Ultra-low energy consumption building energy saving method and system based on scene prediction algorithm

Abstract

The invention discloses an ultra-low energy consumption building energy-saving method and system based on a scene prediction algorithm, and relates to the technical field of electric digital data processing. The ultra-low energy consumption building energy saving method based on the scene prediction algorithm comprises the following steps: data acquisition; obtaining an evaluation value; and judging an evaluation value. According to the method, the energy consumption data and the carbon emission data of the ultra-low energy consumption building in the preset operation time period of the production line are obtained, and the dynamic energy consumption data and the static energy consumption data are obtained at the same time and are evaluated respectively to obtain the dynamic energy consumption evaluation value and the static energy consumption evaluation value; finally, whether dynamic energy consumption adjustment is executed or not is judged according to the dynamic energy consumption evaluation value and the dynamic energy consumption evaluation threshold value, whether static energy consumption adjustment is executed or not is judged according to the static energy consumption evaluation value and the static energy consumption evaluation threshold value, and the effect of improving the energy consumption and energy conservation effectiveness of the ultra-low energy consumption building in the building working process is achieved. The problem that in the prior art, an ultra-low energy consumption building is low in energy consumption and energy conservation effectiveness in the building working process is solved.

Description

Technical Field

[0001] The present invention relates to the technical field of electrical digital data processing, and in particular to an ultra-low energy consumption building energy-saving method and system based on a scene prediction algorithm. Background Art

[0002] With the rapid development of the global economy and the continuous increase in population, the demand for energy has also risen sharply. The problems of energy resource shortage and environmental pollution have become increasingly prominent and have become important factors restricting sustainable development. As one of the main areas of energy consumption, the energy consumption of buildings is particularly noteworthy. In recent years, green buildings have developed rapidly across the country, and a large number of research results on green buildings have emerged. Ultra-low energy consumption buildings, as another improvement of green, have gradually been valued by people and have become an important part of green buildings, achieving a significant reduction in building energy consumption. The ultra-low energy consumption building energy-saving method and system based on scenario prediction algorithm is an innovative technology that came into being under this background.

[0003] In the existing technology, various data during the operation of the building are obtained and analyzed and processed, and the energy consumption of the building under different climatic conditions in the future is predicted. Corresponding energy-saving strategies are formulated according to the prediction results, and then the corresponding energy-saving strategies are dynamically adjusted according to real-time data and prediction results to achieve intelligent management of building energy consumption.

[0004] For example, the invention patent with announcement number: CN112182720B announces a building energy consumption model evaluation method based on a building energy management application scenario, including: obtaining historical building energy consumption data; using a machine learning method to establish a building energy consumption model, and setting characteristic variables and model algorithms in the building energy consumption model; coupling the selection of characteristic variables in the building energy consumption model and the selection configuration of the model algorithm to form an evaluation task; according to the building energy consumption model and in combination with the building energy management application scenario, establishing a multidimensional comprehensive evaluation model for the performance of the building energy consumption model; according to the evaluation task, using the multidimensional comprehensive evaluation model to comprehensively evaluate the predictive performance of the building energy consumption model.

[0005] For example, the invention patent with announcement number: CN116738856B announces a method and system for predicting building energy consumption, including: selecting the type of building energy consumption and mining relevant influencing factors according to the classification of building use functions based on the historical data of building energy consumption; mining the correlation between each building energy consumption type and the influencing factors through the association rule algorithm; setting up energy consumption data collection equipment to collect energy consumption data and generate energy consumption data reports through regional and spatial analysis of building energy consumption; establishing an energy consumption prediction model based on the acquired energy consumption factor data, and predicting the building energy consumption in the future; comparing the prediction results with the energy consumption data report, adjusting the energy consumption prediction model by analyzing the causes of errors and influencing factors, and re-predicting until it is consistent with the energy consumption data report.

[0006] However, in the process of implementing the technical solution of the invention in the embodiments of the present application, the present application found that the above technology has at least the following technical problems:

[0007] In the existing technology, different scenario prediction algorithms are suitable for different data prediction needs. Due to the uncertainty of algorithm parameters, there are deviations in the prediction of building energy consumption in the future. In addition, the uncertainty and randomness of the building's own operating mode make it difficult to accurately estimate the energy consumption. There is a problem of low energy saving effectiveness of ultra-low energy buildings during the construction process. Summary of the invention

[0008] The embodiment of the present application solves the problem of low energy saving effectiveness of ultra-low energy consumption buildings in the construction process in the prior art by providing an ultra-low energy consumption building energy saving method and system based on a scenario prediction algorithm, thereby achieving improved energy saving effectiveness of ultra-low energy consumption buildings in the construction process.

[0009] The embodiment of the present application provides an ultra-low energy consumption building energy-saving method based on a scenario prediction algorithm, comprising the following steps: S1, obtaining energy consumption data and carbon emission data of the ultra-low energy consumption building within a preset operating time period of the production line; S2, obtaining dynamic energy consumption data and static energy consumption data based on the energy consumption data and carbon emission data, evaluating the dynamic energy consumption data to obtain a dynamic energy consumption evaluation value, and at the same time evaluating the static energy consumption data to obtain a static energy consumption evaluation value, the dynamic energy consumption evaluation value is used to evaluate the energy consumption of the ultra-low energy consumption building in the energy consumption stage, and the static energy consumption evaluation value is used to evaluate the carbon emissions of the ultra-low energy consumption building in the energy consumption stage; S3, judging whether to perform dynamic energy consumption adjustment based on the dynamic energy consumption evaluation value and the dynamic energy consumption evaluation threshold, and judging whether to perform static energy consumption adjustment based on the static energy consumption evaluation value and the static energy consumption evaluation threshold.

[0010] Furthermore, the specific process of evaluating the dynamic energy consumption data is as follows: obtaining power load peak and valley data, obtaining power peak duration and power valley duration based on the obtained power load peak and valley data, the power load peak and valley data including power peak data and power valley data, the power peak duration represents the proportion of power peak data in the production line operation time, the power valley duration represents the proportion of power valley data in the production line operation time; obtaining energy conversion loss data, obtaining loss peak duration and loss valley duration based on the obtained energy conversion loss data, The energy conversion loss data includes loss peak data and loss valley data, wherein the loss peak duration indicates the proportion of the loss peak data in the production line operation time, and the loss valley duration indicates the proportion of the loss valley data in the production line operation time; the dynamic energy consumption peak duration and the dynamic energy consumption valley duration are obtained, and the preset operation time period of the production line is adjusted to avoid the dynamic energy consumption peak duration, wherein the dynamic energy consumption peak duration is the overlapping duration of the power peak duration and the loss peak duration, and the dynamic energy consumption valley duration is the overlapping duration of the power valley duration and the loss valley duration.

[0011] Furthermore, the specific process of evaluating the static energy consumption data is as follows: obtaining high-energy lighting areas and low-energy lighting areas according to the distribution of building lighting loads within the preset operating time period of the production line; real-time monitoring of changes in the energy consumption of the enclosure structure and the network transmission energy consumption in the high-energy lighting area within the preset operating time period of the production line, obtaining the peak duration of static energy consumption and the trough duration of static energy consumption, and adjusting the lighting equipment in the high-energy lighting area to avoid the peak duration of static energy consumption; the peak duration of static energy consumption is the overlapping duration of the peak duration of the energy consumption of the enclosure structure and the peak duration of the energy consumption of the network transmission; the trough duration of static energy consumption is the overlapping duration of the corresponding trough duration of the energy consumption of the enclosure structure and the energy consumption of the network transmission.

[0012] Furthermore, the specific limiting expression of the dynamic energy consumption evaluation value is:

[0013]

[0014] Where i is the number of the ultra-low energy consumption building, i = 1, 2, ..., Y, Y is the total number of ultra-low energy consumption buildings, t is the number of the preset operation time period of the production line, t = 1, 2, ..., T, T is the total number of preset operation time periods of the production line, e is a natural constant, DONG i represents the dynamic energy consumption evaluation value of the energy consumption stage of the i-th ultra-low energy consumption building during the preset operation time period of the production line, α1 represents the weight factor of the peak power duration, A1 i.t represents the peak duration of electricity in the energy consumption phase of the i-th ultra-low energy consumption building during the preset operation time period of the t-th production line, α2 represents the weight factor of the peak duration of loss, and A2i.t represents the peak duration of energy consumption in the energy consumption phase of the i-th ultra-low energy consumption building during the preset operation time period of the t-th production line, α3 represents the dynamic energy consumption peak duration weight factor, A3 i.t It represents the duration of the dynamic energy consumption peak of the energy consumption phase of the i-th ultra-low energy consumption building during the preset operation time period of the t-th production line, U i.t It represents the total duration of the energy consumption stage of the i-th ultra-low energy consumption building corresponding to the preset operating time period of the t-th production line.

[0015] Furthermore, the specific steps of determining whether to perform dynamic energy consumption adjustment based on the dynamic energy consumption evaluation value and the dynamic energy consumption evaluation threshold include: A1, determining whether the dynamic energy consumption evaluation value is within the dynamic energy consumption evaluation threshold range, if so, continuing to monitor the changes in dynamic energy consumption data within the preset operating time period of the production line in real time, otherwise executing A2; A2, executing dynamic energy consumption adjustment and re-evaluating the dynamic energy consumption data until the dynamic energy consumption evaluation value is within the dynamic energy consumption evaluation threshold range, and the dynamic energy consumption adjustment includes equipment parameter adjustment and building environment optimization.

[0016] Furthermore, the steps for obtaining the static energy consumption assessment value are as follows: real-time monitoring of the duration of the high-energy lighting area, obtaining a first duration ratio and a second duration ratio according to the peak duration of the energy consumption of the enclosure structure, the peak duration of the network transmission energy consumption and the peak duration of the static energy consumption, wherein the duration of the high-energy lighting area is the duration of the energy consumption stage of the ultra-low energy consumption building in the high-energy lighting area; obtaining a third duration ratio, and obtaining a fourth duration ratio according to the total duration of the preset operating time period of the production line, and obtaining the static energy consumption assessment value in combination with the preset duration weight factor in the preset database; the specific steps of judging whether to perform static energy consumption adjustment according to the static energy consumption assessment value and the static energy consumption assessment threshold include: B1, judging whether the static energy consumption assessment value is within the static energy consumption assessment threshold range, if so, continuing to monitor the changes of the static energy consumption data within the preset operating time period of the production line in real time, otherwise executing B2; B2, executing static energy consumption adjustment and re-evaluating the static energy consumption data until the static energy consumption assessment value is within the static energy consumption assessment threshold range, and the static energy consumption adjustment includes enclosure structure repair and network transmission parameter adjustment.

[0017] The embodiment of the present application provides an ultra-low energy consumption building energy-saving system based on a scenario prediction algorithm, comprising: a data acquisition module, an evaluation value acquisition module and an evaluation value judgment module; wherein the data acquisition module is used to acquire energy consumption data and carbon emission data of the ultra-low energy consumption building within a preset operating time period of the production line; the evaluation value acquisition module is used to acquire dynamic energy consumption data and static energy consumption data according to the energy consumption data and carbon emission data, evaluate the dynamic energy consumption data to acquire a dynamic energy consumption evaluation value, and at the same time evaluate the static energy consumption data to acquire a static energy consumption evaluation value, the dynamic energy consumption evaluation value is used to evaluate the energy consumption of the ultra-low energy consumption building in the energy consumption stage, and the static energy consumption evaluation value is used to evaluate the carbon emissions of the ultra-low energy consumption building in the energy consumption stage; the evaluation value judgment module is used to determine whether to perform dynamic energy consumption adjustment according to the dynamic energy consumption evaluation value and the dynamic energy consumption evaluation threshold, and at the same time determine whether to perform static energy consumption adjustment according to the static energy consumption evaluation value and the static energy consumption evaluation threshold.

[0018] One or more technical solutions provided in the embodiments of the present application have at least the following technical effects or advantages:

[0019] 1. Whether to perform dynamic energy consumption adjustment is determined by the obtained dynamic energy consumption assessment value and the dynamic energy consumption assessment threshold, and whether to perform static energy consumption adjustment is determined according to the obtained static energy consumption assessment value and the static energy consumption assessment threshold, thereby achieving more accurate acquisition of dynamic energy consumption assessment values ​​and static energy consumption assessment values, thereby achieving improved energy consumption and energy-saving effectiveness of ultra-low energy consumption buildings during the construction process, and effectively solving the problem of low energy consumption and energy-saving effectiveness of ultra-low energy consumption buildings during the construction process in the prior art.

[0020] 2. By obtaining the peak and valley data of power load, the peak duration and valley duration of power are obtained according to the obtained peak and valley data of power load, and then the energy conversion loss data is obtained, the peak loss duration and valley loss duration are obtained according to the obtained energy conversion loss data, and finally the dynamic energy consumption peak duration and dynamic energy consumption valley duration are obtained and the preset operating time period of the production line is adjusted to avoid the dynamic energy consumption peak duration, thereby improving the accuracy and reliability of obtaining the dynamic energy consumption peak duration, and then realizing a more accurate evaluation of the dynamic energy consumption data.

[0021] 3. Through real-time monitoring of the duration of high-energy lighting areas, the first duration ratio and the second duration ratio are obtained according to the peak duration of energy consumption of the enclosure structure, the peak duration of network transmission energy consumption and the peak duration of static energy consumption, and then the third duration ratio is obtained, and the fourth duration ratio is obtained according to the total duration of the preset operating time period of the production line. Finally, the static energy consumption assessment value is obtained by combining the preset duration weight factor in the preset database, thereby achieving more accurate monitoring of the duration of high-energy lighting areas and improving the accuracy of obtaining static energy consumption assessment values. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] Figure 1 A flowchart of an ultra-low energy consumption building energy-saving method based on a scenario prediction algorithm provided in an embodiment of the present application;

[0023] Figure 2 A flow chart of dynamic energy consumption data evaluation provided in an embodiment of the present application;

[0024] Figure 3 A schematic diagram of the structure of an ultra-low energy consumption building energy-saving system based on a scenario prediction algorithm provided in an embodiment of the present application;

[0025] Figure 4 A specific flow chart for energy saving in ultra-low energy consumption buildings provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] The embodiment of the present application solves the problem of low energy efficiency of ultra-low energy consumption buildings in the construction process in the prior art by providing an ultra-low energy consumption building energy-saving method and system based on a scenario prediction algorithm. The method obtains energy consumption data and carbon emission data of the ultra-low energy consumption building within a preset operation time period of the production line, and obtains dynamic energy consumption data and static energy consumption data based on the energy consumption data and carbon emission data. The dynamic energy consumption data and the static energy consumption data are then evaluated respectively to obtain a dynamic energy consumption evaluation value and a static energy consumption evaluation value. Subsequently, whether to perform dynamic energy consumption adjustment is determined based on the dynamic energy consumption evaluation value and the dynamic energy consumption evaluation threshold. Finally, whether to perform static energy consumption adjustment is determined based on the static energy consumption evaluation value and the static energy consumption evaluation threshold. At the same time, energy-saving optimization is performed on the ultra-low energy consumption building based on the evaluation results of the dynamic energy consumption adjustment effect and the static energy consumption adjustment effect, thereby improving the energy efficiency of the ultra-low energy consumption building in the construction process.

[0027] The technical solution in the embodiment of the present application is to solve the problem of low energy efficiency of the ultra-low energy consumption building during the construction process. The overall idea is as follows:

[0028] By evaluating the dynamic energy consumption data and the static energy consumption data respectively to obtain the dynamic energy consumption assessment value and the static energy consumption assessment value, and judging whether to perform the dynamic energy consumption adjustment according to the dynamic energy consumption assessment value and the dynamic energy consumption assessment threshold, and judging whether to perform the static energy consumption adjustment according to the static energy consumption assessment value and the static energy consumption assessment threshold, the effect of improving the energy saving effectiveness of ultra-low energy consumption buildings during the construction process is achieved.

[0029] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0030] like Figure 1As shown, it is a flow chart of an ultra-low energy consumption building energy-saving method based on a scenario prediction algorithm provided in an embodiment of the present application, the method comprising the following steps: S1, obtaining energy consumption data and carbon emission data of the ultra-low energy consumption building within a preset operating time period of the production line; S2, obtaining dynamic energy consumption data and static energy consumption data according to the energy consumption data and carbon emission data, evaluating the dynamic energy consumption data to obtain a dynamic energy consumption evaluation value, and at the same time evaluating the static energy consumption data to obtain a static energy consumption evaluation value, the dynamic energy consumption evaluation value is used to evaluate the energy consumption of the ultra-low energy consumption building in the energy consumption stage, and the static energy consumption evaluation value is used to evaluate the carbon emissions of the ultra-low energy consumption building in the energy consumption stage; S3, judging whether to perform dynamic energy consumption adjustment according to the dynamic energy consumption evaluation value and the dynamic energy consumption evaluation threshold, and judging whether to perform static energy consumption adjustment according to the static energy consumption evaluation value and the static energy consumption evaluation threshold, the dynamic energy consumption evaluation threshold and the static energy consumption evaluation threshold are energy consumption evaluation indicators of the reference building.

[0031] In this embodiment, the ultra-low energy consumption building is an energy-consuming building corresponding to the energy consumption stage with the largest carbon emissions per unit area in the entire life cycle of the building. The energy consumption stage includes the building materials production and transportation stage and the construction work stage. The embodiment of the present application realizes the accurate identification of energy consumption sources through the evaluation of dynamic energy consumption data and static energy consumption data, thereby helping managers to take more targeted energy-saving measures. Secondly, by judging whether to perform dynamic energy consumption adjustment and static energy consumption adjustment, the energy consumption management efficiency of ultra-low energy consumption buildings is improved, and the carbon emissions of the entire life cycle are reduced, thereby selecting building materials suppliers with lower energy consumption and carbon emissions to reduce energy consumption and carbon emissions in the energy consumption stage, thereby realizing the improvement of energy consumption and energy-saving effectiveness of ultra-low energy consumption buildings during the construction process.

[0032] Furthermore, the energy consumption data includes first energy consumption data and second energy consumption data; the first energy consumption data is used to reflect the energy consumption in the production and transportation stage of building materials, and the second energy consumption data is used to reflect the energy consumption of ultra-low energy consumption buildings in the construction work stage; the carbon emission data includes first carbon emission data and second carbon emission data; the first carbon emission data is used to reflect the carbon emissions in the production and transportation stage of building materials, and the second carbon emission data is used to reflect the carbon emissions of ultra-low energy consumption buildings in the construction work stage.

[0033] In this embodiment, the construction work stage is the production line operation stage during the construction of ultra-low energy buildings. The first energy consumption data is usually the energy consumption in the building materials production and transportation stage (such as the fuel consumption of the transport vehicle during transportation), and the second energy consumption data is usually the energy consumption in the production line operation stage (such as the power load of the generator set in the production line). The first carbon emission data is usually the carbon dioxide emissions of the transport vehicle, and the second carbon emission data is usually the carbon dioxide emissions generated during the operation of the generator set in the production line. The embodiment of the present application provides a subdivision of energy consumption data and carbon emission data into the first and second categories, which helps to have a more comprehensive understanding of the energy consumption and carbon emissions of ultra-low energy buildings throughout their life cycle. At the same time, this classification method also helps to promote green development in the building materials industry and building operations, and achieve the sustainable development goals of the construction industry.

[0034] Further, such as Figure 2 As shown, it is a flow chart of dynamic energy consumption data evaluation provided in an embodiment of the present application. The specific process of evaluating dynamic energy consumption data is: obtaining power load peak and valley data, obtaining power peak duration and power valley duration according to the obtained power load peak and valley data, the power load peak and valley data include power peak data and power valley data, the power peak duration indicates the proportion of power peak data in the production line operation time period, and the power valley duration indicates the proportion of power valley data in the production line operation time period; obtaining energy conversion loss data, obtaining loss peak according to the obtained energy conversion loss data Peak duration and loss trough duration. Energy conversion loss data includes loss peak data and loss trough data. The loss peak duration indicates the proportion of the loss peak data in the production line operation time, and the loss trough duration indicates the proportion of the loss trough data in the production line operation time. Obtain the dynamic energy consumption peak duration and dynamic energy consumption trough duration and adjust the preset operation time period of the production line to avoid the dynamic energy consumption peak duration. The dynamic energy consumption peak duration is the overlapping duration of the power peak duration and the loss peak duration, and the dynamic energy consumption trough duration is the overlapping duration of the power trough duration and the loss trough duration.

[0035] In this embodiment, the dynamic energy consumption data includes power load peak and valley data and energy conversion loss data, wherein the power load peak and valley data is used to reflect the power demand of the production line of the ultra-low energy consumption building within a preset operating time period, and the energy conversion loss data is used to reflect the loss fluctuation of the energy conversion equipment (such as generator) of the ultra-low energy consumption building during the energy conversion process; real-time monitoring of the power load changes of the production line within the preset operating time period, when the power load is greater than the preset power load, the corresponding power load is recorded as power peak data, otherwise, it is recorded as power valley data, and at the same time, the time periods corresponding to the power peak data and the power valley data are counted and summed to obtain the power peak duration and the power valley duration, wherein the preset power load is represented by the result of summing and averaging the historical power loads within the historical time period in the preset database.

[0036] Similarly, the energy loss load changes of the energy conversion equipment within the preset operating time period of the production line are monitored in real time. When the energy loss load is greater than the preset energy loss load, the corresponding energy loss load is recorded as loss peak data, otherwise, it is recorded as loss valley data. At the same time, the time periods corresponding to the loss peak data and the loss valley data are counted and summed to obtain the loss peak duration and the loss valley duration. Among them, the energy loss load represents the energy loss value generated when the energy conversion equipment converts electrical energy into mechanical energy, and the preset energy loss load is represented by the sum and average of the historical energy loss loads in the historical time period in the preset database; the dynamic energy consumption peak duration is the time period when the power peak duration overlaps with the loss peak duration, and the dynamic energy consumption valley duration is the time period when the power valley duration overlaps with the loss valley duration.

[0037] This embodiment helps to reduce the energy consumption in the time period corresponding to the dynamic energy consumption peak duration by accurately obtaining the time period corresponding to the dynamic energy consumption peak duration and the dynamic energy consumption trough duration. For example, the time period corresponding to the dynamic peak duration is from 11:00 noon to 1:00 p.m., and the time period corresponding to the dynamic trough duration is from 2:00 p.m. to 4:00 p.m. At this time, it is necessary to change the preset operating time period of the production line to 2:00 p.m. to 4:00 p.m., so as to improve the energy efficiency level of the production line, provide strong support for ultra-low energy consumption buildings and green production, and further realize the improvement of the energy saving effectiveness of ultra-low energy consumption buildings during the construction process.

[0038] Furthermore, the specific process for evaluating the static energy consumption data is as follows: high-energy lighting areas and low-energy lighting areas are obtained according to the distribution of the building lighting load within the preset operating time period of the production line, the high-energy lighting area is the production line lighting area where the building lighting generates the maximum load within the preset operating time period of the production line, and the low-energy lighting area is the production line lighting area where the building lighting generates the minimum load within the preset operating time period of the production line; real-time monitoring of the changes in the energy consumption of the enclosure structure and the network transmission energy consumption in the high-energy lighting area within the preset operating time period of the production line, obtaining the peak duration of static energy consumption and the trough duration of static energy consumption, and adjusting the lighting equipment in the high-energy lighting area to avoid the peak duration of static energy consumption; the peak duration of static energy consumption is the overlapping duration of the peak duration of the energy consumption of the enclosure structure and the peak duration of the energy consumption of the network transmission; the trough duration of static energy consumption is the overlapping duration of the corresponding trough duration of the energy consumption of the enclosure structure and the energy consumption of the network transmission.

[0039] In this embodiment, the static energy consumption data includes the energy consumption of the enclosure structure and the network transmission energy consumption. The load sensor is used to monitor in real time the building lighting load of the corresponding production line area during the operation of the production line in the preset operation time period. When the building lighting load is greater than the preset lighting load, the corresponding production line area is recorded as a high-energy lighting area, otherwise, it is recorded as a low-energy lighting area. The heat dissipation temperature of the enclosure structure in the high-energy lighting area during the preset operation time period is monitored in real time (such as the heat dissipation baffle during the operation of the production line. Specifically, the heat dissipation temperature of the heat dissipation baffle is measured in real time through the temperature sensor deployed at the transportation port of the production line). When the heat dissipation temperature of the heat dissipation baffle is greater than the preset heat dissipation temperature, the corresponding heat dissipation temperature is recorded as the peak energy consumption of the enclosure structure; otherwise, it is recorded as the valley energy consumption of the enclosure structure. At the same time, the time periods corresponding to the peak energy consumption and the valley energy consumption of the enclosure structure are counted and summed to obtain the peak duration of the enclosure energy consumption and the valley duration of the enclosure energy consumption. Among them, the preset heat dissipation temperature is represented by the sum and average of the historical heat dissipation temperatures in the historical time period in the preset database. The heat dissipation temperature is the difference between the temperature of the corresponding heat dissipation baffle when the production line ends work within the preset operating time period of the production line and the temperature of the corresponding heat dissipation baffle when the production line just starts working.

[0040] Similarly, the temperature change of the network transmission data line (used to transmit the energy consumption data generated by the ultra-low energy consumption building in the energy consumption stage) in the high-energy lighting area within the preset operation time period is monitored in real time (usually represented by the data line temperature corresponding to the transmission port on the production line, specifically, it is measured in real time by the temperature sensor deployed on the transmission port of the production line). When the temperature of the corresponding network transmission data line on the production line is greater than the preset data line temperature, the corresponding network transmission data line temperature is recorded as the peak energy consumption of the network transmission, otherwise, it is recorded as the valley energy consumption of the network transmission. At the same time, the time periods corresponding to the peak energy consumption of the network transmission and the valley energy consumption of the network transmission are counted and analyzed. The sum is obtained to obtain the peak duration of network transmission energy consumption and the trough duration of network transmission energy consumption, wherein the preset data line temperature is represented by the sum and average of the historical data line temperatures in the historical time period in the preset database, and the data line temperature (measured in real time by the temperature sensor on the network transmission data line) is the difference between the temperature of the corresponding data line when the production line ends work within the preset operation time period of the production line and the temperature of the corresponding data line when the production line just starts working; the peak duration of static energy consumption is the time period when the peak duration of the enclosure structure overlaps with the peak duration of network transmission, and the trough duration of static energy consumption is the time period when the trough duration of the enclosure structure overlaps with the trough duration of network transmission.

[0041] Different from the traditional method that only focuses on the building lighting load, this embodiment also takes into account the energy consumption of the enclosure structure and the network transmission energy consumption. By accurately obtaining the time periods corresponding to the static energy consumption peak duration and the static energy consumption valley duration, it is helpful to reduce the energy consumption in the time period corresponding to the static energy consumption peak duration. For example, the time period corresponding to the static energy consumption peak duration is from 9:00 to 11:00 a.m. in the preset operation time period of the production line in the high-energy lighting area, and the time period corresponding to the static energy consumption valley duration is from 1:00 to 3:00 p.m. in the preset operation time period of the production line in the low-energy lighting area. At this time, it is necessary to adjust the lighting parameters corresponding to the lighting equipment in the high-energy lighting area (such as replacing the 800-watt lighting lamp on the production line in the lighting equipment with a 500-watt lighting lamp, and changing the preset operation time period of the production line in the high-energy lighting area to 1:00 to 3:00 p.m.), thereby reducing the energy consumption in the time period corresponding to the static energy consumption peak duration in the high-energy lighting area, and helping to reduce energy consumption and carbon emissions.

[0042] Furthermore, the static energy consumption data is evaluated, which also includes: obtaining a preset carbon emission factor from a preset database, and converting the carbon emission energy consumption data into carbon emissions according to the carbon emission factor; the carbon emission energy consumption data is the sum of the dynamic energy consumption data and the static energy consumption data multiplied by the carbon emission factor respectively; the dynamic energy consumption data includes power load peak and valley data and energy conversion loss data; the static energy consumption data includes building lighting load data, building envelope structure energy consumption data and network transmission energy consumption data.

[0043] In this embodiment, the carbon emissions are the product of the carbon emission factor and the carbon emission energy consumption data, wherein the carbon emission factor is queried through the carbon emission factor table corresponding to the energy type and equipment type. The carbon emission factor in this embodiment is the carbon emission factor corresponding to the carbon emission factor table in the preset database corresponding to the power load of the production line, which usually represents the amount of carbon dioxide generated per unit of energy consumption. In the power industry, the national average carbon emission factor for power is usually 0.5942 kgCO2 / kWh; since the dynamic energy consumption data contains real-time change information on power load peaks and valleys and energy conversion losses, the method of this embodiment can reflect the fluctuations in energy use and carbon emissions in real time, which is of great significance for formulating and adjusting emission reduction strategies, and helps to improve the pertinence and effectiveness of emission reduction work.

[0044] Furthermore, the specific limiting expression of the dynamic energy consumption evaluation value is:

[0045]

[0046] Where i is the number of the ultra-low energy consumption building, i = 1, 2, ..., Y, Y is the total number of ultra-low energy consumption buildings, t is the number of the preset operation time period of the production line, t = 1, 2, ..., T, T is the total number of preset operation time periods of the production line, e is a natural constant, DONG i represents the dynamic energy consumption evaluation value of the energy consumption stage of the i-th ultra-low energy consumption building during the preset operation time period of the production line, α1 represents the weight factor of the peak power duration, A1 i.t represents the peak duration of electricity in the energy consumption phase of the i-th ultra-low energy consumption building during the preset operation time period of the t-th production line, α2 represents the weight factor of the peak duration of loss, and A2 i.t represents the peak duration of energy consumption in the energy consumption phase of the i-th ultra-low energy consumption building during the preset operation time period of the t-th production line, α3 represents the dynamic energy consumption peak duration weight factor, A3 i.t It represents the duration of the dynamic energy consumption peak of the energy consumption phase of the i-th ultra-low energy consumption building during the preset operation time period of the t-th production line, U i.t It represents the total duration of the energy consumption stage of the i-th ultra-low energy consumption building corresponding to the preset operating time period of the t-th production line.

[0047] Among them, the power peak duration weight factor is used to measure the proportion of the power peak duration of ultra-low energy consumption buildings in the energy consumption stage to the total duration of the preset operating time period of the production line; the loss peak duration weight factor is used to measure the proportion of the loss peak duration of ultra-low energy consumption buildings in the energy consumption stage to the total duration of the preset operating time period of the production line; the dynamic energy consumption peak duration weight factor is used to measure the dynamic energy consumption peak duration of ultra-low energy consumption buildings in the energy consumption stage to the sum of the power peak duration and the loss peak duration.

[0048] In this embodiment, the power peak duration weight factor is the weight factor corresponding to the power peak duration in the preset database during the dynamic energy consumption data evaluation process, which represents the numerical value of the influence of the power peak duration on the dynamic energy consumption evaluation value. When used, the power peak duration weight factor corresponding to the power peak duration can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the power peak duration corresponding to the dynamic energy consumption data in the evaluation process and the weight factor corresponding to the power peak duration in the preset database form a mapping set, and the real-time power peak duration is input into the mapping set to obtain the corresponding power peak duration weight factor, wherein the mapping relationship can be one-to-one or many-to-one. The value range of the power peak duration weight factor in this example is [0,1].

[0049] The loss peak duration weight factor is the weight factor corresponding to the loss peak duration in the preset database during the dynamic energy consumption data evaluation process, which represents the numerical value of the influence of the loss peak duration on the dynamic energy consumption evaluation value. When used, the loss peak duration weight factor corresponding to the loss peak duration can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the loss peak duration corresponding to the dynamic energy consumption data in the evaluation process and the weight factor corresponding to the loss peak duration in the preset database form a mapping set, and the real-time loss peak duration is input into the mapping set to obtain the corresponding loss peak duration weight factor, wherein the mapping relationship can be one-to-one or many-to-one. The value range of the loss peak duration weight factor in this example is [0,1]. It should be noted that the sum of the power peak duration weight factor, the loss peak duration weight factor and the dynamic energy consumption peak duration weight factor in this embodiment is 1.

[0050] Specifically, to simplify the analysis, define P1 i.t P2 represents the estimated value of the peak power duration of the energy consumption phase of the i-th ultra-low energy consumption building during the preset operation time period of the t-th production line, i.t P3 represents the estimated value of the peak loss duration of the energy consumption phase of the i-th ultra-low energy consumption building during the preset operation time period of the t-th production line, i.t It represents the dynamic energy consumption peak duration evaluation value of the energy consumption stage of the i-th ultra-low energy consumption building in the preset operation time period of the t-th production line. The simplified calculation formula of the dynamic energy consumption evaluation value is: The dynamic energy consumption evaluation value change statistics are shown in Table 1:

[0051] Table 1 Statistics of changes in dynamic energy consumption evaluation values

[0052]

[0053] It should be understood that since the dynamic energy consumption peak duration is the overlap of the power peak duration and the loss peak duration, A3 i.t <A1 i.t And A3 i.t <A2 i.t The dynamic energy consumption assessment value increases with the increase of the power peak duration assessment value, the loss peak duration assessment value and the dynamic energy consumption peak duration. Among them, the power peak duration assessment value increases with the increase of the power peak duration, the loss peak duration assessment value increases with the increase of the loss peak duration, and the dynamic energy consumption peak duration assessment value increases with the increase of the dynamic energy consumption peak duration.

[0054] It should be noted that the mutual influence between the peak duration of electricity and the peak duration of loss also indirectly affects the value of the peak duration of dynamic energy consumption. When the peak duration of electricity is extended, it means that the burden on the power transmission and distribution system is increased, which may extend the peak duration of loss. The change of the peak duration of loss may be affected by many factors, including but not limited to the intensity of the peak duration of electricity and the topological structure of the power grid. When the overlapping time of the peak duration of loss and the peak duration of electricity increases, the dynamic peak duration of energy consumption will also increase accordingly. By considering the mutual influence between the peak duration of electricity and the peak duration of loss, it is helpful to accurately obtain the peak time period of electricity consumption, and it is convenient for users to use electricity during non-peak hours to reduce the duration of the peak duration of electricity. This can not only reduce the load on the power grid, but also effectively reduce energy consumption, thereby realizing the improvement of the energy-saving effectiveness of ultra-low energy consumption buildings in the construction process, and effectively solving the problem of low energy-saving effectiveness of ultra-low energy consumption buildings in the construction process in the existing technology.

[0055] Furthermore, the specific steps of determining whether to perform dynamic energy consumption adjustment based on the dynamic energy consumption evaluation value and the dynamic energy consumption evaluation threshold include: A1, determining whether the dynamic energy consumption evaluation value is within the dynamic energy consumption evaluation threshold range, if so, determining that it meets the energy-saving requirements and continuing to monitor the changes in dynamic energy consumption data within the preset operating time period of the production line in real time, otherwise executing A2; A2, executing dynamic energy consumption adjustment and re-evaluating the dynamic energy consumption data until the dynamic energy consumption evaluation value is within the dynamic energy consumption evaluation threshold range, and the dynamic energy consumption adjustment includes equipment parameter adjustment and building environment optimization.

[0056] In this embodiment, the dynamic energy consumption evaluation threshold range is the range corresponding to the maximum and minimum values ​​of the historical dynamic energy consumption evaluation values ​​in the historical time period in the preset database; assuming that when a factory's production line is running at night, due to abnormal fluctuations in power load (such as the power load of the production line drops from 500 to 10 during operation) and aging of the equipment, the dynamic energy consumption evaluation value is higher than the dynamic energy consumption evaluation threshold. In order to reduce energy consumption, dynamic energy consumption adjustment is required: the running speed of the equipment (such as the generator) is usually reduced by 10%, and an automatic shutdown function program is set on the internal circuit board of the generator (for automatically shutting down the machine connected to the production line when the generator is detected to be unloaded). At the same time, the lighting brightness of the lighting equipment in the high-energy lighting area (such as the lighting on the production line) is reduced by 20%, and the night energy saving mode is turned on. After the dynamic energy consumption adjustment, the dynamic energy consumption data is re-evaluated and the dynamic energy consumption evaluation value is re-acquired until the dynamic energy consumption evaluation value is within the dynamic energy consumption evaluation threshold range, which helps to improve the efficiency of dynamic energy consumption adjustment and the accuracy of real-time monitoring of dynamic energy consumption data, and achieves the improvement of energy consumption and energy saving effectiveness.

[0057] Furthermore, the steps for obtaining the static energy consumption assessment value are as follows: real-time monitoring of the duration of the high-energy lighting area, obtaining a first duration ratio and a second duration ratio according to the peak duration of the energy consumption of the envelope structure, the peak duration of the network transmission energy consumption and the peak duration of the static energy consumption, the duration of the high-energy lighting area is the duration of the energy consumption stage of the ultra-low energy consumption building in the high-energy lighting area, the first duration ratio represents the ratio of the difference between the peak duration of the energy consumption of the envelope structure and the peak duration of the static energy consumption to the duration of the high-energy lighting area, the second duration ratio represents the ratio of the difference between the peak duration of the network transmission energy consumption and the peak duration of the static energy consumption to the duration of the high-energy lighting area; obtaining the third duration ratio, and obtaining the fourth duration ratio according to the total duration of the preset operating time period of the production line, and combining the preset duration weight factors in the preset database to obtain the static energy consumption assessment value, the third duration ratio represents the ratio of the peak duration of the static energy consumption to the peak duration of the energy consumption of the envelope structure, the network transmission energy consumption and the peak duration of the static energy consumption. the ratio of the sum of the peak durations of network transmission energy consumption, the fourth duration ratio coefficient represents the ratio of the duration of the high-energy lighting area to the total duration of the preset operating time period of the production line, the duration weight factors include the peak duration weight factor of the enclosure structure energy consumption, the peak duration weight factor of the network transmission energy consumption, the peak duration weight factor of the static energy consumption and the high-energy lighting area duration weight factor; the specific steps of judging whether to perform static energy consumption adjustment according to the static energy consumption evaluation value and the static energy consumption evaluation threshold include: B1, judging whether the static energy consumption evaluation value is within the static energy consumption evaluation threshold range, if so, judging that it meets the energy-saving requirements and continuing to monitor the changes of the static energy consumption data within the preset operating time period of the production line in real time, otherwise executing B2; B2, executing static energy consumption adjustment and re-evaluating the static energy consumption data until the static energy consumption evaluation value is within the static energy consumption evaluation threshold range, and the static energy consumption adjustment includes enclosure structure repair and network transmission parameter adjustment.

[0058] Among them, the weight factor of the peak energy consumption duration of the envelope structure is used to measure the proportion of the peak energy consumption duration of the envelope structure in the high-energy lighting area during the energy consumption stage of the ultra-low energy consumption building to the total duration of the preset operating time period of the production line; the weight factor of the peak network transmission energy consumption duration is used to measure the proportion of the peak network transmission energy consumption duration in the high-energy lighting area during the energy consumption stage of the ultra-low energy consumption building to the total duration of the preset operating time period of the production line; the weight factor of the peak static energy consumption duration is used to measure the proportion of the peak static energy consumption duration of the ultra-low energy consumption building in the energy consumption stage in the high-energy lighting area to the sum of the peak energy consumption duration of the envelope structure and the peak network transmission energy consumption duration; the weight factor of the high-energy lighting area duration is used to measure the proportion of the duration of the energy consumption stage of the ultra-low energy consumption building in the high-energy lighting area to the total duration of the preset operating time period of the production line.

[0059] In this embodiment, the weight factor of the peak duration of energy consumption of the enclosure structure is the weight factor corresponding to the peak duration of energy consumption of the enclosure structure in the static energy consumption data evaluation process in the preset database, which represents the numerical value of the influence of the peak duration of energy consumption of the enclosure structure on the static energy consumption evaluation value. When used, the weight factor of the peak duration of energy consumption of the enclosure structure corresponding to the peak duration of energy consumption of the enclosure structure can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the peak duration of energy consumption of the enclosure structure corresponding to the static energy consumption data in the evaluation process and the weight factor corresponding to the peak duration of energy consumption of the enclosure structure in the preset database form a mapping set, and the real-time peak duration of energy consumption of the enclosure structure is input into the mapping set to obtain the corresponding weight factor of the peak duration of energy consumption of the enclosure structure, wherein the mapping relationship can be a one-to-one correspondence or a many-to-one relationship. In this example, the value range of the weight factor of the peak duration of energy consumption of the enclosure structure is [0,1].

[0060] The network transmission energy consumption peak duration weight factor is the weight factor corresponding to the network transmission energy consumption peak duration in the preset database during the static energy consumption data evaluation process, which represents the numerical value of the influence of the network transmission energy consumption peak duration on the static energy consumption evaluation value. When used, the network transmission energy consumption peak duration weight factor corresponding to the network transmission energy consumption peak duration can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the network transmission energy consumption peak duration corresponding to the static energy consumption data during the evaluation process and the weight factor corresponding to the network transmission energy consumption peak duration in the preset database form a mapping set, and the real-time network transmission energy consumption peak duration is input into the mapping set to obtain the corresponding network transmission energy consumption peak duration weight factor, wherein the mapping relationship can be one-to-one or many-to-one. In this example, the value range of the network transmission energy consumption peak duration weight factor is [0,1].

[0061] The static energy consumption peak duration weight factor is the weight factor corresponding to the static energy consumption peak duration in the preset database during the static energy consumption data evaluation process, which represents the numerical value of the influence of the static energy consumption peak duration on the static energy consumption evaluation value. When used, the static energy consumption peak duration weight factor corresponding to the static energy consumption peak duration can be directly obtained from the preset database, and the corresponding relationship can be a pre-set mapping relationship. For example, the static energy consumption peak duration corresponding to the static energy consumption data during the evaluation process and the weight factor corresponding to the static energy consumption peak duration in the preset database form a mapping set, and the real-time static energy consumption peak duration is input into the mapping set to obtain the corresponding static energy consumption peak duration weight factor, wherein the mapping relationship can be one-to-one or many-to-one. The value range of the static energy consumption peak duration weight factor in this example is [0,1]. It should be noted that the sum of the enclosure structure energy consumption peak duration weight factor, the network transmission peak duration weight factor, the static energy consumption peak duration weight factor and the high-energy lighting area duration weight factor in this embodiment is 1.

[0062] Specifically, the static energy consumption assessment threshold range is the range corresponding to the maximum and minimum values ​​of the historical static energy consumption assessment values ​​within the historical time period in the preset database; when the static energy consumption assessment value is not within the static energy consumption assessment threshold range, it can be achieved by: (1) replacing or adding the insulation material of the heat dissipation baffle corresponding to the production line to reduce the heat loss transmitted through the enclosing structure. Assuming that the roof insulation layer of a factory's production workshop is aging (i.e., the aging of the insulation layer is caused by regular replacement), the indoor temperature in summer increases, and the corresponding air-conditioning energy consumption increases. At this time, a new insulation layer needs to be added to improve the insulation performance and reduce the air-conditioning energy consumption; (2) During the production line operation period, the data transmission frequency increases, resulting in an increase in network load, which in turn increases energy consumption. At this time, it is necessary to increase the time interval for data transmission to reduce unnecessary data transmission, thereby reducing the energy consumption of network equipment.

[0063] Specifically, the specific limiting expression of the static energy consumption evaluation value is:

[0064]

[0065]

[0066] Where i is the number of the ultra-low energy consumption building, i = 1, 2, ..., Y, Y is the total number of ultra-low energy consumption buildings, t is the number of the preset operation time period of the production line, t = 1, 2, ..., T, T is the total number of preset operation time periods of the production line, e is a natural constant, JING i represents the static energy consumption evaluation value of the energy consumption stage of the i-th ultra-low energy consumption building in the high-energy lighting area during the preset operation time period of the production line, ε1 represents the weight factor of the peak duration of energy consumption of the enclosure structure, W1 i.tB1 represents the first duration ratio of the energy consumption stage of the i-th ultra-low energy consumption building in the high-energy lighting area during the preset operation time period of the t-th production line, i.t represents the peak energy consumption duration of the enclosure structure of the i-th ultra-low energy building in the high-energy lighting area during the preset operation time period of the t-th production line, B3 i.t represents the static energy consumption peak duration of the energy consumption stage of the i-th ultra-low energy consumption building in the high-energy lighting area during the preset operation time period of the t-th production line, ε2 represents the weight factor of the network transmission energy consumption peak duration, W2 i.t B2 represents the second duration ratio of the energy consumption phase of the i-th ultra-low energy consumption building in the high-energy lighting area during the preset operation time period of the t-th production line, i.t represents the peak duration of network transmission energy consumption in the high-energy lighting area during the preset operation time period of the t-th production line during the energy consumption stage of the i-th ultra-low energy consumption building, ε3 represents the weight factor of the static energy consumption peak duration, and W3 i.t represents the third duration ratio of the energy consumption stage of the i-th ultra-low energy consumption building in the high-energy lighting area during the preset operation time period of the t-th production line, ε4 represents the duration weight factor of the high-energy lighting area, W4 i.t It represents the fourth duration ratio of the energy consumption stage of the i-th ultra-low energy consumption building in the high-energy lighting area during the preset operation time period of the t-th production line, Q i.t represents the total duration of the energy consumption phase of the i-th ultra-low energy consumption building in the high-energy lighting area during the preset operation time period of the t-th production line, U i.t It represents the total duration of the energy consumption stage of the i-th ultra-low energy consumption building corresponding to the preset operating time period of the t-th production line.

[0067] It should be understood that since the peak duration of static energy consumption is the overlap between the peak duration of enclosure energy consumption and the peak duration of network transmission energy consumption, B3 i.t <B1 i.t And B3 i.t <B2 i.t The static energy consumption assessment value increases with the increase of the first duration proportion coefficient, the second duration proportion coefficient, the third duration proportion coefficient and the fourth duration proportion coefficient. Among them, the first duration proportion coefficient increases with the increase of the peak duration of the energy consumption of the enclosure structure, the second duration proportion coefficient increases with the increase of the peak duration of the network transmission energy consumption, the third duration proportion coefficient increases with the increase of the static energy consumption peak duration, and the fourth duration proportion coefficient increases with the increase of the high-energy lighting area duration. It should be noted that the peak duration of dynamic energy consumption in the dynamic energy consumption data evaluation process also indirectly affects the value of the high-energy lighting area duration in the static energy consumption data evaluation process.

[0068] The duration of dynamic energy consumption peak often reflects the busyness of the production line or work area activities in the ultra-low energy consumption building. In the actual production line application scenario, the lighting demand is synchronized with the time period corresponding to the peak energy consumption duration in the building materials production process. When the production line is synchronously operated within the preset operation time period, it will not only increase the dynamic energy consumption of the equipment operation (such as the generator), but also require higher lighting intensity and longer lighting time to support the operator's work, thereby indirectly increasing the duration of the high-energy lighting area. Through the synchronous analysis of the duration of the dynamic energy consumption peak and the duration of the high-energy lighting area, in order to reduce unnecessary energy consumption while maintaining the necessary working conditions, the static energy consumption of ultra-low energy consumption buildings in the high-energy lighting area can be effectively reduced, thereby achieving the improvement of the energy consumption and energy-saving effectiveness of ultra-low energy consumption buildings in the construction process, and effectively solving the problem of low energy consumption and energy-saving effectiveness of ultra-low energy consumption buildings in the construction process in the prior art.

[0069] Furthermore, whether to perform static energy consumption adjustment is determined based on the static energy consumption assessment value and the static energy consumption assessment threshold, and then includes: step one, real-time monitoring of changes in dynamic energy consumption data during dynamic energy consumption adjustment to evaluate the effect of dynamic energy consumption adjustment; step two, real-time monitoring of changes in static energy consumption data during static energy consumption adjustment to evaluate the effect of static energy consumption adjustment; step three, energy-saving optimization of ultra-low energy consumption buildings is performed based on the evaluation results of dynamic energy consumption adjustment effect and static energy consumption adjustment effect.

[0070] In this embodiment, after the dynamic energy consumption adjustment is completed, the dynamic energy consumption data is re-acquired and re-evaluated to obtain the dynamic energy consumption evaluation value. When the dynamic energy consumption evaluation value is within the dynamic energy consumption evaluation threshold range, it indicates that the dynamic energy consumption adjustment effect meets the preset dynamic energy consumption adjustment requirement (that is, the dynamic energy consumption evaluation value is within the dynamic energy consumption evaluation threshold range); otherwise, the dynamic energy consumption adjustment is continued until the dynamic energy consumption adjustment effect meets the preset dynamic energy consumption adjustment requirement; similarly, after the static energy consumption adjustment is completed, the static energy consumption data is re-acquired and re-evaluated to obtain the static energy consumption evaluation value. When the static energy consumption evaluation value is within the static energy consumption evaluation threshold range, it indicates that the static energy consumption adjustment effect meets the preset static energy consumption adjustment requirement (that is, the static energy consumption evaluation value is within the static energy consumption evaluation threshold range); otherwise, the static energy consumption adjustment is continued until the static energy consumption adjustment effect meets the preset static energy consumption adjustment requirement. After completing the dynamic energy consumption adjustment and the static energy consumption adjustment, it is also necessary to regularly inspect and maintain the operating equipment of the ultra-low energy consumption building (such as generators and lighting equipment) to avoid energy consumption caused by equipment aging, thereby achieving the improvement of the effectiveness of energy-saving optimization of ultra-low energy consumption buildings, and further achieving the improvement of the effectiveness of energy saving in ultra-low energy consumption buildings during the construction process.

[0071] like Figure 3As shown, it is a structural schematic diagram of an ultra-low energy consumption building energy-saving system based on a scenario prediction algorithm provided in an embodiment of the present application. An ultra-low energy consumption building energy-saving system based on a scenario prediction algorithm provided in an embodiment of the present application includes: a data acquisition module, an evaluation value acquisition module and an evaluation value judgment module; wherein the data acquisition module is used to obtain energy consumption data and carbon emission data of the ultra-low energy consumption building within a preset operating time period of the production line; the evaluation value acquisition module is used to obtain dynamic energy consumption data and static energy consumption data according to the energy consumption data and carbon emission data, evaluate the dynamic energy consumption data to obtain a dynamic energy consumption evaluation value, and at the same time evaluate the static energy consumption data to obtain a static energy consumption evaluation value, the dynamic energy consumption evaluation value is used to evaluate the energy consumption of the ultra-low energy consumption building in the energy consumption stage, and the static energy consumption evaluation value is used to evaluate the carbon emissions of the ultra-low energy consumption building in the energy consumption stage; the evaluation value judgment module is used to judge whether to perform dynamic energy consumption adjustment according to the dynamic energy consumption evaluation value and the dynamic energy consumption evaluation threshold, and at the same time judge whether to perform static energy consumption adjustment according to the static energy consumption evaluation value and the static energy consumption evaluation threshold, the dynamic energy consumption evaluation threshold and the static energy consumption evaluation threshold are energy consumption evaluation indicators of the reference building.

[0072] In this embodiment, dynamic energy consumption data and static energy consumption data together constitute energy consumption data. In order to improve the effectiveness of energy saving of ultra-low energy consumption buildings, this embodiment not only considers the energy consumption of ultra-low energy consumption buildings in different operation scenarios (building materials production and transportation stage and building work stage), but also considers the carbon emissions of ultra-low energy consumption buildings in the high-energy lighting area of ​​the production line, such as Figure 4 As shown, the specific flow chart of energy conservation in ultra-low energy consumption buildings provided in the embodiment of the present application realizes more accurate dynamic and static energy consumption adjustments, effectively reduces energy consumption and carbon emissions, thereby improving the effectiveness of energy conservation in ultra-low energy consumption buildings during the construction process.

[0073] To summarize, the embodiment of the present application determines whether to perform dynamic energy consumption adjustment by using the acquired dynamic energy consumption assessment value and the dynamic energy consumption assessment threshold, and determines whether to perform static energy consumption adjustment based on the acquired static energy consumption assessment value and the static energy consumption assessment threshold, thereby achieving more accurate acquisition of dynamic energy consumption assessment values ​​and static energy consumption assessment values, thereby achieving improved energy consumption and energy-saving effectiveness of ultra-low energy consumption buildings during the construction process, and effectively solving the problem of low energy consumption and energy-saving effectiveness of ultra-low energy consumption buildings during the construction process in the prior art.

[0074] Those skilled in the art will appreciate that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0075] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0076] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0077] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0078] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.

[0079] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. An ultra-low energy consumption building energy-saving method based on a scene prediction algorithm, characterized in that: The following steps are involved: S1, obtaining energy consumption data and carbon emission data of the ultra-low energy consumption building during the preset operation time period of the production line; S2, obtaining dynamic energy consumption data and static energy consumption data according to the energy consumption data and the carbon emission data, evaluating the dynamic energy consumption data to obtain a dynamic energy consumption evaluation value, and evaluating the static energy consumption data to obtain a static energy consumption evaluation value, wherein the dynamic energy consumption evaluation value is used to evaluate the energy consumption of the ultra-low energy consumption building in the energy consumption stage, and the static energy consumption evaluation value is used to evaluate the carbon emissions of the ultra-low energy consumption building in the energy consumption stage; S3, judging whether to execute dynamic energy consumption adjustment according to the dynamic energy consumption evaluation value and the dynamic energy consumption evaluation threshold, and judging whether to execute static energy consumption adjustment according to the static energy consumption evaluation value and the static energy consumption evaluation threshold.

2. The ultra-low energy consumption building energy saving method based on scenario prediction algorithm as claimed in claim 1, characterized in that: The energy consumption data includes first energy consumption data and second energy consumption data; The first energy consumption data is used to reflect the energy consumption in the production and transportation stage of building materials, and the second energy consumption data is used to reflect the energy consumption of ultra-low energy consumption buildings in the construction work stage; The carbon emission data includes first carbon emission data and second carbon emission data; The first carbon emission data is used to reflect the carbon emissions during the production and transportation stages of building materials, and the second carbon emission data is used to reflect the carbon emissions during the construction work stage of ultra-low energy consumption buildings.

3. The ultra-low energy consumption building energy saving method based on scenario prediction algorithm as claimed in claim 1, characterized in that: The specific process of evaluating the dynamic energy consumption data is as follows: Obtain power load peak and valley data, and obtain power peak duration and power valley duration according to the acquired power load peak and valley data, wherein the power load peak and valley data include power peak data and power valley data, wherein the power peak duration represents the proportion of the power peak data in the production line operation time, and the power valley duration represents the proportion of the power valley data in the production line operation time; Obtain energy conversion loss data, and obtain loss peak duration and loss valley duration according to the obtained energy conversion loss data, wherein the energy conversion loss data includes loss peak data and loss valley data, wherein the loss peak duration represents the proportion of the loss peak data in the production line operation time, and the loss valley duration represents the proportion of the loss valley data in the production line operation time; Obtain the dynamic energy consumption peak duration and dynamic energy consumption valley duration and adjust the preset operating time period of the production line to avoid the dynamic energy consumption peak duration, wherein the dynamic energy consumption peak duration is the overlapping duration of the power peak duration and the loss peak duration, and the dynamic energy consumption valley duration is the overlapping duration of the power valley duration and the loss valley duration.

4. The ultra-low energy consumption building energy saving method based on scenario prediction algorithm as claimed in claim 1, characterized in that: The specific process of evaluating the static energy consumption data is as follows: Obtain high-energy lighting areas and low-energy lighting areas based on the distribution of building lighting loads within a preset operating time period of the production line; Real-time monitoring of the changes in the energy consumption of the enclosure structure and network transmission energy consumption in the high-energy lighting area during the preset operation time period of the production line, obtaining the peak duration and trough duration of static energy consumption, and adjusting the lighting equipment in the high-energy lighting area to avoid the peak duration of static energy consumption; The static energy consumption peak duration is the overlapping duration of the enclosure structure energy consumption peak duration and the network transmission energy consumption peak duration; The static energy consumption valley duration is the overlapping duration of the energy consumption valley durations corresponding to the energy consumption valley durations of the enclosure structure energy consumption and the network transmission energy consumption.

5. The ultra-low energy consumption building energy saving method based on scenario prediction algorithm as claimed in claim 1, characterized in that: The evaluation of the static energy consumption data further includes: Obtain a preset carbon emission factor from a preset database, and convert the carbon emission energy consumption data into carbon emissions according to the carbon emission factor; The carbon emission energy consumption data is the sum of the dynamic energy consumption data and the static energy consumption data multiplied by the carbon emission factor respectively; The dynamic energy consumption data includes power load peak and valley data and energy conversion loss data; The static energy consumption data includes building lighting load data, building envelope energy consumption data and network transmission energy consumption data.

6. The ultra-low energy consumption building energy saving method based on scenario prediction algorithm as claimed in claim 1, characterized in that: The specific limiting expression of the dynamic energy consumption evaluation value is: Where i is the number of the ultra-low energy consumption building, i = 1, 2, ..., Y, Y is the total number of ultra-low energy consumption buildings, t is the number of the preset operation time period of the production line, t = 1, 2, ..., T, T is the total number of preset operation time periods of the production line, e is a natural constant, DONG i represents the dynamic energy consumption evaluation value of the energy consumption stage of the i-th ultra-low energy consumption building during the preset operation time period of the production line, α1 represents the weight factor of the peak power duration, A1 i.t represents the peak duration of electricity in the energy consumption phase of the i-th ultra-low energy consumption building during the preset operation time period of the t-th production line, α2 represents the weight factor of the peak duration of loss, and A2 i.t represents the peak duration of energy consumption in the energy consumption phase of the i-th ultra-low energy consumption building during the preset operation time period of the t-th production line, α3 represents the dynamic energy consumption peak duration weight factor, A3 i.t It represents the duration of the dynamic energy consumption peak of the energy consumption phase of the i-th ultra-low energy consumption building during the preset operation time period of the t-th production line, U i.t It represents the total duration of the energy consumption stage of the i-th ultra-low energy consumption building corresponding to the preset operating time period of the t-th production line.

7. The ultra-low energy consumption building energy saving method based on scenario prediction algorithm as claimed in claim 1, characterized in that: The specific steps of judging whether to perform dynamic energy consumption adjustment according to the dynamic energy consumption evaluation value and the dynamic energy consumption evaluation threshold include: A1, determine whether the dynamic energy consumption evaluation value is within the dynamic energy consumption evaluation threshold range. If so, continue to monitor the changes of dynamic energy consumption data in real time within the preset operation time period of the production line. Otherwise, execute A2; A2, executing dynamic energy consumption adjustment and re-evaluating dynamic energy consumption data until the dynamic energy consumption evaluation value is within the dynamic energy consumption evaluation threshold range, wherein the dynamic energy consumption adjustment includes equipment parameter adjustment and building environment optimization.

8. The ultra-low energy consumption building energy saving method based on scenario prediction algorithm as claimed in claim 1, characterized in that: The steps for obtaining the static energy consumption evaluation value are as follows: Real-time monitoring of the duration of the high-energy lighting area, and obtaining a first duration ratio coefficient and a second duration ratio coefficient according to the peak duration of the energy consumption of the enclosure structure, the peak duration of the network transmission energy consumption, and the peak duration of the static energy consumption. The duration of the high-energy lighting area is the duration of the energy consumption stage of the ultra-low energy consumption building in the high-energy lighting area; Obtain the third duration ratio coefficient, and obtain the fourth duration ratio coefficient according to the total duration of the preset operation time period of the production line, and obtain the static energy consumption assessment value in combination with the duration weight factor preset in the preset database; The specific steps of judging whether to perform static energy consumption adjustment according to the static energy consumption evaluation value and the static energy consumption evaluation threshold include: B1, determine whether the static energy consumption evaluation value is within the static energy consumption evaluation threshold range. If so, continue to monitor the change of the static energy consumption data in the preset operation time period of the production line in real time, otherwise execute B2; B2, executing static energy consumption adjustment and re-evaluating the static energy consumption data until the static energy consumption evaluation value is within the static energy consumption evaluation threshold range, wherein the static energy consumption adjustment includes enclosure structure repair and network transmission parameter adjustment.

9. The ultra-low energy consumption building energy saving method based on scenario prediction algorithm as claimed in claim 8, characterized in that: The step of determining whether to perform static energy consumption adjustment according to the static energy consumption evaluation value and the static energy consumption evaluation threshold value further includes: Step 1: Real-time monitoring of the changes in dynamic energy consumption data during the dynamic energy consumption adjustment process to evaluate the effect of the dynamic energy consumption adjustment; Step 2: Real-time monitoring of the change of static energy consumption data during the static energy consumption adjustment process to evaluate the effect of the static energy consumption adjustment; Step three: optimize energy conservation of ultra-low energy consumption buildings based on the evaluation results of dynamic energy consumption adjustment effects and static energy consumption adjustment effects.

10. An ultra-low energy consumption building energy-saving system based on a scene prediction algorithm, characterized in that: include: Data acquisition module, evaluation value acquisition module and evaluation value judgment module; Wherein, the data acquisition module is used to acquire the energy consumption data and carbon emission data of the ultra-low energy consumption building within the preset operation time period of the production line; The evaluation value acquisition module is used to acquire dynamic energy consumption data and static energy consumption data according to the energy consumption data and the carbon emission data, evaluate the dynamic energy consumption data to acquire a dynamic energy consumption evaluation value, and evaluate the static energy consumption data to acquire a static energy consumption evaluation value. The dynamic energy consumption evaluation value is used to evaluate the energy consumption of the ultra-low energy consumption building in the energy consumption stage, and the static energy consumption evaluation value is used to evaluate the carbon emissions of the ultra-low energy consumption building in the energy consumption stage; The evaluation value judgment module is used to judge whether to perform dynamic energy consumption adjustment according to the dynamic energy consumption evaluation value and the dynamic energy consumption evaluation threshold, and to judge whether to perform static energy consumption adjustment according to the static energy consumption evaluation value and the static energy consumption evaluation threshold.

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