Building room temperature analysis method based on heat supply measured data

Abstract

The invention belongs to the technical field of building room temperature analysis, and particularly relates to a building room temperature analysis method based on heat supply measured data. Comprising the following steps that indoor temperature data and outdoor environment data are obtained, after dimension unification is conducted on the indoor temperature data and the outdoor environment data, the incidence relation between the building room temperature and the indoor temperature data and the incidence relation between the building room temperature and the outdoor environment data are established, and the building room temperature is obtained through calculation according to the incidence relation; the established incidence relation is evaluated and corrected through historical building room temperature data, the indoor temperature data and the outdoor environment data, the corrected incidence relation is obtained, the corrected incidence relation is redefined as a determined incidence relation, the indoor temperature data and the outdoor environment data serve as variables to be input into the determined incidence relation, and therefore the indoor temperature data and the outdoor environment data are obtained. And obtaining the building room temperature of the heat supply measured data. The method is reliable in data and simple.

Description

Technical Field

[0001] The present invention relates to the technical field of building room temperature analysis, and particularly to a building room temperature analysis method based on measured heating data. Background Art

[0002] The description of the background art in the present invention belongs to the related technologies related to the present invention, and is only used to illustrate and facilitate the understanding of the content of the present invention, and should not be construed as the applicant clearly believing or presuming that the applicant believes it to be the prior art on the filing date of the first application of the present invention.

[0003] With the continuous improvement of people's requirements for indoor environmental quality and the increasingly severe problem of energy shortage, building room temperature analysis has become an important research topic in the building field. Traditional building room temperature analysis methods mainly rely on empirical formulas and simple statistical analysis, and it is difficult to accurately reflect the complex relationship between indoor temperature and outdoor environment and other factors.

[0004] Currently, although there are some building room temperature analysis methods based on data analysis, these methods have the following deficiencies:

[0005] Single data source: Most methods only consider a few factors such as indoor temperature and outdoor air temperature, and ignore the influence of other important outdoor environmental factors such as radiation, illuminance, humidity, wind speed, wind direction, and perceived temperature on building room temperature.

[0006] Simple analysis method: Mainly adopt traditional data analysis methods such as linear regression, and cannot effectively handle non-linear relationships and multi-factor interactions.

[0007] Lack of practicality: Some methods are theoretically feasible to a certain extent, but there are problems such as difficult data collection, complex calculation, and inaccurate results in actual applications.

[0008] Therefore, there is an urgent need for a more scientific, accurate and practical building room temperature analysis method to meet the needs of the building field for room temperature analysis. Summary of the Invention

[0009] The purpose of the present invention is to provide a building room temperature analysis method based on measured heating data, which can comprehensively consider multiple factors such as indoor temperature and outdoor environment (radiation, illuminance, air temperature, humidity, wind speed, wind direction, perceived temperature, etc.), and adopt scientific quantitative or artificial intelligence-based data analysis methods to analyze and summarize the measured data, and excavate the characteristics of building room temperature and its associated influencing factors, so as to provide scientific basis and technical support for building energy conservation, indoor environmental quality assessment, heating system optimization, etc.

[0010] A building room temperature analysis method based on measured heating data includes the following steps:

[0011] Obtain indoor temperature data and outdoor environment data, unify the dimensions of the indoor temperature data and outdoor environment data, establish the correlation between the building room temperature and the indoor temperature data and outdoor environment data, and calculate the building room temperature according to the correlation; use the historical building room temperature data, indoor temperature data, and outdoor environment data to evaluate and correct the established correlation to obtain the corrected correlation, redefine the corrected correlation as the determined correlation, and use the indoor temperature data and outdoor environment data as variables to input into the determined correlation to obtain the building room temperature of the measured heating data.

[0012] Further, the correlation is:

[0013]

[0014] Among them, T_building is the building room temperature, e is a constant, M is the irradiance intensity, M0 is the standard irradiance intensity; L is the illuminance, L0 is the standard illuminance, t is the external air temperature, t0 is the standard external air temperature; K is the humidity, K0 is the standard humidity, X is the perceived temperature, X0 is the standard perceived temperature, D is the wind speed, D0 is the standard wind speed.

[0015] Further, in the formula When the wind direction is southeast, take +, and when it is northwest, take -.

[0016] 4. The method for analyzing the building room temperature based on the measured heating data according to claim 2, characterized in that the method for correcting the above formula using historical data is:

[0017] Input the irradiance intensity, illuminance, external air temperature, humidity, perceived temperature, and wind speed in the historical data into the formula to obtain the predicted value of the building room temperature, compare it with the building room temperature in the historical data, if the deviation is within 10%, then update the formula to:

[0018]

[0019] Among them, T_building is the building room temperature, e is a constant, M is the irradiance intensity, M0 is the standard irradiance intensity; L is the illuminance, L0 is the standard illuminance, t is the external air temperature, t0 is the standard external air temperature; K is the humidity, K0 is the standard humidity, X is the perceived temperature, X0 is the standard perceived temperature, D is the wind speed, D0 is the standard wind speed, Q is the deviation value between the prediction and the historical data, less than 10%.

[0020] Further, the Q is the average value of the deviation values calculated from multiple historical data.

[0021] Further, if the number of deviation values greater than 10% among the multiple calculated deviation values exceeds the set ratio, the standard sensible temperature and standard wind speed are modified so that the number of deviation values greater than 10% is less than the set ratio, and then the above formula is executed and the correction step is performed.

[0022] The embodiments of the present invention have the following beneficial effects:

[0023] Comprehensive consideration of multiple factors: The method of the present invention comprehensively considers multiple factors such as indoor temperature, outdoor environment (radiation, illuminance, air temperature, humidity, wind speed, wind direction, sensible temperature, etc.), house type, etc., and can analyze the characteristics of building room temperature and its associated influencing factors more comprehensively and accurately.

[0024] Adoption of scientific quantitative and artificial intelligence analysis methods: The method of the present invention adopts scientific quantitative analysis methods such as multiple linear regression analysis, principal component analysis, correlation analysis, etc. and artificial intelligence-based analysis methods such as artificial neural network, support vector machine, genetic algorithm, etc., and can effectively handle non-linear relationships and multi-factor interactions, improving the accuracy and reliability of the analysis results.

[0025] Strong practicality: The method of the present invention collects measured data by installing devices such as temperature sensors and weather stations. The data source is reliable, the calculation method is simple, the results are accurate, and it has strong practicality.

[0026] Wide application fields: The method of the present invention can be applied to multiple fields such as building energy conservation, indoor environmental quality assessment, heating system optimization, etc., providing technical support for the sustainable development of the building field. Specific implementation manners

[0027] The following further introduces the present application in combination with embodiments.

[0028] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, in the following description, different "one embodiment" or "embodiment" do not necessarily refer to the same embodiment. Different embodiments can be replaced or combined, and for those of ordinary skill in the art, other implementation manners can also be obtained based on these embodiments without creative efforts.

[0029] A method for analyzing building room temperature based on measured heating data includes the following steps:

[0030] Obtain indoor temperature data and outdoor environmental data, unify the dimensions of the indoor temperature data and outdoor environmental data, establish the correlation between the building room temperature and the indoor temperature data and outdoor environmental data, and calculate the building room temperature according to the correlation; use the historical building room temperature data, indoor temperature data, and outdoor environmental data to evaluate and correct the established correlation to obtain the corrected correlation, redefine the corrected correlation as the determined correlation, and use the indoor temperature data and outdoor environmental data as variables to input into the determined correlation to obtain the building room temperature of the measured heating data.

[0031] Further, the correlation is as follows:

[0032]

[0033] Among them, T_building is the building room temperature, e is a constant, M is the irradiance intensity, M0 is the standard irradiance intensity; L is the illuminance, L0 is the standard illuminance, t is the external air temperature, t0 is the standard external air temperature; K is the humidity, K0 is the standard humidity, X is the perceived temperature, X0 is the standard perceived temperature, D is the wind speed, D0 is the standard wind speed.

[0034] Further, in the formula When the wind direction is southeast, take +, and when it is northwest, take -.

[0035] Further, the method of correcting the above formula using historical data is as follows:

[0036] Input the irradiance intensity, illuminance, external air temperature, humidity, perceived temperature, and wind speed in the historical data into the formula to obtain the predicted value of the building room temperature, compare it with the building room temperature in the historical data. If the deviation is within 10%, then update the formula to:

[0037]

[0038] Among them, T_building is the building room temperature, e is a constant, M is the irradiance intensity, M0 is the standard irradiance intensity; L is the illuminance, L0 is the standard illuminance, t is the external air temperature, t0 is the standard external air temperature; K is the humidity, K0 is the standard humidity, X is the perceived temperature, X0 is the standard perceived temperature, D is the wind speed, D0 is the standard wind speed, Q is the deviation value between the prediction and historical data, less than 10%.

[0039] Further, the Q is the average value of the deviation values calculated from multiple historical data.

[0040] Further, if the number of deviation values greater than 10% among the multiple calculated deviation values exceeds the set proportion, then modify the standard perceived temperature and standard wind speed so that the number of deviation values greater than 10% is less than the set proportion, and then execute the above formula and the correction steps.

[0041] Data collection

[0042] (1) Indoor temperature data collection: Install temperature sensors in the building interior to collect indoor temperature data in real time. The installation locations of the temperature sensors should be representative and able to reflect the overall indoor temperature level.

[0043] (2) Outdoor environment data collection: Collect outdoor environment data such as radiation, illuminance, air temperature, humidity, wind speed, wind direction, and perceived temperature through weather stations installed around the building. The installation locations of the weather stations should be avoided from being blocked by buildings and affected by other interference factors.

[0044] Result output and application

[0045] (1) Result output: Output the analyzed building room temperature characteristics and their associated influencing factors in the form of charts, reports, etc., providing a scientific basis for building energy conservation, indoor environmental quality assessment, heating system optimization, etc.

[0046] (2) Application fields

[0047] Building energy conservation: Optimize the design of the building envelope structure and adjust the operating parameters of the heating system according to the analysis results to improve the building energy utilization efficiency.

[0048] Indoor environmental quality assessment: Evaluate the indoor thermal comfort of the building and provide suggestions for improving the indoor environmental quality.

[0049] Heating system optimization: Provide decision-making support for the planning, design, operation, and management of the heating system, improving the reliability and stability of the heating system.

[0050] (3) Beneficial effects

[0051] Comprehensive consideration of multiple factors: The method of the present invention comprehensively considers multiple factors such as indoor temperature and outdoor environment (radiation, illuminance, air temperature, humidity, wind speed, wind direction, perceived temperature, etc.), and can analyze the building room temperature characteristics and their associated influencing factors more comprehensively and accurately.

[0052] Adoption of scientific quantitative and artificial intelligence analysis methods: The method of the present invention adopts scientific quantitative analysis methods such as multiple linear regression analysis, principal component analysis, and correlation analysis, as well as artificial intelligence-based analysis methods such as artificial neural networks, support vector machines, and genetic algorithms, which can effectively handle non-linear relationships and multi-factor interactions, improving the accuracy and reliability of the analysis results.

[0053] Strong practicability: The method of the present invention collects measured data by installing devices such as temperature sensors and weather stations. The data source is reliable, the calculation method is simple, the results are accurate, and it has strong practicability.

[0054] Wide range of application fields: The method of the present invention can be applied to multiple fields such as building energy conservation, indoor environmental quality assessment, and heating system optimization, providing technical support for the sustainable development of the building field.

[0055] (I) Effects

[0056] Accurately analyze the characteristics of building room temperature: Through the analysis of measured data, it can accurately reveal the change rules and characteristics of building room temperature, providing a scientific basis for building energy conservation and indoor environmental quality assessment.

[0057] Determine the associated influencing factors: It can determine the correlation between indoor temperature and outdoor environmental factors, find out the main factors affecting building room temperature, and provide guidance for heating system optimization and building design.

[0058] Improve energy utilization efficiency: According to the analysis results, optimizing the design of building envelopes and the operating parameters of heating systems can effectively improve the energy utilization efficiency of buildings and reduce energy consumption.

[0059] Improve indoor thermal comfort: By evaluating the indoor thermal comfort of buildings, it provides suggestions for improving indoor environmental quality and enhances people's quality of life.

[0060] (II) Advantages

[0061] Comprehensive data: The method of the present invention considers multiple factors of indoor temperature and outdoor environment, with more comprehensive data, and can more accurately reflect the actual situation of building room temperature.

[0062] Advanced method: Adopting scientific quantitative and artificial intelligence-based data analysis methods, it can handle complex non-linear relationships and multi-factor interactions, improving the accuracy and reliability of analysis results.

[0063] Strong real-time performance: By collecting and analyzing data in real time, it can timely understand the changes in building room temperature, providing a basis for the dynamic adjustment of heating systems.

[0064] Good scalability: The method of the present invention can flexibly increase or decrease analysis factors according to actual needs, with good scalability.

[0065] Low cost: Using common devices such as temperature sensors and weather stations for data collection, the cost is low and it is easy to promote and apply.

[0066] (I) Establishment of data acquisition system

[0067] Selection and installation of temperature sensors

[0068] Select temperature sensors with high precision and good stability, such as digital temperature sensors.

[0069] According to the layout of the building and the types of rooms, reasonably determine the installation positions of temperature sensors to ensure that the indoor temperature can be accurately reflected. Generally speaking, at least one temperature sensor should be installed in each room. For larger rooms or important areas, the number of sensors can be appropriately increased.

[0070] Connect the temperature sensors to the data acquisition device to ensure accurate data transmission.

[0071] Selection and installation of weather stations

[0072] Select a weather station with complete functions and high precision, which can measure outdoor environmental parameters such as radiation, illuminance, air temperature, humidity, wind speed, wind direction, and perceived temperature.

[0073] The installation position of the weather station should be selected in an open and unobstructed place around the building to avoid being affected by buildings and other obstacles. At the same time, ensure that the weather station is firmly installed and can withstand various adverse weather conditions.

[0074] Connect the weather station to the data acquisition device and set the data acquisition frequency and transmission method.

[0075] Data preprocessing

[0076] Clean and screen the collected temperature data and outdoor environmental data to remove outliers and noise data.

[0077] Normalize the data to convert data with different dimensions into a unified standard form for subsequent analysis.

[0078] Development of an analysis module based on artificial intelligence

[0079] Artificial neural network: Select a suitable artificial neural network structure, such as a feedforward neural network, a feedback neural network, etc. Use the collected measured data to train the neural network and adjust the network parameters to improve the accuracy of the model. Existing neural network development tools such as TensorFlow and PyTorch can be used, or neural network programs can be written independently.

[0080] Support vector machine: Use the support vector machine algorithm to establish a non-linear relationship model between indoor temperature and outdoor environmental factors. Select suitable kernel functions and parameters to improve the prediction ability of the model. Existing support vector machine software packages such as LIBSVM can be used, or support vector machine programs can be written independently.

[0081] Genetic algorithm: Use the genetic algorithm to optimize the parameters of models such as artificial neural networks or support vector machines to improve the performance of the models. Write a genetic algorithm program and set appropriate genetic parameters such as population size, crossover probability, and mutation probability.

[0082] Result Output and Visualization

[0083] Output the analyzed building room temperature characteristics and their associated influencing factors in the form of charts, reports, etc., to facilitate users to intuitively understand the analysis results.

[0084] Develop a data visualization tool to display the collected measured data and analysis results in a graphical way, such as line charts, bar charts, heat maps, etc., to facilitate users for data analysis and decision-making.

[0085] (III) System Testing and Optimization

[0086] Data Acquisition Accuracy Testing

[0087] Calibrate the temperature sensors and weather stations to ensure the accuracy and reliability of the collected data.

[0088] Conduct data acquisition tests under different environmental conditions, compare the error between the collected data and the actual values, analyze the reasons for the error, and make corresponding adjustments and optimizations.

[0089] Data Analysis Accuracy Testing

[0090] Use the known building room temperature data and influencing factor data to test the developed data analysis software, compare the difference between the analysis results and the actual situation, and evaluate the accuracy and reliability of the analysis method.

[0091] Adjust the parameters and model structure of the analysis method to improve the accuracy and stability of the analysis results.

[0092] System Performance Testing

[0093] Test the running performance of the data acquisition system and data analysis software, including data acquisition frequency, data processing speed, system response time, etc.

[0094] Optimize the hardware configuration and software algorithm of the system to improve the performance and stability of the system.

[0095] (IV) Practical Application Cases

[0096] Building Energy Conservation Application

[0097] Select a typical building as the test object and install the data acquisition system and analysis software of the present invention.

[0098] Conduct on-site measurements of the building for a period of time and collect data such as indoor temperature and outdoor environment.

[0099] Use the analysis software to analyze the measured data and find out the main factors affecting the building room temperature, such as the heat transfer coefficient of the enclosure structure, the shading coefficient of the windows, the operating parameters of the heating system, etc.

[0100] According to the analysis results, propose building energy-saving renovation plans, such as replacing thermal insulation materials, adding sunshade facilities, optimizing the operating parameters of the heating system, etc.

[0101] Conduct another on-site measurement of the renovated building to evaluate the energy-saving effect.

[0102] Indoor environmental quality assessment application

[0103] Select multiple buildings of different types as test objects and install a data acquisition system and analysis software.

[0104] Conduct long-term on-site measurements of the buildings to collect indoor temperature, outdoor environment, and data.

[0105] Use the analysis software to analyze the measured data and evaluate the indoor thermal comfort of the buildings, such as calculating indicators like PMV (Predicted Mean Vote) and PPD (Predicted Percentage of Dissatisfied).

[0106] According to the evaluation results, propose suggestions for improving the indoor environmental quality, such as adjusting the air conditioning temperature, increasing the ventilation rate, improving indoor lighting, etc.

[0107] Heating system optimization application

[0108] Select a heating area as a test object and install a data acquisition system and analysis software.

[0109] Conduct on-site measurements of the buildings in the heating area to collect indoor temperature and outdoor environment data.

[0110] Use the analysis software to analyze the measured data, establish a mathematical model of the heating system, and predict the indoor temperature of the buildings under different working conditions.

[0111] According to the analysis results, optimize the operating parameters of the heating system, such as supply water temperature, return water temperature, flow rate, etc., to improve the efficiency and stability of the heating system.

[0112] Monitor and evaluate the optimized heating system to verify the optimization effect.

[0113] It should be noted that the above embodiments can be freely combined as needed. The above introduction is only the preferred embodiments of the present invention and is not used to limit the present invention. For those skilled in the art, the present invention can have various changes and modifications. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for analyzing the indoor temperature of a building based on measured heating data, characterized in that, The steps include: Obtain indoor temperature data and outdoor environmental data, unify the dimensions of the indoor temperature data and outdoor environmental data, establish the correlation between the building room temperature and the indoor temperature data and outdoor environmental data, and calculate the building room temperature according to the correlation; Use historical building room temperature data, indoor temperature data, and outdoor environmental data to evaluate and correct the established correlation, obtain the corrected correlation, redefine the corrected correlation as the determined correlation, and input the indoor temperature data and outdoor environmental data as variables into the determined correlation to obtain the building room temperature of the measured heating data.

2. The method for analyzing the indoor temperature of a building based on the measured heating data according to claim 1, characterized in that The said correlation is: Among them, T_building is the building room temperature, e is a constant, M is the irradiance intensity, M0 is the standard irradiance intensity; L is the illuminance, L0 is the standard illuminance, t is the external air temperature, t0 is the standard external air temperature; K is the humidity, K0 is the standard humidity, X is the perceived temperature, X0 is the standard perceived temperature, D is the wind speed, D0 is the standard wind speed.

3. The method for analyzing the indoor temperature of a building based on the actual measured heating data according to claim 2, characterized in that, In the formula described When the wind direction is southeast, take +, and when it is northwest, take -.

4. The method for analyzing the indoor temperature of a building based on the measured heating data according to claim 2, wherein The method of correcting the above formula using historical data is: Input the irradiance intensity, illuminance, external air temperature, humidity, perceived temperature, and wind speed in the historical data into the formula to obtain the predicted value of the building room temperature, compare it with the building room temperature in the historical data. If the deviation is within 10%, then update the formula to: Among them, T_building is the building room temperature, e is a constant, M is the irradiance intensity, M0 is the standard irradiance intensity; L is the illuminance, L0 is the standard illuminance, t is the external air temperature, t0 is the standard external air temperature; K is the humidity, K0 is the standard humidity, X is the perceived temperature, X0 is the standard perceived temperature, D is the wind speed, D0 is the standard wind speed, Q is the deviation value between the prediction and the historical data, less than 10%.

5. The method for analyzing the room temperature of a building based on the measured heating data according to claim 4, wherein The said Q is the average value of the deviation values calculated from multiple historical data.

6. The method for analyzing the indoor temperature of a building based on the actual measured heating data according to claim 5, characterized in that, If the number of deviation values greater than 10% among the multiple calculated deviation values exceeds the set ratio, then modify the standard perceived temperature and standard wind speed so that the number of deviation values greater than 10% is less than the set ratio, and then execute the above formula and the correction steps.

Images