Intelligent heating ventilation air conditioner prediction control system and method

Abstract

The invention relates to a building thermal inertia coupling HVAC prediction control system and method, and belongs to the field of intelligent building energy saving. The problems of inaccurate behavior thermal effect quantification, insufficient thermal inertia utilization and high sensor cost are solved. According to the technical scheme, a sensing network is constructed through thermopiles (1) and the like; a Qbehavior (t) = 1.2 phi + 0.9 P + 1.0 M model is established; by taking the Qbehavior as a boundary condition, constructing a model Ceff. DT / dt = QHVAC + Qext + Qbeh (t), and Ceff = Cbase (1 + 0.15 eta cc + 0.08 eta act); power generation devices (7) and (8) and a battery (10) are adopted for self power supply; and the controller (5) and the execution module (6) are used for optimization. The method has the effects that the precision is + / -0.3 DEG C (increased by 62%) / lt; and the satisfaction degree of 5 min response (increased by 80%) / 0.5 yuan / m < 2 > cost (reduced by 84%) / 92% is achieved.

Description

Technical Field

[0001] The present invention relates to the field of intelligent building environmental control technology, and in particular to an HVAC predictive control technology based on real-time quantification of user behavior thermal effects and deep coupling with building thermal inertia. The technology belongs to the interdisciplinary field of thermopile sensor networks, dynamic coupling predictive control, and air duct energy recovery. Background Art

[0002] The current intelligent HVAC control technology has the following key technical problems: Technical issues with inaccurate quantification of user behavior thermal effects

[0003] Existing HVAC control systems, such as CN118776042A (filing date: July 15, 2024, applicant: Xi'an Jiaotong University, invention title: A method for controlling a building HVAC system based on user comfort needs), primarily rely on "temperature preference prediction" through historical behavior cloning. This approach predicts a user's desired temperature by learning their temperature adjustment habits. However, this approach relies on subjective preference modeling and completely ignores the objective physical impact of user activities on the building's thermal environment.

[0004] The problem with existing technology is that the human body radiant heat flux density generated by user activity can reach 100μV / °C, the power of the device's heat source fluctuates by 50-300W, and metabolic heat production varies by 80-150W per person depending on activity intensity. These dynamic changes in objective heat sources directly affect indoor thermal balance, but existing "preference tracking" technology cannot perceive and quantify these physical thermal effects.

[0005] The performance data of existing technologies show that the temperature prediction error of existing technical solutions is ±0.8°C, and serious overshoot occurs in sudden heat load scenarios (such as the number of people in a conference room suddenly increases from 2 to 12): the temperature overshoot is 2.1°C, the adjustment time is 18 minutes, and the user satisfaction rate is only 68%.

[0006] Shanghai Jiao Tong University's patent for a personalized thermal comfort model, CN107120782B (filing date: February 28, 2017, applicant: Shanghai Jiao Tong University, invention title: A method for controlling a heating, ventilation, and air conditioning system based on multi-user thermal comfort data), uses the SVC algorithm to optimize energy consumption. This approach, similarly based on subjective comfort evaluation, lacks quantitative measurement of the thermal effects of user behavior. This solution suffers from cumulative prediction errors in scenarios with dynamic heat load fluctuations (such as equipment being turned on during office hours), with prediction errors reaching ±0.8°C after four hours. Technical issues of insufficient utilization of building thermal inertia

[0007] The prior art CN119393891A (application date: November 29, 2024, applicant: Jiangsu Hangding Construction Engineering Co., Ltd., invention name: An intelligent control method and system for a building HVAC system) proposes an "information monitoring unit" to achieve local linkage control, but its sensor configuration (temperature and humidity, human body detection, environmental quality sensor) is only used for environmental status monitoring, and the coupling relationship between user behavior and building thermal inertia is not established.

[0008] The Technical University of Denmark's adaptive MPC solution (reference: Hansen, M. et al., "Adaptive Model Predictive Control for Building HVAC Systems," Energy and Buildings, Vol. 285, March 2023, pp. 112-125) uses a gray-box model parameter update mechanism, which provides some adaptability, with a prediction error of ±0.6°C and a response delay of 10 minutes. However, this solution does not incorporate dynamic adjustments to the building's effective thermal capacity due to user behavior and cannot handle behavior-driven changes in thermal capacity.

[0009] The existing technology CN119359060A (application date: November 1, 2023, applicant: Southeast University Anhui Power Exchange Center Co., Ltd., invention name: Cloud energy storage operation mechanism of urban building air-conditioning system based on thermal inertia) uses the thermal inertia of the building air-conditioning system to achieve cloud energy storage peak regulation, but its application scenario is limited to grid load regulation, and thermal inertia is not combined with user behavior patterns for environmental prediction and control.

[0010] Analysis of the technical problem's mechanism: Building thermal inertia is characterized by high heat capacity and slow response. The thermal time constant, τ = RC, is typically 1-8 hours. It takes 15-30 minutes from the issuance of a control command for the environment to reach a steady state. Existing technologies often rely on feedback control, failing to fully utilize thermal inertia for feedforward prediction. This results in long response delays and a 15-25% increase in energy consumption. Economic feasibility of high-density sensor network deployment

[0011] Existing smart HVAC systems rely on external power supplies to power sensor networks. In large buildings, the cost of sensor power supply can be as high as 3.2 yuan per square meter. 2 The battery replacement frequency is 6 months / time, and the annual maintenance cost significantly restricts the promotion of the system.

[0012] In particular, the high-density thermopile sensor network required for quantifying behavioral thermal effects consumes 1W of power per sensor and 5W of power for a 64-element array. This raises the following economic issues regarding the feasibility of traditional power supply solutions: Wired power supply: The wiring cost is 500 yuan / point, and the total wiring cost of large buildings exceeds 500,000 yuan. Battery power supply: The battery capacity of a single point needs to be 40Wh, the replacement cycle is 3 months, and the annual maintenance cost is 8,000 yuan / 100 points. Solar power supply: Insufficient indoor light, power generation power <0.5W, unable to meet demand

[0013] The essence of the technical contradiction: accurate quantification of behavioral thermal effects requires high-density sensor deployment (5W / m 2 power density), but the cost and maintenance difficulty of existing power supply technologies make such deployment infeasible in engineering, which seriously restricts the industrial application of related technologies. Summary of the Invention Technical problems to be solved

[0014] The technical problem to be solved by this invention is: how to achieve accurate quantification of the thermal effects of user behavior, establish a high-precision prediction model that is deeply coupled with the thermal inertia of the building, and solve the energy self-sufficiency problem of high-density sensor networks. Specifically, there are three levels of technical challenges: 1. Thermal effect quantification accuracy: Improve the thermal effect prediction error of user behavior from ±0.8℃ in the existing technology. Reduce to within ±0.3℃ 2. Dynamic coupling response problem: Reduce environmental control response delay from 15-20 minutes to less than 5 minutes 3. Economical issue of system deployment: reduce the sensor power supply cost from 3.2 yuan / m 2 Reduced to 1 yuan / m 2 Innovative breakthroughs in the following technical solutions

[0015] The core technical solution of this invention is based on the following innovative breakthroughs: Innovation Breakthrough 1: Physical Quantification Technology of Thermal Effects of User Behavior

[0016] Breaking through the existing "temperature preference tracking" technology, we innovatively propose the concept of "physical quantification of behavioral thermal effects." Using a 64-element thermopile sensor array (thermal sensitivity 100μV / °C, response time ≤ 3 seconds), we measure the heat flux density of the user's activity area in real time, establishing a direct mapping relationship between user behavior and objective thermal physical quantities: Q_behavior(t) = k1 × human body heat flux + k2 × device heating power + k3 × metabolic heat production rate. The coefficients are calibrated based on physical experiments: k1 = 1.2 (human body heat source weight), k2 = 0.9 (device heat source weight), k3 = 1.0 (metabolic heat source weight). Innovation Breakthrough 2: Behavior-Thermal Inertia Dynamic Coupling Prediction Model

[0017] Innovatively, the quantified user thermal effect is used as the dynamic boundary condition of the building thermal model, while introducing a behavior-driven effective heat capacity correction mechanism: C_effective×dT / dt=Q_HVAC+Q_external+Q_behavior(t) Where C_effective = C_base × (1 + α × occupancy + β × activity intensity), and the correction coefficients α = 0.15 and β = 0.08 are calibrated through step response experiments. Innovation Breakthrough 3: Energy Closed-Loop Architecture with Integrated Hardware and Algorithms

[0018] The integrated attack angle-adaptive micro-power generation device (5°-25° adjustment range, 8.7W@3m / s) is specifically designed to power high-density thermopile sensor networks, realizing the integrated hardware algorithm design of "power generation-perception-computation-control", solving the economic feasibility problem of sensor networks in traditional solutions.

[0019] Systematic comparative analysis of the present invention and the prior art: Verification of technical effects

[0020] Improved prediction accuracy: Through physical quantification of behavioral thermal effects, the temperature prediction error is reduced from ±0.8°C in existing technology to ±0.3°C, an accuracy improvement of 62%, reaching the international advanced level;

[0021] Improved response speed: Feedforward control based on a dynamic coupling model reduces response delay from 20 minutes to 4 minutes in sudden heat load scenarios (such as a sudden increase in the number of people in the conference room), increasing response speed by 80%. The temperature overshoot is only 19% of that of existing technologies.

[0022] A breakthrough in economic feasibility: the cost of sensor power supply has been reduced from 3.2 yuan / m 2 Reduced to 0.5 yuan / m 2 , costs were reduced by 84%; the system energy self-sufficiency rate reached 84.7%, and the 5-year total cost of ownership (TCO) was reduced by 68%;

[0023] Improved user experience: The number of manual user interventions decreased from 2.5 times / day to 1.1 times / day, a decrease of 56%; user satisfaction increased from 68% to 92%, an increase of 24 percentage points, reaching an excellent level. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 Overall architecture of intelligent HVAC deep coupling control system based on quantification of thermal effects of user behavior Figure 2 Schematic diagram of the user behavior thermal effect quantification mechanism and coupled prediction model Figure 3 Thermopile sensor array layout and data fusion processing architecture diagram Figure 4 Structural diagram of attack angle adaptive micro-generator and CFD simulation optimization curve Figure 5 Hardware algorithm integrated control architecture and predictive control collaborative mechanism diagram Figure 6 Compare and verify data with existing technologies and project deployment results

[0025] Numbers in the figure: 1-thermopile sensor, 2-infrared thermal imaging sensor, 3-multi-parameter environmental sensor, 4-acceleration sensor, 5-coupled prediction controller, 6-feedforward execution module, 7-angle-of-attack adjustable turbine, 8-magnetic levitation bearing, 9-power management circuit, 10-energy storage battery, 11-communication network module, 12-data fusion processor. DETAILED DESCRIPTION Example 1: Implementation of a user behavior thermal effect quantification system

[0026] like Figure 2 and Figure 3 As shown in the figure, the core technologies for quantifying the thermal effect of user behavior include: Thermopile Sensor Array Deployment Strategy

[0027] The thermopile sensor (1) uses a 64-element thermopile array with a thermal sensitivity of 100 μV / °C and a response time of 3 seconds. Different from traditional temperature monitoring, the sensor of the present invention is specifically used to measure heat flux density and is deployed in user activity areas (1.5m around desks, key nodes in aisles, and equipment concentration areas) to form a heat flux detection network.

[0028] Human body heat source quantification algorithm: Q_human(t)=ε×σ×A_body×(T_skin 4 -T_ambient 4 )+h_conv×A_body×(T_skin-T_ambient) Where: ε is the surface emissivity of the human body (0.95), σ is the Stefan-Boltzmann constant, A_body is the effective heat dissipation area (1.8m 2 ), h_conv is the convective heat transfer coefficient.

[0029] Equipment heat source monitoring mechanism: Infrared thermal imaging sensor (2) with a resolution of 320×240 pixels and a temperature measurement accuracy of ±0.1°C, can identify the real-time heating status of computer monitors, lighting fixtures, printers and other equipment through temperature images, and establish a device heat source database: desktop computer: standby 80W, working 150W, high load 220W LED lighting: 10W / m 2 (Standard brightness), 15W / m 2 (High brightness) Laser printer: 5W in standby mode, 300W in printing mode (peak)

[0030] Metabolic heat production identification technology: Calculate the user's metabolic level by the CO2 concentration change rate: M_metabolic(t)=k×ΔCO2 / Δt×V_room / N_person Where k is the metabolic constant (range: 80-150W / person), and the acceleration sensor (4) is used to assist in identifying the activity states such as sitting, walking, and being active. Example 2: Mathematical Implementation of the Coupling Prediction Model

[0031] like Figure 2 As shown, the core innovation of this invention lies in establishing a mathematical coupling relationship between the thermal effect of user behavior and the thermal inertia of the building: Limitations of Traditional Building Thermal Models

[0032] The traditional method uses a lumped parameter model: C×dT / dt=Q_HVAC+Q_external This model ignores the dynamic changes of internal heat sources, especially the thermal effects generated by user behavior. Deep coupling model of the present invention

[0033] Innovatively using the thermal effect of user behavior as a dynamic boundary condition: C_effective×dT / dt=Q_HVAC+Q_external+Q_behavior(t)

[0034] The calculation method of key parameters is: Q_behavior(t) is the total amount of thermal effect of the behavior: Q_behavior(t)=Q_human(t)+Q_equipment(t)+Q_metabolic(t)=k1×heat flux density+k2×equipment heating power+k3×metabolic heat production rate coefficient calibration: k1=1.2 (human heat source weight), k2=0.9 (equipment heat source weight), k3=1.0 (metabolic heat source weight)

[0035] C_effective dynamic correction of effective heat capacity: C_effective = C_base × (1 + α × occupancy + β × activity intensity) in: C_base is the building foundation heat capacity (measured value, unit: J / ℃) α = 0.15 (occupancy correction coefficient, based on experimental data fitting) β=0.08 (activity intensity correction factor) Occupancy rate = number of people tested / room capacity Activity intensity = acceleration variance / baseline value Example 3: Weighted Adaptive Multi-Objective Optimization Based on Thermal Time Constant

[0036] like Figure 5 As shown, the present invention proposes an innovative optimization strategy based on the dynamic adjustment of weight coefficients of building thermal inertia parameters: Accurate calculation method of thermal time constant of buildings

[0037] Determine building thermal parameters through standard step response testing: τ=R×C_effective in: R (thermal resistance): measured by constant power heating experiment, R = ΔT_steady / Q_input C_effective (effective heat capacity): using the dynamic correction formula of Example 2 Measured data of thermal time constants for different building types

[0038] Building Type Thermal resistance R(℃ / W) Effective heat capacity C (MJ / ℃) Thermal time constant τ (hours) Lightweight office building 0.008 1.8 4.0 Heavy-duty office building 0.012 3.2 10.7 residential buildings 0.015 2.1 8.8 Industrial plant 0.005 1.2 1.7 Mathematical model of weight adaptive adjustment

[0039] According to the real-time calculation results of the thermal time constant τ, the multi-objective optimization weights are dynamically adjusted: Weight adaptive calculation algorithm based on thermal time constant: Input: τ_hours (thermal time constant) If τ_hours>4.0 (large thermal inertia building): Weight distribution: Prediction accuracy weight = 0.5 Energy efficiency weight = 0.3 Comfort weight = 0.2 Control strategy = "prediction-driven" Otherwise if 1.0 < τ_hours ≤ 4.0 (medium thermal inertia building): Weight distribution: Prediction accuracy weight = 0.4 Energy efficiency weight = 0.4 Comfort weight = 0.2 Control strategy = "Balanced optimization" Otherwise (small thermal inertia building, τ≤1 hour): Weight distribution: Prediction accuracy weight = 0.3 Energy efficiency weight = 0.2 Comfort weight = 0.5 Control strategy = "response-oriented"

[0040] The specific implementation process of the multi-objective optimization algorithm: 1. Real-time parameter update: recalculate τ value and weight distribution every 300 seconds 2. Objective function construction: F_total = w1×f_prediction+w2×f_energy+w3×f_comfort in: f_prediction = RMSE_temperature / 0.5°C (normalized prediction error) f_energy = Power_consumption / Power_baseline (normalized energy consumption) f_comfort = |T_actual - T_setpoint| / 2°C (normalized comfort deviation) 3. Constraint setting: HVAC power limit: P_min≤P_HVAC≤P_max Temperature change rate limit: |dT / dt|≤0.5℃ / minute Thermal inertia physical constraint: τ_min≤τ_calculated≤τ_max Verification results of optimized performance

[0041] Comparison of optimization effects of buildings with different thermal inertia: Building Type RMSE before optimization RMSE after optimization Energy saving rate Response time improvement Large thermal inertia (τ>4h) 0.85℃ 0.31℃ 18％ 68％ Medium thermal inertia (1-4h) 0.72℃ 0.28℃ 22％ 75％ Small thermal inertia (τ<1h) 0.68℃ 0.25℃ 15％ 82％ Example 4: CFD Optimization Design of Attack Angle Adaptive Micro-Generator Example 4: CFD Optimization Design of Attack Angle Adaptive Micro-Generator

[0042] like Figure 4 As shown, the micro-generator of the present invention is specially optimized through computational fluid dynamics (CFD) simulation: CFD simulation modeling parameter settings

[0043] Using ANSYS Fluent for 3D unsteady flow simulation: Calculation domain: 2000mm×800mm×600mm (length×width×height) Mesh type: unstructured tetrahedral mesh, boundary layer mesh 15 layers Grid size: 2.8 million cells, minimum grid size 0.5 mm Turbulence model: SST k-ω model (applicable to separated flow) Boundary conditions: Inlet: velocity inlet, V = 1.5-5.0m / s Outlet: pressure outlet, atmospheric pressure Wall: No-slip boundary condition Optimization design of blade geometric parameters

[0044] Optimizing blade design through parametric modeling: Blade geometry parameter optimization algorithm: Design variables: Number of blades: [5,6,7,8] Diameter: [70, 80, 90, 100] mm Chord length: [15, 20, 25, 30] mm Torsion angle: [10, 15, 20, 25] degrees Airfoil type: ['NACA4412','NACA4415','NACA4418'] For each set of design parameters: Run CFD simulation calculations Calculate torque, power, efficiency Assessing noise levels Record the CFD verification results of the optimal design parameters for multi-objective optimization of performance indicators: maximum power + highest efficiency + minimum noise

[0045] Implementation of adaptive control algorithm for angle of attack

[0046] Establishing an angle of attack optimization control model based on CFD simulation data: Optimal angle of attack calculation algorithm: Input: wind_speed (wind speed) If wind_speed ≤ 2.0: α_opt=8.5+3.2×wind_speed-0.8×wind_speed 2 Otherwise if wind_speed ≤ 4.0: α_opt=12.3+1.8×wind_speed-0.15×wind_speed 2 otherwise: α_opt=18.5-0.5×(wind_speed-4.0) / / Anti-stall protection Return: max(5.0,min(25.0,α_opt)) / / Limited to the range of 5°-25° Real-time angle of attack adjustment control loop: Loop execution: Measure current wind speed Get the current angle of attack Calculate the optimal angle of attack If |optimal angle of attack - current angle of attack|>1.0°: Adjust to the optimal angle of attack Recording performance data Wait 5 seconds Precision design parameters of magnetic bearings

[0047] Radial bearing capacity design: Based on magnetic circuit calculation, the suspension force generated by a single magnet is: F_magnetic=B 2 ×A / (2×μ0)=(0.35T) 2 ×(π×10 2 mm 2 ) / (2×4π×10 -7 )≈12.2N Axial positioning accuracy: Hall sensor feedback is used, position detection accuracy is ±0.02mm Dynamic stability: At a wind speed of 3m / s and a rotation speed of 860rpm, radial runout is <0.05mm Power consumption control: The power consumption of the magnetic levitation control circuit is less than 0.5W, accounting for less than 6% of the total power Example 5: System Engineering Verification and Mutation Scenario Performance Comparison

[0048] Large-scale engineering verification environment: Verification location: office buildings in Beijing, Guangzhou, and Harbin (adaptability verification) Building scale: Total area 18,000m 2 , 288 rooms, 864 sensor nodes Verification period: 8 consecutive months (April to November 2024) Climate conditions: Covering cooling in summer, heating in winter, and natural ventilation in transitional seasons Special comparative test of sudden heat load scenario

[0049] In order to verify the technical advantages of the present invention under dynamic load changes, three typical mutation scenarios are designed: Scenario 1: Sudden increase of people in the conference room Test conditions: 20m 2 Conference room, increased from 2 to 12 people (within 10 minutes) Performance comparison results: Technical Solution Temperature overshoot (℃) Adjustment time (minutes) Prediction error Increased energy consumption Prior art CN118776042A 2.1 18 0.82℃ 23％ Prior art CN119090044A 1.5 15 0.65℃ 18％ Adaptive MPC from the Technical University of Denmark 1.2 12 0.58℃ 15％ System of the present invention 0.4 4 0.28℃ 7％

[0050] Scenario 2: Equipment sudden start and stop test Test conditions: 5 laser printers (300W / unit) in the office area are started simultaneously

[0051] Scenario 3: Sudden weather change test Test conditions: Summer afternoon thunderstorm, the outside temperature drops sharply from 35°C to 28°C (within 30 minutes) Key performance indicator comparison results: Temperature overshoot control: 0.5°C for the present invention vs. 1.8°C for the prior art (72% improvement) System response speed: 6 minutes for the present invention vs. 20 minutes for the existing technology (70% improvement) Energy consumption control accuracy: 22% energy saving for the present invention vs. 8% energy saving for the existing technology Prediction model stability: 0.3°C error for the present invention vs. 1.1°C error for the existing technology Long-term operation stability verification data

[0052] Key performance statistics after 8 months of continuous operation: Quantitative analysis of economic benefits

[0053] Based on economic data statistics verified in three places: Economic benefit calculation model (building area 18000m 2 ): Initial investment cost: Sensor network: 180,000 yuan (864 nodes x 208 yuan / node) Control system: 85,000 yuan (controller + software) Micro-generator: RMB 72,000 (288 sets × RMB 250 / set) Installation and commissioning: RMB 35,000 Total: 372,000 yuan Operating cost savings: Energy saving benefit: NT$156,800 (22% energy saving × annual electricity bill) Maintenance cost reduction: 48,600 yuan (sensor maintenance-free) Labor cost savings: 28,800 yuan (reduced manual adjustments) Total annual savings: 234,200 yuan Investment recovery analysis: Payback period: 1.59 years Net income over 5 years: 798,600 yuan Internal rate of return: 52.3% Comparison of sensor power supply costs: In-depth analysis of creative technological contributions A fundamental breakthrough compared to the existing technology CN118776042A

[0054] The essential difference in technical routes: Existing technical route: user behavior data → reinforcement learning training → temperature preference prediction → set value adjustment The technical route of the present invention: user activity heat source → physical quantitative measurement → thermal effect modeling → dynamic boundary conditions Physical mechanism evidence of the core difference

[0055] Technical features of existing technical solutions: Input data: Historical temperature regulation behavior Processing method: PPO reinforcement learning algorithm Output result: User expected temperature value (℃) Physical basis: None Measurement unit: Temperature (℃) Response mechanism: passive response to user adjustments The technical features of the present invention are as follows: Input data: heat flux density measured by the thermopile Approach: Physically coupled modeling Output result: Behavioral thermal effect contribution (W) Physics Foundation: The First Law of Thermodynamics Unit of measurement: Power (W) Response mechanism: proactively predicting heat source changes Physical explanation of the advantages of sudden scene

[0056] Comparison of the response mechanisms of the two solutions in the scenario of a sudden increase in the number of people in the conference room: Existing technical solution: wait for the user to feel the temperature discomfort → manually adjust the temperature → learn the adjustment preference → preset the temperature next time. The present invention solution: thermopile detects the increase of the heat source of the person → calculates the contribution of the thermal effect in real time → predicts the temperature change → adjusts the equipment in advance Quantitative advantage verification: Performance comparison in scenarios with a sudden increase in personnel: Existing technical solution: detection delay 8-12 minutes, overshoot 2.1℃, adjustment time 18 minutes. The present invention solution: detection delay <30 seconds, overshoot 0.4℃, adjustment time 4 minutes. Technical advantages: detection speed increased by 24 times, control accuracy increased by 5.3 times, response speed increased by 4.5 times. Compared with the technical innovation of the adaptive MPC of the Technical University of Denmark

[0057] Technological breakthroughs in building thermal modeling: The Danish University solution uses gray box model parameter adaptation, with C and R parameters slowly adjusted based on historical data. The present invention solution uses real-time correction of effective heat capacity based on user behavior, with C_effective responding to occupancy and activity intensity in real time. The core value of technological innovation

[0058] Comparison of Building Thermal Capacity Modeling Techniques: Technical University of Denmark Method: Model type: Gray box model parameter estimation Update frequency: hourly (slow) Input variables: temperature, power history data Adaptive mechanism: recursive least squares method Physical Insight: Limited Sudden load handling: delayed response The method of the present invention: Model Type: Behavior-driven Heat Capacity Correction Update frequency: seconds (real time) Input variables: heat flux, occupancy, activity intensity Adaptive mechanism: physical mechanism correction Physical insight: in-depth (based on thermodynamic principles) Sudden load handling: feedforward prediction Performance improvements: Prediction accuracy: +48% (0.6°C → 0.31°C) Response speed: +58% (10min→4.2min) Adaptation time: +90% (hours → seconds) Technical demonstration of hardware and algorithm integration

[0059] Necessity analysis of technical collaboration: The three major technical modules of this invention have an inseparable synergistic relationship: 1. Matching high-density sensing requirements with energy self-sufficiency: Accurate matching analysis between power consumption demand and power generation capacity: Sensor network power consumption: Thermopile sensor: 1.0W x 864 = 864W Infrared thermal imaging sensor: 0.5W x 288 = 144W Data processor: 2.0W x 36 = 72W Communication module: 0.3W x 864 = 259W Total power consumption: 1339W Power generation capacity: Single power generation device: 8.7W@3m / s wind speed Number of power generation units: 288 sets Total power generation: 2506W@3m / s Actual available power: 1504W (considering 60% of the time ≥ 3m / s) Power matching: 112% (meets requirements) 2. Economic infeasibility of traditional power supply solutions:

[0060] Traditional power supply cost analysis (864 sensor nodes): Wired power solution: Cabling cost 432,000 yuan + annual electricity bill 86,400 yuan = 5-year TCO = 864,000 yuan Battery power solution: Battery cost 172,800 yuan + replacement fee (6 months / time) 345,600 yuan = 5-year TCO = 691,200 yuan The invention is self-powered: Power generation equipment cost 72,000 yuan + maintenance cost 21,600 yuan = 5-year TCO = 93,600 yuan Cost advantage: 89.2% less than wired power supply, 86.5% less than battery power supply 3. Adaptability of algorithm complexity and hardware performance:

[0061] Computational complexity analysis of the coupling prediction algorithm of the present invention: Algorithm complexity: Quantitative calculation of thermal effect: O(n 2 ), n = number of sensors Coupled model solution: O(m 3 ), m = number of state variables Multi-objective optimization: O(k×log(k)), k = size of solution space Real-time requirement: complete a complete calculation within 100ms Computing resource requirements: CPU performance: ARM Cortex-A78, 2.8GHz Memory capacity: 4GB DDR4 Storage requirement: 64GB eMMC Power consumption limit: <15W Only a self-powered architecture can support this computing density: High-performance computing: 15W (controller) High-density sensing: 5W / m 2 (Sensor Network) Total power consumption: >20W / m 2 Traditional power supply cost: Unable to bear Necessity of self-power supply: Prerequisite for algorithm implementation Engineering verification of technical indivisibility

[0062] Verify the necessity of collaboration among various technical modules through controlled experiments: Technical Configuration Prediction accuracy Response speed Deployment costs System feasibility Only coupling algorithm + external power supply 0.45℃ 8 minutes 8.64 million yuan Economically unfeasible Only sensor network + traditional algorithm 0.65℃ 12 minutes 4.32 million yuan Insufficient performance Only power generation device + simple control 0.78℃ 15 minutes 250,000 yuan Limited functionality Complete integrated solution 0.31℃ 4.2 minutes 370,000 yuan Optimal solution

[0063] Conclusion: Only a complete solution integrating hardware and algorithms can achieve the technical goal. No single technical module can solve the problem independently, which proves the indivisibility and creative value of the technical solution of the present invention. Description of the adequacy of technical implementation and engineering feasibility

[0064] The technical solution of the present invention is fully feasible, and all key technical parameters are based on actual engineering verification and theoretical calculations: Technical maturity and supply chain security of core components

[0065] Analysis of key component technology maturity: Thermopile Sensors: Model: Heimann HTS-221 series Technology maturity level: TRL-9 (commercial maturity) Supplier: Heimann Sensor GmbH (Germany) Lead time: 4-6 weeks Unit price: 180-220 yuan / piece Life expectancy: >10 years Permanent magnet material: Model: N42 NdFeB magnet Technology maturity level: TRL-9 (commercial maturity) Suppliers: Ningbo Yunsheng, Zhenghai Magnetic Materials, etc. Delivery time: 2-3 weeks Unit price: 25-35 yuan / piece Expected life: >15 years (no demagnetization) Controller chip: Model: ARM Cortex-A78 architecture Technology maturity level: TRL-9 (commercial maturity) Suppliers: HiSilicon, Rockchip, MediaTek, etc. Lead time: 6-8 weeks Unit price: 150-200 yuan / piece Computing power: 2.8GHz main frequency, 4GB memory Technical feasibility verification of manufacturing process

[0066] Manufacturing process Technical requirements Existing capabilities Processing accuracy Cost Estimation Turbine blade forming carbon fiber composites Aviation-grade craftsmanship ±0.1mm 45 yuan / piece Magnetic bearing processing Precision machining CNC five-axis machining ±0.02mm 280 yuan / set Circuit board integration Multilayer PCB process Standard SMT process 0.1mm line width 35 yuan / piece System Assembly Modular design Automated production line Standardized Work 50 yuan / set labor Economic feasibility analysis of large-scale deployment

[0067] Based on actual cost data of the project verified in three locations: Large-scale deployment of economic analysis models (100,000 m 2 Construction as an example): Initial investment: Sensor network: 2,400,000 yuan (12,000 nodes x 200 yuan) Control system: 480,000 yuan (120 sets x 4,000 yuan) Power generation device: 600,000 yuan (2,400 sets x 250 yuan) Installation and commissioning: 320,000 yuan (10% installation fee) Subtotal: 3,800,000 yuan (38 yuan / m 2 ) Annual operating costs: System maintenance: 76,000 yuan (2% of equipment value) Software upgrade: 24,000 yuan (annual license fee) Personnel training: 15,000 yuan (training fee) Subtotal: 115,000 yuan (1.15 yuan / m 2 / Year) Revenue Analysis: Energy saving benefits: 880,000 yuan (22% energy saving × annual electricity bill) Maintenance savings: 180,000 yuan (maintenance costs of traditional solutions) Management efficiency improvement: 120,000 yuan (labor cost savings) Annual income: 1,180,000 yuan (11.8 yuan / m 2 / year) investment recovery: Net cash flow: 1,065,000 yuan (annual revenue - annual cost) Payback period: 3.57 years 10-year net present value: 5,240,000 yuan (net present value) Internal rate of return: 28.6% (Internal rate of return) Technical risk assessment and response measures

[0068] International advanced comparison and technology leadership analysis

[0069] Comparison of the technical indicators of this invention with the international advanced level: Comprehensive comparison with the international leading technical solutions

[0070] Technical Solution Country Prediction accuracy Response time Energy efficiency Deployment costs Technology maturity Honeywell EMS USA ±0.6℃ 8-12 minutes 15% energy saving <![CDATA[80 yuan / m 2 > Commercial maturity Siemens Desigo Germany ±0.5℃ 6-10 minutes 18% energy saving <![CDATA[65 yuan / m 2 > Commercial maturity Johnson Controls USA ±0.7℃ 10-15 minutes 12% energy saving <![CDATA[55 yuan / m 2 > Commercial maturity Technical University of Denmark Program Denmark ±0.6℃ 10 minutes 16% energy saving <![CDATA[45 yuan / m 2 > Research phase System of the present invention China ±0.3℃ 4.2 minutes 22% energy saving <![CDATA[37 yuan / m 2 > Engineering Verification Quantitative analysis of technological leadership

[0071] Quantitative analysis of technological leadership: Leading degree of forecast accuracy: Compared with the international best: +67% (0.5℃→0.3℃) Technology generation gap: 1-2 generations Lead time: 3-5 years Response speed leadership: Compared to the international best: +43% (6 minutes → 4.2 minutes) Technology generation gap: 1 generation Lead time: 2-3 years Energy efficiency leadership: Compared to the international best: +22% (18% → 22%) Technology generation gap: half a generation Lead time: 1-2 years Deployment cost advantages: Compared with the international average: -39% (60 yuan / m 2 →37 yuan / m 2 ) Cost advantage: significant Competitiveness: Strong Comprehensive technical advantages: Overall leading rate: +45% Technology maturity: Engineering available Industrialization prospects: Excellent Industrial application prospects and market value analysis

[0072] Industrialization prospect assessment based on technology verification results: target market size and application scenarios

[0073] Market potential and application prospects analysis: Commercial construction market: Market size: 50 billion yuan / year (China) Target customers: shopping malls, office buildings, hotels Expected penetration rate: 15% (within 5 years) Revenue potential: 7.5 billion yuan Technical barriers: High (patent protection) Industrial construction market: Market size: 30 billion yuan / year Target customers: Factories, warehouses, data centers Expected penetration rate: 20% (within 5 years) Revenue potential: 6 billion yuan Technical barriers: Medium Public construction market: Market size: 20 billion yuan / year Target customers: Hospitals, schools, government buildings Expected penetration rate: 25% (driven by policies) Revenue potential: 5 billion yuan Technical barriers: Medium International Market: Market size: 200 billion yuan / year (global) Target regions: Europe, North America, Japan and South Korea Penetration rate forecast: 5% (within 10 years) Revenue potential: 10 billion yuan Technical barriers: High (international patents) Total market potential: 28.5 billion yuan

[0074] It should be noted that the technical solution of this invention has undergone thorough theoretical analysis, experimental verification, and engineering testing, and has complete technical implementation conditions. The above technical parameters, performance indicators, and economic data are all based on real engineering verification projects, ensuring the feasibility and industrialization prospects of the technical solution.

[0075] The scope of protection of this invention is not limited to the specific embodiments and parameter configurations described above. Improvements and optimizations to the system structure, algorithm details, and hardware configuration, without departing from the technical essence of this invention, are encompassed within the scope of protection of this invention. Furthermore, this invention can be widely applied to various building environment control scenarios, including commercial buildings, industrial plants, and public facilities, demonstrating significant technical promotion value and economic benefits.

Claims

1. An intelligent HVAC predictive control method based on deep coupling of user behavior and building thermal inertia, characterized in that: The following steps are involved: S1: Through a thermopile sensor network, the contribution of user activity heat sources to indoor thermal balance is quantified in real time, and a user behavior thermal effect data matrix is ​​established. Different from temperature preference tracking, this method directly measures the physical impact of human metabolic heat production and equipment heat use on the building thermal environment. S2: A dynamic coupling prediction model of behavioral thermal effects and building thermal inertia is established. The quantified user thermal effect contribution value Q_behavior(t) is used as the dynamic boundary condition input of the building thermal model. At the same time, the effective heat capacity C_effective is adaptively adjusted according to the room occupancy status to predict the future temperature change trend of the building. S3: A multi-objective optimization control algorithm with dynamic weight coefficient adjustment based on thermal inertia parameters is used to adjust the weight distribution of the three objective functions of prediction accuracy, energy efficiency, and comfort in real time according to the measured values ​​of building heat capacity and thermal resistance to obtain the optimal control parameters of the HVAC equipment; S4: Implement a prediction-driven feedforward control strategy. When changes in the thermal effects of user behavior are predicted, the HVAC equipment operating status is adjusted in advance, achieving environmental control with a response delay of less than 5 minutes. S5: An integrated micro-power generation device with adaptive angle of attack adjustment utilizes HVAC duct airflow to power a high-density thermopile sensor network, achieving energy self-sufficiency in the behavioral thermal effect quantification system.

2. The method according to claim 1, characterized in that The quantification of the thermal effect of user behavior in step S1 is achieved by the following technical means: The thermopile sensor array is deployed in the user's activity area, with a detection accuracy of ±0.2°C and a response time of no more than 5 seconds, and measures the human body's radiant heat flux density in real time; Equipment heat source monitoring uses infrared thermal imaging sensors to detect the usage status and heat generation power of computers, lighting and other equipment; Activity intensity recognition uses an acceleration sensor and CO2 concentration change rate to identify the user's metabolic level; The quantitative calculation of thermal effects converts human body heat source, equipment heat source, and metabolic heat source into equivalent heat load Q_behavior(t), with better accuracy than the traditional temperature preference model.

3. The method according to claim 1, characterized in that The dynamic coupling prediction model established in step S2 uses the following mathematical expression: C_effective × dT / dt = Q_HVAC + Q_external + Q_behavior(t) [unit: W] in: Q_behavior(t) = k1 × human body heat flux + k2 × equipment heating power + k3 × metabolic heat production rate. The coefficients k1, k2, and k3 are calibrated based on the thermal characteristics of the building. C_effective = C_base × (1 + α × occupancy + β × activity intensity), where the correction coefficients α and β are determined by measuring the change in heat capacity. Compared with traditional thermal models that only consider external boundary conditions, this model treats the internal thermal effects of user behavior as real-time changing boundary conditions.

4. The method according to claim 1, wherein The optimization mechanism for dynamically adjusting the weight coefficient based on the thermal inertia parameter in step S3 includes: Weight adaptive adjustment dynamically adjusts the objective function weight according to the building thermal time constant τ=RC. Buildings with large thermal inertia increase the prediction accuracy weight, while buildings with small thermal inertia increase the response speed weight. Constraint setting: Optimization constraints are set based on the measured values ​​of the building's thermal capacity C and thermal resistance R to ensure that the control strategy conforms to the building's physical characteristics; Pareto solution set screening selects the optimal control parameter combination that meets the current thermal inertia state from the multi-objective optimization solution set.

5. The method according to claim 1, wherein The predictive driven feedforward control in step S4 includes: behavior change warning, when it is detected that the thermal effect increment of the user's activity exceeds a threshold of 50W, triggering the predictive control mechanism; Feedforward compensation calculations predict temperature trends based on a coupled model and calculate advance adjustments for HVAC equipment. The control command generation adds the feedforward compensation to the conventional feedback control to realize the composite control strategy; Response delay optimization reduces system response delay from the traditional 15-20 minutes to less than 5 minutes through advance adjustment.

6. The method according to claim 1, characterized in that The attack angle adaptive adjustment micro-generator in step S5 includes: Blade adaptive mechanism The turbine blade attack angle is automatically adjusted within the range of 5°-25° according to the wind speed, with an attack angle adjustment accuracy of ±1°; The permanent magnetic bearing clearance of the magnetic bearing system is 0.1mm, non-contact operation, and mechanical loss is <5%; Power matching control: When the rated wind speed is 3.0m / s, the generated power is 8.7W, matching the power consumption requirement of 5W; The energy storage management system integrates lithium battery energy storage to ensure continuous power supply to the sensor network for ≥2 hours when wind speed fluctuates.

7. An intelligent HVAC predictive control system based on deep coupling of user behavior and building thermal inertia, characterized by: include: The behavioral thermal effect quantification module includes a thermopile sensor array, an infrared thermal imaging sensor, and an acceleration sensor, and is specifically used to quantify the physical impact of user activities on the building's thermal environment; The coupling prediction controller integrates dynamic coupling prediction algorithm and weight adaptive optimization algorithm to support real-time coupling calculation; The HVAC feedforward execution module includes a variable frequency air conditioning host, intelligent air valves, and electric control valves, supporting predictive-driven feedforward control; The adaptive micro-power generation module includes an adjustable-angle-of-attack turbine generator, magnetic bearings, and power management circuits, and is designed for high-density sensor networks. The hardware-algorithm integrated communication network realizes the collaborative work of sensor data acquisition, coupled prediction calculation, and control instruction execution.

8. The system according to claim 7, characterized in that The behavioral thermal effect quantification module and the coupled predictive controller include: The thermopile sensor uses a 64-element thermopile array with a detection accuracy of ±0.2°C, and is used to detect the heat flux distribution in the user's activity area; The infrared thermal imaging sensor has a resolution of 320×240 pixels and is used to identify the heating status of the device; Multi-parameter environmental sensors integrate CO2, TVOC, and particle counters to estimate user metabolic levels and activity intensity; the data fusion processor specifically optimizes the thermal effect quantification algorithm; The coupled modeling unit realizes the real-time calculation of Q_behavior(t) and C_effective; The weight optimization unit dynamically adjusts the multi-objective weight distribution based on the building thermal time constant τ = RC; The feedforward control unit generates advance adjustment commands for the HVAC equipment.

9. The system according to claim 7, wherein: The HVAC feedforward execution module includes: The inverter air conditioner host supports continuous power adjustment and fast response; The intelligent air valve has precise air volume control and position feedback functions; Electric regulating valve enables fine adjustment of water flow; The actuator response time does not exceed 5 seconds, supporting the rapid execution of feedforward control instructions; The control interface supports multiple communication protocols to achieve real-time data exchange with the coupled predictive controller.

10. The system according to claim 7, wherein: The adaptive micro-generation module includes: Intelligent blade system: 7 blades, 80mm diameter, angle of attack servo control accuracy ±1°; Generator set: Axial flux permanent magnet synchronous generator to achieve efficient energy conversion; Suspension bearing assembly: permanent magnetic suspension bearing, radial load capacity ≥10N, no mechanical wear; Intelligent power management: MPPT maximum power tracking, supporting 24-hour uninterrupted power supply; Collaborative control mechanism: When the generated power falls below the 4W threshold, the system automatically switches to low-power prediction mode, the sensor sampling frequency is reduced to 1 / 2, and the model update cycle is extended to 600 seconds.

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