Passive and green building coupled indoor environment dynamic regulation and control method

Abstract

The invention discloses a passive and green building coupled indoor environment dynamic regulation and control method, and belongs to the field of green buildings and building automation control. According to the method, environment, energy consumption and user behavior data are collected through a distributed sensor, after normalization and other fusion processing, a time sequence algorithm and building energy consumption are used for simulating and predicting a trend, a multi-target decision matrix is constructed, regulation and control are preferentially carried out through passive assemblies such as a sun-shading system and a phase change material, and the energy consumption and user behavior data are obtained. When the comfort level does not reach the comfort level threshold value, active systems such as an air conditioner and photovoltaic-energy storage are linked for cooperative adjustment, and closed-loop optimization is conducted on a regulation and control strategy through algorithms such as reinforcement learning. The problems that in the prior art, a passive system and an active system are separated, data utilization is extensive, and energy distribution is low in efficiency are solved, and cooperative regulation and control of passive priority and active compensation are achieved. According to the method, the building energy consumption is reduced, the comfort reaching rate is increased, the renewable energy utilization rate is increased, and the method is suitable for intelligent regulation and control of the indoor environment of various green buildings.

Description

Technical Field

[0001] The present invention relates to the technical field of green buildings and building automation control, and in particular to a method for dynamically controlling an indoor environment coupled with a passive green building. Background Art

[0002] Current building indoor environmental control technologies are primarily categorized as passive and active. Passive technologies utilize natural energy flows through building structural design to regulate the environment, offering the advantage of zero energy consumption. However, their control capabilities are significantly constrained by climatic conditions, such as extreme temperatures and humidity or windless environments, with their effectiveness plummeting. Active technologies rely on forced control by equipment such as air conditioners, heat pumps, and fresh air. While highly adaptable, they consume a lot of energy. Traditional control strategies often employ a "fixed threshold trigger" model, such as activating the air conditioner when the temperature exceeds 26°C. These strategies lack dynamic perception and integrated analysis of environmental parameters, energy consumption data, and user behavior. Furthermore, while green buildings are gradually introducing renewable energy devices such as photovoltaic-energy storage systems, the coordinated control of passive structures and active systems remains at the "independent operation" level, lacking a linkage mechanism based on predictive models. This makes it difficult to maximize the overall energy efficiency of the system.

[0003] The core problems of existing indoor environmental control technologies are: the passive structure and the active system control logic are separated, and the "passive priority-active compensation" synergy mechanism has not been formed, resulting in insufficient passive technology control capabilities and high energy consumption of active systems in extreme climates; data utilization remains at the level of single parameter collection, lacking multi-source data fusion, future trend prediction and closed-loop optimization mechanisms, making it difficult to adapt to dynamic environmental changes, and long-term energy consumption rebounds significantly; user behavior data and renewable energy management are missing, equipment operation is out of touch with personnel needs, green energy such as photovoltaics and energy storage is distributed extensively, and utilization rates are generally below 30%, resulting in insufficient overall energy efficiency and comfort synergistic optimization capabilities. Summary of the Invention

[0004] To solve the above technical problems, the present invention provides a method for dynamic control of indoor environment by coupling passive and green buildings based on multi-source data fusion prediction, passive-active collaborative control and closed-loop optimization, and integrating user behavior perception with efficient distribution of renewable energy.

[0005] The method for dynamically controlling an indoor environment by coupling a passive and green building system of the present invention comprises the following steps:

[0006] Step 1: Collect indoor and outdoor environmental parameters, building energy consumption data, and user behavior data through a distributed sensor network, and transmit the data to a processing unit through communication technology;

[0007] Step 2: The processing unit performs fusion processing on the collected data and predicts the environmental change trend in the future period;

[0008] Step 3: Based on the prediction results, the processing unit preferentially adjusts the indoor environment through the building's passive structural components. When the passive adjustment fails to reach a preset comfort threshold, the green building's active system is linked for coordinated regulation.

[0009] Step 4: The processing unit dynamically optimizes the fusion processing rules and control strategies through real-time feedback of the control effect to form a closed-loop control system.

[0010] Furthermore, the indoor and outdoor environmental parameters in step 1 include at least one of temperature, humidity, light intensity, CO2 concentration, wind speed, wind direction and solar radiation intensity.

[0011] Furthermore, the data fusion processing in step 2 specifically includes normalizing the environmental parameters and constructing a multi-objective decision matrix including the building envelope thermal response model, the natural ventilation flow model and the user comfort requirements.

[0012] The normalization process adopts one of the following methods:

[0013] (1) Z-score normalization: The calculation formula is Where μ is the mean of the historical data of the environmental parameter, and σ is the standard deviation of the historical data. It is suitable for scenarios where the data distribution is close to the normal distribution (such as temperature and humidity);

[0014] (2) Min-Max normalization: The calculation formula is x′=(xx min ) / (x max -x min ), where x min and x max It is the minimum and maximum historical data of the environmental parameter, which is applicable to scenarios with a clear value range (such as light intensity and CO2 concentration).

[0015] Furthermore, the construction rules of the multi-objective decision matrix are as follows:

[0016] (1) Matrix dimension: Rows represent control solutions (e.g., adjusting shading angles, opening vents), and columns represent evaluation targets, including thermal response efficiency of building envelopes (W / (m 2 ·K)), natural ventilation flow (m 3 / h), user comfort index (PMV-PPD value) and energy consumption cost (yuan / h);

[0017] (2) Weight allocation: The analytic hierarchy process (AHP) is used to determine the weight of each objective. The specific steps include:

[0018] a. Build a hierarchical structure, breaking down the overall goal into primary objectives (thermal response efficiency, ventilation flow, comfort, and energy cost) and secondary indicators (e.g., sub-indicators of thermal response efficiency include wall thermal conductivity and roof insulation thickness);

[0019] b. Construct a judgment matrix based on expert scoring and compare the relative importance of each objective pairwise (e.g., the importance ratio of thermal response efficiency to natural ventilation flow is 3:1);

[0020] c. Calculate the maximum eigenvalue and eigenvector of the judgment matrix and normalize them to obtain the weights of each target (e.g., thermal response efficiency weight 0.35, comfort weight 0.3);

[0021] (3) Constraints: including shading angle ≤ 90°, ventilation opening ≤ 80%, PMV-PPD∈[-0.5,0.5], CO2 concentration ≤ 1000ppm, and phase change material heat exchange rate ≤ 500W / m 2 .

[0022] Furthermore, the passive structural components of step three include at least one of a shading system, vents, phase change material walls, sunrooms, and ground heat storage systems or high-airtightness enclosure structures, and the passive adjustment includes dynamically adjusting the angle or opening degree of the shading system, controlling the opening and closing or opening degree of the vents, triggering phase change material heat exchange, or using a heat storage system to store and release heat.

[0023] Furthermore, the green building active system in step three includes at least one of an air-conditioning system, a fresh air system, a photovoltaic-energy storage system, and an air source heat pump or a ground source heat pump. The coordinated regulation includes optimizing the distribution of renewable energy based on real-time energy consumption data and predicted load, or adjusting indoor temperature and humidity, air quality and ventilation volume through active equipment.

[0024] Furthermore, the preset comfort threshold in step three is set based on the PMV-PPD index of the ISO7730 standard, the temperature and humidity limits specified in GB50736, or the CO2 concentration limit specified in GB / T18883.

[0025] Furthermore, the prediction of environmental change trends in the future period in step 2 is achieved by the following method:

[0026] a. Train historical data using time series forecasting algorithms (such as ARIMA) or machine learning models (such as LSTM neural networks);

[0027] b. Conduct physical simulation using building energy simulation software (such as EnergyPlus);

[0028] c. Correct the forecast results by combining external meteorological forecast data obtained through the meteorological data interface.

[0029] Furthermore, the dynamic optimization in step 4 iteratively updates the data fusion rules, equipment control parameters and control strategies through reinforcement learning algorithm, genetic algorithm, adaptive control algorithm or fuzzy logic algorithm.

[0030] The real-time feedback control effect includes the following indicators:

[0031] (1) Comfort compliance rate: the proportion of time when the PMV-PPD index is within the range of [-0.5, 0.5] after adjustment, or the proportion of time when the temperature, humidity, and CO2 concentration meet the limits of GB50736 / GB / T18883;

[0032] (2) Energy consumption deviation rate: the ratio of the difference between the actual energy consumption and the energy consumption predicted in step 2 to the predicted value. The calculation formula is:

[0033] (3) Renewable energy utilization rate: The ratio of electricity provided by the photovoltaic-energy storage system to the total electricity consumption of the building. The calculation formula is:

[0034] (4) Equipment operating efficiency: the ratio of the actual COP of the air conditioning system to the rated COP, or the matching degree between the actual air volume of the fresh air system and the target air volume. COP refers to the coefficient of performance, which is an important indicator for measuring the energy conversion efficiency of cooling, heating or heat pump systems.

[0035] (5) User demand matching: the frequency of manual device adjustment by the user or the deviation between the occupancy sensor data and the predicted population density, where the occupancy sensor data is used to detect whether there are people in a specific area (such as a room, corridor, etc.), as well as the density and activity status of people.

[0036] Furthermore, the distributed sensor network in step one includes temperature and humidity sensors, light sensors, and anemometers deployed on the building's exterior envelope, and CO2 sensors, PMV-PPD monitors, or occupancy sensors deployed indoors. Data is transmitted to a processing unit via wireless or wired communication technology. The processing unit is configured as an edge computing node or a central control system. The wireless communication technology is configured as LoRa, Zigbee, or WiFi technology, and the wired communication technology is configured as an RS485 communication protocol. The edge computing node is used to perform real-time preprocessing of sensor data (such as noise filtering) and execute local control instructions. The central control system is used to globally fuse data from multiple edge nodes to generate a cross-regional control strategy.

[0037] Furthermore, the method is applied to the design of new green buildings or energy-saving renovation of existing buildings, and the building types include residential, office, public or industrial buildings, and the number of floors covers low-rise, multi-story or high-rise residential buildings.

[0038] The user behavior data in step 1 includes:

[0039] (1) Personnel activity pattern data: daily entry and exit times, length of stay in an area, and personnel density collected by occupancy sensors;

[0040] (2) Equipment usage habit data: air conditioning temperature adjustment records, lighting brightness adjustment frequency, and fresh air system on / off time recorded by the equipment control module;

[0041] (3) Comfort preference data: ideal temperature and humidity range, CO2 concentration sensitivity, and wind speed acceptance submitted by users through the terminal APP.

[0042] User behavior data is incorporated into regulatory strategies through the following methods:

[0043] (1) As input to the prediction model: data on occupant activity patterns are used to correct indoor heat load predictions (e.g., calculating human heat dissipation in combination with occupant density), and data on equipment usage habits are used to optimize air conditioning / lighting energy consumption predictions;

[0044] (2) Adjust the weight of the decision matrix: Dynamically adjust the weight of the 'user comfort index' in the multi-objective decision matrix (e.g., from 0.3 to 0.4) according to the user's comfort preferences (e.g., temperature and humidity settings);

[0045] (3) Directly trigger control instructions: turn off the lighting and lower the air conditioning temperature when it detects that the room is empty, or start the fresh air system in advance according to the user's reservation information.

[0046] The data fusion processing in step 2 includes the following sub-steps:

[0047] (1) Data cleaning: outliers (|Z|>3) were removed through the Z-score test, and missing values ​​were filled using linear interpolation. Z-score is a standard score, which is a commonly used standardization method in statistics and machine learning. It is used to measure the degree of deviation of a data point from the mean of the data set (in units of standard deviation);

[0048] (2) Feature extraction: extracting time labels (hours, weekdays / weekends) and space labels (room orientation, floor);

[0049] (3) Multi-source fusion: associate the normalized environmental parameters with the user behavior data (e.g., temperature = ambient temperature and humidity + 0.5°C × population density), and update the normalized parameters (μ, σ, x) through a sliding window (previous 24-hour data). min 、x max ).

[0050] The prediction of environmental change trends in the future period in step 2 includes the following training details:

[0051] (1) Data division: Historical data is divided into training set, validation set, and test set according to the ratio of 7:2:1;

[0052] (2) Hyperparameter adjustment: Optimize the machine learning model using grid search method;

[0053] (3) Verification and correction: The prediction accuracy is evaluated by RMSE (≤0.5℃) and MAPE (≤3%). If the accuracy is not met, the prediction result is corrected by combining meteorological data (e.g., rainfall forecast lowers the temperature forecast value). RMSE refers to the root mean square error, which measures the absolute deviation between the predicted value and the true value. The unit is consistent with the predicted variable. MAPE refers to the mean absolute percentage error, which expresses the relative error in percentage form.

[0054] Compared with the prior art, the present invention has the following beneficial effects:

[0055] This invention effectively addresses the problems inherent in traditional technologies, such as the separation of passive and active systems, extensive data utilization, and inefficient energy allocation, through multi-source data fusion, passive-active coordinated control, and closed-loop optimization. By implementing a coordinated mechanism of "passive priority regulation of baseloads and active system precise compensation," this significantly reduces building energy consumption and improves indoor comfort compliance. The integration of user behavior data allows for more tailored control to actual needs, reducing the frequency of manual adjustments. Combined with a dynamic renewable energy allocation strategy, this technology improves the utilization of green energy sources such as photovoltaics and energy storage. This approach is suitable for energy-saving renovation and intelligent control in various buildings, offering both high energy efficiency and a comfortable experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0056] Figure 1 It is a flow chart of the method implementation of the present invention;

[0057] Figure 2 It is a system architecture diagram of the implementation method of the present invention; DETAILED DESCRIPTION

[0058] The following embodiments of the present invention are described in further detail with reference to the accompanying drawings and examples. The following examples are used to illustrate the present invention but are not intended to limit the scope of the present invention.

[0059] like Figures 1 to 2 As shown,

[0060] 1. Example 1

[0061] Low-rise residential (3-story independent villa)

[0062] Building parameters

[0063] Number of floors: 3, building area 200㎡, enclosure structure is 200mm thick aerated concrete wall (heat transfer coefficient 0.45W / (m 2 ·K)), equipped with external sunshade louvers and ground heat storage system.

[0064] Specific implementation steps:

[0065] 1. Data collection and transmission

[0066] Sensor deployment:

[0067] External protective structure: One temperature and humidity sensor (accuracy ±0.5°C) and one light sensor (0-20,000 lux) are deployed on each exterior wall;

[0068] Indoor: One CO2 sensor (accuracy ±50ppm) and one occupancy sensor (detection range 5m) are deployed in the living room on each floor;

[0069] Communication technology: LoRa transmits external protective structure data (distance ≤ 800m), Zigbee transmits indoor data;

[0070] User behavior: Set the ideal bedroom temperature to 22±1°C and the CO2 concentration threshold in the children's room to ≤800ppm through the app.

[0071] 2. Data fusion and prediction

[0072] Normalization: Temperature uses Z-score (historical mean 23°C, standard deviation 1.8°C), and light intensity uses Min-Max (0-15000 lux);

[0073] Multi-objective decision matrix weights: thermal response efficiency 0.35, comfort 0.3, ventilation flow 0.25, energy cost 0.1;

[0074] Prediction model: LSTM predicts the room temperature for the next 12 hours. Input parameters include historical temperature and occupancy density (night bedroom density is 1 person / room).

[0075] 3. Regulation strategy

[0076] Passive adjustment:

[0077] When the solar radiation is predicted to be ≥800W / ㎡ at 13:00, the west-facing sunshade blinds will be automatically adjusted to 70°, and the ground heat storage system will be turned on at the same time (storing heat at night and releasing it during the day);

[0078] If the room temperature is greater than 25°C (PMV=0.6), the phase change material wall heat exchange is triggered (cooling down by 1.5°C);

[0079] Active system linkage:

[0080] When PMV=0.7 after passive adjustment, start the air source heat pump (cooling mode, set to 23℃), and at the same time start the fresh air system (air volume 300m 3 / h).

[0081] 4. Closed-loop optimization

[0082] Feedback indicators: Daily average comfort level compliance rate 96%, energy consumption deviation rate ≤ 4%;

[0083] Dynamic optimization: The decision matrix weights are adjusted weekly through reinforcement learning to increase the "comfort" weight to 0.32.

[0084] 5. Implementation Effect

[0085] The average daily energy consumption in summer is reduced by 22% compared with traditional control, the utilization rate of renewable energy is 38%, and the user's manual adjustment frequency is ≤0.5 times / day.

[0086] 2. Example 2

[0087] Multi-storey residential building (6-storey apartment)

[0088] Building parameters

[0089] Number of floors: 6, single-family building area 120㎡, the whole building is equipped with ventilation atrium, high airtightness enclosure structure (heat transfer coefficient 0.3W / (m 2 ·K)), photovoltaic roof (installed capacity 10kW).

[0090] Specific implementation steps:

[0091] 1. Data collection and transmission

[0092] Sensor deployment:

[0093] External protective structure: Two temperature and humidity sensors and one anemometer are deployed on each of the east, west, south, and north sides of the building;

[0094] Indoor: One PMV-PPD monitor and two CO2 sensors are deployed in each living room;

[0095] Communication technology: RS485 connects photovoltaic meters with the central control system, and WiFi transmits indoor sensor data;

[0096] User behavior: The ventilation preferences of users in the entire building are collected through the property APP (for example, 30% of users prefer natural ventilation).

[0097] 2. Data fusion and prediction

[0098] Normalization processing: wind speed uses Z-score (historical mean 2.5m / s, standard deviation 1.2m / s), CO2 concentration uses Min-Max (400-1500ppm);

[0099] Multi-objective decision matrix: Add the secondary indicator of "atrium ventilation efficiency" with weights of 0.3 for thermal response efficiency and 0.35 for comfort;

[0100] Prediction model: Combined with EnergyPlus to simulate the natural ventilation volume of the atrium, LSTM predicts the temperature and humidity of each household for the next 8 hours.

[0101] 3. Regulation strategy

[0102] Passive adjustment:

[0103] When the outdoor wind speed is ≥3m / s and the indoor CO2 concentration is >900ppm, open the atrium vents (opening 60%) to introduce fresh air using wind pressure;

[0104] Automatically adjust the shading system angle for west-facing units (dynamically changes with the sun's altitude angle);

[0105] Active system linkage:

[0106] If the CO2 concentration is still >1000ppm after ventilation in the atrium, start the fresh air system for the entire building (total air volume 5000m 3 / h), give priority to photovoltaic power supply (when the utilization rate is ≥40%).

[0107] 4. Closed-loop optimization

[0108] Feedback indicators: The building's overall comfort level achieved 94% and renewable energy utilization rate reached 42%;

[0109] Dynamic optimization: Genetic algorithms are used to optimize the combination of shading angles and ventilation openings for each household, reducing energy consumption by 15%.

[0110] 5. Implementation Effect

[0111] During the transition season, the utilization rate of natural ventilation increased to 75%, air conditioning energy consumption decreased by 30%, and the PMV-PPD compliance time accounted for 97%.

[0112] 3. Example 3

[0113] High-rise residential building (18-story commercial and residential building)

[0114] Building parameters

[0115] Number of floors: 18, single-family building area 85㎡, building exterior structure is broken bridge aluminum window (heat transfer coefficient 2.0W / (m 2 ·K)), phase change material exterior wall, equipped with ground source heat pump system, photovoltaic-energy storage system (energy storage capacity 50kWh).

[0116] Specific implementation steps:

[0117] 1. Data collection and transmission

[0118] Sensor deployment:

[0119] External protective structure: one temperature and humidity sensor and one solar radiation sensor are deployed every three floors;

[0120] Indoor: Two occupancy sensors are deployed in public areas on each floor, and one CO2 sensor is deployed in each kitchen;

[0121] Processing unit: edge computing node (1 per floor) + central control system (basement server);

[0122] User behavior: Smart terminals are used to collect information on the preferences of high-rise users regarding elevator hall ventilation (e.g., high-rise users are more concerned about wind speeds ≤ 0.3 m / s).

[0123] 2. Data fusion and prediction

[0124] Normalization processing: solar radiation intensity uses Min-Max (0-1200W / ㎡), and high-rise wind speed uses Z-score (historical mean 4m / s, standard deviation 1.5m / s);

[0125] Multi-objective decision matrix: Add the "high-rise wind pressure impact" indicator with a weight of 0.2 to prioritize ventilation comfort for high-rise residents;

[0126] Prediction model: LSTM combines meteorological data (high-rise wind load prediction) to predict the temperature, humidity, and wind pressure of each floor for the next 24 hours.

[0127] 3. Regulation strategy

[0128] Passive adjustment:

[0129] Residents on high floors open their high-airtightness windows (adjustable opening 0-30%), using wind pressure for natural ventilation and triggering the phase change material exterior wall (storing cold at night and releasing it during the day);

[0130] An adjustable ventilation cap is installed on the top of the building, which automatically adjusts its angle according to wind pressure (optimizing ventilation efficiency in high-rise buildings);

[0131] Active system linkage:

[0132] When the PMV of high-rise residents is greater than 0.5 and the wind pressure is less than 2m / s, the ground source heat pump is started (high-rise areas are given priority for cooling), and the photovoltaic-energy storage system gives priority to powering high-rise fans.

[0133] 4. Closed-loop optimization

[0134] Feedback indicators: 93% of high-rise residents’ comfort level meets standards, and energy consumption deviation rate is ≤3.5%;

[0135] Dynamic Optimization: An adaptive control algorithm adjusts the angle of high-rise vent caps and the distribution of ground-source heat pump flow, reducing energy consumption in high-rise areas by 12%.

[0136] 5. Implementation Effect

[0137] The indoor temperature fluctuation of high-rise residents in summer is ≤1°C, the utilization rate of renewable energy is increased to 50%, and the actual / rated COP ratio of the ground source heat pump is 0.92.

[0138] 4. Comparative Example 1

[0139] Low-rise residential buildings (passive control only, no active green building systems)

[0140] Building parameters

[0141] Number of floors: 3, building area 200 m2, the enclosure structure is the same as that in Example 1, but active systems such as air conditioning, fresh air, and photovoltaics are not installed.

[0142] Technical defects:

[0143] 1. Limitations of regulatory capabilities:

[0144] In summer, when solar radiation is ≥1000W / ㎡ at 14:00, the room temperature can drop to 27℃ (PMV=1.2) with only sunshade louvers and phase change material walls, which cannot reach the comfort threshold (PMV≤0.5). The comfort level compliance rate is only 75%;

[0145] When the outdoor temperature is ≤5℃ at night in winter, the room temperature is maintained at 18℃ (lower than the lower limit of 20℃ specified in GB50736) by relying solely on the ground heat storage system.

[0146] 2. Uncontrollable energy consumption:

[0147] Without active system adjustment, users need to use electric heaters to improve comfort (average daily power consumption of 15kWh), total energy consumption increases by 40% compared with Example 1, and no renewable energy is used (utilization rate 0%).

[0148] 3. Poor user experience:

[0149] The frequency of manual adjustment of the sunshade louvers is ≥ 3 times / day, active ventilation is impossible when the CO2 concentration is greater than 1200ppm, and the user complaint rate increases by 2 times compared with Example 1.

[0150] V. Comparative Example 2

[0151] Multi-story residential buildings (green building active systems only, no passive components)

[0152] Building parameters

[0153] Number of floors: 6, the building area is the same as that of Example 2, but the enclosure structure is ordinary brick wall (heat transfer coefficient 1.2W / (m 2 K)) There are no passive components such as sunshade systems and ventilated atriums.

[0154] 1. Technical defects

[0155] Energy consumption remains high:

[0156] In summer, the air conditioner runs for an average of 12 hours per day (8 hours in Example 2), and the total energy consumption increases by 35% compared to Example 2. Although the utilization rate of renewable energy reaches 30%, the total power consumption is high (the proportion of photovoltaic power supply is still low).

[0157] The actual / rated COP ratio of the air conditioning system is 0.7 (0.9 in the second embodiment), and the excessive load on the equipment causes the failure rate to increase by 15%.

[0158] 2. Delayed control response:

[0159] Without passive pre-conditioning, the air conditioner is activated only when the room temperature is detected to be greater than 26°C. The indoor temperature fluctuates by ±3°C (±1.5°C in Example 2), and the PMV-PPD compliance time accounts for only 82%;

[0160] Without natural ventilation, the fresh air system needs to run at full load (air volume increased by 40%), and energy consumption further increases.

[0161] 3. High initial investment and operation and maintenance costs:

[0162] The active system capacity needs to be designed according to the maximum load (20% increase in equipment capacity compared to Example 2), the initial investment increases by 25%, and the operation and maintenance costs (such as air conditioning maintenance) increase by 18% annually.

[0163] Implementation Effect Comparison Table

[0164]

[0165] in conclusion:

[0166] Through verification of implementation examples in low-rise, multi-story and high-rise residential buildings, the technical solution of the present invention has achieved significant energy savings and comfort improvements in different building types: the collaboration of passive and active systems has reduced energy consumption by 18%-22%, the comfort compliance rate has exceeded 95%, the utilization rate of renewable energy has reached up to 45%, and the integration of user behavior data has increased the control accuracy by 20%. The comparative examples show that the control ability of a single passive control is insufficient under extreme climates, while the energy consumption of a single active system is 35% higher and the equipment load is large. In summary, the technical architecture of the present invention of "passive priority + active collaboration + data closed loop" is both universal and advanced, fully meets the technical requirements of the specification, and provides an efficient solution for the control of indoor environments in green buildings.

[0167] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A method for dynamic control of indoor environment by coupling passive and green building, characterized in that: The following steps are involved: Step 1: Collect indoor and outdoor environmental parameters, building energy consumption data, and user behavior data through a distributed sensor network, and transmit the data to a processing unit through communication technology; Step 2: The processing unit performs fusion processing on the collected data and predicts the environmental change trend in the future period; Step 3: Based on the prediction results, the processing unit preferentially adjusts the indoor environment through the building's passive structural components. When the passive adjustment fails to reach a preset comfort threshold, the green building's active system is linked for coordinated regulation. Step 4: The processing unit dynamically optimizes the fusion processing rules and control strategies through real-time feedback of the control effect to form a closed-loop control system.

2. The method for dynamic control of indoor environment by coupling passive and green building as claimed in claim 1, characterized in that: The indoor and outdoor environmental parameters in step 1 include at least one of temperature, humidity, light intensity, CO2 concentration, wind speed, wind direction and solar radiation intensity.

3. The method for dynamic control of indoor environment by coupling passive and green building as claimed in claim 1, characterized in that: The data fusion processing in step 2 specifically includes normalizing the environmental parameters and constructing a multi-objective decision matrix including a building envelope thermal response model, a natural ventilation flow model, and user comfort requirements.

4. The method for dynamic control of indoor environment by coupling passive and green building as claimed in claim 1, characterized in that: The passive structural components of step three include at least one of a shading system, vents, phase change material walls, sunrooms, ground heat storage systems, or high-airtightness enclosure structures. The passive adjustment includes dynamically adjusting the angle or opening degree of the shading system, controlling the opening and closing or opening degree of the vents, triggering phase change material heat exchange, or utilizing a heat storage system to store and release heat.

5. The method for dynamic control of indoor environment of passive and green building coupling according to claim 4, characterized in that: The green building active system in step three includes at least one of an air-conditioning system, a fresh air system, a photovoltaic-energy storage system, an air source heat pump or a ground source heat pump. The coordinated regulation includes optimizing the distribution of renewable energy based on real-time energy consumption data and predicted load, or adjusting indoor temperature and humidity, air quality and ventilation volume through active equipment.

6. The method for dynamic control of indoor environment of passive and green building coupling according to claim 5, characterized in that: The preset comfort threshold in step 3 is set based on the PMV-PPD index, temperature and humidity limits or CO2 concentration limit.

7. The method for dynamic control of indoor environment by coupling passive and green building as claimed in claim 3, characterized in that: The following method is used to predict the environmental change trend in the future period in step 2: a. Train historical data through time series prediction algorithms or machine learning models; b. Conduct physical simulation through building energy consumption simulation software; c. Correct the forecast results by combining external meteorological forecast data obtained through the meteorological data interface.

8. The method for dynamic control of indoor environment by coupling passive and green building as claimed in claim 1, characterized in that: The dynamic optimization in step 4 iteratively updates the data fusion rules, equipment control parameters and regulation strategies through a reinforcement learning algorithm, a genetic algorithm, an adaptive control algorithm or a fuzzy logic algorithm.

9. The method for dynamic control of indoor environment by coupling passive and green building as claimed in claim 2, characterized in that: The distributed sensor network in step 1 includes temperature and humidity sensors, light sensors, and anemometers deployed on the building's exterior structure, as well as CO2 sensors, PMV-PPD monitors, or occupancy sensors deployed indoors. Data is transmitted to a processing unit via wireless or wired communication technology. The processing unit is configured as an edge computing node or a central control system. The wireless communication technology is configured as LoRa, Zigbee, or WiFi technology, and the wired communication technology is configured as the RS485 communication protocol.

10. Application of the method for dynamic control of indoor environment of passive and green building coupling according to any one of claims 1 to 9, characterized in that: The method is applied to the design of new green buildings or energy-saving renovation of existing buildings, and the building types include residential buildings, office buildings, public buildings or industrial buildings, and the number of floors of buildings covers low-rise, multi-story or high-rise residential buildings.

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