A method for analyzing energy consumption of ultra-low energy buildings

Abstract

The present invention discloses a method for analyzing energy consumption of ultra-low energy consumption buildings, which belongs to the technical field of ultra-low energy consumption buildings and includes the following steps: S1: collecting and processing historical data of ultra-low energy consumption buildings to determine characteristic data of ultra-low energy consumption buildings; S2: testing and optimizing the energy consumption prediction model of ultra-low energy consumption buildings to determine the optimal energy consumption prediction model of ultra-low energy consumption buildings; S3: predicting energy consumption of ultra-low energy consumption buildings to determine the predicted energy consumption value of ultra-low energy consumption buildings; S4: analyzing the predicted energy consumption value of ultra-low energy consumption buildings to determine the energy consumption analysis result of ultra-low energy consumption buildings. The present invention solves the existing problem that ultra-low energy consumption buildings cannot be effectively analyzed for energy consumption and timely managed for energy consumption, resulting in poor use effect of ultra-low energy consumption buildings. The present invention can effectively analyze the energy consumption of ultra-low energy consumption buildings and can timely manage the energy consumption of ultra-low energy consumption buildings, which can improve the use effect of ultra-low energy consumption buildings.

Description

Technical Field

[0001] The present invention relates to the technical field of ultra-low energy consumption buildings, and in particular to an energy consumption analysis method for ultra-low energy consumption buildings. Background Art

[0002] With the intensification of global climate change and the energy crisis, people are paying more and more attention to energy efficiency in building design. Ultra-low energy buildings are a new architectural concept that aims to achieve efficient energy utilization and reduce environmental impact through the use of advanced energy-saving technologies and design strategies. However, to achieve these goals, it is necessary to analyze and manage the energy consumption of buildings.

[0003] Chinese patent publication number CN110708824A discloses an ultra-low energy consumption building system. Through an intelligent dimmer, the brightness of corridor lights, user lights, and public facility lights can be adjusted according to different lighting conditions, thereby avoiding the increase in electricity consumption caused by higher daytime lighting power consumption. A power consumption collection module can quickly collect the power consumption generated by lighting, and a power consumption calculation module calculates the power consumption and controls the lighting in different areas. However, this patent has the following drawbacks:

[0004] The existing technology cannot conduct effective energy consumption analysis on ultra-low energy consumption buildings, and cannot conduct timely energy consumption management on ultra-low energy consumption buildings, resulting in poor use effect of ultra-low energy consumption buildings. Summary of the Invention

[0005] The purpose of the present invention is to provide an energy consumption analysis method for ultra-low energy consumption buildings, which can effectively analyze the energy consumption of ultra-low energy consumption buildings and timely manage the energy consumption of ultra-low energy consumption buildings, thereby improving the use effect of ultra-low energy consumption buildings and solving the problems raised in the above-mentioned background technology.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] An energy consumption analysis method for ultra-low energy consumption buildings comprises the following steps:

[0008] S1: According to the energy consumption analysis requirements of ultra-low energy consumption buildings, collect and process the historical data of ultra-low energy consumption buildings to determine the characteristic data of ultra-low energy consumption buildings;

[0009] S2: Construct an energy consumption prediction model for ultra-low energy consumption buildings, test and optimize the energy consumption prediction model for ultra-low energy consumption buildings, and determine the optimal energy consumption prediction model for ultra-low energy consumption buildings;

[0010] S3: Based on the optimal ultra-low energy consumption building energy consumption prediction model, perform energy consumption prediction for the ultra-low energy consumption building and determine the energy consumption prediction value of the ultra-low energy consumption building;

[0011] S4: Based on the energy consumption standards for ultra-low energy consumption buildings, analyze the predicted energy consumption values of ultra-low energy consumption buildings and determine the energy consumption analysis results of ultra-low energy consumption buildings.

[0012] Preferably, in S1, historical data of ultra-low energy consumption buildings is collected, and the following operations are performed:

[0013] According to the energy consumption analysis requirements of ultra-low energy consumption buildings, the area, shape, geographical location, orientation, enclosure materials, window configuration and foundation configuration of ultra-low energy consumption buildings are collected to obtain basic data of ultra-low energy consumption buildings;

[0014] According to the energy consumption analysis requirements of ultra-low energy consumption buildings, the temperature, humidity, light and wind speed of the environment in which the ultra-low energy consumption buildings are located are collected to obtain the environmental data of ultra-low energy consumption buildings;

[0015] Among them, the historical data of ultra-low energy consumption buildings are determined based on the basic data of ultra-low energy consumption buildings and the environmental data of ultra-low energy consumption buildings.

[0016] Preferably, in S1, the historical data of ultra-low energy consumption buildings is processed to perform the following operations:

[0017] Obtain historical data on ultra-low energy buildings;

[0018] Cleaning and processing of historical data of ultra-low energy buildings, including:

[0019] Conduct consistency checks on historical data of ultra-low energy buildings;

[0020] According to the data consistency requirements, check whether there is inconsistent data in the historical data of ultra-low energy consumption buildings that is useless for the energy consumption analysis of ultra-low energy consumption buildings, and remove the inconsistent data in the historical data of ultra-low energy consumption buildings;

[0021] Process invalid and missing values in historical data of ultra-low energy consumption buildings;

[0022] According to the requirements of data validity and completeness, check whether there are invalid values and missing values in the historical data of ultra-low energy consumption buildings that are useless for the energy consumption analysis of ultra-low energy consumption buildings, and remove the invalid values and missing values in the historical data of ultra-low energy consumption buildings;

[0023] Identify historical data of ultra-low energy buildings that are useful for energy consumption analysis of ultra-low energy buildings.

[0024] Preferably, in S1, the historical data of ultra-low energy consumption buildings is processed, and the following operations are further performed:

[0025] Obtain historical data of ultra-low energy buildings that are useful for energy consumption analysis of ultra-low energy buildings;

[0026] Convert and process historical data of ultra-low energy buildings that are useful for energy consumption analysis of ultra-low energy buildings;

[0027] Eliminate the dimensional differences between historical data of ultra-low energy consumption buildings that are useful for energy consumption analysis of ultra-low energy consumption buildings, unify the formats of historical data of ultra-low energy consumption buildings that are useful for energy consumption analysis of ultra-low energy consumption buildings, and determine standardized historical data of ultra-low energy consumption buildings;

[0028] Extract features from standardized historical data of ultra-low energy buildings;

[0029] Extract features that can reflect the energy consumption analysis of ultra-low energy buildings;

[0030] Determine the characteristic data of ultra-low energy consumption buildings.

[0031] Preferably, in S2, an ultra-low energy consumption building energy consumption prediction model is constructed, and the following operations are performed:

[0032] Obtain characteristic data of ultra-low energy consumption buildings;

[0033] Divide the characteristic data of ultra-low energy consumption buildings to determine the training set and test set;

[0034] Select a convolutional neural network model framework suitable for energy consumption prediction of ultra-low energy buildings;

[0035] Based on the training set, the convolutional neural network model framework suitable for ultra-low energy consumption building energy consumption prediction is trained to determine the ultra-low energy consumption building energy consumption prediction model.

[0036] Preferably, the ultra-low energy consumption building characteristic data is divided to determine a training set and a test set, including:

[0037] Extracting the amount of invalid values in the historical data of the ultra-low energy consumption building;

[0038] Extracting the number of missing data corresponding to the missing values in the historical data of the ultra-low energy consumption building;

[0039] Obtaining an outlier degree coefficient by combining the amount of invalid values in the ultra-low energy consumption building historical data with the number of missing data corresponding to the missing values in the ultra-low energy consumption building historical data;

[0040] The outlier degree coefficient is obtained by the following formula:

[0041]

[0042] Among them, E represents the outlier degree coefficient; C w Indicates the amount of invalid values in the historical data of the ultra-low energy consumption building; C zRepresents the total amount of historical data of ultra-low energy consumption buildings; N x N represents the number of missing data corresponding to the missing values in the historical data of the ultra-low energy consumption building; z The total number of historical data representing ultra-low energy consumption buildings;

[0043] Comparing the outlier degree coefficient with a preset coefficient threshold;

[0044] Determining target ratios of the training set and the test set based on a comparison result between the outlier degree coefficient and a preset coefficient threshold;

[0045] The ultra-low energy consumption building characteristic data is divided according to the target ratios corresponding to the training set and the test set to determine the training set and the test set.

[0046] Preferably, determining the corresponding ratios of the training set and the test set according to the comparison result between the outlier degree coefficient and a preset coefficient threshold comprises:

[0047] Retrieving a comparison result between the outlier degree coefficient and a preset coefficient threshold;

[0048] When the comparison result between the abnormal value degree coefficient and the preset coefficient threshold value indicates that the abnormal value degree coefficient is lower than the preset coefficient threshold value, the preset initial ratio value is retrieved as the target ratio;

[0049] When the comparison result between the abnormal value degree coefficient and the preset coefficient threshold value indicates that the abnormal value degree coefficient is not lower than the preset coefficient threshold value, a preset initial ratio value is retrieved;

[0050] The target ratio is obtained by using the outlier degree coefficient and the preset initial ratio value, wherein the target ratio is obtained by the following formula:

[0051]

[0052] Among them, R represents the target ratio; R0 represents the preset initial ratio value; E represents the abnormal value degree coefficient; N x N represents the number of missing data corresponding to the missing values in the historical data of the ultra-low energy consumption building; c represents the number of data corresponding to invalid values in the historical data of ultra-low energy consumption buildings; T represents the preset coefficient threshold; C w Indicates the amount of invalid values in the historical data of the ultra-low energy consumption building; C z Represents the total amount of historical data of ultra-low energy consumption buildings; N z represents the total number of historical data of ultra-low energy consumption buildings; x represents the adjustment coefficient, and the value range of the adjustment coefficient is 0.21-1.88.

[0053] Preferably, in S2, the ultra-low energy consumption building energy consumption prediction model is tested and optimized, and the following operations are performed:

[0054] Obtain energy consumption prediction models for ultra-low energy buildings;

[0055] Based on the test set, the performance of the ultra-low energy consumption building energy consumption prediction model is tested to determine whether the performance of the ultra-low energy consumption building energy consumption prediction model can achieve the expected effect;

[0056] Determine the performance test results based on the ultra-low energy building energy consumption prediction model;

[0057] Based on the performance test results, the energy consumption prediction model of ultra-low energy consumption buildings is mined and analyzed, the parameter configuration of the energy consumption prediction model of ultra-low energy consumption buildings is adjusted, the structural setting of the energy consumption prediction model of ultra-low energy consumption buildings is optimized, and the energy consumption prediction model of ultra-low energy consumption buildings is repeatedly iterated and optimized to determine the optimal energy consumption prediction model of ultra-low energy consumption buildings.

[0058] Preferably, in S3, energy consumption prediction is performed on the ultra-low energy consumption building by performing the following operations:

[0059] Collect real-time data of ultra-low energy consumption buildings based on energy consumption analysis requirements of ultra-low energy consumption buildings;

[0060] Input the real-time data of ultra-low energy consumption buildings into the optimal ultra-low energy consumption prediction model;

[0061] Based on the optimal ultra-low energy consumption prediction model, the real-time data of ultra-low energy consumption buildings is mined and analyzed to predict the energy consumption of ultra-low energy consumption buildings;

[0062] Determine the predicted energy consumption of ultra-low energy buildings.

[0063] Preferably, in S4, the predicted energy consumption value of the ultra-low energy consumption building is analyzed, and the following operations are performed:

[0064] According to the energy consumption analysis requirements of ultra-low energy consumption buildings, the energy consumption standards of ultra-low energy consumption buildings are set in advance;

[0065] Based on the energy consumption standards of ultra-low energy buildings, analyze the predicted energy consumption values of ultra-low energy buildings and determine the energy consumption analysis results of ultra-low energy buildings;

[0066] Among them, if the predicted energy consumption value of the ultra-low energy consumption building is within the energy consumption standard range of the ultra-low energy consumption building, the energy consumption analysis result of the ultra-low energy consumption building is normal;

[0067] Among them, if the predicted energy consumption value of the ultra-low energy consumption building is not within the energy consumption standard range of the ultra-low energy consumption building, the energy consumption analysis result of the ultra-low energy consumption building is abnormal energy consumption of the ultra-low energy consumption building;

[0068] When the energy consumption of ultra-low energy consumption buildings is abnormal, early warning reminders will be issued to guide management personnel to take energy-saving measures in a timely manner, and the design and layout of ultra-low energy consumption buildings will be optimized to ensure normal energy consumption of ultra-low energy consumption buildings.

[0069] Compared with the prior art, the present invention has the following beneficial effects:

[0070] 1. According to the energy consumption analysis requirements of ultra-low energy consumption buildings, the present invention collects historical data of ultra-low energy consumption buildings, processes the historical data of ultra-low energy consumption buildings, determines characteristic data of ultra-low energy consumption buildings, divides the characteristic data of ultra-low energy consumption buildings, determines a training set and a test set, and based on the training set, trains a convolutional neural network model framework selected for energy consumption prediction of ultra-low energy consumption buildings, constructs an energy consumption prediction model for ultra-low energy consumption buildings, tests and optimizes the energy consumption prediction model for ultra-low energy consumption buildings, and determines the optimal energy consumption prediction model for ultra-low energy consumption buildings.

[0071] 2. The present invention is based on the optimal ultra-low energy consumption prediction model for ultra-low energy consumption buildings, mines and analyzes the real-time data of ultra-low energy consumption buildings, predicts the energy consumption of ultra-low energy consumption buildings, determines the energy consumption prediction value of ultra-low energy consumption buildings, analyzes the energy consumption prediction value of ultra-low energy consumption buildings based on the energy consumption standard of ultra-low energy consumption buildings, and determines the energy consumption analysis result of ultra-low energy consumption buildings. When the energy consumption of ultra-low energy consumption buildings is abnormal, an ultra-low energy consumption energy consumption early warning reminder is issued to guide management personnel to take energy-saving measures in time, and the design and layout of ultra-low energy consumption buildings are optimized to make the energy consumption of ultra-low energy consumption buildings normal. The ultra-low energy consumption buildings can be effectively analyzed for energy consumption, and the energy consumption of ultra-low energy consumption buildings can be managed in time, which can improve the use effect of ultra-low energy consumption buildings. BRIEF DESCRIPTION OF THE DRAWINGS

[0072] Figure 1 This is an operational flow chart of the ultra-low energy consumption building energy consumption analysis method of the present invention;

[0073] Figure 2 This is a flow chart of the algorithm for analyzing the predicted energy consumption value of ultra-low energy consumption buildings based on the ultra-low energy consumption building energy consumption standards of the present invention. DETAILED DESCRIPTION

[0074] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0075] In order to solve the existing problem of not being able to conduct effective energy analysis on ultra-low energy buildings and not being able to conduct timely energy management on ultra-low energy buildings, which leads to poor use of ultra-low energy buildings, please refer to Figure 1-Figure 2 , this embodiment provides the following technical solutions:

[0076] An energy consumption analysis method for ultra-low energy consumption buildings comprises the following steps:

[0077] S1: According to the energy consumption analysis requirements of ultra-low energy consumption buildings, collect and process the historical data of ultra-low energy consumption buildings to determine the characteristic data of ultra-low energy consumption buildings;

[0078] In this embodiment, historical data of ultra-low energy consumption buildings is collected and the following operations are performed:

[0079] According to the energy consumption analysis requirements of ultra-low energy consumption buildings, the area, shape, geographical location, orientation, enclosure materials, window configuration and foundation configuration of ultra-low energy consumption buildings are collected to obtain basic data of ultra-low energy consumption buildings;

[0080] It should be noted that the foundation refers to the soil or rock mass supporting the foundation of the building. The soil layer used as the foundation of a building is divided into rock, crushed stone soil, sand, silt, clay soil and artificial fill. There are two types of foundations: natural foundations and artificial foundations. Natural foundations are natural soil layers that do not require human reinforcement, while artificial foundations require human reinforcement. Common methods include stone chip cushions, sand cushions, mixed lime soil backfill and compaction.

[0081] According to the energy consumption analysis requirements of ultra-low energy consumption buildings, the temperature, humidity, light and wind speed of the environment in which the ultra-low energy consumption buildings are located are collected to obtain the environmental data of ultra-low energy consumption buildings;

[0082] Among them, the historical data of ultra-low energy consumption buildings are determined based on the basic data of ultra-low energy consumption buildings and the environmental data of ultra-low energy consumption buildings.

[0083] It should be noted that by collecting historical data on ultra-low energy consumption buildings, it is convenient to subsequently build an energy consumption prediction model for ultra-low energy consumption buildings, and further predict the energy consumption of ultra-low energy consumption buildings.

[0084] In this embodiment, the historical data of ultra-low energy consumption buildings is processed to perform the following operations:

[0085] Obtain historical data on ultra-low energy buildings;

[0086] Cleaning and processing of historical data of ultra-low energy buildings, including:

[0087] Conduct consistency checks on historical data of ultra-low energy buildings;

[0088] According to the data consistency requirements, check whether there is inconsistent data in the historical data of ultra-low energy consumption buildings that is useless for the energy consumption analysis of ultra-low energy consumption buildings, and remove the inconsistent data in the historical data of ultra-low energy consumption buildings;

[0089] Process invalid and missing values in historical data of ultra-low energy consumption buildings;

[0090] According to the requirements of data validity and completeness, check whether there are invalid values and missing values in the historical data of ultra-low energy consumption buildings that are useless for the energy consumption analysis of ultra-low energy consumption buildings, and remove the invalid values and missing values in the historical data of ultra-low energy consumption buildings;

[0091] Identify historical data of ultra-low energy buildings that are useful for energy consumption analysis of ultra-low energy buildings.

[0092] It should be noted that data cleaning refers to the process of preprocessing data in data governance work, with the aim of correcting or deleting errors, duplications and inconsistencies in the data to ensure the accuracy and completeness of the data. Through data cleaning, data can be converted into high-quality data to facilitate subsequent data analysis and use.

[0093] Among them, consistency check is to check whether the data meets the requirements based on the reasonable value range and mutual relationship of each parameter, and to find data that exceeds the normal range, is logically unreasonable or contradictory; for example, if a variable measured by a 1-7 scale has a value of 0, or a negative number appears in the weight, they should be regarded as exceeding the normal value range; computer software such as SPSS, SAS and Excel can automatically identify each variable value that exceeds the range based on the defined value range; logically inconsistent answers may appear in various forms: for example, many respondents said that they drove to work, but reported that they did not have a car; or respondents reported that they were heavy buyers and users of a certain brand, but at the same time gave a very low score on the familiarity scale; when inconsistencies are found, the questionnaire number, record number, variable name, error category, etc. should be listed to facilitate further verification and correction.

[0094] Therefore, by cleaning the historical data of ultra-low energy consumption buildings, inconsistent data, invalid values and missing values in the historical data of ultra-low energy consumption buildings can be removed, and the historical data of ultra-low energy consumption buildings that are useful for energy consumption analysis of ultra-low energy consumption buildings can be determined, which can improve the processing accuracy and efficiency of subsequent historical data of ultra-low energy consumption buildings.

[0095] In this embodiment, the historical data of ultra-low energy consumption buildings is processed, and the following operations are further performed:

[0096] Obtain historical data of ultra-low energy buildings that are useful for energy consumption analysis of ultra-low energy buildings;

[0097] Convert and process historical data of ultra-low energy consumption buildings that are useful for energy consumption analysis of ultra-low energy consumption buildings;

[0098] Eliminate the dimensional differences between historical data of ultra-low energy consumption buildings that are useful for energy consumption analysis of ultra-low energy consumption buildings, unify the formats of historical data of ultra-low energy consumption buildings that are useful for energy consumption analysis of ultra-low energy consumption buildings, and determine standardized historical data of ultra-low energy consumption buildings;

[0099] Extract features from standardized historical data of ultra-low energy buildings;

[0100] Extract features that can reflect the energy consumption analysis of ultra-low energy buildings;

[0101] Determine the characteristic data of ultra-low energy consumption buildings.

[0102] It should be noted that by converting and processing the historical data of ultra-low energy consumption buildings that are useful for the energy consumption analysis of ultra-low energy consumption buildings, standardized historical data of ultra-low energy consumption buildings are determined, which facilitates the subsequent feature extraction of the standardized historical data of ultra-low energy consumption buildings. By extracting the features of the standardized historical data of ultra-low energy consumption buildings, the characteristic data of ultra-low energy consumption buildings are determined, which facilitates the subsequent construction of an energy consumption prediction model for ultra-low energy consumption buildings.

[0103] S2: Construct an energy consumption prediction model for ultra-low energy consumption buildings, test and optimize the energy consumption prediction model for ultra-low energy consumption buildings, and determine the optimal energy consumption prediction model for ultra-low energy consumption buildings;

[0104] In this embodiment, an ultra-low energy consumption building energy consumption prediction model is constructed, and the following operations are performed:

[0105] Obtain characteristic data of ultra-low energy consumption buildings;

[0106] Divide the characteristic data of ultra-low energy consumption buildings to determine the training set and test set;

[0107] Select a convolutional neural network model framework suitable for energy consumption prediction of ultra-low energy buildings;

[0108] Based on the training set, the convolutional neural network model framework suitable for ultra-low energy consumption building energy consumption prediction is trained to determine the ultra-low energy consumption building energy consumption prediction model.

[0109] It should be noted that by constructing an energy consumption prediction model for ultra-low energy consumption buildings, it is convenient to conduct subsequent energy consumption predictions for ultra-low energy consumption buildings and determine the energy consumption prediction values for ultra-low energy consumption buildings.

[0110] In this embodiment, the ultra-low energy consumption building energy consumption prediction model is tested and optimized, and the following operations are performed:

[0111] Obtain energy consumption prediction models for ultra-low energy buildings;

[0112] Based on the test set, the performance of the ultra-low energy consumption building energy consumption prediction model is tested to determine whether the performance of the ultra-low energy consumption building energy consumption prediction model can achieve the expected effect;

[0113] Determine the performance test results based on the ultra-low energy building energy consumption prediction model;

[0114] Based on the performance test results, the energy consumption prediction model of ultra-low energy consumption buildings is mined and analyzed, the parameter configuration of the energy consumption prediction model of ultra-low energy consumption buildings is adjusted, the structural setting of the energy consumption prediction model of ultra-low energy consumption buildings is optimized, and the energy consumption prediction model of ultra-low energy consumption buildings is repeatedly iterated and optimized to determine the optimal energy consumption prediction model of ultra-low energy consumption buildings.

[0115] Specifically, the characteristic data of ultra-low energy consumption buildings are divided to determine the training set and test set, including:

[0116] Extracting the amount of invalid values in the historical data of the ultra-low energy consumption building;

[0117] Extracting the number of missing data corresponding to the missing values in the historical data of the ultra-low energy consumption building;

[0118] Obtaining an outlier degree coefficient by combining the amount of invalid values in the ultra-low energy consumption building historical data with the number of missing data corresponding to the missing values in the ultra-low energy consumption building historical data;

[0119] The outlier degree coefficient is obtained by the following formula:

[0120]

[0121] Among them, E represents the outlier degree coefficient; C w Indicates the amount of invalid values in the historical data of the ultra-low energy consumption building; C z Represents the total amount of historical data of ultra-low energy consumption buildings; N x N represents the number of missing data corresponding to the missing values in the historical data of the ultra-low energy consumption building; z The total number of historical data representing ultra-low energy consumption buildings;

[0122] Comparing the outlier degree coefficient with a preset coefficient threshold;

[0123] Determining target ratios of the training set and the test set based on a comparison result between the outlier degree coefficient and a preset coefficient threshold;

[0124] The ultra-low energy consumption building characteristic data is divided according to the target ratios corresponding to the training set and the test set to determine the training set and the test set.

[0125] The technical effect of the above technical solution is that by extracting invalid and missing values from historical data of ultra-low energy buildings, the solution can quantify the degree of anomalies in the data. Invalid and missing values are common problems in data cleaning and can affect the accuracy and performance of machine learning models. By calculating the outlier degree coefficient (E), the solution can comprehensively assess the overall data quality, providing an important reference for subsequent data preprocessing and model training. Based on the comparison of the outlier degree coefficient with a preset coefficient threshold, the solution can dynamically adjust the target ratio of the training and test sets. This means that when the data quality is high (low outlier degree coefficient), more data can be used for model training; when the data quality is low (high outlier degree coefficient), the proportion of training data should be appropriately reduced to avoid model overfitting or performance degradation. This dynamic adjustment strategy helps improve the generalization and stability of the model under different data quality conditions. By optimizing the division of the training and test sets, the technical solution can provide more accurate and representative training and test data for the machine learning model, which helps improve the accuracy, robustness, and generalization of the model. This solution also reduces model performance fluctuations caused by data quality issues, improving the model's reliability and stability in practical applications. By calculating the outlier degree coefficient and dynamically adjusting the training / test set ratio, this technical solution automates and intelligently partitions the dataset. This reduces reliance on manual intervention and improves data processing efficiency and accuracy. Furthermore, the solution offers scalability and flexibility, adapting to characteristic data of low-energy buildings of varying sizes and types.

[0126] In summary, this technical solution provides strong support for the machine learning application of ultra-low energy building characteristic data through technical effects such as quantifying data quality, optimizing data set division, and improving model performance.

[0127] Specifically, the ratios of the training set and the test set are determined based on the comparison result between the outlier degree coefficient and the preset coefficient threshold, including:

[0128] Retrieving a comparison result between the outlier degree coefficient and a preset coefficient threshold;

[0129] When the comparison result between the abnormal value degree coefficient and the preset coefficient threshold value indicates that the abnormal value degree coefficient is lower than the preset coefficient threshold value, the preset initial ratio value is retrieved as the target ratio;

[0130] When the comparison result between the abnormal value degree coefficient and the preset coefficient threshold value indicates that the abnormal value degree coefficient is not lower than the preset coefficient threshold value, a preset initial ratio value is retrieved;

[0131] The target ratio is obtained by using the outlier degree coefficient and the preset initial ratio value, wherein the target ratio is obtained by the following formula:

[0132]

[0133] Among them, R represents the target ratio; R0 represents the preset initial ratio value; E represents the abnormal value degree coefficient; N x N represents the number of missing data corresponding to the missing values in the historical data of the ultra-low energy consumption building; c represents the number of data corresponding to invalid values in the historical data of ultra-low energy consumption buildings; T represents the preset coefficient threshold; C w Indicates the amount of invalid values in the historical data of the ultra-low energy consumption building; C z Represents the total amount of historical data of ultra-low energy consumption buildings; N z represents the total number of historical data of ultra-low energy consumption buildings; x represents the adjustment coefficient, and the value range of the adjustment coefficient is 0.21-1.88.

[0134] The technical effect of the above-mentioned technical solution is that by comparing the outlier degree coefficient with a preset coefficient threshold, the solution can dynamically adjust the ratio of the training set to the test set. This dynamic adjustment mechanism ensures that the machine learning model is provided with an appropriate ratio of training and test data under different data quality conditions. When the outlier degree coefficient is low, it indicates high data quality. In this case, using the preset initial ratio as the target ratio can fully utilize the high-quality data for model training, thereby improving the model's accuracy and generalization ability. When the outlier degree coefficient is at least the preset coefficient threshold, it indicates that there are a large number of outliers in the data. In this case, the target ratio calculated by the formula is adjusted accordingly to reduce the impact of outliers on model training and further improve the robustness of the model. This technical solution introduces an adjustment coefficient x, whose value range is between 0.21 and 1.88. By adjusting the value of the adjustment coefficient, the range of the target ratio can be flexibly controlled to adapt to the characteristic data of low-energy buildings of different sizes and types. In addition, this solution also considers multiple data quality indicators (such as the amount of invalid values and the number of missing data), making the technical solution more comprehensive and flexible. By calculating the outlier degree coefficient and dynamically adjusting the training / test set ratio, this technical solution achieves automated and intelligent dataset partitioning. This reduces reliance on manual intervention and improves the efficiency and accuracy of data processing. When data quality is high, maintaining the original training set ratio allows for more efficient utilization of limited data resources and improves model training efficiency. When data quality is low, increasing the training set ratio and correspondingly increasing the test set ratio ensures a more comprehensive assessment of model performance during the testing phase, thus avoiding overfitting or underfitting.

[0135] In summary, this technical solution provides strong support for the machine learning application of ultra-low energy building characteristic data through dynamic adjustment of data set ratio, improvement of model generalization ability, flexibility and scalability, automation and intelligence, and optimization of resource utilization.

[0136] S3: Based on the optimal ultra-low energy consumption building energy consumption prediction model, perform energy consumption prediction for the ultra-low energy consumption building and determine the energy consumption prediction value of the ultra-low energy consumption building;

[0137] In this embodiment, energy consumption prediction is performed for ultra-low energy consumption buildings by performing the following operations:

[0138] Collect real-time data of ultra-low energy consumption buildings based on energy consumption analysis requirements of ultra-low energy consumption buildings;

[0139] Input the real-time data of ultra-low energy consumption buildings into the optimal ultra-low energy consumption building energy consumption prediction model;

[0140] Based on the optimal ultra-low energy consumption prediction model, the real-time data of ultra-low energy consumption buildings is mined and analyzed to predict the energy consumption of ultra-low energy consumption buildings;

[0141] Determine the predicted energy consumption of ultra-low energy buildings.

[0142] It should be noted that by mining and analyzing the real-time data of ultra-low energy consumption buildings through the optimal ultra-low energy consumption prediction model, the energy consumption of ultra-low energy consumption buildings can be predicted, and the energy consumption prediction value of ultra-low energy consumption buildings can be determined, which is convenient for subsequent analysis of the energy consumption of ultra-low energy consumption buildings and also convenient for subsequent timely management of the energy consumption of ultra-low energy consumption buildings.

[0143] S4: Based on the energy consumption standards for ultra-low energy consumption buildings, analyze the predicted energy consumption values of ultra-low energy consumption buildings and determine the energy consumption analysis results of ultra-low energy consumption buildings.

[0144] In this embodiment, the predicted energy consumption value of the ultra-low energy consumption building is analyzed, and the following operations are performed:

[0145] According to the energy consumption analysis requirements of ultra-low energy consumption buildings, the energy consumption standards of ultra-low energy consumption buildings are set in advance;

[0146] Based on the energy consumption standards of ultra-low energy buildings, analyze the predicted energy consumption values of ultra-low energy buildings and determine the energy consumption analysis results of ultra-low energy buildings;

[0147] Among them, if the predicted energy consumption value of the ultra-low energy consumption building is within the energy consumption standard range of the ultra-low energy consumption building, the energy consumption analysis result of the ultra-low energy consumption building is normal;

[0148] Among them, if the predicted energy consumption value of the ultra-low energy consumption building is not within the energy consumption standard range of the ultra-low energy consumption building, the energy consumption analysis result of the ultra-low energy consumption building is abnormal energy consumption of the ultra-low energy consumption building;

[0149] When the energy consumption of ultra-low energy consumption buildings is abnormal, early warning reminders will be issued to guide management personnel to take energy-saving measures in a timely manner, and the design and layout of ultra-low energy consumption buildings will be optimized to ensure normal energy consumption of ultra-low energy consumption buildings.

[0150] It should be noted that based on the energy consumption standards for ultra-low energy consumption buildings, the predicted energy consumption values of ultra-low energy consumption buildings are analyzed. The energy consumption analysis results of ultra-low energy consumption buildings are shown in Table 1:

[0151] Table 1: Energy consumption analysis results of ultra-low energy buildings

[0152]

[0153]

[0154] Therefore, based on the energy consumption standards of ultra-low energy consumption buildings, the predicted energy consumption values of ultra-low energy consumption buildings are analyzed to determine the energy consumption analysis results of ultra-low energy consumption buildings. Based on the energy consumption analysis results of ultra-low energy consumption buildings, an energy consumption management and control plan for ultra-low energy consumption buildings is formulated. Based on the energy consumption management and control plan for ultra-low energy consumption buildings, the energy consumption of ultra-low energy consumption buildings is managed and controlled, such as: issuing early warning reminders for energy consumption of ultra-low energy consumption buildings, guiding management personnel to take energy-saving measures in a timely manner, and optimizing the design and layout of ultra-low energy consumption buildings to ensure normal energy consumption of ultra-low energy consumption buildings.

[0155] It should be noted that, in this document, relational terms such as first and second, etc., are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the terms "comprises," "comprising," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus.

[0156] While embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions, and variations may be made to these embodiments without departing from the principles and spirit of the invention, and that the scope of the invention is defined by the appended claims and their equivalents.

Claims

1. A method for analyzing energy consumption of ultra-low energy buildings, characterized in that: The steps include: S1: According to the energy consumption analysis requirements of ultra-low energy consumption buildings, collect and process the historical data of ultra-low energy consumption buildings to determine the characteristic data of ultra-low energy consumption buildings; S2: Construct an energy consumption prediction model for ultra-low energy consumption buildings, test and optimize the energy consumption prediction model for ultra-low energy consumption buildings, and determine the optimal energy consumption prediction model for ultra-low energy consumption buildings; S3: Based on the optimal ultra-low energy consumption building energy consumption prediction model, perform energy consumption prediction for the ultra-low energy consumption building and determine the energy consumption prediction value of the ultra-low energy consumption building; S4: Based on the energy consumption standards for ultra-low energy consumption buildings, analyze the predicted energy consumption values of ultra-low energy consumption buildings and determine the energy consumption analysis results of ultra-low energy consumption buildings; In S2, an ultra-low energy consumption building energy consumption prediction model is constructed, and the following operations are performed: Obtain characteristic data of ultra-low energy consumption buildings; Divide the characteristic data of ultra-low energy consumption buildings to determine the training set and test set; Select a convolutional neural network model framework suitable for energy consumption prediction of ultra-low energy buildings; Based on the training set, the convolutional neural network model framework suitable for ultra-low energy consumption building energy consumption prediction is trained to determine the ultra-low energy consumption building energy consumption prediction model; The characteristic data of ultra-low energy consumption buildings are divided to determine the training set and test set, including: Extracting the amount of invalid values in the historical data of the ultra-low energy consumption building; Extracting the number of missing data corresponding to the missing values in the historical data of the ultra-low energy consumption building; Obtaining an outlier degree coefficient by combining the amount of invalid values in the ultra-low energy consumption building historical data with the number of missing data corresponding to the missing values in the ultra-low energy consumption building historical data; The outlier degree coefficient is obtained by the following formula: Among them, E represents the outlier degree coefficient; C w Indicates the amount of invalid values in the historical data of the ultra-low energy consumption building; C z Represents the total amount of historical data of ultra-low energy consumption buildings; N x N represents the number of missing data corresponding to the missing values in the historical data of the ultra-low energy consumption building; z The total number of historical data representing ultra-low energy consumption buildings; Comparing the outlier degree coefficient with a preset coefficient threshold; Determining target ratios of the training set and the test set based on a comparison result between the outlier degree coefficient and a preset coefficient threshold; The ultra-low energy consumption building characteristic data is divided according to the target ratios corresponding to the training set and the test set to determine the training set and the test set.

2. The method for analyzing energy consumption of ultra-low energy buildings according to claim 1, characterized in that: In S1, historical data of ultra-low energy consumption buildings is collected and the following operations are performed: According to the energy consumption analysis requirements of ultra-low energy consumption buildings, the area, shape, geographical location, orientation, enclosure materials, window configuration and foundation configuration of ultra-low energy consumption buildings are collected to obtain basic data of ultra-low energy consumption buildings; According to the energy consumption analysis requirements of ultra-low energy consumption buildings, the temperature, humidity, light and wind speed of the environment in which the ultra-low energy consumption buildings are located are collected to obtain the environmental data of ultra-low energy consumption buildings; Among them, the historical data of ultra-low energy consumption buildings are determined based on the basic data of ultra-low energy consumption buildings and the environmental data of ultra-low energy consumption buildings.

3. The method for analyzing energy consumption of ultra-low energy buildings according to claim 2, characterized in that: In S1, the historical data of ultra-low energy consumption buildings is processed, and the following operations are performed: Obtain historical data on ultra-low energy buildings; Cleaning and processing of historical data of ultra-low energy buildings, including: Conduct consistency checks on historical data of ultra-low energy buildings; According to the data consistency requirements, check whether there is inconsistent data in the historical data of ultra-low energy consumption buildings that is useless for the energy consumption analysis of ultra-low energy consumption buildings, and remove the inconsistent data in the historical data of ultra-low energy consumption buildings; Process invalid and missing values in historical data of ultra-low energy consumption buildings; According to the requirements of data validity and completeness, check whether there are invalid values and missing values in the historical data of ultra-low energy consumption buildings that are useless for the energy consumption analysis of ultra-low energy consumption buildings, and remove the invalid values and missing values in the historical data of ultra-low energy consumption buildings; Identify historical data of ultra-low energy buildings that are useful for energy consumption analysis of ultra-low energy buildings.

4. The method for analyzing energy consumption of ultra-low energy buildings according to claim 3, characterized in that: In S1, the historical data of ultra-low energy consumption buildings is processed, and the following operations are performed: Obtain historical data of ultra-low energy buildings that are useful for energy consumption analysis of ultra-low energy buildings; Convert and process historical data of ultra-low energy buildings that are useful for energy consumption analysis of ultra-low energy buildings; Eliminate the dimensional differences between historical data of ultra-low energy consumption buildings that are useful for energy consumption analysis of ultra-low energy consumption buildings, unify the formats of historical data of ultra-low energy consumption buildings that are useful for energy consumption analysis of ultra-low energy consumption buildings, and determine standardized historical data of ultra-low energy consumption buildings; Extract features from standardized historical data of ultra-low energy buildings; Extract features that can reflect the energy consumption analysis of ultra-low energy buildings; Determine the characteristic data of ultra-low energy consumption buildings.

5. The method for analyzing energy consumption of ultra-low energy buildings according to claim 4, characterized in that: Determining the corresponding ratios of the training set and the test set according to a comparison result between the outlier degree coefficient and a preset coefficient threshold includes: Retrieving a comparison result between the outlier degree coefficient and a preset coefficient threshold; When the comparison result between the abnormal value degree coefficient and the preset coefficient threshold value indicates that the abnormal value degree coefficient is lower than the preset coefficient threshold value, the preset initial ratio value is retrieved as the target ratio; When the comparison result between the abnormal value degree coefficient and the preset coefficient threshold value indicates that the abnormal value degree coefficient is not lower than the preset coefficient threshold value, a preset initial ratio value is retrieved; The target ratio is obtained by using the outlier degree coefficient and the preset initial ratio value, wherein the target ratio is obtained by the following formula: Among them, R represents the target ratio; R0 represents the preset initial ratio value; E represents the abnormal value degree coefficient; N x N represents the number of missing data corresponding to the missing values in the historical data of the ultra-low energy consumption building; c represents the number of data corresponding to invalid values in the historical data of ultra-low energy consumption buildings; T represents the preset coefficient threshold; C w Indicates the amount of invalid values in the historical data of the ultra-low energy consumption building; C z Represents the total amount of historical data of ultra-low energy consumption buildings; N z represents the total number of historical data of ultra-low energy consumption buildings; x represents the adjustment coefficient, and the value range of the adjustment coefficient is 0.21-1.

88.

6. The method for analyzing energy consumption of ultra-low energy buildings according to claim 5, characterized in that: In S2, the energy consumption prediction model for ultra-low energy consumption buildings is tested and optimized, and the following operations are performed: Obtain energy consumption prediction models for ultra-low energy buildings; Based on the test set, the performance of the ultra-low energy consumption building energy consumption prediction model is tested to determine whether the performance of the ultra-low energy consumption building energy consumption prediction model can achieve the expected effect; Determine the performance test results based on the ultra-low energy building energy consumption prediction model; Based on the performance test results, the energy consumption prediction model of ultra-low energy consumption buildings is mined and analyzed, the parameter configuration of the energy consumption prediction model of ultra-low energy consumption buildings is adjusted, the structural setting of the energy consumption prediction model of ultra-low energy consumption buildings is optimized, and the energy consumption prediction model of ultra-low energy consumption buildings is repeatedly iterated and optimized to determine the optimal energy consumption prediction model of ultra-low energy consumption buildings.

7. The method for analyzing energy consumption of ultra-low energy buildings according to claim 6, characterized in that: In S3, energy consumption prediction is performed on ultra-low energy consumption buildings by performing the following operations: Collect real-time data of ultra-low energy consumption buildings based on energy consumption analysis requirements of ultra-low energy consumption buildings; Input the real-time data of ultra-low energy consumption buildings into the optimal ultra-low energy consumption building energy consumption prediction model; Based on the optimal ultra-low energy consumption prediction model, the real-time data of ultra-low energy consumption buildings is mined and analyzed to predict the energy consumption of ultra-low energy consumption buildings; Determine the predicted energy consumption of ultra-low energy buildings.

8. The method for analyzing energy consumption of ultra-low energy buildings according to claim 7, characterized in that: In S4, the predicted energy consumption value of the ultra-low energy consumption building is analyzed, and the following operations are performed: According to the energy consumption analysis requirements of ultra-low energy consumption buildings, the energy consumption standards of ultra-low energy consumption buildings are set in advance; Based on the energy consumption standards of ultra-low energy buildings, analyze the predicted energy consumption values of ultra-low energy buildings and determine the energy consumption analysis results of ultra-low energy buildings; Among them, if the predicted energy consumption value of the ultra-low energy consumption building is within the energy consumption standard range of the ultra-low energy consumption building, the energy consumption analysis result of the ultra-low energy consumption building is normal; Among them, if the predicted energy consumption value of the ultra-low energy consumption building is not within the energy consumption standard range of the ultra-low energy consumption building, the energy consumption analysis result of the ultra-low energy consumption building is abnormal energy consumption of the ultra-low energy consumption building; When the energy consumption of ultra-low energy consumption buildings is abnormal, early warning reminders will be issued to guide management personnel to take energy-saving measures in a timely manner, and the design and layout of ultra-low energy consumption buildings will be optimized to ensure normal energy consumption of ultra-low energy consumption buildings.

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