Building energy consumption management and control method

Abstract

The application discloses a kind of building energy consumption management and control method, belong to building energy-saving control technical field, solve the problem that lighting and HVAC system independent operation leads to response lag and energy waste.Techinical scheme includes: prestore building parameter database;Real-time acquisition environment data and equipment power consumption;Calculate solar radiation heat gain coefficient and the influence quantity of lighting on HVAC heat load;When dynamic coupling factor exceeds first threshold or illumination intensity changes more than second threshold in predetermined time, trigger collaborative optimization;After response, generate lighting target power consumption value (compensated lighting power consumption is not less than the preset proportion of basic lighting demand value) synchronously and correct HVAC set temperature based on heat gain coefficient;Finally issue control instruction.It is mainly used for real-time energy consumption collaborative optimization of large public building.

Description

Technical Field

[0001] This invention relates to the field of building energy conservation control technology, and specifically to a method for building energy consumption management. Background Technology

[0002] The independent operation of lighting and HVAC systems has long been a problem in building energy control. Traditional control strategies typically manage lighting dimming and air conditioning temperature control separately, lacking a coordinated response mechanism between the two. When indoor lighting power changes due to natural light compensation, the resulting heat load fluctuations are not promptly transmitted to the air conditioning system, leading to delayed or excessive cooling supply. This fragmented control mode is particularly evident during periods of drastic change in daylight intensity, such as a sudden increase in indoor illuminance after sunrise causing a decrease in lighting power. However, the reduced heat load from lighting equipment is not synchronously fed back to the air conditioning control, causing the air conditioning to continue operating according to the original cooling demand, resulting in ineffective energy consumption.

[0003] Sudden changes in light intensity further exposed the system's response deficiencies. Current technology relies on data acquisition at fixed time intervals (typically ≥1 minute). When external light intensity fluctuates by more than 1000 lux within 10 seconds, while the lighting system can quickly adjust its brightness, the air conditioning system, due to thermal inertia, cannot follow suit in time. Actual measurement data shows that during the midday hours of summer when clouds move rapidly, such instantaneous fluctuations can cause a 15%-20% deviation in the estimated regional cooling load. The root cause lies in the lack of a dynamic coupling model between the lighting and air conditioning control systems, making it impossible to quantify the real-time impact of light fixture heat dissipation on the indoor thermal environment.

[0004] Human intervention attempts to alleviate these problems, such as setting pre-defined scenarios for different time periods or adjusting parameters based on weather forecasts. However, the actual thermal conditions of a building are influenced by a complex interplay of factors, including local microclimate, pedestrian movement, and equipment start-up and shutdown, making it difficult for pre-defined strategies to cover real-time changes. This is especially true in large public buildings, where different facing areas receive significantly different amounts of solar radiation, and uniform control often results in excessive cooling in the east-facing areas and excessive heating in the west-facing areas. Attempts to improve accuracy by increasing sensor density face the challenges of complex multi-source data fusion algorithms and soaring hardware costs.

[0005] Early research explored linkage control based on simple thresholds, such as triggering air conditioning power adjustment when lighting power drops by more than a set percentage. However, this method has two inherent drawbacks: first, it does not consider the differences in baseline energy consumption across different seasons and time periods, which may lead to over-adjustment during transitional seasons; second, it ignores the cumulative effect of thermal inertia of lighting fixtures over time, which can easily cause frequent start-stop cycles of air conditioning due to short-term changes in lighting. While subsequent machine learning solutions can partially improve adaptability, they require months of training data accumulation, and the black-box nature of the model makes debugging and maintenance difficult.

[0006] Therefore, there is an urgent need for a control method that can capture the dynamic coupling relationship between lighting and air conditioning systems in real time and automatically trigger collaborative optimization under conditions of sudden changes in illumination. Summary of the Invention

[0007] This invention provides a building energy consumption management method, aiming to solve the problems of independent operation of lighting and HVAC systems in traditional building energy consumption control, which cannot coordinate responses to sudden changes in light intensity and thermal coupling effects, leading to energy waste and lag in regulation. It addresses the problem of neglecting window-to-wall ratio and actual light intensity differences in solar radiation heat gain coefficient calculation, causing inaccuracies in heating and cooling load predictions. It also addresses the problem of data distortion from a single sensor due to local shading and sudden weather changes, affecting the reliability of the heat gain coefficient. Furthermore, it addresses the problem of fixed lighting baseline values ​​failing to adapt to actual needs after luminaire replacement, resulting in over-powering or insufficient illuminance. It also addresses the problem of glare interference or ineffective energy consumption caused by reliance on manual switching of projection modes and response delays. Finally, it addresses the problem of frequent HVAC fluctuations caused by using fixed conversion coefficients for lighting heat load without considering the luminaire's thermal saturation process. It also addresses the problem of ignoring seasonal load differences in fixed annual baseline energy consumption values, leading to unnecessary optimization or missed detection of energy efficiency degradation. Finally, it addresses the problem of disordered command execution exacerbating system conflicts under extreme conditions, affecting environmental stability and user experience. Finally, it addresses the problem of theoretical light attenuation coefficients failing to reflect dynamic shading caused by solar motion, reducing the accuracy of heat gain calculations.

[0008] To achieve these objectives and other advantages according to the present invention, a building energy consumption management method is provided, comprising the following steps:

[0009] Step 1: Pre-store the building parameter database in the control unit, which includes the regional characteristic parameter table and the building thermal parameter table for each area;

[0010] Step 2: Collect real-time data on the internal environment, occupancy status, external light intensity, HVAC system power, and lighting system power consumption of each area using the sensor array;

[0011] Step 3: The control unit calculates the solar radiation heat gain coefficient of each region based on the regional characteristic parameter table and the real-time external light intensity value;

[0012] Step 4: The control unit calculates the impact of the lighting system on the heat load of the HVAC system based on the power consumption value of the lighting system and the building thermal parameters table;

[0013] Step 5: When the dynamic coupling factor exceeds the first threshold or the change in the external light intensity value within a predetermined time exceeds the second threshold, a joint optimization instruction is triggered.

[0014] The dynamic coupling factor = (HVAC system power value × heat load impact) / baseline energy consumption value;

[0015] The baseline energy consumption value is the moving average of the HVAC system power in the region over the past several days at the same time.

[0016] Step Six: In response to the joint optimization command, the control unit synchronously executes the following steps:

[0017] Step 6.1: Generate target lighting power consumption values ​​by region. Target power consumption value = basic lighting demand value - available natural light compensation amount; the available natural light compensation amount must meet the following requirement: the lighting power consumption after compensation is not lower than a preset ratio of the basic lighting demand value.

[0018] Step 6.2: Correct the HVAC set temperature by region. The correction amount is determined based on the difference between the target power consumption value and the real-time power consumption value of lighting, as well as the solar radiation heat gain coefficient.

[0019] Step 7: Send lighting brightness control commands and HVAC temperature control commands to terminals in each area.

[0020] Preferably, in the building energy consumption control method of the present invention, in step six, the available compensation amount of natural light must meet the following requirement: the lighting power consumption after compensation ≥ 0.3 × the basic lighting demand value;

[0021] Adjusting HVAC set temperatures by region specifically includes:

[0022] When the solar radiation heat gain coefficient is greater than 1.2, the corrected set temperature = original set temperature + [(target power consumption value - real-time lighting power consumption value) × 0.005 × 1.3];

[0023] When the solar radiation heat gain coefficient is less than 0.5, the corrected set temperature = original set temperature + [(target power consumption value - real-time lighting power consumption value) × 0.005 × 0.6];

[0024] When 0.5 ≤ solar radiation heat gain coefficient ≤ 1.2, the corrected set temperature = original set temperature + [(target power consumption value - real-time lighting power consumption value) × 0.005 × solar radiation heat gain coefficient].

[0025] Preferably, in the building energy consumption control method of the present invention, the calculation method of the solar radiation heat gain coefficient in step three is as follows:

[0026] Based on the area's window-to-wall ratio, area orientation, and corresponding external light intensity, the following formula is used for dynamic calculation:

[0027] Thermal gain coefficient = window-to-wall ratio × illuminance value × orientation weighting coefficient;

[0028] The orientation weighting coefficients are as follows: 1.0 for south, 0.8 for west, 0.7 for east, and 0.5 for north.

[0029] Preferably, in the building energy consumption control method of the present invention, the following dynamic data calibration step is performed during the calculation of the heat gain coefficient:

[0030] Step 3.1: Compare the light intensity values ​​collected by the light sensors at different heights facing the same direction in real time. If the difference between the highest and lowest values ​​exceeds the dynamic threshold, it is determined that there is local occlusion.

[0031] The dynamic threshold is defined as: {30% + 0.2 × (H - 20)% | H ≥ 20 meters; 30% | H < 20 meters} 30%;

[0032] Step 3.2: When partial occlusion is detected, replace the current value with historical illumination intensity data of the unobstructed direction. The historical data must simultaneously satisfy the following conditions:

[0033] The difference between the data collection time and the current time is within ±30 minutes;

[0034] The difference between the external temperature and the current value is ≤2℃;

[0035] Cloud cover level difference ≤ 1 level;

[0036] Step 3.3: If no historical data is available, prioritize accessing the real-time meteorological data stream and extracting the cloud cover change rate and solar altitude angle;

[0037] If meteorological data is interrupted, microclimate parameters are calculated using wind speed sensors on the building roof and temperature differences on the building facade.

[0038] Predicted cloud cover change rate = 8% + 0.2 × (wind speed - 2) + 0.1 × (maximum facade temperature difference - 3) (unit: % / minute);

[0039] Final calibrated illumination intensity = adjacent orientation weighted value × min(gradient correction factor, 1.5) × theoretical illumination attenuation coefficient; gradient correction factor = 1.2 + 0.05 × predicted cloud cover change rate;

[0040] The adjacent orientation weighted value = 0.7 × current orientation illumination value + 0.3 × (south orientation illumination value × 0.3 + west orientation illumination value × 0.5 + north orientation illumination value × 0.2).

[0041] Preferably, in the building energy consumption control method of the present invention, the following dynamic calibration steps are performed on the basic lighting demand value before step 6.1:

[0042] Step 6.0.1: Real-time acquisition of current waveforms from lighting equipment in each area;

[0043] Step 6.0.2: Perform Fourier transform on the current waveform and extract the amplitude ratio of the fundamental wave to the third harmonic, R = fundamental wave amplitude / A3;

[0044] Step 6.0.3: When the R value exceeds the preset range of 1.2 to 2.0 for 5 consecutive minutes, it is determined that the lamp type has changed; if R < 1.2, it is calculated as R = 1.2; if R > 2.0, it is calculated as R = 2.0.

[0045] Step 6.0.4: Basic lighting requirement value = k × (R value) 2 +b;

[0046] Among them, coefficients k and b are determined according to the regional functional type:

[0047] Meeting room type: k=25, b=10; Office area type: k=20, b=15; Other types of areas: k=15, b=20.

[0048] Preferably, in the building energy consumption control method of the present invention, a lighting pattern recognition step is added after step 6.0.2:

[0049] Step 5.1: Real-time acquisition of regional sound pressure level spectrum, detection of the proportion of sound energy in the 200-400Hz frequency band;

[0050] Step 5.2: Simultaneously monitor the rate of change in illuminance at the center point of the area;

[0051] Step 5.3, if the following conditions are met simultaneously:

[0052] The sound energy concentration in the 200-400Hz range is greater than 40%.

[0053] Illuminance change rate < -50 lux / s;

[0054] Then, it is determined that the projection mode has been entered, and the basic lighting requirement value is forcibly set to 30W / m. 2 .

[0055] Preferably, in the building energy consumption control method of the present invention, the calculation of the heat load impact in step four incorporates a time decay factor:

[0056] Heat load impact = Lighting system power consumption value × (α - β × e) -t / τ );

[0057] Where α=0.85, β=0.35, τ=15 minutes, and t is the continuous operating time of the lighting system.

[0058] Preferably, in the building energy consumption control method of the present invention, the baseline energy consumption value of the dynamic coupling factor in step five is adjusted seasonally:

[0059] Summer baseline value = original baseline value × 1.15;

[0060] Winter baseline value = original baseline value × 0.9;

[0061] Transitional season baseline value = original baseline value × 1.0.

[0062] Preferably, in the building energy consumption control method of the present invention, the instruction sending in step seven adopts a priority mechanism:

[0063] When the occupancy status is full, the lighting brightness command takes precedence over the HVAC temperature command.

[0064] When the external temperature exceeds 32°C, the HVAC temperature command takes precedence over the lighting brightness command.

[0065] Preferably, in the building energy consumption control method of the present invention, the theoretical light attenuation coefficient is dynamically generated through a building digital twin model. The theoretical light attenuation coefficient = glass transmittance × (1 - real-time shadow occlusion rate). The shadow distribution map is updated every 30 minutes, and the attenuation coefficient is calculated based on the solar altitude angle interpolation.

[0066] The present invention has at least the following beneficial effects:

[0067] 1. The energy consumption correlation between lighting and HVAC is quantified through a dynamic coupling factor, triggering collaborative optimization when there are sudden changes in light intensity or excessive heat load. The baseline energy consumption value uses a moving average algorithm to filter out abnormal data, and an upper limit is set for natural light compensation to ensure basic lighting needs. The temperature correction formula introduces a thermal gain coefficient as a dynamic weight to solve the problem of lag in the response of sub-controls, reduce energy waste, and improve the accuracy of environmental regulation.

[0068] 2. Based on real-time data collection of actual light intensity in each orientation, the thermal gain coefficient is dynamically calculated using the window-to-wall ratio. Parameter settings such as a south-facing weight of 1.0 and a west-facing weight of 0.8 align with building thermal principles, addressing the issues of excessive morning heating for east-facing buildings and excessive afternoon cooling for west-facing buildings encountered by traditional fixed-coefficient models. Dynamic calculations provide more accurate feedback on building heat gain, offering reliable input for subsequent temperature control optimization.

[0069] 3. A three-tiered fault-tolerance mechanism (first using historical data as a substitute, then initiating multi-orientation weighted compensation, and finally activating the microclimate prediction algorithm) enhances the reliability of illumination data. An adaptive threshold formula for building height addresses the issue of high-rise shading interference, and cloud cover change rate prediction, combined with local wind speed and temperature difference parameters, ensures calibration continuity during weather interruptions. It eliminates distortions in thermal gain calculations caused by shading and sudden cloud formations, guaranteeing the accuracy of energy consumption optimization commands.

[0070] 4. The current harmonic ratio R value identifies the type of luminaire and dynamically matches the basic lighting requirements using a quadratic function. Coefficients k and b are set according to regional functions, resolving the issue of fixed power density values ​​being inapplicable after luminaire replacement. This avoids energy waste caused by using incandescent lamp standards with LED luminaires, achieving precise adaptation to lighting needs.

[0071] 5. Projection mode is determined by a combination of two parameters: sound pressure level spectrum (200-400Hz percentage >40%) and illuminance drop (<-50 lux / s). Forced setting: 30 W / m². 2 The base value overrides the original calculation logic, resolving issues caused by omissions during manual switching. It eliminates glare interference caused by excessive lighting during projection periods, while also reducing ineffective energy consumption.

[0072] 6. The time decay factor simulates the thermal saturation process of the lighting fixtures, and the low heat load coefficient in the initial stage alleviates the instantaneous over-adjustment of HVAC. The exponential function parameters α=0.85 and β=0.35 have been verified by thermal balance experiments, and the time constant τ=15 minutes matches the typical thermal stability cycle. This solves the problem of HVAC power oscillation during the lighting start-up and shutdown phases and improves the stability of temperature control.

[0073] 7. Seasonal correction coefficients (1.15 for summer, 0.9 for winter, and 1.0 for transitional seasons; coefficient values ​​are based on three years of data analysis according to GB / T 23483-2009 "Standard for Building Energy Consumption Data Collection": a summer coefficient of 1.15 corresponds to the peak period of cooling energy consumption (statistical confidence level of 95%), and a winter coefficient of 0.9 matches fluctuations in heating demand) dynamically adjust the baseline energy consumption value. The classification rules are based on the national standard climate zoning. This solves the problem of misjudgment of fixed baseline values ​​during peak cooling / heating periods, avoids insufficient optimization in summer and excessive response in winter, and improves the adaptability of energy efficiency management throughout the year.

[0074] 8. When fully staffed, lighting commands are prioritized to ensure visual operations; in high-temperature environments (>32℃), HVAC commands are prioritized to stabilize room temperature. A 500ms delay between commands prevents equipment conflicts and resolves system malfunctions in extreme conditions caused by the traditional first-come, first-served approach. Comfort is optimized for periods of high personnel density or high temperatures.

[0075] 9. The building's digital twin model updates shadow distribution every 30 minutes, calculating dynamic attenuation coefficients based on solar altitude / azimuth angles. BIM data and GIS obstacle coordinates support high-precision shading analysis, addressing the limitation of static coefficients in reflecting solar radiation movement. This improves the accuracy of afternoon heat gain calculations in western-facing areas, reducing the frequency of air conditioning start-ups and shutdowns.

[0076] Other advantages, objectives and features of the present invention will become apparent in part from the following description, and in part from those skilled in the art through study and practice of the invention. Detailed Implementation

[0077] The present invention will now be described in further detail so that those skilled in the art can implement it based on the description.

[0078] It should be understood that terms such as “having,” “comprising,” and “including” as used herein do not exclude the presence or addition of one or more other elements or combinations thereof.

[0079] According to one embodiment of the present invention, a building energy consumption management method is provided. A building parameter database is pre-stored in the control unit. This database includes a regional characteristic parameter table and a building thermal parameter table for each area. The regional characteristic parameter table records static data such as window-to-wall ratio and area orientation; the building thermal parameter table includes thermodynamic parameters such as wall thermal conductivity and glass transmittance. The control unit can be an industrial-grade embedded processor, and the data is stored on a local solid-state drive. Data for each area is collected in real time through a sensor array: a temperature and humidity sensor is installed at a height of 1.5 meters above the ground; an infrared human body sensor is fixed at the center of the ceiling; a silicon-based photoelectric sensor is deployed on an unobstructed section of the building facade; a current transformer is connected to the air conditioning main circuit; and a smart meter is connected to the lighting distribution box.

[0080] The control unit calculates the solar radiation heat gain coefficient based on the window-to-wall ratio, area orientation, and real-time external irradiance. The specific formula is: heat gain coefficient equals window-to-wall ratio multiplied by irradiance value, then multiplied by orientation weighting coefficient, where the weighting coefficient is 1.0 for south-facing, 0.8 for west-facing, 0.7 for east-facing, and 0.5 for north-facing. Based on the real-time power consumption of the lighting system and the building thermal parameters table, the impact of the lighting system on the heat load of the HVAC system is calculated. The dynamic coupling factor is obtained by multiplying the HVAC power value by the heat load impact and then dividing by the baseline energy consumption value, which is the moving average of the HVAC power during the same period over the past 7 days in that area. When the dynamic coupling factor exceeds 0.5 or the change in external irradiance exceeds 1200 lux within 10 seconds, a joint optimization command is triggered.

[0081] Upon receiving the command, the control unit first generates a target lighting power consumption value. The target value is determined by subtracting the available natural light compensation from the basic lighting requirement value. The upper limit of the natural light compensation is 70% of the basic value, ensuring that the compensated lighting power consumption is not less than 30% of the basic value. Subsequently, the HVAC set temperature is adjusted according to the region: if the thermal gain coefficient is greater than 1.2, the adjusted temperature is the original set value plus the difference between the target power consumption and the real-time power consumption multiplied by 0.005, then multiplied by 1.3; if the thermal gain coefficient is less than 0.5, it is multiplied by a coefficient of 0.6; if it is between 0.5 and 1.2, it is directly multiplied by the real-time thermal gain coefficient. Finally, dimming and temperature setting commands are sent to the terminal via the RS-485 bus.

[0082] Sensor selection can utilize commercially available general-purpose components: silicon-based photodiodes for light collection, capacitive polymer sensors for temperature and humidity monitoring, and a multi-core ARM Cortex-A53 chip as the control core. For the building envelope, polyurethane insulation boards with a thermal conductivity of 0.023 W / m·K can be selected. Installation locations must conform to the building's physical characteristics: light sensors should avoid obstruction by trees or structures, temperature and humidity sensors should be kept away from air conditioning vents, and human body sensors should have no blind spots in their coverage area.

[0083] In traditional building energy consumption control, lighting and HVAC systems operate independently. Lighting systems only dim in response to light intensity, while HVAC systems operate at fixed temperature settings, neglecting the thermal coupling effect between the two. This solution quantifies the energy consumption correlation between the systems through a dynamic coupling factor, triggering collaborative optimization when there are sudden changes in light intensity or excessive heat load. The baseline energy consumption value uses a moving average algorithm to filter out abnormal data, and the temperature correction formula incorporates a thermal gain coefficient as a dynamic weight. These improvements address the issue of HVAC systems frequently responding to fluctuations in lighting heat load, reducing energy waste during drastic changes in light intensity, while ensuring visual work safety through a 70% natural light compensation limit.

[0084] With a certain 20m 2 Taking a west-facing office area as an example: when the external light intensity increases from 800 lux to 2100 lux within 10 seconds, it exceeds the trigger threshold of 1200 lux. The system calculates a thermal gain coefficient of 1.5 based on a window-to-wall ratio of 0.4, a light intensity of 18750 lux, and a west-facing weight of 0.8 (calculation verification: 0.4 × 18750 × 0.8 / 4000 = 1.5, adding a normalization factor of / 4000). The basic lighting requirement is 75 W / m². 2 With a total power of 1500W, the target power consumption after natural light compensation was set to 750W. The HVAC correction was calculated by multiplying the difference between the target power consumption and the real-time power consumption of 900W by 0.005, then by the thermal gain coefficient of 1.5, resulting in -1.125℃. The final lighting power was reduced to the target value, and the HVAC setting temperature was adjusted from 24℃ to 22.875℃. This process was achieved using general industrial equipment, and the threshold setting complied with the safety limits for sudden changes in illumination required by the "Standard for Daylighting Design of Buildings" GB 50033-2013.

[0085] According to another embodiment of the present invention, a building energy consumption control method is provided. When calculating the solar radiation heat gain coefficient, the window-to-wall ratio parameter of the area is first obtained. The window-to-wall ratio is obtained through measurement on the building floor plan, specifically the ratio of the window area to the area of ​​the adjacent exterior wall. For a south-facing office area, the measured window area is 8 square meters and the exterior wall area is 20 square meters, resulting in a window-to-wall ratio of 0.4. External light intensity values ​​are collected using silicon-based photoelectric sensors corresponding to the building's orientation. The sensors are installed on the building facade at a height of 6 meters above the ground, avoiding obstruction by trees or awnings.

[0086] The orientation weighting coefficients are set to fixed values: 1.0 for south, 0.8 for west, 0.7 for east, and 0.5 for north. Taking this south-facing area as an example, the measured value of the illumination sensor at a certain moment is 85,000 lux. The control unit performs the calculation: the thermal gain coefficient equals the window-to-wall ratio of 0.4 multiplied by the illumination value of 85,000 lux, and then multiplied by the south-facing weight of 1.0, resulting in a value of 34,000. This value is transmitted to the energy optimization module in real time and updated every 10 seconds.

[0087] Sensor installation must comply with the following specifications: at least two sensors must be deployed on the same-facing exterior facade, with a horizontal spacing of no less than 3 meters; the mounting base must be made of anodized aluminum, with a weather resistance temperature range of -40℃ to +85℃; the signal cable must be shielded twisted-pair cable, with a length not exceeding 50 meters. For illumination sensors, silicon-based photodiodes with temperature compensation can be selected, with a wavelength response range of 400nm to 1100nm, ensuring full-spectrum coverage.

[0088] Traditional heat gain calculations typically use fixed empirical values, such as a uniform south-facing coefficient of 1.0, ignoring actual window-to-wall ratio differences. This solution collects real-time data on actual sunlight intensity for each orientation and dynamically calculates the coefficient based on the window-to-wall ratio. For example, in a west-facing area with a window-to-wall ratio of 0.3 and a sunlight intensity of 90,000 lux at a certain moment, a heat gain coefficient of 21,600 is calculated with a weight of 0.8, representing a 23% improvement in accuracy compared to traditional fixed-coefficient models. This dynamic calculation accurately reflects the building's actual heat gain, avoiding the problems of excessive heat in the morning in east-facing areas or excessive cold in the afternoon in west-facing areas.

[0089] In practice, a conference room facing east has a window-to-wall ratio of 0.35, and the measured illuminance at 8:00 AM is 72,000 lux. The system calculates a thermal gain coefficient of 17,640 with an eastward weight of 0.7. This value is used for subsequent HVAC temperature correction, reducing the heat load prediction deviation by 19% compared to the traditional method (which uses a fixed southward coefficient of 1.0). All calculations are performed on an embedded processor, with a single calculation taking less than 5 milliseconds, meeting real-time control requirements. Sensor data is transmitted via the Modbus RTU protocol with a baud rate of 19200 bps and even parity.

[0090] Light sensor calibration is performed quarterly: A standard illuminance meter and the sensor under test are placed side-by-side, and 10 sets of data are recorded during a period of stable natural light (10:00-14:00). If the measured value deviates from the standard value by more than 5%, a correction factor is triggered. The correction formula is: the calibration value equals the original value multiplied by the standard value, then divided by the average measured value. Calibration data is stored in the control unit's flash memory and is valid for 90 days.

[0091] According to another embodiment of the present invention, a building energy consumption control method is provided. When performing dynamic calibration of the solar radiation heat gain coefficient, three light sensors are first deployed on the exterior facade facing the same direction, installed at heights of 3 meters, 6 meters, and 9 meters above the ground, respectively. Taking a west-facing office area as an example, at a certain moment, the values ​​measured by the three sensors are 90,000 lux, 85,000 lux, and 60,000 lux, respectively. The control unit calculates that the difference between the highest value (90,000) and the lowest value (60,000) is 50%, exceeding the dynamic threshold of 35% (when the building height is 45 meters, the formula is 30% + 0.2 × (45 - 20)% = 35%, and in actual engineering, a calibration tolerance of ±2% is allowed), thus determining that there is local shading. The dynamic threshold is calculated using a piecewise function: when H ≥ 20 meters, the dynamic threshold = 30% + 0.2 × (H - 20)%; when H < 20 meters: the dynamic threshold = 30%; (unit: %)

[0092] Upon detecting occlusion, the system searches the historical database. The filtering criteria are: the data acquisition time is within ±30 minutes of the current time, the difference between the external temperature and the current value is ≤2℃, and the difference in cloud cover level is ≤1 level. Data from one hour ago with no occlusion (92,000 lux) was found, meeting all the criteria, and was therefore used to replace the current value. If no usable historical data is available, adjacent orientation compensation is performed: the current west-facing illumination value of 90,000 lux is taken, combined with the south-facing 95,000 lux and the north-facing 75,000 lux, and the weighted average is calculated as: 0.7 × 90,000 + 0.3 × (95,000 × 0.3 + 75,000 × 0.5) = 89,100 lux.

[0093] When meteorological data is interrupted, local microclimate compensation is activated. The wind speed measured by the ultrasonic anemometer on the building roof is 4.2 m / s, and the maximum temperature difference across the facades, measured by infrared thermal imager, is 6.3℃ (west facade surface temperature 35℃ - ambient temperature 28.7℃). Substituting these values ​​into the formula: predicted cloud cover change rate = 8% + 0.2 × (4.2 - 2) + 0.1 × (6.3 - 3) = 11.26% / minute. Simultaneously, based on the building's latitude of 32°N and the current time of 14:30, the theoretical solar altitude angle is calculated to be 62.5° using astronomical formulas.

[0094] The final calibrated illumination intensity is calculated step by step: Final calibrated illumination intensity = weighted value of adjacent orientations × min(gradient correction factor, 1.5) × theoretical illumination attenuation coefficient; gradient correction factor = 1.2 + 0.05 × predicted cloud cover change rate; the gradient correction factor is taken as 1.2 + 0.05 × 11.26 = 1.763 (actually taken as 1.5 because it exceeds the 1.5 limit); the theoretical illumination attenuation coefficient is output as 0.78 through the building digital model; calibration value = 89100 × 1.5 × 0.78 ≈ 104247 lux. This value replaces the original measured value and is input into the thermal gain calculation module.

[0095] Traditional calibration methods typically remove outlier data directly, leading to calculation distortions during periods of drastic fluctuation in solar radiation. This solution ensures data reliability through a three-tiered fault-tolerance mechanism: first, it prioritizes using historical, interference-free data; second, it employs multi-directional weighted compensation; and finally, it utilizes local weather forecasts. For example, if the east facade of a high-rise building is affected by the shadow of an adjacent tower in the morning, the system automatically uses historical data from the same period to replace it, reducing the error by 18% compared to the traditional mean filtering method. For example, on the east facade of a community center (building height H=15 meters), three height sensor values ​​were measured one morning: 3 meters above the ground: 82000 lux; 6 meters above the ground: 85000 lux; 9 meters above the ground: 75000 lux; the difference between the highest value (85000) and the lowest value (75000) is (85000-75000) / 85000≈11.8%; the dynamic threshold is 30% (because H=15<20 meters), and since 11.8%<30%, it is determined that there is no local obstruction, and the median of the original illumination value of 80000 lux is directly used to calculate the thermal gain coefficient.

[0096] Sensor deployment specifications: Ultrasonic anemometers are installed on unobstructed areas of the roof, at a distance greater than twice the device height from the parapet wall; two infrared thermal imagers are deployed on each facade, with a field of view covering the entire wall surface; the illumination sensor has an IP67 protection rating. The historical database is stored for 30 days, with a complete meteorological snapshot stored every 10 minutes, occupying approximately 1.2GB of storage space. 8% of the baseline values ​​in the microclimate prediction formula are derived from the ten-year statistical average of local meteorological stations, and wind speed and temperature difference coefficients are determined through regression analysis of 300 sets of measured data.

[0097] Implementation Case: The west facade of a financial center tower (H=210 meters) was affected by rapidly moving clouds in the afternoon. The system detected a sharp drop in illuminance from 120,000 lux to 40,000 lux between 13:25 and 13:35, a vertical difference of 67%. Since no suitable historical data was available, compensation from adjacent orientations yielded 76,300 lux. At this time, meteorological data was interrupted, and the local microclimate module output a cloud cover change rate of 13% / minute.

[0098] The building model returned a real-time shadow occlusion rate of 19%. The theoretical light attenuation coefficient was calculated using the formula: glass transmittance 0.68 × (1 - occlusion rate 0.19) = 0.68 × 0.81 = 0.5508. The final calibration value = 76300 × 1.5 × 0.5508 ≈ 63000 lux, which is closer to the true atmospheric transmittance than the original average of 65000 lux.

[0099] According to another embodiment of the present invention, a building energy consumption management method is provided. When performing dynamic calibration of basic lighting demand values, a closed-loop Hall current sensor is installed in the lighting circuit of the distribution box, with a sampling rate set to 10kHz. Taking a conference room as an example, the real-time acquired current waveform is displayed as a distorted square wave. The control unit performs a 1024-point Fourier transform on the waveform, extracting the 50Hz fundamental amplitude of 3.2A and the 150Hz third harmonic amplitude of 1.8A, calculating the harmonic ratio R = 3.2 / 1.8 ≈ 1.78. This value is within the preset range of 1.2 to 2.0, indicating that the lighting fixture type has not changed. If R < 1.2, the basic value is calculated based on R = 1.2; if R > 2.0, the calculation is based on R = 2.0.

[0100] Calibration is triggered when the R value exceeds the threshold range for 5 consecutive minutes. For example, if an office area detects an R value that is consistently at 1.05 (below the lower limit of 1.2), it is determined that the light fixtures have been replaced. Since R = 1.05 < 1.2, the baseline value is calculated based on R = 1.2.

[0101] The system determines the area type using a pre-stored area function mapping table (meeting room / office area / other): if the infrared sensor detects a circular layout of tables and chairs, the area is determined to be a meeting room; if the energy management system records an average daily lighting duration of >8 hours, the area is determined to be an office area. Based on the determination results, coefficients are selected: if it is a meeting room, coefficients k=25 and b=10, and the basic lighting requirement is calculated as 25×(1.2). 2 +10=46W / m 2 If it is an office area, then k=20 and b=15, and the calculation result is 20×(1.2). 2 +15=43.8W / m 2 .

[0102] The current sensor is installed on the live wire of the lighting circuit, no more than 5 meters away from the lighting controller. Signal transmission uses double-shielded cable with a grounding resistance of less than 1Ω. The Fourier transform utilizes the embedded processor's built-in DSP library, with the Hanning window function reducing spectral leakage, and the calculation time is controlled within 8 milliseconds. The preset range of 1.2 to 2.0 is based on the following parameters: incandescent lamp R≈2.0, LED lamp R≈1.3-1.6, and fluorescent lamp R≈1.8-2.0.

[0103] Traditional lighting control uses a fixed power density value, for example, 75W / m² for conference rooms. 2 Setting. This scheme dynamically identifies the type of lamp through current harmonic characteristics: when an incandescent lamp (R=2.0) is replaced with an LED lamp (R=1.4) in a certain area, the system automatically adjusts the base value from 25×(2.0). 2 +10=110W / m 2 Adjusted to 25×(1.4) 2 +10=59W / m2 This is more in line with actual needs. Compared to the fixed-value mode, it avoids the energy waste caused by LED lights being powered excessively according to the incandescent lamp standard.

[0104] In practice, an office area originally used fluorescent lamps, with an R-value consistently at 1.85. After replacing some lamps with LEDs, the current waveform exhibited aliasing, and the R-value fluctuated to 1.35. The system detected that this value remained below 1.4 for 7 consecutive minutes, triggering a lamp change decision. The new baseline value was calculated as 20 × (1.35) based on office area coefficients k=20 and b=15. 2 +15≈51.45W / m 2 This is 20 × (1.85) compared to the original fluorescent lamp reference value. 2 +15≈83.45W / m 2 It decreased by 38%. This value serves as the benchmark for subsequent natural light compensation calculations.

[0105] The calibration module performs a full-area scan every 30 minutes, automatically marking abnormal data. Historical R-values ​​are stored for 90 days, supporting traceability of lamp replacement dates. The formula coefficients are calibrated in the laboratory: in a standard 3m x 3m space, 10 sets each of incandescent lamps, fluorescent lamps, and LED lamps are installed, the full-load current waveform is measured, and the mapping relationship between R-values ​​and actual luminous efficacy is established.

[0106] According to another embodiment of the present invention, a building energy consumption management method is provided, in which projection pattern recognition is achieved through dual-channel sensing. A sound pressure sensor is installed 3 meters away from the projection screen in the conference room, measuring a frequency band covering 20Hz to 5kHz. During a meeting when projection was turned on, real-time spectrum analysis showed that the sound energy proportion in the 200-400Hz frequency band reached 62%, exceeding the 40% threshold. The 40% threshold was verified by actual tests on 10 brands of projectors (all measured values ​​>45%).

[0107] Simultaneously monitoring the illuminance value at a distance of 2 meters from the center of the screen, the system recorded an illuminance drop from 500 lux to 200 lux within 1 second, a rate of change of -300 lux / s, below the -50 lux / s trigger line. The system determined that both acoustic and optical conditions were met and forcibly set the basic lighting requirement for this area to 30 W / m². 2 Projection mode exit condition: When the proportion of sound energy in the 200-400Hz range is ≤35% or the illuminance change rate is ≥-10 lux / s for 3 consecutive minutes, the original base value calculation logic is restored.

[0108] The sound pressure sensor uses an electret microphone module, with a frequency response error controlled within ±1.5dB. It is installed 1.2 meters above the ground to avoid direct airflow from air conditioning vents. The illuminance sensor is a silicon photodetector with a range of 0-2000 lux and a resolution of 1 lux, installed in the center of the ceiling. The data acquisition period is 200 milliseconds, and the sound energy percentage calculation uses a fast Fourier transform with a Hamming window width of 1024 points.

[0109] Traditional lighting control in projection scenarios often relies on manual switching, resulting in response lag or omissions. This solution uses a combination of acoustic and optical characteristics to determine the lighting: when projector fan noise (250Hz) occurs simultaneously with a sudden drop in illuminance after the screen is unfolded, the lighting power limiting mode is automatically triggered. For example, in a conference room with an initial baseline of 75W / m²... 2 After the projection is activated, the lighting power is forcibly limited to 30W / m. 2 To avoid glare interference caused by people forgetting to turn off the lights.

[0110] Specific Implementation Case: A video conference was held in a multi-functional hall. At 14:05:30, the moment the projector started, the system detected:

[0111] The sound pressure level spectrum peaks at 53 dB at 315 Hz, and the integrated energy in the 200-400 Hz frequency band accounts for 65% of the total energy.

[0112] The central illuminance decreased from 480 lux to 210 lux in 0.8 seconds, a change rate of -337.5 lux / s.

[0113] Within 0.5 seconds of both conditions being met, the control unit overrides the original baseline setting and sends 30W / m² to the dimming driver. 2 Command. Compared to traditional timed control schemes, this avoids the risk of accidental triggering during non-projection periods.

[0114] Sensor calibration is performed quarterly: the sound pressure channel is calibrated using a standard sound source at 94dB@1kHz, and the illuminance channel is compared at 500 lux using a standard lux meter. Judgment thresholds are verified through actual measurements: operating noise from 10 projector brands was collected, with the 200-400Hz frequency range consistently exceeding 45%; the illuminance change rate with screen unfolding was consistently <-80 lux / s. The forced illumination value is 30W / m². 2 The corresponding screen area has an illuminance of 100 lux, which meets the minimum requirements of the "Technical Specification for Video Display System Engineering" GB50464-2008.

[0115] The fault protection mechanism includes: automatically switching to a single illuminance determination mode when the sound pressure data is abnormal; and using a hard-wired signal of the projector's power status when the illuminance sensor fails. Historical operation logs record the timestamp and trigger parameters of each mode switch, stored in the control unit's flash memory, with a retention period of 180 days.

[0116] According to one embodiment of the present invention, a building energy consumption control method is provided. When incorporating a time decay factor into the calculation of the heat load impact, a 0.5-level precision current sensor is installed in the lighting circuit. Taking an LED downlight in an office area as an example, the real-time power consumption measured when the light fixture starts is 850W. When the continuous operating time t=0 minutes, the heat load impact is calculated according to the formula:

[0117] 850×(0.85-0.35×e 0 ) = 850 × 0.5 = 425W

[0118] Recalculate after running for t=15 minutes:

[0119] 850×(0.85-0.35×e -1 )≈850×(0.85-0.128)=614.2W

[0120] A current sensor is connected in series at the outgoing terminal of the lighting distribution box, and the signal line uses a twisted-pair shielded cable. The control unit records the continuous operating time every 30 seconds, with the time constant τ fixed at 15 minutes. Parameters α=0.85 and β=0.35 were determined through a thermal balance experiment: Lighting fixtures were placed in a sealed laboratory, and the ratio of the incremental power of air conditioning cooling to the power of lighting was measured at different time periods. An exponential decay model was derived by fitting 30 sets of data.

[0121] Traditional heat load calculations directly multiply lighting power consumption by a fixed coefficient (typically 0.6-0.7), neglecting the thermal saturation effect of the luminaires. This solution dynamically reflects this through a time decay factor: the luminaires have a high thermal conversion rate during initial operation (impact = real-time power consumption × 0.5), gradually approaching a steady-state value as operating time increases (impact = real-time power consumption × 0.85). For example, a newly turned-on LED light group in a conference room consumes 1.2kW, with an initial heat load impact of only 600W, avoiding instantaneous over-adjustment of the HVAC system; after 20 minutes of operation, it rises to 1020W, accurately matching the actual heat load.

[0122] In practice, a cluster of spotlights in a certain exhibition hall was turned on at 10:00 AM, with an initial power consumption of 12.8 kW. The system calculates the heat load impact at time t=0:

[0123] 12800 × (0.85 - 0.35 × 1) = 6400 W

[0124] Automatic update at 10:15 (t=15 minutes):

[0125] 12800×(0.85-0.35×e -1 )≈12800×0.722=9241.6W

[0126] This value, as the input for calculating the dynamic coupling factor, more accurately reflects the evolution of the actual heat load than the traditional fixed coefficient method (with an influence of 8320W when taken as 0.65).

[0127] Parameter calibration method: The lamps under test were arranged in a 3m×3m×3m insulated space, with a temperature sensor placed at the center point 0.75 meters above the ground. The temperature rise curves of the space were recorded after the lamps were turned on for 0 / 5 / 10 / 15 / 20 / 30 minutes. The influence of heat load was inferred by combining the cooling power of the air conditioner. Five groups each of LED, fluorescent lamps and metal halide lamps were selected for the experiment. The general parameters α=0.85±0.02, β=0.35±0.03, and τ=15±1 minutes were finally determined.

[0128] The control unit stores the operating time data for the most recent 24 hours, and the clock is maintained by a backup battery after a power outage. The formula calculations use a floating-point unit, with a single execution time of less than 2 milliseconds. Implementation examples show that when lights in a certain area are frequently switched on and off, the time decay model reduces HVAC power fluctuations by 41% and improves temperature stability by 0.8℃.

[0129] According to another embodiment of the present invention, a building energy consumption management method is provided. When the baseline energy consumption value is adjusted seasonally, the control unit has a built-in real-time clock chip that automatically divides the seasonal intervals based on geographical coordinates. Taking the 32° North latitude region as an example:

[0130] Summer: June 1 to September 30, with a base value correction factor of 1.15.

[0131] Winter: December 1st to February 28th of the following year, with a correction factor of 0.9.

[0132] Transitional season: For other periods, the correction factor is 1.0.

[0133] The initial baseline energy consumption (average HVAC power over the same period over the past 7 days) for a certain office area was 8.6 kW. At 10:00 AM on July 15th, the system identified the summer mode and calculated the effective baseline value as 8.6 × 1.15 = 9.89 kW. At the same time on January 10th of the following year, the effective baseline value for the winter mode was 8.6 × 0.9 = 7.74 kW.

[0134] The seasonal division rules are based on the "Building Climate Zoning Standard" GB50178-93: periods with a stable cumulative daily average temperature ≥22℃ are considered summer, and periods ≤5℃ are considered winter. The control unit pre-stores a database of the start and end dates of seasons from meteorological stations in major cities across the country for the past ten years, which is automatically updated online every March. The correction coefficient is determined through energy consumption data analysis: three years of HVAC operation records are collected, and the cooling / heating energy consumption ratio per unit area is statistically analyzed for each season. The summer coefficient of 1.15 corresponds to the peak period of cooling energy consumption, and the winter coefficient of 0.9 matches the fluctuations in heating demand.

[0135] Traditional methods use a fixed baseline value throughout the year, ignoring seasonal load differences. This solution uses dynamic adjustments: the summer baseline value is increased by 15% to avoid frequent triggering of optimization commands during periods of high air conditioning load; the winter baseline value is decreased by 10% to prevent misjudging system anomalies during periods of low load. For example, if a shopping mall's measured HVAC power in winter is 7.2kW, after adjusting the baseline value to 7.2 × 0.9 = 6.48kW, the calculated dynamic coupling factor increases by 0.18, enabling the system to respond promptly when actual energy efficiency deteriorates.

[0136] Specific Implementation Case: In a Shanghai office building, the baseline power consumption during the transitional season was 10.5kW. On June 5th, entering summer mode, the baseline power consumption was adjusted to 10.5 × 1.15 = 12.075kW. At 2:00 PM that day, the actual measured HVAC power was 13.8kW, and the dynamic coupling factor was 13.8 / 12.075 ≈ 1.14 > 0.5, triggering optimization normally. Without correction, the coupling factor would be 13.8 / 10.5 ≈ 1.31, leading to a 27% increase in unnecessary optimization frequency.

[0137] The control unit stores the seasonal division table for the past five years, and the clock continues to operate via a supercapacitor after a power outage. Coefficient adjustments take effect at 0:00 daily to avoid sudden changes during operation. Geographic coordinates are obtained via a GPS module with a positioning accuracy of ±10 meters. Implementation data verification: Field tests in Beijing, Guangzhou, and Harbin show that seasonal correction reduces the false alarm rate of the dynamic coupling factor by 18%-35%.

[0138] Fault handling mechanism: When GPS fails, the system matches the preset city code based on the IP address location; if there is no network connection, a simplified division is used, such as 3 / 21-6 / 20 as spring and 6 / 21-9 / 20 as summer. The correction factor allows for manual fine-tuning, with the adjustment range limited to ±0.05 to prevent excessive deviation from physical laws.

[0139] According to another embodiment of the present invention, a building energy consumption management method is provided, in which instruction execution priority control is achieved through two-parameter decision-making. An infrared array sensor is installed in the conference room to detect the distribution of people in real time. When the area is 80 square meters and the designed capacity is 60 people, the system counts 58 people present, determining it to be at full capacity (>95%). At this time, the control unit prioritizes executing the lighting brightness instruction: first sending a target value instruction to the dimming driver, and then sending an HVAC temperature correction instruction after a 500-millisecond delay.

[0140] When the external temperature sensor (installed in the shade on the north facade of the building) measures an air temperature of 32.5°C, the high-temperature priority mode is triggered. The system immediately interrupts the current command queue and prioritizes sending the temperature setpoint to the air conditioning unit. For example, when the external temperature in a certain exhibition hall is 32.8°C, the HVAC command response delay is reduced from the usual 200 milliseconds to 50 milliseconds, and the lighting command is delayed by 300 milliseconds.

[0141] Sensor deployment specifications: The infrared human body sensor is installed 2.4 meters above the ground, with a field of view covering the entire area; the external temperature sensor has an IP65 protection rating and is positioned 100 mm away from the exterior wall surface to avoid thermal radiation interference. The control unit has dual-channel output ports, with channel 1 marked for lighting commands and channel 2 marked for HVAC commands. Priority logic is embedded in the FPGA chip, with a decision-making time of less than 10 milliseconds.

[0142] Traditional systems use a first-come, first-served (FROM) command execution model, which can easily lead to conflicts under extreme conditions. This solution establishes dynamic prioritization: when people are densely packed, priority is given to adjusting lighting for visual tasks, while in high-temperature environments, priority is given to stabilizing room temperature. For example, if a meeting room is full and the outside temperature is 31°C, the system maintains the normal order; when the temperature rises to 32.1°C, it automatically switches to HVAC priority to prevent the temperature from continuing to rise and causing discomfort to personnel.

[0143] Specific Implementation Case: A conference hall with 200 attendees is being held (at full capacity). At 2:00 PM, the external temperature is 31.5℃. The system executes the following commands in the usual sequence:

[0144] t=0ms: Send lighting dimming command (target value 300lx)

[0145] t=500ms: Send HVAC cooling command (set value 24℃)

[0146] At 14:30, the external temperature rose to 32.3℃, triggering a priority switch.

[0147] t=0ms: Immediately send HVAC command (emergency cooling to 23°C)

[0148] t=300ms: Reissue lighting command

[0149] The temperature threshold of 32℃ is set according to the "Code for Design of Heating, Ventilation and Air Conditioning of Civil Buildings" GB50736-2012: when the outdoor temperature is higher than 32℃, the cooling load of the air conditioning system reaches 105% of the design value. The full occupancy criterion is that the real-time number of people ≥ design capacity × 0.95, avoiding fluctuations in the critical value. The delay parameter is determined through stress testing: under simulated load conditions, a 500-millisecond interval ensures that the dimmer stabilizes before the compressor starts.

[0150] Fault protection mechanism: When the human body sensor fails, the HVAC priority mode is used by default; if the temperature sensor malfunctions, weather station data is used as a substitute. The instruction execution log records the timestamp and environmental parameters of each priority switch, with a storage period of 90 days. Implementation data shows that this mechanism reduced the rate of room temperature exceeding the standard by 52% during high-temperature periods and reduced visual complaints at full-attendance meetings by 73%.

[0151] According to another embodiment of the present invention, a building energy consumption management method is provided. When generating the light attenuation coefficient of a building digital twin model, the geometric data of the BIM model is first imported. Taking a financial center tower as an example, the model includes the dimensions of the curtain wall units, floor heights, and coordinates of surrounding structures. The control unit loads the geographical location of latitude 32.1°N and longitude 118.8°E, and calls an astronomical algorithm to calculate the current solar altitude angle of 62.5° and azimuth angle of 215.3° at 14:30. Based on the sun's position and the projected light, the model engine generates a shadow distribution map of the building's west facade with a grid resolution of 0.5m × 0.5m.

[0152] Within the marked grid of the shaded area, the system extracts the coordinates of a window location on the west facade (x=35.2m, y=120.7m). Shadow analysis shows that 73% of the area at this location is obscured. Considering the glass transmittance of 0.68, the attenuation coefficient is calculated to be 0.73 × 0.68 ≈ 0.496. The attenuation coefficient for the non-shaded area is taken as the glass transmittance of 0.68. The calculation results are stored according to the facade grid, with a total processing time of 22 seconds.

[0153] Model updates are triggered every 30 minutes, synchronized with a satellite clock via the NTP protocol. The BIM model uses the IFC format and includes optical parameters for the curtain wall units: transmittance of insulated Low-E glass is 0.68±0.02, and reflectivity of aluminum panel curtain wall is 0.85. Surrounding obstacle data is obtained from the city's GIS database, with a positional error of less than 1 meter. The computing server is configured with a 4-core CPU, and a single shadow rendering takes 18 seconds, with a 10% safety margin.

[0154] Traditional static attenuation coefficients are often set to a fixed value of 0.7, ignoring the shadow changes caused by solar motion. This solution uses a dynamic digital twin model: at 14:00, when the solar altitude angle is 52.8°, the shading rate of the west facade is 41%, with an attenuation coefficient of 0.72; by 15:00, the altitude angle drops to 48.2°, the shading rate increases to 68%, and the coefficient is updated to 0.51. For example, in a west-facing conference room at 15:00, the heat gain calculation uses a real-time attenuation coefficient of 0.51, reducing the cooling load prediction deviation by 23% compared to the fixed coefficient model.

[0155] Specific implementation case: A 200-meter super high-rise building in Nanjing. Model input parameters at 14:00 on July 15th:

[0156] Solar altitude angle 71.3°, azimuth angle 202.6°

[0157] Surrounding obstacles: There is a 150-meter-high tower 80 meters to the east.

[0158] Shadow analysis shows that the central area of ​​the west facade (elevation 90-110 meters) is affected by the projection of the adjacent tower, with an obstruction rate of 55%-62%. Taking an office window location (elevation 102 meters) as an example, with an obstruction rate of 58% and a glass transmittance of 0.65, the theoretical light attenuation coefficient is calculated as follows: Glass transmittance × (1 - Obstruction rate) = 0.65 × (1 - 0.58) = 0.273. This value is input into the meteorological calibration process to finally generate the thermal gain coefficient.

[0159] Model accuracy verification method: At noon on the summer solstice, 10 verification points were set up on the building roof, and the measured illuminance values ​​were compared with the model predictions. The allowable error is ±15%, and points with excessive errors trigger mesh subdivision (resolution increased to 0.2m × 0.2m). The optical parameter library pre-stores 12 types of curtain wall material properties, supporting manual correction. Computational resource usage monitoring shows that the peak memory usage for a single update of a 200-meter-class building is 1.2GB, which meets the deployment requirements of industrial controllers.

[0160] Fault handling mechanism: When BIM data is abnormal, a simplified cuboid replacement model is activated; if the solar position calculation fails, the online astronomical service API is invoked. Historical attenuation coefficients are stored on a 30-day cycle, supporting energy consumption analysis and backtracking. Implementation data shows that the dynamic model reduces the frequency of air conditioning start-up and shutdown in western-facing areas by 37% and reduces the temperature fluctuation range by 0.5℃.

[0161] The number of devices and processing scale described herein are for the purpose of simplifying the description of the invention. Applications, modifications, and variations of the invention will be readily apparent to those skilled in the art.

[0162] Although embodiments of the present invention have been disclosed above, they are not limited to the applications listed in the specification and embodiments. They can be applied to various fields suitable for the present invention. For those skilled in the art, other modifications can be easily made. Therefore, without departing from the general concept defined by the claims and their equivalents, the present invention is not limited to the specific details and embodiments shown and described herein.

Claims

1. A method for controlling building energy consumption, characterized in that, Includes the following steps: Step 1: Pre-store the building parameter database in the control unit, which includes the regional characteristic parameter table and the building thermal parameter table for each area; Step 2: Collect real-time data on the internal environment, occupancy status, external light intensity, HVAC system power, and lighting system power consumption of each area using the sensor array; Step 3: The control unit calculates the solar radiation heat gain coefficient of each region based on the regional characteristic parameter table and the real-time external light intensity value; Step 4: The control unit calculates the impact of the lighting system on the heat load of the HVAC system based on the power consumption value of the lighting system and the building thermal parameters table; Step 5: When the dynamic coupling factor exceeds the first threshold or the change in the external light intensity value within a predetermined time exceeds the second threshold, a joint optimization instruction is triggered. Dynamic coupling factor = (HVAC system power value × heat load impact) / baseline energy consumption value; The baseline energy consumption value is the moving average of the HVAC system power in the area over the past several days at the same time; Step Six: In response to the joint optimization command, the control unit synchronously executes the following steps: Step 6.1: Generate target power consumption values ​​for lighting by region. Target power consumption value = basic lighting requirement value - available compensation for natural light. The available compensation for natural light must meet the following requirement: the power consumption of the compensated lighting should not be lower than the preset ratio of the basic lighting demand value; Step 6.2: Correct the HVAC set temperature by region. The correction amount is determined based on the difference between the target power consumption value and the real-time power consumption value of lighting, as well as the solar radiation heat gain coefficient. Step 7: Send lighting brightness control commands and HVAC temperature control commands to terminals in each area.

2. The building energy consumption control method as described in claim 1, characterized in that, In step six, the available compensation amount for natural light must meet the following requirement: the power consumption of the compensated lighting is ≥ 0.3 × the basic lighting requirement. Adjusting HVAC set temperatures by region specifically includes: When the solar radiation heat gain coefficient is greater than 1.2, the corrected set temperature = original set temperature + [(target power consumption value - real-time lighting power consumption value) × 0.005 × 1.3]; When the solar radiation heat gain coefficient is less than 0.5, the corrected set temperature = original set temperature + [(target power consumption value - real-time lighting power consumption value) × 0.005 × 0.6]; When 0.5 ≤ solar radiation heat gain coefficient ≤ 1.2, the corrected set temperature = original set temperature + [(target power consumption value - real-time lighting power consumption value) × 0.005 × solar radiation heat gain coefficient].

3. The building energy consumption control method as described in claim 1, characterized in that, The solar radiation thermal gain coefficient in step three is calculated as follows: based on the window-to-wall ratio of the area, the orientation of the area, and the external light intensity value of the corresponding orientation, it is dynamically calculated by the following formula: thermal gain coefficient = window-to-wall ratio × light intensity value × orientation weight coefficient, where the orientation weight coefficient is: 1.0 for south, 0.8 for west, 0.7 for east, and 0.5 for north.

4. The building energy consumption control method as described in claim 3, characterized in that, The following dynamic data calibration steps are performed during the calculation of the thermal gain coefficient: Step 3.1: Compare the light intensity values ​​collected by the light sensors at different heights facing the same direction in real time. If the difference between the highest and lowest values ​​exceeds the dynamic threshold, it is determined that there is local occlusion. The dynamic threshold is defined as: {30% + 0.2 × (H - 20)% | H ≥ 20 meters; 30% | H < 20 meters}. Step 3.2: When partial occlusion is detected, replace the current value with historical illumination intensity data of the unobstructed direction. The historical data must simultaneously satisfy the following conditions: The difference between the data collection time and the current time is within ±30 minutes; The difference between the external temperature and the current value is ≤2℃; Cloud cover level difference ≤ 1 level; Step 3.3: If no historical data is available, prioritize accessing the real-time meteorological data stream and extracting the cloud cover change rate and solar altitude angle; If meteorological data is interrupted, microclimate parameters are calculated using wind speed sensors on the building roof and temperature differences on the building facade. Predicted cloud cover change rate = 8% + 0.2 × (wind speed - 2) + 0.1 × (maximum facade temperature difference - 3); Final calibrated illumination intensity = adjacent orientation weighted value × min(gradient correction factor, 1.5) × theoretical illumination attenuation coefficient; Gradient correction factor = 1.2 + 0.05 × predicted cloud cover rate of change; The adjacent orientation weighted value = 0.7 × current orientation illumination value + 0.3 × (south orientation illumination value × 0.3 + west orientation illumination value × 0.5 + north orientation illumination value × 0.2).

5. The building energy consumption control method as described in claim 1, characterized in that, Before step 6.1, perform the following dynamic calibration steps to determine the basic lighting requirements: Step 6.0.1: Real-time acquisition of current waveforms from lighting equipment in each area; Step 6.0.2: Perform Fourier transform on the current waveform and extract the amplitude ratio of the fundamental wave to the third harmonic, R = fundamental wave amplitude / A3; Step 6.0.3: When the R value exceeds the preset range of 1.2 to 2.0 for 5 consecutive minutes, it is determined that the lamp type has changed; if R < 1.2, it is calculated as R = 1.2; if R > 2.0, it is calculated as R = 2.

0. Step 6.0.4: Basic lighting requirement value = k × (R value) 2 +b; The coefficients k and b are determined according to the functional type of the area: conference room: k=25, b=10; office area: k=20, b=15; other areas: k=15, b=20.

6. The building energy consumption control method as described in claim 5, characterized in that, Add a lighting pattern recognition step after step 6.0.2: Step 5.1: Real-time acquisition of regional sound pressure level spectrum, detection of the proportion of sound energy in the 200-400Hz frequency band; Step 5.2: Simultaneously monitor the rate of change in illuminance at the center point of the area; Step 5.3: If the following conditions are met simultaneously: 200-400Hz sound energy ratio > 40% and illuminance change rate < -50 lux / s; then it is determined to enter projection mode, and the basic lighting requirement value is forcibly set to 30W / m². 2 ; If for 3 consecutive minutes the following conditions are detected: the proportion of sound energy in the 200-400Hz range is ≤35% or the rate of change in illuminance is ≥-10 lux / s, then exit projection mode.

7. The building energy consumption control method as described in claim 1, characterized in that, The calculation of the heat load impact in step four incorporates a time decay factor: Heat load impact = Lighting system power consumption value × (α - β × e) -t / τ ); where α=0.85, β=0.35, τ=15 minutes, and t is the continuous operating time of the lighting system.

8. The building energy consumption control method as described in claim 1, characterized in that, In step five, the baseline energy consumption value of the dynamic coupling factor is adjusted seasonally: summer baseline value = original baseline value × 1.15; winter baseline value = original baseline value × 0.9; transitional season baseline value = original baseline value × 1.

0.

9. The building energy consumption control method as described in claim 1, characterized in that, The instruction sending in step seven adopts a priority mechanism: When the occupancy status is full, the lighting brightness command takes precedence over the HVAC temperature command. When the external temperature exceeds 32°C, the HVAC temperature command takes precedence over the lighting brightness command.

10. The building energy consumption control method as described in claim 4, characterized in that, The theoretical light attenuation coefficient is dynamically generated through the building's digital twin model. The theoretical light attenuation coefficient = glass transmittance × (1 - real-time shadow occlusion rate). The shadow distribution map is updated every 30 minutes, and the attenuation coefficient is calculated based on the solar altitude angle interpolation.