Dynamic Management Platform and Method for Multi-Dimensional Energy Saving Analysis of Buildings

Abstract

The present invention discloses a multi-dimensional energy-saving analysis dynamic management platform and method for buildings, which relates to the technical field of building energy-saving management, and includes the following steps: The indoor temperature is monitored in real time through a temperature monitoring system, and the collected temperature data is transmitted to a control system; The obtained current indoor temperature is compared with the preset start value and stop value of the air-conditioning system, and the start and stop of the air-conditioning system are controlled through the control system; The real-time indoor temperature data is collected and constructed into an analysis data set, and key features capable of reflecting the temperature change trend are extracted therefrom. Through real-time temperature data analysis and machine learning prediction, the present invention accurately divides the temperature change stage, adopts conventional start and stop control in the normal stage, and dynamically adjusts the output power through fuzzy logic in the rapid stage, avoiding frequent start and stop and energy consumption waste, improving the temperature control accuracy and comfort, significantly improving the energy efficiency, reducing the operation cost, prolonging the service life of air-conditioning equipment, and providing an efficient and intelligent energy-saving management solution.

Description

Technical Field

[0001] The present invention relates to the technical field of building energy conservation management, and particularly to a multi-dimensional energy conservation analysis dynamic management platform and method for buildings. Background Art

[0002] The multi-dimensional energy conservation analysis dynamic management platform is an integrated system designed to achieve comprehensive monitoring and optimized management of energy consumption through real-time data collection, analysis, and prediction. The platform inputs energy consumption data from multiple dimensions, such as equipment usage status, environmental parameters, workload, energy consumption type, etc., combines advanced data analysis algorithms and machine learning models, dynamically evaluates various energy efficiency indicators, and provides targeted energy conservation suggestions. The platform can monitor the energy consumption trend in real time, identify potential waste areas, and adjust energy efficiency according to different time periods, working condition changes, or external environmental factors. Through flexible management strategies and feedback mechanisms, the platform helps enterprises achieve continuous energy optimization, reduce energy consumption costs, improve energy usage efficiency, and has strong adaptability and scalability, capable of being customized according to different application scenarios and requirements.

[0003] The prior art has the following deficiencies:

[0004] In the prior art, traditional air conditioners usually control start and stop by setting the temperature. When the indoor temperature reaches the set stop value, the air conditioner stops working; when the temperature deviates from the set start value, the air conditioner restarts. Although this control method is simple and easy to implement, in the case of rapid temperature environment changes, the frequent start and stop of the air conditioner will cause indoor temperature fluctuations and it is difficult to maintain a stable comfortable environment. At the same time, each time it starts, the air conditioner compressor needs to consume a large amount of energy to quickly adjust the temperature, and after shutdown, the system enters the standby state, resulting in energy waste. Especially when starting, the compressor requires a large power to overcome the initial load, leading to low energy efficiency. Therefore, the traditional on / off control method is not efficient in some environments. The frequent start and stop not only affect comfort but also increase unnecessary energy consumption.

[0005] The above information disclosed in the background art section is only used to enhance the understanding of the background of the present disclosure, and thus it may include information that does not constitute the prior art known to those of ordinary skill in the art. Summary of the Invention

[0006] The object of the present invention is to provide a multi-dimensional energy-saving analysis dynamic management platform and method for buildings. Through the collection and analysis of real-time temperature data, combined with the prediction of machine learning models, it can accurately divide the temperature change stages. Thus, during the normal temperature change stage, conventional start-stop control is adopted to reduce system complexity and energy consumption; during the rapid temperature change stage, the output power of the air conditioner is dynamically adjusted through fuzzy logic to ensure continuous operation of the air conditioner, maintain a stable room temperature, and avoid energy waste and equipment wear caused by frequent start-up of the compressor. This not only improves the comfort of the indoor environment and temperature control accuracy, but also significantly improves the energy efficiency of the system, reduces the operating cost, and extends the service life of the air conditioner equipment to solve the problems in the above-mentioned background technology.

[0007] To achieve the above object, the present invention provides the following technical solutions: A multi-dimensional energy-saving analysis dynamic management method for buildings, including the following steps:

[0008] Real-time monitor the indoor temperature through a temperature monitoring system, and transmit the collected temperature data to the control system;

[0009] Compare the obtained current indoor temperature with the preset start value and stop value of the air conditioning system, and control the start and stop of the air conditioning system through the control system;

[0010] Collect the real-time indoor temperature data and construct it into an analysis data set, and extract the key features that can reflect the temperature change trend from it;

[0011] Under the monitoring window, deeply analyze the extracted features, and input the processed data into a pre-trained machine learning model to identify and understand the law of indoor temperature change and predict the future temperature change trend;

[0012] Predict the indoor temperature change through the machine learning model, and divide the indoor temperature change into a normal temperature change stage and a rapid temperature change stage;

[0013] During the normal temperature change stage, the air conditioning system continues to monitor the current indoor temperature, compare it with the set start value and stop value for analysis, and control the start and stop of the air conditioner in a conventional manner;

[0014] During the rapid temperature change stage, adopt fuzzy logic to control the output power of the air conditioner according to the indoor temperature change situation, so that the current indoor temperature is always between the start value and the stop value, ensuring that the air conditioner always maintains an operating state, maintaining the indoor temperature while avoiding frequent start and stop of the air conditioner.

[0015] Preferably, the temperature monitoring system is a plurality of temperature sensors evenly arranged in the indoor area. Temperature data is collected through the plurality of temperature sensors, and the average value of the data collected by the plurality of temperature sensors is calculated to obtain the current indoor temperature.

[0016] Preferably, key features that can reflect the temperature change trend are extracted. Among them, the extracted key features include the speed at which the indoor real-time temperature deviates from the set value, and the frequency and intensity of the real-time temperature fluctuation. After obtaining them, under the monitoring window, in-depth analysis is carried out on the speed at which the indoor real-time temperature deviates from the set value, and the frequency and intensity of the real-time temperature fluctuation, and a temperature deviation speed quantization value and a temperature fluctuation quantization value are respectively generated. The rapidity of temperature change is reflected by the temperature deviation speed quantization value; the volatility and amplitude of temperature change are reflected by the temperature fluctuation quantization value.

[0017] After obtaining the temperature deviation speed quantization value and the temperature fluctuation quantization value generated by in-depth analysis of the extracted key features, the temperature deviation speed quantization value and the temperature fluctuation quantization value are input into a pre-trained machine learning model to generate a dynamic temperature change coefficient, and the future temperature change trend is predicted through the dynamic temperature change coefficient.

[0018] Preferably, the dynamic temperature change coefficient generated during the prediction of the indoor temperature change trend through a pre-trained machine learning model under the monitoring window is compared and analyzed with a pre-set dynamic temperature change coefficient reference threshold to divide the indoor temperature change. The specific division steps are as follows:

[0019] If the dynamic temperature change coefficient generated during the prediction of the indoor temperature change trend through a pre-trained machine learning model under the monitoring window is greater than the dynamic temperature change coefficient reference threshold, the indoor temperature change is divided into a rapid temperature change stage;

[0020] If the dynamic temperature change coefficient generated during the prediction of the indoor temperature change trend through a pre-trained machine learning model under the monitoring window is less than or equal to the dynamic temperature change coefficient reference threshold, the indoor temperature change is divided into a normal temperature change stage.

[0021] Preferably, the specific steps for in-depth analysis of the speed at which the indoor real-time temperature deviates from the set value to generate a temperature deviation speed quantization value under the monitoring window are as follows:

[0022] Under the monitoring window, record the indoor real-time temperature at each time point and the target temperature set by the air-conditioning system, calculate the temperature deviation rate at each time point, and the calculation expression is:

[0023]

[0024] , where T(t) is the indoor real-time temperature at time t, T s is the target temperature set by the air conditioner, and R(t) is the temperature deviation rate at time t, expressed as a percentage;

[0025] At each time point, calculate the rate of change of the temperature deviation rate with respect to time, that is, the deviation rate acceleration. The calculation expression is:

[0026]

[0027] , where A(t) is the deviation rate acceleration at time t, representing the speed of change of the temperature deviation rate, and Δt is the time interval between adjacent time points;

[0028] Based on the deviation rate acceleration, calculate the quantization value of the temperature deviation speed to quantify the rapidity of temperature change. The formula is:

[0029]

[0030] , where TDSI xy is the quantization value of the temperature deviation speed, and Δ and β are weight factors used to control the sensitivity to the deviation rate and the temperature deviation rate. e -β·|R(t)| is the exponential decay term, and t 0 and t 1 are the start time point and the end time point of the monitoring window respectively.

[0031] Preferably, under the monitoring window, the specific steps for in-depth analysis of the frequency and intensity of the indoor real-time temperature fluctuation to generate the temperature fluctuation quantization value are as follows:

[0032] Extract the time series characteristics of the indoor real-time temperature fluctuation data. Suppose the temperature data at N time points are collected within the monitoring window and denoted as T i ={T 1 , T 2 , …, T N}, where T i is the temperature value at the i-th time point;

[0033] Calculate the amplitude of the temperature change between adjacent time points to obtain the temperature difference sequence. The calculation expression of the temperature difference sequence is:

[0034] ΔT i =T i+1 -T i

[0035] , where ΔT i represents the i-th value of the temperature difference sequence, indicating the temperature change amplitude between two adjacent sampling points, and i = 1, 2, 3, …, N - 1;

[0036] Define a cumulative fluctuation energy function to quantify the fluctuation intensity. Combining the sum of squares of the difference sequence and the time weight, it is used to measure the overall severity of the temperature fluctuation. The expression of the cumulative fluctuation energy function is:

[0037]

[0038] , where E T Cumulative fluctuation energy, representing the intensity of temperature fluctuation within the monitoring window, w i is the time weight factor, used to highlight the contribution of fluctuations at different times to the overall energy;

[0039] Combining the cumulative fluctuation energy and the consistency of the temperature change direction to calculate the temperature fluctuation quantization value to comprehensively reflect the amplitude of the fluctuation. The comprehensive calculation expression of the temperature fluctuation quantization value is:

[0040]

[0041] , in the formula, TBI xy represents the temperature fluctuation quantization value, reflecting the volatility and amplitude of the temperature change, sign(ΔT i ·ΔT i+1 ) represents calculating the direction consistency of the temperature change by judging whether the signs of adjacent temperature differences (ΔT i ·ΔT i+1 ) are consistent. δ is the direction consistency weight coefficient, used to balance the contributions of the fluctuation intensity and the direction consistency.

[0042] Preferably, in the rapid temperature change stage, use fuzzy logic to control the output power of the air conditioner according to the indoor temperature change situation, so that the current indoor temperature is always between the start value and the stop value. The specific steps are as follows:

[0043] In the rapid temperature change stage, first compare the dynamic temperature change coefficient with the reference threshold of the dynamic temperature change coefficient to determine the severity of the current temperature change, and use it as one of the input variables of the fuzzy logic. Calculate the relative position between the current indoor temperature and the start value and stop value of the air conditioner to generate a temperature deviation factor. The expression for generating the temperature deviation factor is:

[0044]

[0045] , where, ΔT relative is the temperature deviation factor, quantifying the deviation degree of the current temperature within the target range. The range of the temperature deviation factor is [0, 1], indicating the position deviation degree of the current temperature relative to the start value and the stop value. T current is the current indoor temperature, T start is the start value when the air conditioner starts, T stop is the stop value when the air conditioner stops;

[0046] Calculate the normalized value of the dynamic temperature change amplitude. The calculation expression is:

[0047]

[0048] , where Temper dyna represents the dynamic temperature change coefficient, DTC_ref represents the reference threshold of the dynamic temperature change coefficient, and ΔT dynamic is the normalized value of the dynamic temperature change, quantifying the ratio of the dynamic temperature change coefficient to the reference threshold of the dynamic temperature change coefficient;

[0049] The temperature deviation factor ΔT relative and the normalized value of the dynamic temperature change ΔT dynamic will be used as the core inputs of the fuzzy logic system to analyze the drastic change of the current temperature and its position in the target range.

[0050] Preferably, based on the input temperature deviation factor ΔT relative and the normalized value of the dynamic temperature change ΔT dynamic , an air conditioner output power adjustment coefficient is generated through fuzzy logic rules. The fuzzy logic rules consider two aspects:

[0051] When the temperature is close to the start value or the stop value and the temperature changes drastically, increase the output power to quickly stabilize the temperature;

[0052] When the temperature is far from the start value and the stop value and the temperature changes smoothly, reduce the power to avoid over-response;

[0053] The calculation expression of the fuzzy logic output is:

[0054] P adjust = min(1, max(0, k p ·ΔT relative + k q ·ΔT dynamic ))

[0055] , where k p and k q are weight coefficients, respectively controlling the influence of the relative temperature deviation and the dynamic change on the output power adjustment. P adjust is the air conditioner output power adjustment coefficient, used to dynamically adjust the output power of the air conditioner. min and max are used to limit the value range of the output power adjustment coefficient.

[0056] Preferably, the actual output power of the air conditioner is calculated according to the generated adjustment coefficient. At this stage, it is necessary to ensure that the indoor temperature is always controlled between the start value and the stop value. Then the calculation expression of the actual output power is:

[0057] P output = P max ·(1 - ΔT relativ e·P adjust )

[0058] , where P output is the actual output power of the air conditioner, which is used to maintain the indoor temperature, and P max is the maximum output power of the air conditioner, which represents the energy output capacity of the air conditioner under full load;

[0059] By dynamically adjusting the actual output power P output of the air conditioner, the indoor temperature is controlled between the start value and the stop value.

[0060] The multi-dimensional energy-saving analysis dynamic management platform of a building includes a temperature monitoring module, a temperature start / stop control module, a temperature data analysis module, a temperature change prediction module, a temperature change stage division module, a conventional start / stop control module, and a fuzzy logic power regulation module;

[0061] The temperature monitoring module monitors the indoor temperature in real time through a temperature monitoring system and transmits the collected temperature data to the control system;

[0062] The temperature start / stop control module compares the obtained current indoor temperature with the preset start value and stop value of the air conditioning system, and controls the start and stop of the air conditioning system through the control system;

[0063] The temperature data analysis module collects the real-time indoor temperature data and constructs it into an analysis data set, and extracts the key features that can reflect the temperature change trend from it;

[0064] The temperature change prediction module deeply analyzes the extracted features under the monitoring window, and inputs the processed data into a pre-trained machine learning model to identify and understand the law of indoor temperature change and predict the future temperature change trend;

[0065] The temperature change stage division module predicts the indoor temperature change through a machine learning model, and divides the indoor temperature change into a normal temperature change stage and a rapid temperature change stage;

[0066] The conventional start / stop control module, in the normal temperature change stage, the air conditioning system continues to monitor the current indoor temperature, compares it with the set start value and stop value for analysis, and controls the start and stop of the air conditioner in a conventional manner;

[0067] The fuzzy logic power regulation module, in the rapid temperature change stage, uses fuzzy logic to control the output power of the air conditioner according to the indoor temperature change situation, so that the current indoor temperature is always between the start value and the stop value, ensuring that the air conditioner always remains in operation, maintaining the indoor temperature while avoiding frequent start and stop of the air conditioner.

[0068] In the above technical solution, the technical effects and advantages provided by the present invention:

[0069] Through the collection and analysis of real-time temperature data and the prediction of a machine learning model, the present invention can accurately divide the temperature change stages. Thus, during the normal temperature change stage, conventional start-stop control is adopted to reduce the system complexity and energy consumption. During the rapid temperature change stage, the output power of the air conditioner is dynamically adjusted through fuzzy logic to ensure the continuous operation of the air conditioner, maintain the room temperature stability, and avoid the energy waste and equipment wear caused by the frequent startup of the compressor. This not only improves the comfort of the indoor environment and the temperature control accuracy but also significantly improves the energy efficiency of the system, reduces the operating cost, and extends the service life of the air conditioner equipment, providing an efficient and intelligent solution for the energy-saving management of buildings. BRIEF DESCRIPTION OF THE DRAWINGS

[0070] To more clearly illustrate the technical solutions in the embodiments of the present application or the prior art, the following will briefly introduce the drawings required for use in the embodiments. Obviously, the drawings described below are only some embodiments recorded in the present invention. For those of ordinary skill in the art, other drawings can also be obtained based on these drawings.

[0071] Figure 1 It is a method flow chart of the multi-dimensional energy-saving analysis and dynamic management method for the buildings of the present invention.

[0072] Figure 2 It is a module schematic diagram of the multi-dimensional energy-saving analysis and dynamic management platform for the buildings of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0073] Now, the exemplary embodiments will be described more comprehensively with reference to the drawings. However, the exemplary embodiments can be implemented in various forms and should not be construed as limited to the examples set forth herein. On the contrary, these exemplary embodiments are provided so that the present disclosure will be more complete and comprehensive, and the concept of the exemplary embodiments will be fully conveyed to those skilled in the art.

[0074] The present invention provides a multi-dimensional energy-saving analysis and dynamic management method for buildings as Figure 1 shown, including the following steps:

[0075] The indoor temperature is monitored in real time through a temperature monitoring system, and the collected temperature data is transmitted to the control system;

[0076] The obtained current indoor temperature is compared with the preset start value and stop value of the air conditioning system, and the start and stop of the air conditioning system are controlled through the control system;

[0077] The temperature monitoring system is a plurality of temperature sensors evenly arranged in the indoor area. The temperature data is collected through the plurality of temperature sensors, and the average value of the data collected by the plurality of temperature sensors is calculated to obtain the current indoor temperature;

[0078] By evenly arranging multiple temperature sensors in the indoor area and calculating the average value of the data collected by these sensors to obtain the current indoor temperature, the accuracy and reliability of temperature measurement can be significantly improved. The indoor temperature distribution is usually uneven and affected by factors such as air flow, heat source distribution, wall insulation, and window orientation. A single temperature sensor may only reflect the temperature of a certain local area and cannot comprehensively represent the true temperature situation of the entire room or area. Through the data fusion of multiple temperature sensors, these local differences can be eliminated to obtain a more representative temperature value. In addition, multiple temperature sensors can also improve the stability and fault tolerance of the system. Even if one temperature sensor fails or has data deviation, the data of other temperature sensors can still ensure the accuracy of the overall temperature monitoring. This method can not only improve the control accuracy of the air conditioning system, avoid instability caused by temperature fluctuations or temperature sensor errors, but also effectively reduce energy waste, optimize the adjustment strategy of the air conditioner, and improve indoor comfort and energy utilization efficiency.

[0079] Based on the collected real-time temperature data, the control system will compare and analyze the current indoor temperature with the preset start temperature value and stop temperature value of the air conditioning system. This process determines whether the air conditioner starts or stops. The set start value and stop value are usually set according to the user's comfort requirements and the design of the air conditioning system. For example, the start value is usually lower than the stop value to ensure that the air conditioner is turned on in advance before the temperature drops to the set value.

[0080] Collect the real-time indoor temperature data and construct it into an analysis data set, and extract the key features that can reflect the temperature change trend;

[0081] Under the monitoring window, deeply analyze the extracted features, and input the processed data into a pre-trained machine learning model to identify and understand the law of indoor temperature change and predict the future temperature change trend;

[0082] Extract the key features that can reflect the temperature change trend. Among them, the extracted key features include the speed at which the indoor real-time temperature deviates from the set value and the frequency and intensity of the real-time temperature fluctuation. After obtaining them, under the monitoring window, deeply analyze the speed at which the indoor real-time temperature deviates from the set value and the frequency and intensity of the real-time temperature fluctuation, and generate a temperature deviation speed quantization value and a temperature fluctuation quantization value respectively. The rapidity of temperature change is reflected by the temperature deviation speed quantization value; the volatility and amplitude of temperature change are reflected by the temperature fluctuation quantization value.

[0083] After obtaining the temperature deviation speed quantization value and the temperature fluctuation quantization value generated by deeply analyzing the extracted key features, input the temperature deviation speed quantization value and the temperature fluctuation quantization value into a pre-trained machine learning model to generate a dynamic temperature change coefficient, and predict the future temperature change trend through the dynamic temperature change coefficient.

[0084] Pre-trained machine learning models are mainly used to analyze and predict the trend of indoor temperature changes. This model is usually trained based on historical temperature data, environmental factors, and the known behavior of temperature control systems. By learning the patterns, volatility, and response characteristics of temperature changes, it identifies complex temperature change laws. During the training process, the model captures the relationships between key features such as the speed of temperature deviation from the set value, and the frequency and intensity of temperature fluctuations, and the trend of temperature changes. The training data can cover various scenarios such as different indoor environments, external weather changes, and equipment working states, thus helping the model to have a certain generalization ability. Even when facing unknown situations in actual use, the model can make accurate predictions. Common machine learning algorithms include regression models (such as linear regression, support vector regression), ensemble learning methods (such as random forest, XGBoost), and deep learning methods (such as neural networks), etc. These algorithms can efficiently extract features when processing a large amount of historical data and establish mathematical relationships between temperature changes and various factors.

[0085] During the application process, after the quantified values of temperature deviation speed and temperature fluctuation are input into the pre-trained machine learning model, the model will calculate a "dynamic temperature change coefficient" based on these input features. This dynamic temperature change coefficient quantifies the possible trend of future temperature changes. The dynamic temperature change coefficient reflects the direction, amplitude, and time scale of indoor temperature changes, and can predict whether the future temperature will continue to be stable, fluctuate rapidly, or show a gradually rising or falling trend in the short term. In this way, the control system can obtain the prediction results of temperature changes in advance, thus providing accurate information for temperature regulation. The dynamic temperature change coefficient provides a basis for the system to respond dynamically, ensuring that when there are large fluctuations in indoor temperature, the control system can respond in a timely manner, and reduce the frequency and intensity of temperature fluctuations through adaptive adjustments, so as to maintain a comfortable indoor environment. With the prediction ability of the machine learning model, the system can not only provide more intelligent and efficient temperature control management under complex environmental changes, but also reduce energy consumption and avoid the burden on the air conditioning system caused by frequent start and stop.

[0086] A relatively fast speed at which the indoor real-time temperature deviates from the set value usually indicates that the indoor temperature is changing rapidly. The speed at which the temperature deviates from the set value (i.e., the temperature change rate) reflects the rapidity of temperature fluctuations. When the temperature changes rapidly, it means that environmental factors (such as changes in the external weather, changes in the load of the air conditioning system, or the influence of other heat sources) are quickly affecting the indoor temperature. Such rapid changes may make it difficult for the temperature control system to adjust in a timely manner, and may further lead to temperature instability or exceeding the comfortable range. Usually, if the deviation speed of the indoor temperature exceeds the preset threshold, it indicates that the temperature change is beyond the normal range, and immediate temperature control adjustment may be required. Therefore, the rapid temperature deviation speed is an important indication signal of temperature change, which can help the intelligent temperature control system identify and quickly respond to temperature changes, and avoid fluctuations in environmental comfort.

[0087] Under the monitoring window, the specific steps for deeply analyzing the speed at which the indoor real-time temperature deviates from the set value to generate a temperature deviation speed quantization value are as follows:

[0088] Under the monitoring window, record the indoor real-time temperature and the target temperature set by the air conditioning system at each time point, calculate the temperature deviation rate at each time point, and the calculation expression is:

[0089]

[0090] where T(t) is the indoor real-time temperature at time t, T s is the target temperature set by the air conditioner, and R(t) is the temperature deviation rate at time t, expressed as a percentage;

[0091] By calculating the relative deviation of the real-time temperature from the target temperature, the temperature change amplitude at each moment is quantified in percentage form. The relative deviation is more general than the absolute difference and can adapt to the change range of different target temperatures.

[0092] At each time point, calculate the change rate of the temperature deviation rate with respect to time, that is, the deviation rate acceleration, and the calculation expression is:

[0093]

[0094] where A(t) is the deviation rate acceleration at time t, representing the speed of change of the temperature deviation rate, and Δt is the time interval between adjacent time points, with the unit of seconds;

[0095] By calculating the change rate of the temperature deviation rate, the trend intensity of temperature change can be captured. If the deviation rate acceleration A(t) is large, it means that the speed of temperature deviation change is accelerating, indicating that the indoor temperature is in a state of rapid change.

[0096] Calculate the quantization value of the temperature deviation velocity based on the deviation rate acceleration to quantify the rapidity of temperature change. The formula is as follows:

[0097]

[0098] , where TDSI xy is the quantization value of the temperature deviation velocity, and α and β are weighting factors used to control the sensitivity to the deviation rate (i.e., the deviation rate acceleration A(t)) and the temperature deviation rate. e -β·|R(t)| is the exponential decay term. The higher the temperature deviation rate, the stronger the inhibitory effect. t 0 and t 1 are the start time point and end time point of the monitoring window respectively;

[0099] The settings of the parameters α and β need to be combined with the actual application scenario and determined through experimental adjustment to achieve reasonable sensitivity to the deviation rate and deviation rate:

[0100] α controls the influence of the deviation rate acceleration in the formula on the quantization value of the temperature deviation velocity. A larger α value will enhance the system's response to rapid temperature changes, making the system more sensitive and suitable for scenarios that require quick response, such as areas with drastic environmental temperature fluctuations or high air conditioner startup frequencies. A smaller α value is suitable for environments with stable temperature changes to avoid over-response and unnecessary adjustments. It is recommended to set the initial value within the range of 1 - 5 and adjust it gradually according to the actual operating state of the system.

[0101] β is used to adjust the sensitivity of the exponential decay term in the formula to the temperature deviation rate. A larger β value will make the exponential decay faster, thus reducing the weight when the deviation rate is large, and is suitable for scenarios where the control system gives priority to handling small-scale temperature fluctuations. A smaller β value will amplify the influence of the deviation rate and is more suitable for handling environments with a larger temperature change range. It is recommended to set the initial value in the range of 0.1 - 1 and optimize it by observing the system's performance under different temperature change conditions through experiments.

[0102] Finally, the adjustment of α and β needs to aim at the stability, sensitivity, and adjustment accuracy of the system, and be optimized through experiments and data analysis according to the actual operating environment (such as external climate conditions, indoor load fluctuations) to ensure that the system can not only respond quickly to rapid temperature changes but also maintain precise control under stable conditions.

[0103] From the temperature deviation speed quantization value, it can be seen that under the monitoring window, the larger the performance value of the temperature deviation speed quantization value generated by deeply analyzing the speed of the indoor real-time temperature deviating from the set value, the faster the speed of the indoor temperature deviating from the set value, and the more rapid the change of the indoor temperature; conversely, the smaller the performance value of the temperature deviation speed quantization value, the slower the temperature change, approaching a stable state. The temperature deviation speed quantization value synthesizes the time change rate of the temperature deviation rate (deviation rate acceleration) and the current temperature deviation degree, and can quantitatively evaluate the dynamic characteristics of temperature change. When the temperature deviation speed quantization value is relatively high, the system can determine that the temperature is changing rapidly, such as significant fluctuations in the external environment or significant changes in indoor heat sources; while when the temperature deviation speed quantization value is relatively low, it indicates that the temperature is basically in a stable state.

[0104] A relatively large frequency and intensity of the indoor real-time temperature fluctuations generally indicate that the indoor temperature is changing rapidly. A relatively large frequency means that the temperature fluctuates up and down frequently within a short period of time, while a relatively large intensity reflects a relatively high amplitude for each fluctuation. These characteristics are usually caused by changes in the external environment (such as sudden changes in external air temperature or strong wind infiltration), operation of indoor equipment (such as sudden startup or stop of air conditioners, heating equipment), or significant changes in the density and activities of people. When both the frequency and intensity increase simultaneously, it indicates that the indoor temperature fails to remain stable but is changing significantly and rapidly. Therefore, abnormal fluctuations in frequency and intensity are important signals for judging rapid temperature changes.

[0105] Under the monitoring window, the specific steps for deeply analyzing the frequency and intensity of the indoor real-time temperature fluctuations to generate the temperature fluctuation quantization value are as follows:

[0106] Extract the time series characteristics of the indoor real-time temperature fluctuation data. Suppose the temperature data at N time points are collected within the monitoring window, denoted as T i ={T 1 ,T 2 ,…,T N}, where T i is the temperature value at the i-th time point;

[0107] To analyze the frequency and intensity of the temperature fluctuations, first calculate the amplitude of the temperature change between adjacent time points to obtain the temperature difference sequence. The calculation expression of the temperature difference sequence is:

[0108] ΔT i =T i+1 -T i

[0109] In the formula, ΔT i represents the i-th value of the temperature difference sequence, indicating the amplitude of the temperature change between two adjacent sampling points, and i = 1, 2, 3, …, N - 1;

[0110] Through the temperature difference sequence, the dynamic characteristics of temperature change over time can be captured. The goal of this step is to identify the core information of temperature fluctuations, eliminate the trend influence of absolute temperature, and only focus on the characteristics of the fluctuation amplitude.

[0111] To quantify the fluctuation intensity, a cumulative fluctuation energy function is defined, which combines the sum of squares of the difference sequence and the time weight to measure the overall severity of temperature fluctuations. The expression of the cumulative fluctuation energy function is:

[0112]

[0113] , where E T Cumulative fluctuation energy, representing the intensity of temperature fluctuations within the monitoring window, w i is the time weight factor, used to highlight the contribution of fluctuations at different times to the overall energy;

[0114] The weight factor can be dynamically set according to the following rules:

[0115]

[0116] , where is the sensitivity coefficient, controlling the influence of time on energy calculation. The larger the value, the more sensitive to fluctuations at the center position of the monitoring window;

[0117] Through the cumulative fluctuation energy, not only the amplitude is concerned, but also the time distribution characteristics of the fluctuations are reflected.

[0118] Combining the cumulative fluctuation energy and the consistency of the temperature change direction to calculate the temperature fluctuation quantization value, in order to comprehensively reflect the amplitude of the fluctuations. The comprehensive calculation expression of the temperature fluctuation quantization value is:

[0119]

[0120] , where TBI xy represents the temperature fluctuation quantization value, reflecting the volatility and amplitude of temperature changes. sign(ΔT i ·ΔT i+1 ) represents calculating the direction consistency of temperature changes by judging whether the signs of adjacent temperature differences (ΔT i ·ΔT i+1 ) are consistent. If the sign consistency is high, it indicates that the fluctuations are relatively smooth; if the consistency is low, it indicates that the fluctuations are more complex. δ is the direction consistency weight coefficient, used to balance the contributions of fluctuation intensity and direction consistency.

[0121] From the temperature fluctuation quantization value, it can be seen that under the monitoring window, the larger the performance value of the temperature fluctuation quantization value generated by in-depth analysis of the frequency and intensity of the indoor real-time temperature fluctuation, the faster the indoor temperature changes; conversely, the smaller the performance value, the slower the indoor temperature changes. The temperature fluctuation quantization value is an index generated by comprehensively analyzing the frequency and intensity of temperature fluctuations, and its value directly reflects the severity of temperature changes. A larger temperature fluctuation quantization value means that within the monitoring window, the temperature changes more frequently and with a larger fluctuation amplitude, while a smaller index value indicates that the temperature changes smoothly with a lower fluctuation amplitude and frequency. Therefore, the temperature fluctuation quantization value is a key index for quantifying the speed of indoor temperature changes and provides basic support for further predicting the temperature change trend.

[0122] The machine learning model is not specifically limited here, and it can achieve quantifying the temperature deviation speed quantization value TDSI xy and the temperature fluctuation quantization value TBI xy to generate the dynamic temperature change coefficient Temper through comprehensive analysis dyna Any machine learning model that can do this is acceptable. To implement the technical solution of the present invention, the present invention provides a specific implementation method; the expression for generating the dynamic temperature change coefficient Temper dyna is:

[0123]

[0124] , where k 1 , k 2 are respectively the preset proportionality coefficients of the temperature deviation speed quantization value TDSI xy and the temperature fluctuation quantization value TBI xy , and both k 1 , k 2 are greater than 0.

[0125] From the dynamic temperature change coefficient, it can be seen that under the monitoring window, the larger the performance value of the temperature deviation speed quantization value generated by in-depth analysis of the speed at which the indoor real-time temperature deviates from the set value, and the larger the performance value of the temperature fluctuation quantization value generated by in-depth analysis of the frequency and intensity of the indoor real-time temperature fluctuation, that is, the larger the performance value of the dynamic temperature change coefficient generated when predicting the indoor temperature change trend through a pre-trained machine learning model under the monitoring window, the faster the indoor temperature dynamically changes, and vice versa, it indicates that the indoor temperature dynamically changes gently.

[0126] Predict the indoor temperature change situation through a machine learning model, and divide the indoor temperature change into a normal temperature change stage and a rapid temperature change stage;

[0127] During the prediction of the indoor temperature change trend through a pre-trained machine learning model under the monitoring window, the dynamic temperature change coefficient generated is compared with a pre-set reference threshold of the dynamic temperature change coefficient to classify the indoor temperature change. The specific classification steps are as follows:

[0128] If the dynamic temperature change coefficient generated during the prediction of the indoor temperature change trend through a pre-trained machine learning model under the monitoring window is greater than the reference threshold of the dynamic temperature change coefficient, the indoor temperature change is classified as a rapid temperature change stage;

[0129] If the dynamic temperature change coefficient generated during the prediction of the indoor temperature change trend through a pre-trained machine learning model under the monitoring window is less than or equal to the reference threshold of the dynamic temperature change coefficient, the indoor temperature change is classified as a normal temperature change stage.

[0130] The purpose of dividing the temperature change window period is to enable the air conditioner to respond more precisely to temperature changes under different environmental conditions.

[0131] During the normal temperature change stage, the air conditioning system continues to monitor the current indoor temperature, compares it with the set start value and stop value, and controls the start and stop of the air conditioner in a conventional manner;

[0132] During the normal temperature change stage, by continuously monitoring the current indoor temperature, comparing it with the set start value and stop value, and controlling the start and stop of the air conditioner in a conventional manner, it can effectively avoid unnecessary frequent adjustments and maintain the stability of the indoor temperature. The advantage of this method is that it can not only meet the user's temperature comfort requirements but also reduce the system's operating load and extend the service life of the air conditioning equipment. In addition, the conventional control method can avoid complex algorithm calculations and unnecessary over-responses during this stage, thereby reducing energy consumption and achieving a balance between economy and comfort. Through precise and simple control logic, the air conditioning system can operate efficiently during the normal temperature change stage, ensuring the best combination of energy consumption and environmental comfort.

[0133] During the rapid temperature change stage, fuzzy logic is used to control the output power of the air conditioner according to the indoor temperature change situation, so that the current indoor temperature is always between the start value and the stop value, ensuring that the air conditioner always remains in an operating state, maintaining the indoor temperature while avoiding frequent start and stop of the air conditioner;

[0134] During the rapid temperature change stage, fuzzy logic is used to control the output power of the air conditioner according to the indoor temperature change situation, so that the current indoor temperature is always between the start value and the stop value. The specific steps are as follows:

[0135] During the rapid temperature change stage, first, the severity of the current temperature change is determined by comparing the dynamic temperature change coefficient with the reference threshold of the dynamic temperature change coefficient, and it is used as one of the input variables of the fuzzy logic. The relative position between the current indoor temperature and the start value and stop value of the air conditioner is calculated to generate a temperature deviation factor. The expression for generating the temperature deviation factor is:

[0136]

[0137] , where ΔT relative is the temperature deviation factor, quantifying the deviation degree of the current temperature within the target range. The range of the temperature deviation factor is [0, 1], indicating the position deviation degree of the current temperature relative to the start value and stop value. T current is the current indoor temperature, T start is the start value when the air conditioner starts, and T stop is the stop value when the air conditioner stops;

[0138] Calculate the normalized value of the dynamic temperature change amplitude. The calculation expression is:

[0139]

[0140] , where Temper dyna represents the dynamic temperature change coefficient, DTC_ref represents the reference threshold of the dynamic temperature change coefficient, and ΔT dynamic is the normalized value of the dynamic temperature change, quantifying the ratio of the dynamic temperature change coefficient to the reference threshold of the dynamic temperature change coefficient;

[0141] The temperature deviation factor ΔT relative and the normalized value of the dynamic temperature change ΔT dynamic will be used as the core inputs of the fuzzy logic system to analyze the drastic change of the current temperature and its position in the target interval.

[0142] Based on the input temperature deviation factor ΔT relative and the normalized value of the dynamic temperature change ΔT dynamic , an air conditioner output power adjustment coefficient is generated through fuzzy logic rules. The fuzzy logic rules consider two aspects:

[0143] When the temperature is close to the start value or stop value (the temperature deviation factor ΔT relative is close to 0 or 1) and the temperature change is drastic (the normalized value of the dynamic temperature change ΔT dynamic > 1), increase the output power to quickly stabilize the temperature;

[0144] When the temperature is far from the start value and stop value (the temperature deviation factor ΔT relative is in the middle range) and the temperature change is relatively stable (the normalized value of the dynamic temperature change ΔTdynamic When (≤1), reduce the power to avoid excessive response;

[0145] The calculation expression of the fuzzy logic output is:

[0146] P adjust = min(1, max(0, k p ·ΔT relative + k q ·ΔT dynamic ))

[0147] , where k p and k q are weight coefficients, respectively controlling the influence of the relative temperature deviation and dynamic change on the output power adjustment. P adjust is the air conditioner output power adjustment coefficient, used to dynamically adjust the output power of the air conditioner. min and max are used to limit the value range of the output power adjustment coefficient to ensure safety;

[0148] Calculate the actual output power of the air conditioner according to the generated adjustment coefficient. At this stage, it is necessary to ensure that the indoor temperature is always controlled between the start value and the stop value. Therefore, the actual output power is calculated by the following formula:

[0149] P output = P max ·(1 - ΔT relative ·P adjust )

[0150] , where P output is the actual output power of the air conditioner, used to maintain the indoor temperature, and P max is the maximum output power of the air conditioner, indicating the energy output capacity of the air conditioner under full load;

[0151] Finally, through the dynamically adjusted actual output power P output of the air conditioner, the air conditioner can maintain a continuous running state during the rapid temperature change stage, stably control the indoor temperature between the start value and the stop value, and at the same time avoid the energy consumption waste and system pressure caused by frequent start and stop.

[0152] During the rapid temperature change stage, fuzzy logic is used to control the output power of the air conditioner according to the indoor temperature change. Its specific function is to ensure that the indoor temperature always remains between the set start value and stop value by dynamically adjusting the output power, thereby avoiding unnecessary energy consumption and equipment wear caused by frequent start and stop of the air conditioner. This method can keep the air conditioner running continuously when the temperature changes violently and achieve stable temperature control through refined power adjustment. On the one hand, it can quickly respond to environmental changes and prevent the indoor temperature from deviating from the comfortable range; on the other hand, by gradually adjusting the power, it avoids the frequent start and stop of the compressor, thereby extending the service life of the air conditioner, improving the system energy efficiency at the same time, and ensuring user comfort and economy.

[0153] Through the collection and analysis of real-time temperature data and the prediction of the machine learning model, the present invention can accurately divide the temperature change stage. Therefore, during the normal temperature change stage, conventional start-stop control is adopted to reduce system complexity and energy consumption; during the rapid temperature change stage, the output power of the air conditioner is dynamically adjusted through fuzzy logic to ensure continuous operation of the air conditioner, maintain stable room temperature, and avoid energy waste and equipment wear caused by frequent start of the compressor. It not only improves the comfort and temperature control accuracy of the indoor environment, but also significantly improves the energy efficiency of the system, reduces the operating cost, extends the service life of the air conditioning equipment, and provides an efficient and intelligent solution for building energy-saving management.

[0154] The present invention provides a Figure 2 multi-dimensional energy-saving analysis dynamic management platform for buildings as shown, including a temperature monitoring module, a temperature start-stop control module, a temperature data analysis module, a temperature change prediction module, a temperature change stage division module, a conventional start-stop control module, and a fuzzy logic power adjustment module;

[0155] The temperature monitoring module monitors the indoor temperature in real time through a temperature monitoring system and transmits the collected temperature data to the control system;

[0156] The temperature start-stop control module compares the obtained current indoor temperature with the preset start value and stop value of the air conditioning system, and controls the start and stop of the air conditioning system through the control system;

[0157] The temperature data analysis module collects the real-time indoor temperature data and constructs it into an analysis data set, and extracts key features that can reflect the temperature change trend from it;

[0158] The temperature change prediction module deeply analyzes the extracted features under the monitoring window, and inputs the processed data into a pre-trained machine learning model to identify and understand the law of indoor temperature change and predict the future temperature change trend;

[0159] The temperature change stage division module predicts the indoor temperature change situation through a machine learning model, and divides the indoor temperature change into a normal temperature change stage and a rapid temperature change stage;

[0160] The conventional start-stop control module, in the normal temperature change stage, the air conditioning system continues to monitor the current indoor temperature, compares and analyzes it with the set start value and stop value, and controls the start and stop of the air conditioner in a conventional manner;

[0161] The fuzzy logic power adjustment module, in the rapid temperature change stage, uses fuzzy logic to control the output power of the air conditioner according to the indoor temperature change situation, so that the current indoor temperature is always between the start value and the stop value, ensuring that the air conditioner always remains in an operating state, maintaining the indoor temperature while avoiding frequent start and stop of the air conditioner;

[0162] The multi-dimensional energy-saving analysis dynamic management method for buildings provided by the embodiments of the present invention is implemented through the above-mentioned multi-dimensional energy-saving analysis dynamic management platform for buildings. For the specific methods and processes of the multi-dimensional energy-saving analysis dynamic management platform for buildings, please refer to the embodiments of the above-mentioned multi-dimensional energy-saving analysis dynamic management method for buildings, which will not be elaborated here.

[0163] The above formulas are all dimensionless and take their numerical values for calculation. The formula is a formula obtained by collecting a large amount of data for software simulation to approximate the real situation. The preset parameters in the formula are set by those skilled in the art according to the actual situation.

[0164] As mentioned above, only the specific embodiments of the present application are described, but the protection scope of the present application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in the present application, and all should be covered by the protection scope of the present application. Therefore, the protection scope of the present application should be subject to the protection scope of the claimed rights.

[0165] Only some exemplary embodiments of the present invention have been described by way of illustration above. Undoubtedly, for those of ordinary skill in the art, the described embodiments can be modified in various different ways without departing from the spirit and scope of the present invention. Therefore, the above drawings and description are illustrative in nature and should not be construed as limiting the protection scope of the claims of the present invention.

Claims

1. A multi-dimensional energy-saving analysis and dynamic management method for buildings, characterized in that: The following steps are involved: Monitor the indoor temperature in real time through the temperature monitoring system and transmit the collected temperature data to the control system; The current indoor temperature is compared with the preset start value and stop value of the air conditioning system, and the start and stop of the air conditioning system are controlled by the control system; Collect and construct real-time indoor temperature data into an analytical dataset, from which key features that can reflect temperature change trends are extracted; Under the monitoring window, the extracted features are deeply analyzed and the processed data is input into the pre-trained machine learning model to identify and understand the law of indoor temperature changes and predict future temperature change trends; The indoor temperature change is predicted through the machine learning model, and the indoor temperature change is divided into the normal temperature change stage and the rapid temperature change stage; During the normal temperature change phase, the air conditioning system continues to monitor the current indoor temperature, compares it with the set start and stop values, and controls the start and stop of the air conditioning in the usual way; During the rapid temperature change stage, fuzzy logic is used to control the output power of the air conditioner according to the indoor temperature changes, so that the current indoor temperature is always between the start value and the stop value, ensuring that the air conditioner always remains in operation, maintaining the indoor temperature while avoiding frequent start and stop of the air conditioner.

2. The multi-dimensional energy-saving analysis and dynamic management method for buildings according to claim 1 is characterized in that: The temperature monitoring system is a system that evenly distributes multiple temperature sensors in the indoor area. The system collects temperature data through multiple temperature sensors and calculates the average value of the data collected by the multiple temperature sensors to obtain the current indoor temperature.

3. The multi-dimensional energy-saving analysis and dynamic management method for buildings according to claim 1 is characterized in that: Extract key features that can reflect the temperature change trend, where the extracted key features include the speed at which the indoor real-time temperature deviates from the set value and the frequency and intensity of the real-time temperature fluctuation. After acquisition, in the monitoring window, conduct in-depth analysis on the speed at which the indoor real-time temperature deviates from the set value and the frequency and intensity of the real-time temperature fluctuation, and generate a temperature deviation speed quantification value and a temperature fluctuation quantification value respectively. The temperature deviation speed quantification value reflects the rapidity of the temperature change; the temperature fluctuation quantification value reflects the volatility and amplitude of the temperature change; After obtaining the temperature deviation rate quantification value and temperature fluctuation quantification value generated by in-depth analysis of the extracted key features, the temperature deviation rate quantification value and the temperature fluctuation quantification value are input into a pre-trained machine learning model to generate a dynamic temperature change coefficient, and the future temperature change trend is predicted through the dynamic temperature change coefficient.

4. The multi-dimensional energy-saving analysis and dynamic management method for buildings according to claim 3 is characterized in that: The dynamic temperature change coefficient generated by the pre-trained machine learning model when predicting the indoor temperature change trend under the monitoring window is compared and analyzed with the pre-set dynamic temperature change coefficient reference threshold, and the indoor temperature change is divided. The specific division steps are as follows: If the dynamic temperature change coefficient generated when predicting the indoor temperature change trend through the pre-trained machine learning model under the monitoring window is greater than the dynamic temperature change coefficient reference threshold, the indoor temperature change is divided into a rapid temperature change stage; If the dynamic temperature change coefficient generated when predicting the indoor temperature change trend through the pre-trained machine learning model under the monitoring window is less than or equal to the dynamic temperature change coefficient reference threshold, the indoor temperature change is classified as the normal temperature change stage.

5. The multi-dimensional energy-saving analysis and dynamic management method for buildings according to claim 3 is characterized in that: In the monitoring window, the specific steps for in-depth analysis of the speed at which the indoor real-time temperature deviates from the set value to generate a quantitative value of the temperature deviation speed are as follows: In the monitoring window, record the real-time indoor temperature at each time point and the target temperature set by the air conditioning system, and calculate the temperature deviation rate at each time point. The calculation expression is: Where, T(t) is the real-time indoor temperature at time t, T s is the target temperature set for the air conditioner, R(t) is the temperature deviation rate at time t, expressed as a percentage; At each time point, the rate of change of the temperature deviation rate relative to time, that is, the deviation rate acceleration, is calculated. The calculation expression is: Where A(t) is the deviation rate acceleration at time t, indicating the speed of change of temperature deviation rate, and Δt is the time interval between adjacent time points; The temperature deviation velocity quantification value is calculated based on the deviation rate acceleration to quantify the rapidity of temperature change. The formula is: Where, TDSI xy is the quantitative value of the temperature deviation rate, and the α and β weighting factors are used to control the sensitivity to the deviation rate and temperature deviation rate, e -β·|R(t)| is an exponential decay term, t0 and t1 are the starting time point and the ending time point of the monitoring window respectively.

6. The multi-dimensional energy-saving analysis and dynamic management method for buildings according to claim 3 is characterized in that: In the monitoring window, the specific steps for in-depth analysis of the frequency and intensity of indoor real-time temperature fluctuations to generate a quantitative value of temperature fluctuations are as follows: The time series feature extraction of indoor real-time temperature fluctuation data is performed. Assume that the temperature data of N time points are collected in the monitoring window, which is recorded as T i ={T1, T2, ..., T N }, where T i is the temperature value at the i-th time point; Calculate the amplitude of temperature change at adjacent time points to obtain the temperature difference sequence. The calculation expression of the temperature difference sequence is: ΔT i =T i+1 -T i In the formula, ΔT i represents the i-th value of the temperature difference sequence, which represents the temperature variation amplitude of two adjacent sampling points, i=1, 2, 3, ..., N-1; A cumulative fluctuation energy function is defined to quantify the fluctuation intensity. It is combined with the sum of squares of the difference sequence and the time weight to measure the overall severity of the temperature fluctuation. The expression of the cumulative fluctuation energy function is: Among them, E T The accumulated fluctuation energy represents the intensity of temperature fluctuation within the monitoring window, w i is the time weight factor, which is used to highlight the contribution of fluctuations at different moments to the overall energy; The temperature fluctuation quantification value is calculated by combining the accumulated fluctuation energy and the consistency of the temperature change direction to comprehensively reflect the amplitude of the fluctuation. The comprehensive calculation expression of the temperature fluctuation quantification value is: In the formula, TBI xy Indicates the quantitative value of temperature fluctuation, reflecting the volatility and amplitude of temperature change, sign(ΔT i ΔT i+1 ) indicates that the adjacent temperature difference (ΔT i ΔT i+1 ) is consistent to calculate the directional consistency of temperature change. δ is the directional consistency weight coefficient, which is used to balance the contribution of fluctuation intensity and directional consistency.

7. The multi-dimensional energy-saving analysis and dynamic management method for buildings according to claim 4 is characterized in that: In the stage of rapid temperature change, fuzzy logic is used to control the output power of the air conditioner according to the indoor temperature change, so that the current indoor temperature is always between the start value and the stop value. The specific steps are as follows: In the rapid temperature change stage, the dynamic temperature change coefficient is first compared with the dynamic temperature change coefficient reference threshold to determine the severity of the current temperature change, and as one of the input variables of the fuzzy logic, the relative position between the current indoor temperature and the start value and stop value of the air conditioner is calculated to generate the temperature deviation factor. The expression for generating the temperature deviation factor is: Where, ΔT relative is the temperature deviation factor, which quantifies the degree of deviation of the current temperature within the target range. The temperature deviation factor ranges from [0, 1] and indicates the degree of deviation of the current temperature relative to the start value and the stop value. current is the current indoor temperature, T start is the starting value when the air conditioner is started, T stop It is the stop value when the air conditioner stops; Calculate the normalized value of the dynamic temperature change amplitude. The calculation expression is: Among them, Temper dyna Indicates the dynamic temperature change coefficient, DTC_ref indicates the dynamic temperature change coefficient reference threshold, ΔT dynamic is a normalized value of dynamic temperature change, quantifying the ratio of the dynamic temperature change coefficient to a reference threshold value of the dynamic temperature change coefficient; The temperature deviation factor ΔT relative and the normalized value of dynamic temperature change ΔT dynamic It will serve as the core input of the fuzzy logic system to analyze the drastic change of the current temperature and its position in the target range.

8. The multi-dimensional energy-saving analysis and dynamic management method for buildings according to claim 7 is characterized in that: Temperature deviation factor ΔT based on input relative and the normalized value of dynamic temperature change ΔT dynamic , the air conditioner output power adjustment coefficient is generated by fuzzy logic rules. The fuzzy logic rules consider two aspects: When the temperature is close to the start value or stop value and the temperature changes dramatically, increase the output power to quickly stabilize the temperature; When the temperature is far away from the start value and the stop value and the temperature changes smoothly, reduce the power to avoid excessive response; The calculation expression of fuzzy logic output is: P adjust =min((1,max(0,k p ·ΔT relatiue +k q ·ΔT dynamic )) where k p and k q are weight coefficients, which respectively control the influence of relative temperature deviation and dynamic change on output power adjustment, P adjust is the air conditioner output power adjustment coefficient, which is used to dynamically adjust the output power of the air conditioner. Min and max are used to limit the value range of the output power adjustment coefficient.

9. The multi-dimensional energy-saving analysis and dynamic management method for buildings according to claim 8 is characterized in that: The actual output power of the air conditioner is calculated based on the generated adjustment coefficient. At this stage, it is necessary to ensure that the indoor temperature is always controlled between the start value and the stop value. The calculation expression of the actual output power is: P output =P max ·(1-ΔT relative ·P adjust ) Where P output is the actual output power of the air conditioner, used to maintain the indoor temperature, P max The maximum output power of the air conditioner, indicating the energy output capacity of the air conditioner under full load; The actual output power P of the air conditioner is adjusted dynamically output , control the indoor temperature between the start value and the stop value.

10. A multi-dimensional energy-saving analysis dynamic management platform for buildings, used to implement the multi-dimensional energy-saving analysis dynamic management method for buildings described in any one of claims 1 to 9, characterized in that: It includes a temperature monitoring module, a temperature start-stop control module, a temperature data analysis module, a temperature change prediction module, a temperature change stage division module, a conventional start-stop control module and a fuzzy logic power regulation module; Temperature monitoring module, which monitors the indoor temperature in real time through the temperature monitoring system and transmits the collected temperature data to the control system; The temperature start-stop control module compares the current indoor temperature with the preset start value and stop value of the air-conditioning system, and controls the start and stop of the air-conditioning system through the control system; The temperature data analysis module collects real-time indoor temperature data and constructs it into an analysis data set, from which key features that can reflect the temperature change trend are extracted; The temperature change prediction module conducts in-depth analysis of the extracted features under the monitoring window and inputs the processed data into the pre-trained machine learning model to identify and understand the laws of indoor temperature changes and predict future temperature change trends; The temperature change stage division module predicts the indoor temperature change through a machine learning model and divides the indoor temperature change into a normal temperature change stage and a rapid temperature change stage; Conventional start-stop control module: During the normal temperature change stage, the air conditioning system continues to monitor the current indoor temperature, compares it with the set start and stop values, and controls the start and stop of the air conditioning in a conventional manner; The fuzzy logic power regulation module uses fuzzy logic to control the output power of the air conditioner according to the indoor temperature changes during the rapid temperature change stage, so that the current indoor temperature is always between the start value and the stop value, ensuring that the air conditioner always remains in operation, maintaining the indoor temperature while avoiding frequent start and stop of the air conditioner.

Images