data plus one standard deviation. In hotel buildings, the air conditioners operate continuously at night, and the preset intensity threshold can be increased to the average plus three standard deviations.

[0033] If there are multiple air-conditioning cooling energy consumption peaks during the same period, the highest energy consumption peak should be used for intensity comparison. For example, if there are two energy consumption peaks of 620 watts and 580 watts respectively during a certain nighttime period, 620 watts is selected for comparison with the preset intensity threshold.

[0034] The period data in the effective nighttime energy consumption dataset will be used for the calculation of additional cooling energy consumption in the subsequent steps to ensure that only the real energy consumption increment caused by the thermal inertia effect of the glass curtain wall is corrected.

[0035] S5. Based on the screened indoor air-conditioning cooling energy consumption data during the nighttime periods without light, calculate the additional cooling energy consumption caused by the daytime heat storage of the glass curtain wall according to the preset delay period weights, including: When dynamically allocating the delay period weights for each period according to the ratio of the dynamic delay duration to the stable delay threshold, the dynamic delay duration is the result corrected by the weather type mapping relationship and the heat transfer attenuation characteristics in step S3, the stable delay threshold is the threshold dynamically adjusted based on the multi-weather type data distribution in step S3, and the ratio is the value obtained by dividing the dynamic delay duration by the stable delay threshold. For example, when the dynamic delay duration is 8 hours and the stable delay threshold is 7 hours, the ratio is 1.14. The delay period weight is positively correlated with the ratio, and the positive correlation is achieved through a linear proportionality coefficient, which is preset according to the building type. For example, the linear proportionality coefficient for an office building is 0.8, and for a hotel building is 1.2. The delay period weight corresponding to the office building is 1.14×0.8 = 0.91, and for the hotel building is 1.14×1.2 = 1.37.

[0036] When performing thermodynamic correction on the delay period weight in combination with the heat transfer attenuation rate of the glass curtain wall material, the heat transfer attenuation rate is a physical quantity calculated through the thermal conductivity and specific heat capacity parameters of the material in step S3. The thermal conductivity and specific heat capacity parameters are obtained from the material test report or the technical parameter table provided by the supplier. For example, the thermal conductivity of a certain glass curtain wall is 1.05 W / (m·K), and the specific heat capacity is 840 J / (kg·K), then the heat transfer attenuation rate is 1.05 / 840 = 0.00125 (m² / s). The thermodynamic correction is achieved by multiplying the delay period weight by a correction factor, and the correction factor is 1 plus the product of the heat transfer attenuation rate and the ratio of the curtain wall thickness to the heat flow path length. For example, when the curtain wall thickness is 12 mm and the heat flow path length is 0.5 m, the correction factor is 1 + 0.00125×12 / 0.5 = 1.03, and the corrected weight is 0.91×1.03 = 0.94. If the curtain wall uses double-layer insulating glass, the heat flow path length needs to be recalculated according to the sum of the thicknesses of the outer glass, air layer, and inner glass. For example, when the total thickness is 44 mm, the correction factor is adjusted accordingly to 1 + 0.00125×44 / 0.5 = 1.11.

[0037] When calculating the additional cooling energy consumption by weighted calculation based on the corrected weight and the energy consumption intensity of each period in the effective night energy consumption dataset, the effective night energy consumption dataset is the period data screened in step S4 where the dynamic delay duration exceeds the stable delay threshold and the energy consumption intensity is higher than the preset intensity threshold. The weighted calculation is to accumulate the energy consumption intensity of each period multiplied by the corresponding corrected weight. For example, if the energy consumption intensity of a certain period is 650 watts and the corrected weight is 0.94, then the additional cooling energy consumption contributed by this period is 650×0.94 = 611 watts. For the case where there are multiple adjacent eligible periods, it is necessary to calculate the overall duration after merging the periods. For example, when calculating the 22:00 - 24:00 period by merging, the energy consumption intensity takes the two-hour average value, the corrected weight takes the two-hour average value, and the additional cooling energy consumption is the average value × the average value weight × 2 hours.

[0038] When there is a local thermal bridge effect in a building, it is necessary to calculate the dynamic delay duration and correction weight separately for the thermal bridge area. For example, due to the thermal bridge, heat is transferred rapidly through the metal frame at the edge of the curtain wall, and the dynamic delay duration is 5 hours (lower than the overall threshold of 7 hours). Then, the weight is distributed according to the ratio of 5 / 7 = 0.71, and the correction is made separately in combination with the material parameters of this area. For different climate regions, the linear proportionality coefficient needs to be adjusted according to the local typical meteorological data. For example, in tropical regions, due to the small diurnal temperature difference, the dynamic delay duration is generally short, and the linear proportionality coefficient is set to 0.6, while in temperate regions, it is set to 1.0. If the night-time air-conditioning operation mode is intermittent start-stop (such as in office buildings), only the data during the air-conditioning operation period need to be selected for calculation. For example, only calculate the product of the correction weight and the energy consumption intensity when the air-conditioning is running from 22:00 to 23:00.

[0039] When adjustable sunshading devices are added to the glass curtain wall, the heat transfer attenuation rate needs to be adjusted dynamically according to the sunshading state. For example, when the sunshading is closed, the heat storage capacity of the curtain wall increases, and the heat transfer attenuation rate decreases to 0.0008 (m² / s), the correction coefficient decreases accordingly, and the weight distribution ratio of the delay period increases. If the curtain wall material is replaced with a material with a low thermal conductivity (such as vacuum glass), it is necessary to recalculate the heat transfer attenuation rate based on the new material parameters. For example, when the thermal conductivity is 0.8 W / (m·K), the attenuation rate is 0.8 / 840 = 0.00095 (m² / s), and the correction coefficient is updated synchronously.

[0040] S6. Superimpose the additional refrigeration energy consumption and the indoor air-conditioning refrigeration energy consumption during the daytime lighting period to generate an energy-saving effect evaluation result that includes the dynamic heat transfer delay effect, including: When aligning the additional refrigeration energy consumption and the indoor air-conditioning refrigeration energy consumption during the daytime lighting period according to the time correlation, the time correlation is based on the dynamic delay duration determined in step S3. Align the period corresponding to the additional refrigeration energy consumption at night with the daytime lighting period according to the dynamic delay duration offset. For example, if the dynamic delay duration is 8 hours, the period corresponding to the additional refrigeration energy consumption at night corresponding to the peak outer surface temperature at 14:00 during the day is 22:00. It is necessary to correlate the additional energy consumption at 22:00 with the daytime energy consumption at 14:00. When determining the period weight factor of the daytime and night-time energy consumption based on the dynamic delay duration, the period weight factor is set according to the ratio of the dynamic delay duration to the stable delay threshold. For example, when the dynamic delay duration is 8 hours and the stable delay threshold is 7 hours, the ratio is 1.14, and the period weight factor is set to 1.14, indicating that the contribution weight of the additional energy consumption at night to the daytime energy consumption is increased by 14%.

[0041] When dynamically correcting the time period weight factor in combination with the heat transfer attenuation rate of the glass curtain wall, the heat transfer attenuation rate is a physical quantity calculated by the thermal conductivity and specific heat capacity of the material in step S3. For example, the attenuation rate is 0.00125 (m² / s), and the correction coefficient is 1 plus the product of the attenuation rate and the ratio of the curtain wall thickness to the heat flow path length. When the curtain wall thickness is 12 mm and the heat flow path length is 0.5 m, the correction coefficient is 1 + 0.00125×12 / 0.5 = 1.03, and the corrected superposition weight is 1.14×1.03 = 1.17. For a double-layer insulating glass curtain wall, the heat flow path length is adjusted to the sum of the thicknesses of the outer glass, air interlayer, and inner glass. For example, when the total thickness is 44 mm, the correction coefficient is 1 + 0.00125×44 / 0.5 = 1.11, and the superposition weight is adjusted to 1.14×1.11 = 1.27.

[0042] When weighted cumulatively by time period the air-conditioning cooling energy consumption and additional cooling energy consumption during the daytime lighting period based on the corrected superposition weight, the air-conditioning cooling energy consumption data during the daytime lighting period is obtained through the sub-item metering system, and the additional cooling energy consumption data is obtained through the calculation in step S5. The weighted accumulation is the direct addition of the product of the daytime energy consumption multiplied by the superposition weight and the additional energy consumption. For example, if the energy consumption during a certain daytime period is 500 watts, the superposition weight is 1.17, and the additional energy consumption is 611 watts, then the total evaluated energy consumption is 500×1.17 + 611 = 1196 watts. For the case where there are multiple associated daytime and nighttime time periods, it is necessary to calculate and accumulate the sum hour by hour. For example, if there is an association between 10:00 - 12:00 during the day and 20:00 - 22:00 at night, the weighted values for each hour period are calculated and then accumulated.

[0043] When there is a local thermal bridge effect in the building, it is necessary to calculate the superposition weight separately for the thermal bridge area. For example, in the area of the metal frame at the edge of the curtain wall, the dynamic delay duration is shortened to 5 hours, the stable delay threshold is 7 hours, the ratio is 0.71, and the initial value of the superposition weight is 0.71. Combining the material parameters of this area (such as the metal thermal conductivity is 50 W / (m·K)), the correction coefficient is calculated as 1 + 0.00125×5 / 0.5 = 1.0125, and the corrected superposition weight is 0.71×1.0125 = 0.72. The additional energy consumption in this area participates in the accumulation according to the weight of 0.72. For different climate regions, the superposition weight is generally higher in temperate regions with large day-night temperature differences than in tropical regions. For example, in temperate regions, the dynamic delay duration is 9 hours, the stable threshold is 7 hours, and the superposition weight is 1.29, while in tropical regions, the delay duration is 5 hours, the threshold is 6 hours, and the superposition weight is 0.83.

[0044] If the glass curtain wall adopts an adjustable sunshading device, it is necessary to adjust the heat transfer attenuation rate according to the sunshading state. For example, when the sunshading is closed, the attenuation rate is reduced to 0.0008 (m² / s), the correction factor is adjusted to 1 + 0.0008×12 / 0.5 = 1.019, and the superimposed weight is 1.14×1.019 = 1.16. When the sunshading is open, the attenuation rate is restored to 0.00125, and the superimposed weight is synchronously restored to 1.17. When there are multiple peaks in the additional energy consumption at night, it is necessary to calculate the superimposed weights separately according to the dynamic delay durations corresponding to each peak and then accumulate them. For example, the first peak has a delay duration of 8 hours and a weight of 1.17, and the second peak has a delay duration of 6 hours and a weight of 0.86. The total additional energy consumption is the sum of the energy consumptions of the two segments multiplied by their respective weights.

[0045] For the continuous period data of the mixed weather type, it is necessary to allocate the superimposed weights according to the proportion of sunny days and cloudy days. For example, the proportion of sunny days is 60% and the proportion of cloudy days is 40%. The superimposed weight for sunny days is 1.17 and the weight for cloudy days is 0.92. Then the overall superimposed weight is 1.17×0.6 + 0.92×0.4 = 1.07. All parameters involved in the calculation process (such as the attenuation rate and the heat flow path length) are obtained from the existing building data or material parameters, and there is no need to rely on unpublished models or algorithms. The final generated energy-saving effect evaluation result is the total energy consumption value including the dynamic heat transfer delay effect. When comparing the energy consumption differences before and after the transformation, it is necessary to ensure that the comparison data are all calculated using the same superimposed weight to eliminate the evaluation deviation.

[0046] Embodiment 2: Figure 2 The structural schematic diagram of an energy-saving effect evaluation system for a high-rise building according to the present invention is given. An energy-saving effect evaluation system for a high-rise building includes a data acquisition module, a peak extraction module, a delay calculation module, a threshold screening module, an energy consumption calculation module, and a result generation module.

[0047] Data acquisition module: Obtain the time series data of the outer surface temperature and the corresponding indoor air-conditioning cooling energy consumption data of the glass curtain wall of the target building in multiple continuous periods. Each continuous period includes a daytime lighting period and a nighttime non-lighting period; Peak extraction module: Extract the peak time points of the outer surface temperature in the daytime lighting period and the peak time points of the indoor air-conditioning cooling energy consumption in the nighttime non-lighting period for each period; Delay calculation module: Determine the dynamic delay duration according to the time difference between the peak time point of the outer surface temperature and the peak time point of the indoor air-conditioning cooling energy consumption in the corresponding nighttime non-lighting period; Threshold screening module: Screen the indoor air-conditioning cooling energy consumption data in the nighttime non-lighting periods whose dynamic delay durations exceed the stable delay threshold; Energy consumption calculation module: Based on the screened indoor air-conditioning cooling energy consumption data in the nighttime non-lighting periods, calculate the additional cooling energy consumption caused by the daytime heat storage of the glass curtain wall according to the preset delay period weights; Result generation module: superimpose the additional refrigeration energy consumption and the indoor air-conditioning refrigeration energy consumption during the daytime lighting period to generate an energy-saving effect evaluation result including the dynamic heat transfer delay effect.

[0048] The above formulas are all dimensionless and take their numerical calculations. The formulas are obtained by collecting a large amount of data for software simulation to obtain a formula closest to the actual situation. The preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.

[0049] It should be noted that the present invention can be deployed on the device itself to achieve embedded applications, or can also run on a PC or other terminals with a user interface, so as to meet various hardware environments and usage requirements.

[0050] The above embodiments can be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented using software, the above embodiments can be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, the processes or functions described in the embodiments of the present application are generated in whole or in part. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable devices. The computer instructions can be stored in a computer-readable storage medium, or transmitted from one computer-readable storage medium to another computer-readable storage medium. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (such as infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device such as a server or a data center that includes one or more collections of available media. The available medium can be a magnetic medium (such as a floppy disk, a hard disk, a magnetic tape), an optical medium (such as a DVD), or a semiconductor medium. The semiconductor medium can be a solid-state drive.

[0051] Those skilled in the art can clearly understand that for the convenience and simplicity of description, the specific working processes of the systems, devices, and modules described above can refer to the corresponding processes in the foregoing method embodiments, and will not be described herein again.

[0052] In several embodiments provided by this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. For example, the device embodiments described above are merely illustrative. For example, the division of the modules is only a logical function division. In actual implementation, there can be other division methods. For example, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections to each other can be through some interfaces. The indirect couplings or communication connections of the devices or modules can be in electrical, mechanical, or other forms.

[0053] The modules described as separate components may or may not be physically separated. The components shown as modules may or may not be physical modules. They can be located in one place or distributed to multiple network modules. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0054] In addition, in each embodiment of this application, the functional modules can be integrated into one processing module, or each module can exist physically alone, or two or more modules can be integrated into one module.

[0055] If the above functions are implemented in the form of software function modules and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of this application. The foregoing storage medium includes: USB flash drives, mobile hard disks, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical discs and other various media that can store program codes.

[0056] The above is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0057] Finally, the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. An energy-saving effect evaluation method for high-rise buildings, characterized in that It includes the following steps: S1. Obtain the time series data of the outer surface temperature of the target building glass curtain wall in multiple consecutive periods and the corresponding indoor air-conditioning cooling energy consumption data. Each consecutive period includes a daytime lighting period and a nighttime non-lighting period; S2. Extract the outer surface temperature peak time point in the daytime lighting period and the indoor air-conditioning cooling energy consumption peak time point in the nighttime non-lighting period for each period; S3. Determine the dynamic delay duration according to the time difference between the outer surface temperature peak time point and the indoor air-conditioning cooling energy consumption peak time point in the corresponding nighttime non-lighting period; S4. Screen the indoor air-conditioning cooling energy consumption data in the nighttime non-lighting periods with a dynamic delay duration exceeding the stable delay threshold; S5. Based on the screened indoor air-conditioning cooling energy consumption data in the nighttime non-lighting periods, calculate the additional cooling energy consumption caused by the daytime heat storage of the glass curtain wall according to the preset delay period weight; S6. Superimpose the additional cooling energy consumption on the indoor air-conditioning cooling energy consumption in the daytime lighting period to generate an energy-saving effect evaluation result including the dynamic heat transfer delay effect.

2. The energy-saving effect evaluation method for a high-rise building according to claim 1, characterized in that, S1 includes: S1-1. Continuously collect the time series data of the outer surface temperature of the target building glass curtain wall through an infrared temperature sensor; S1-2. Synchronously obtain the indoor air-conditioning cooling energy consumption data in the corresponding period through a sub-item metering system; S1-3. Align the time series data of the outer surface temperature with the indoor air-conditioning cooling energy consumption data according to consecutive periods, and mark the daytime lighting period and the nighttime non-lighting period; S1-4. Screen the consecutive period data including at least two weather types of sunny days and cloudy days to construct a multi-condition data set.

3. The energy-saving effect evaluation method for a high-rise building according to claim 1, characterized in that S2 It includes: S2-1. For the time series data of the outer surface temperature in the daytime lighting period of each consecutive period, determine the interval where the temperature change rate exceeds the preset threshold through the sliding window method, and take the time point corresponding to the maximum temperature in the corresponding interval as the outer surface temperature peak time point; S2-2. For the indoor air-conditioning cooling energy consumption data in the nighttime non-lighting period of each consecutive period, identify the start time point of the energy consumption sudden increase interval through the difference method, and take the time point corresponding to the maximum energy consumption in the corresponding interval as the air-conditioning cooling energy consumption peak time point; S2-3. Record the time difference between the outer surface temperature peak time point and the air-conditioning cooling energy consumption peak time point in the same consecutive period as the preliminary delay duration.

4. The energy-saving effect evaluation method for a high-rise building according to claim 1, wherein S3 It includes: S3-1. Based on the consecutive period data of different weather types, calculate the average value of the preliminary delay duration under sunny and cloudy conditions respectively, and construct a mapping relationship between the weather type and the delay duration; S3-2. According to the mapping relationship, combined with the heat transfer attenuation characteristics of the glass curtain wall material, physically correct the preliminary delay duration of sunny and cloudy days to generate the corrected dynamic delay duration; S3-3. Based on the maximum value of the distribution density of the corrected dynamic delay duration, dynamically adjust the stable delay threshold so that the stable delay threshold changes adaptively according to the weather type distribution ratio.

5. The energy-saving effect evaluation method for a high-rise building according to claim 1, wherein S4 It includes: S4-1. Based on the comparison result between the dynamic delay duration and the stable delay threshold, screen out the indoor air-conditioning cooling energy consumption data in the nighttime non-lighting periods with a dynamic delay duration exceeding the stable delay threshold; S4-2. For the indoor air-conditioning cooling energy consumption data during the night-time without light selected, combine the secondary comparison of the energy consumption intensity during the corresponding period with the preset intensity threshold, and eliminate the period data with the energy consumption intensity lower than the preset intensity threshold. S4-3. Integrate the period data with the dynamic delay duration exceeding the stable delay threshold and the energy consumption intensity exceeding the preset intensity threshold into an effective night-time energy consumption data set.

6. The energy-saving effect evaluation method for a high-rise building according to claim 1, characterized in that S5 Including: S5-1. According to the ratio of the dynamic delay duration to the stable delay threshold, dynamically allocate the delay period weights for each period. The delay period weights are positively correlated with the ratio of the dynamic delay duration to the stable delay threshold. S5-2. Combine the heat transfer attenuation rate of the glass curtain wall material to perform thermodynamic correction on the delay period weights to generate corrected weights. S5-3. Based on the corrected weights and the energy consumption intensity of each period in the effective night-time energy consumption data set, calculate the additional cooling energy consumption caused by the daytime heat storage of the glass curtain wall through weighted calculation.

7. The energy-saving effect evaluation method for a high-rise building according to claim 1, wherein S6 Including: S6-1. Align the additional cooling energy consumption and the indoor air-conditioning cooling energy consumption during the daytime light period according to the time correlation, and determine the period weight factors for the daytime and night-time energy consumption based on the dynamic delay duration. S6-2. Combine the heat transfer attenuation rate of the glass curtain wall to perform dynamic correction on the period weight factors to generate the corrected superimposed weights. S6-3. Based on the corrected superimposed weights, perform weighted accumulation of the air-conditioning cooling energy consumption during the daytime light period and the additional cooling energy consumption by period to generate an energy-saving effect evaluation result including the dynamic heat transfer delay effect.

8. An energy-saving effect evaluation system for high-rise buildings, which is used to implement the energy-saving effect evaluation method for high-rise buildings according to any one of claims 1-7, characterized in that, Including a data acquisition module, a peak extraction module, a delay calculation module, a threshold screening module, an energy consumption calculation module, and a result generation module; Data acquisition module: Obtain the time series data of the outer surface temperature and the corresponding indoor air-conditioning cooling energy consumption data of the glass curtain wall of the target building in multiple consecutive periods. Each consecutive period includes a daytime light period and a night-time without light period. Peak extraction module: Extract the peak time points of the outer surface temperature during the daytime light period and the peak time points of the indoor air-conditioning cooling energy consumption during the night-time without light period period by period. Delay calculation module: Determine the dynamic delay duration according to the time difference between the peak time point of the outer surface temperature and the peak time point of the indoor air-conditioning cooling energy consumption during the corresponding night-time without light period. Threshold screening module: Screen the indoor air-conditioning cooling energy consumption data during the night-time without light period with the dynamic delay duration exceeding the stable delay threshold. Energy consumption calculation module: Based on the screened indoor air-conditioning cooling energy consumption data during the night-time without light period, calculate the additional cooling energy consumption caused by the daytime heat storage of the glass curtain wall according to the preset delay period weights. Result generation module: Superimpose the additional cooling energy consumption and the indoor air-conditioning cooling energy consumption during the daytime light period to generate an energy-saving effect evaluation result including the dynamic heat transfer delay effect.

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