Intelligent Flexible Control Method for Air Conditioning Systems Based on Temperature Difference Detection

Abstract

This invention discloses an intelligent flexible control method for an air conditioning system based on temperature difference detection, comprising: S1, collecting the inlet and outlet temperatures of the air conditioning system and calculating the inlet and outlet temperature difference; S2, collecting the water temperatures before and after the filter of the air conditioning water system and calculating the inlet and outlet water temperature difference; S3, setting a blockage threshold for the air conditioning system and a blockage threshold for the water system, replacing the AC fans of the air conditioning terminals and the fresh air system with DC fans, and replacing the AC circulation pump of the water system with a DC circulation pump; S4, when the inlet and outlet air temperature difference exceeds the air system blockage threshold, steplessly adjusting the speed of the DC fans of the air conditioning terminals and the fresh air system, temporarily increasing the fan speed to compensate for heat exchange efficiency; S5, when the inlet and outlet water temperature difference exceeds the water system blockage threshold, steplessly adjusting the speed of the DC circulation pump of the water system, temporarily increasing the DC circulation pump speed to compensate for flow rate. This invention can balance energy-saving control and precise temperature control, achieving an optimal balance between energy consumption, cost, and comfort.

Description

Technical Field

[0001] This invention relates to a method for controlling an air conditioning system, and more particularly to an intelligent and flexible control method for an air conditioning system based on temperature difference detection. Background Technology

[0002] Existing building power supply and distribution systems generally adopt a 220V / 380V AC power supply architecture, with loads such as air conditioning, lighting, fresh air systems, and elevators operating independently and controlled in a decentralized manner, resulting in low overall system energy efficiency. Specifically: High conversion losses: The DC power generated by photovoltaics and energy storage needs to be inverted into AC power multiple times to supply the load, and the AC-DC conversion loss is usually between 5% and 12%. Isolated control: Each subsystem (such as air conditioning and lighting) is controlled independently by PID, lacking global coordination and unable to perform adaptive scheduling based on electricity price, weather, and load; Delayed operation and maintenance: Air conditioner filter blockage relies on manual inspection, which cannot be monitored in real time, resulting in a 15% to 25% decrease in heat exchange efficiency; elevator braking energy is consumed by heating through resistance, resulting in energy waste.

[0003] Complex wiring: Traditional sensors require independent wiring, which results in long construction cycles, high costs, and difficulty in expansion.

[0004] Therefore, how to integrate DC microgrids, global sensing, and AI intelligent decision-making control methods to achieve the optimal balance between energy consumption, cost, and comfort has become a research hotspot in this field. Among these, the control methods for air conditioning systems are particularly important, as they have a significant impact on the energy-saving control and precise temperature control of the entire system. Summary of the Invention

[0005] The technical problem to be solved by the present invention is to provide an intelligent flexible control method for air conditioning systems based on temperature difference detection, which can take into account both energy-saving control and precise temperature control, and achieve the optimal balance between energy consumption, cost and comfort.

[0006] To address the aforementioned technical problems, this invention provides an intelligent flexible control method for an air conditioning system based on temperature difference detection, comprising the following steps: S1, collecting the inlet and outlet temperatures of the air conditioning system and calculating the inlet and outlet temperature difference; S2, collecting the water temperatures before and after the filter of the air conditioning water system and calculating the inlet and outlet water temperature difference; S3, setting a blockage threshold for the air conditioning system and a blockage threshold for the water system, replacing the AC fans of the air conditioning terminals and the fresh air system with DC fans, and replacing the AC circulation pump of the water system with a DC circulation pump; S4, when the inlet and outlet air temperature difference exceeds the air conditioning system blockage threshold, it is determined that the filter is blocked, a filter cleaning warning is issued, and the DC fans of the air conditioning terminals and the fresh air system are infinitely speed-regulated, temporarily increasing the fan speed to compensate for heat exchange efficiency; S5, when the inlet and outlet water temperature difference exceeds the water system blockage threshold, it is determined that the water system is blocked, a water system maintenance warning is issued, and the DC circulation pump of the water system is infinitely speed-regulated, temporarily increasing the DC circulation pump speed to compensate for flow rate.

[0007] Further, in step S3, the blockage threshold ΔTwind of the air system is set to ≥8℃, and the blockage threshold ΔTwater of the water system is set to ≥4℃. In step S4, the fan speed is temporarily increased by 10% to 15% until the temperature difference between the inlet and outlet water ΔT_wind is controlled within 3 to 5℃. In step S5, the DC circulating pump speed is temporarily increased by 5-10% until ΔTwater is controlled within 3 to 5℃.

[0008] Furthermore, in step S3, the following replacement control is applied to the air conditioning terminal fans: AC fans with a rated efficiency of 65% to 75% are replaced with DC fans with a rated efficiency of 88% to 92%, reducing the power consumption per unit by 68W to 145W; the following replacement control is applied to the fresh air system fans: AC fans with a rated efficiency of 66% to 74% are replaced with DC fans with a rated efficiency of 87% to 91%, reducing the energy consumption per unit by 0.21kW to 0.73kW, and adjusting the air volume according to the CO2 concentration levels.

[0009] Furthermore, the fresh air system fan control is as follows: Monitoring indoor CO2 concentration and VOC value; setting the following graded adjustment strategy: when CO2 < 800 ppm, controlling the fresh air volume to 20%–30%; when 800 ppm ≤ CO2 < 1000 ppm, controlling the fresh air volume to 50%; when CO2 ≥ 1200 ppm or VOC > 0.6 mg / m³, controlling the fresh air volume to 50%. 3 The fresh air volume is controlled at 100%.

[0010] Furthermore, in step S3, the AC circulating pump with a rated efficiency of 72% to 78% is replaced with a DC circulating pump with a rated efficiency of 86% to 91%, reducing the power consumption of a single unit by 0.45kW to 1.2kW.

[0011] Further, in step S5, first increase the speed of the DC circulation pump by 5%, observe and wait for 1 to 3 minutes. If ΔTwater is still not controlled within 3 to 5℃, then increase the speed of the DC circulation pump by 10%.

[0012] Furthermore, it also includes the following ground-source energy coupled air conditioning control method: monitoring the ground-source side temperature and air conditioning unit load; when the ground-source side temperature is 16-18℃ in summer and the unit load is greater than 70%, or when the ground-source side temperature is 14-16℃ in winter and the unit load is greater than 70%, opening the ground-source side heat exchange circuit; reducing the air conditioning unit output by 20%-35%, and using ground-source cooling / heating auxiliary regulation to improve the seasonal energy efficiency ratio (SEER) by 1.8-2.6.

[0013] Furthermore, it also includes arranging temperature sensors at multiple height levels indoors to finely regulate the indoor temperature; when the temperature difference between different height levels is greater than 1.5℃, adjusting the DC fan speed and the opening of the air conditioning water valve until the temperature difference between different height levels is controlled within 0.5℃.

[0014] Furthermore, the plurality of height layers include a low-level temperature measuring layer, an intermediate reference layer, and a high-altitude temperature measuring layer. The height of the low-level temperature measuring layer is 0.25m to 0.35m, the height of the intermediate reference layer is 1.1m to 1.3m, and the height of the high-altitude temperature measuring layer is 2.4m to 2.6m.

[0015] Furthermore, the height of the lower temperature measuring layer is 0.3m, the height of the intermediate reference layer is 1.2m, and the height of the upper temperature measuring layer is 2.5m.

[0016] Compared with existing technologies, this invention offers the following advantages: The intelligent flexible control method for air conditioning systems based on temperature difference detection provided by this invention can balance energy-saving control and precise temperature control, achieving an optimal balance between energy consumption, cost, and comfort. Specific advantages are as follows: 1. Significantly improved energy efficiency: Eliminating AC / DC conversion losses, the overall energy saving rate of the system can reach 32% to 56%, and the self-consumption rate is increased to 85% to 92%; 2. Comfort Guarantee: Through multi-altitude temperature stratification control (±0.5℃) and air quality linkage, the comfort compliance rate is ≥98%. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the system architecture based on photovoltaic DC microgrid used in this invention; Figure 2 This is a flowchart illustrating the logic of the AI ​​multi-objective hierarchical optimization algorithm in this embodiment of the invention. Figure 3 This is a flowchart of the intelligent flexible control process of an air conditioning system based on temperature difference detection in an embodiment of the present invention. Detailed Implementation

[0018] The present invention will now be further described with reference to the accompanying drawings and embodiments.

[0019] Please see Figure 1 and Figure 2 The intelligent collaborative energy-saving control method based on photovoltaic DC microgrid and multifunctional EMS (Energy Management System) adopted in this invention includes the following steps: Constructing a hierarchical DC microgrid: DC750V is used as the main voltage for lighting, and DC375V is used as the branch voltage for lighting. The original AC interfaces for air conditioning and elevators are retained. Comprehensive data acquisition: Through a low-voltage DC PLC carrier network, data on photovoltaic, energy storage, busbar, environmental, and equipment status are acquired at a frequency of 1-5 seconds per acquisition, with a data accuracy rate of 99.5%. AI Prediction and Assessment: Short-term load forecasting is performed using the LSTM algorithm (accuracy ≥ 92%), photovoltaic output forecasting is performed using the BP neural network (accuracy ≥ 88%), and the equipment health index HI is calculated. Multi-objective hierarchical optimization: Establish an objective function F=α that includes minimum energy consumption (E), minimum electricity cost (C), and maximum comfort (S). E+β C+γ (1 S), under the premise of satisfying hard constraints (temperature, voltage, CO2 concentration), dynamically adjust the weight coefficients to generate the globally optimal strategy; The constraint boundaries are set as follows: indoor temperature T∈[24,26]℃, illuminance Lux∈[300,500], and DC bus voltage U∈[712.5,787.5]V.

[0020] Scenario simulation: Work mode: When personnel are detected entering, AI prioritizes comfort, sets γ>0.5, and maintains standard illuminance and temperature.

[0021] Peak electricity price mode: When the system detects that the electricity price is in a peak period, the AI ​​prioritizes electricity costs, sets β>0.6, automatically raises the air conditioner temperature by 1°C, lowers the lighting brightness by 10%, and starts energy storage discharge.

[0022] Nighttime Idle Mode: When no one is detected and it is during off-peak hours, AI prioritizes energy consumption, sets α>0.7, turns off unnecessary lighting, and fully charges the energy storage system; Flexible control execution: Based on the optimal strategy, the DC fan, water pump, lighting and energy storage are controlled in milliseconds via PLC carrier communication.

[0023] I. System Architecture and Power Supply Mode This embodiment constructs a hybrid AC / DC microgrid. Photovoltaic modules (conversion efficiency 21.5%) and an energy storage system (charge / discharge efficiency 94%–96%) are connected to a DC 750V bus. The lighting system is directly connected to a DC 375V branch; the air conditioner and elevator retain their original 380V AC interfaces and are connected to the bus via bidirectional inverters.

[0024] This invention is based on a multi-functional EMS energy management platform. Without altering the existing heavy-duty air conditioning and elevator circuits, it enables the DC conversion of light-duty equipment such as lighting, and has extremely strong compatibility for retrofitting.

[0025] II. Load forecasting, flexible regulation and global optimization based on AI algorithms 1. Real-time monitoring Full-domain sensor network: The entire system adopts low-voltage DC PLC carrier communication sensors, eliminating the need for additional wiring; Monitoring objects: photovoltaic, energy storage, air conditioning, lighting, fresh air, elevator, sensors, DC bus; Monitoring parameters: voltage, current, power, temperature, humidity, CO2, VOC, illuminance, equipment start / stop status, fault codes, filter status, speed, flow rate, and cumulative energy consumption.

[0026] Acquisition frequency: adjustable from 1 to 5 times per second; Transmission method: sensor → local acquisition module → PLC carrier communication → multi-functional EMS system → AI algorithm flexible control.

[0027] Data latency: ≤200ms, data accuracy: ≥99.5%, anomaly response: ≤1 second alarm, communication distance: ≤1200m, maximum number of access points: a single EMS system supports ≤1024 measurement points; (1) Photovoltaic monitoring Installation locations: photovoltaic module combiner box, inverter DC side, AC output terminal; Sensor types: DC voltage sensor (0~1000V), DC current sensor (0~100A), light intensity sensor (0~200000lx), photovoltaic panel temperature sensor (-20℃~85℃) Arrangement: Each photovoltaic string is equipped with one set of voltage / current sensors; the module array is equipped with 1 to 2 light and temperature sensors each.

[0028] Data acquisition method: Sensor → Data acquisition module → RS485 / PLC carrier → EMS system; Monitoring parameters: voltage, current, power, power generation, temperature, light intensity, operating status, and fault alarms.

[0029] (2) Energy storage monitoring Installation locations: battery cluster main positive and main negative, BMS main board, inside battery cabinet; Sensor types: Battery voltage sensor (cell / cluster level), battery current sensor (Hall DC sensor), NTC temperature sensor (-20℃~60℃), insulation monitoring sensor; Arrangement: Each battery cluster is equipped with voltage / current / total temperature sensors; each battery layer is equipped with 1-2 temperature sensors. Data acquisition method: BMS internal acquisition → PLC carrier / RS485 → EMS system; Monitoring parameters: individual cell voltage, total voltage, charge / discharge current, SOC / SOH, temperature, insulation resistance, and fault status.

[0030] (3) DC bus monitoring Installation locations: DC750V main busbar, DC375V branch busbar, distribution box busbar; Sensor types: DC voltage sensor (0~1000V), DC current sensor (open-close Hall effect), bus temperature sensor; Arrangement: Install one set of voltage / current / temperature sensors at the entrance of each main / branch bus; Data acquisition method: Acquisition module → PLC carrier → EMS system; Monitoring parameters: bus voltage, load current, total power, line temperature, overload / undervoltage / overvoltage alarms.

[0031] (4) Monitoring of air conditioning system Installation locations: air conditioner inlet and outlet, before and after the water filter of the water heater, water supply and return pipes, fan control cabinet, ground source side; Sensor types: Duct temperature sensor (NTC / PT100), water temperature sensor (surface mount / insertion mount), fan speed sensor, multi-height ambient temperature sensor (0.3m / 1.2m / 2.5m); Arrangement method: Air system: Each fan coil unit has one air inlet and one air outlet; Water system: one temperature sensor before the filter and one temperature sensor after the filter; Indoors: One temperature measuring point is set up at each of the following heights: 0.3m, 1.2m, and 2.5m above the ground; Data acquisition method: PLC carrier temperature acquisition unit → EMS system; Monitoring parameters: inlet and outlet air temperature difference, inlet and outlet water temperature difference, ambient temperature at multiple points, fan speed, and filter clogging status.

[0032] (6) Monitoring of fresh air system Installation locations: fresh air unit inlet / outlet, indoor return air area, fan control cabinet; Sensor types: CO2 sensor (0~5000ppm), VOC sensor (0~2mg / m³), temperature and humidity sensor (-10℃~60℃, 0~95% RH), fan speed sensor; Arrangement: A CO2 + VOC + temperature and humidity integrated sensor is installed at the return air vent; a speed sensor is installed at the fan end; Data acquisition method: PLC carrier → EMS system; Monitoring parameters: CO2 concentration, VOC, temperature and humidity, fan speed, operating status, and air volume.

[0033] (7) Elevator system monitoring Installation locations: elevator control cabinet, traction machine, energy feedback device, braking circuit; Sensor types: voltage / current sensors, traction machine temperature sensors, and feedback power metering modules; Arrangement: The output end of the energy feedback device is equipped with a power meter and a current sensor; the traction machine is equipped with a temperature sensor; Data acquisition method: RS485 / PLC carrier → EMS system; Monitoring parameters: operating status, floor, number of braking cycles, feedback power, feedback current, temperature, and fault codes.

[0034] 2. Core Functions of AI Algorithms (1) Short-term load forecasting algorithm: Load forecasting accuracy ≥92% for the next 15 minutes to 24 hours; Predict the total building load for the next 15 minutes to 24 hours; Inputs: historical load, time, day of the week, weather, temperature, and light intensity; Output: predicted load P_pred(t); P_pred(t) = f( L(t-1), L(t-2), …, L(tn), T(t), W(t), T_env(t)); L(t) represents the historical load at time t, T(t) represents the time characteristic, W(t) represents the weather characteristic, and T_env(t) represents the ambient temperature.

[0035] (2) Photovoltaic output prediction algorithm (BP neural network + light fitting), based on light, weather and time period prediction, with an accuracy of ≥88%; Predict future photovoltaic (PV) power generation. Inputs: irradiance, PV panel temperature, ambient temperature, time, weather; Output: Predicted PV power PV_pred(t) PV_pred(t) = K_light × I(t) × η(T_pv) × S; K_light is the illumination correction factor, I(t) is the illumination intensity, η(T_pv) is the photovoltaic temperature efficiency coefficient, and S is the total photovoltaic area.

[0036] (3) Equipment health AI assessment: predicting faults based on current, temperature and efficiency trends. Function: Automatically determines whether the air conditioner / water pump / fan / fresh air system is aging, blocked, or abnormal; Input: Current, efficiency, temperature difference, speed, running time; Output: Health Index HI (0~1); HI = ω1×η + ω2×I_nor + ω3×ΔT_nor + ω4×R_nor; η is the equipment operating efficiency, I_nor is the normalized current value, ΔT_nor is the normalized temperature difference value, R_nor is the normalized speed value, and ω1~ω4 are weighting coefficients (0~1, summing to 1). The judgment rules are as follows: HI ≥ 0.9, healthy; 0.7 ≤ HI<0.9, slightly abnormal; HI<0.7, fault warning (such as air conditioner filter blockage, automatically issue a command to increase fan speed by 10% to 15% to compensate for air volume), and notify maintenance personnel.

[0037] (4) Energy consumption mode recognition: Automatically recognize idle / working / peak / night mode Automatically identify building energy consumption patterns: Idle / Working / Peak / Nighttime; Inputs: Real-time power, time, equipment activation rate; Output: Pattern label M ∈ {0,1,2,3} M = argmin(distance( P(t), C_i)); C_i is the center of the i-th pattern class, and distance is the Euclidean distance; Mode: 0 = Idle, 1 = During work hours, 2 = Peak hours, 3 = Night.

[0038] (5) Optimal operation strategy optimization: with the goal of the lowest energy consumption, the lowest electricity cost, and the highest comfort. The AI ​​of this invention simultaneously seeks optimization for three indivisible objectives: i. Minimal system energy consumption (air conditioning, lighting, fresh air, elevators, and minimal energy loss) ii. Lowest electricity cost (peak-valley electricity pricing, self-consumption of photovoltaic power, peak shaving through energy storage) iii. Maximum environmental comfort (temperature, illuminance, and air quality meet standards) Existing technologies involve independent control of each device, making global coordination impossible; they lack prediction and load forecasting, allowing only passive adjustment; multiple objectives conflict (energy saving vs. comfort), making automatic balancing impossible; and the hybrid architecture of DC microgrid + AC load lacks mature optimization methods.

[0039] This invention completely solves the above difficulties through AI multi-objective hierarchical optimization. The specific steps are as follows: Step 1: AI makes predictions (15 minutes to 24 hours in advance), enabling the system to act proactively rather than react passively. This includes: load forecasting: future energy consumption P(t); photovoltaic forecasting: future power generation PV(t); electricity price forecasting: peak / slow / valley time prices; and environmental forecasting: temperature, sunlight, and population status.

[0040] Step Two: AI Establishes "Constraint Boundaries" All optimization efforts must not compromise safety and comfort, ensuring comfort, safety, and no equipment damage: Temperature: 24℃ ≤ T ≤ 26℃; Illuminance: 300 lx ≤ Lux ≤ 500 lx; CO2: ≤ 1000ppm; DC bus: DC750V±5%; The equipment's current, temperature, and speed do not exceed the limits.

[0041] Step 3: AI Multi-Objective Weighted Optimization min F = α·E + β·C + γ·(1 S); Where F: global objective function (the smaller the better), E: total system energy consumption, C: electricity cost, S: comfort index; α, β, γ: AI adaptive weighting coefficients (summing to 1).

[0042] AI automatically adjusts weights to achieve dynamic optimization, rather than a fixed strategy: during work hours: comfort takes priority; during peak electricity prices: electricity cost takes priority; during off-peak / nighttime hours: lowest energy consumption takes priority.

[0043] The optimal strategy is determined as follows: Step 1: Input all data, including photovoltaic, energy storage, busbar, air conditioning, lighting, fresh air, elevator, sensor, electricity price, time, and weather.

[0044] Step 2: The AI ​​iterates through all feasible combinations, exhaustively / intelligently searching for the following adjustable variables: Air conditioner fan speed 10%–100%; air conditioner water pump speed 10%–100%; ground source coupling ratio 0%–100%; lighting brightness 10%–100%; fresh air volume 20%–100%; energy storage charging / discharging power; bus power distribution.

[0045] Step 3: AI eliminates solutions that do not meet the constraints. Temperature exceeding the limit → Reject; Illumination insufficient → Reject; CO2 exceeding the limit → Reject; Voltage exceeding the limit → Reject; Equipment overload → Reject.

[0046] Step 4: AI selects the unique combination that satisfies all constraints and has the lowest energy consumption, lowest electricity cost, and highest comfort level.

[0047] The optimal control strategy of this invention is as follows: (1) Optimal energy consumption strategy: reduce the output of unnecessary equipment, operate the fan / pump at the highest efficiency point, reduce AC / DC conversion losses, and prioritize the self-use of the energy recovered by the elevator.

[0048] (2) Electricity cost optimization strategy Off-peak electricity: Fully charge energy storage; Peak electricity: Discharge energy storage + maximize self-use of photovoltaic power; Peak hours: Adjust air conditioning temperature by 1°C, reduce lighting temperature by 10%, and maintain fresh air supply at the minimum comfort level.

[0049] (3) Comfort Optimal Strategy Three-point temperature measurement ensures precise temperature control, staff maintain standard illumination, air quality strictly meets standards, and fans operate silently.

[0050] As can be seen from the above, this invention achieves three-objective collaborative optimization for the first time under the DC microgrid + AC hybrid architecture, using prediction instead of feedback to achieve feedforward optimization rather than passive adjustment; it adopts adaptive weights to solve the conflict between energy saving, electricity costs, and comfort; it unifies the control of all equipment instead of individual equipment independent PID control; and it provides hard protection of constraint boundaries to ensure both optimality and absolute safety. The measured results are as follows: (1) the overall energy saving rate is increased by 12%–25%; (2) electricity costs are reduced by 12%–25%; (3) the comfort compliance rate is ≥98%; (4) the equipment operates in the highest efficiency range, and the lifespan is extended by 20%–30%; (5) the global response time is ≤1 second.

[0051] 3. AI-based flexible regulation This invention, AI, automatically performs flexible adjustments based on real-time data, including: Air conditioning: AI dynamically adjusts fan speed, water supply temperature, and ground source coupling ratio; such as... Figure 3 As shown.

[0052] Lighting: AI automatically adjusts the brightness based on people, ambient light, and time of day; Fresh air system: AI automatically adjusts airflow based on CO2, VOC, temperature, and humidity; Energy storage: AI-powered peak shaving and valley filling, charging during off-peak hours and discharging during peak hours, can reduce electricity costs by 12% to 25% per transaction; DC microgrids: AI coordinates photovoltaic output and load demand, increasing self-consumption rate to 85%–92%; Global energy consumption: AI enables multi-device linkage, avoiding simultaneous peak start-up and reducing peak power by 15% to 30%.

[0053] 3.1 AI-based Flexible Control of Air Conditioning Systems (1) Control of air system filter blockage Monitoring parameter: Temperature difference between air conditioner inlet and outlet air ΔT_wind; Judgment conditions: Normal: ΔT_wind = 3~5℃; Blocked: ΔT_wind ≥ 8℃; Status assessment: Filter screen clogged, heat exchange efficiency decreased by ≥18%; AI execution strategy: The system issues a filter cleaning warning, temporarily increases the fan speed by 10% to 15% to compensate for heat exchange capacity, records the fault and uploads it to EMS; Control target: Maintain heat exchange efficiency ≥90%.

[0054] (2) Water circuit blockage control of water pump Monitoring parameters: Temperature difference between inlet and outlet water of the filter screen ΔT_water; Judgment criteria: Normal: 1.5~2.5℃; Blockage: ≥4℃; Status assessment: Excessive water resistance causes the main unit's power consumption to increase by 1.2–2.8 kW; AI-driven strategy: Issue water system maintenance warning; slightly increase DC circulating pump speed by 5-10% to compensate for flow rate; Control objectives: To restore stable water flow and reduce the load on the main unit.

[0055] (3) Fine-tuning of indoor temperature Preferred standard measuring points: Temperature measurement at lower levels: 0.25m~0.35m, with a preferred setting of 0.3m; Human comfort reference layer: 1.1m~1.3m, with a preferred value of 1.2m; Upper-level high-altitude temperature measurement: 2.4m~2.6m, with a preferred setting of 2.5m; Monitoring parameters: Temperatures at three points: 0.3m, 1.2m, and 2.5m (T1, T2, T3); Judgment conditions: Three-point temperature difference > 1.5℃, average temperature > 26℃ (cooling) / < 20℃ (heating); Condition determination: Uneven temperature distribution, undercooling / overheating; AI execution strategies: Adjusting the stepless speed of the DC fan; Adjusting the opening of the air conditioning water valve; Coupling ground source cooling / heating assistance; Control targets: Three-point temperature difference ≤ 0.5℃, room temperature stable at 24~26℃.

[0056] (4) Coupling regulation of ground-source energy Monitoring parameters: ground source temperature T_ground, main unit power P_main; Judgment conditions: Summer: T_ground = 16~18℃ and host load > 70%; Winter: T_ground = 14~16℃ and host load > 70%; Status assessment: The air conditioning unit is operating under high load, resulting in high energy consumption; AI execution strategy: Activate ground source heat exchange and reduce main unit output by 20% to 35%; Control targets: reduce main unit power consumption by 2.5 to 6.8 kW and increase SEER (Seasonal Energy Efficiency Ratio) by 1.8 to 2.6.

[0057] 3.2 AI-powered flexible control of the lighting system: AI automatically adjusts the dimming based on people, lighting conditions, and time of day. Monitoring parameters: ambient illuminance (Lux), human presence signal (People); Judgment conditions: i. If there are people and the illuminance is <200 lx, the brightness will be 100%. ii. For people, illuminance 200-300 lx → luminance 60%-80%; iii. If there are people and the illuminance is >500 lx, reduce the brightness by 10% to 20%; iv. No one is present → Brightness 10% or off; Status determination: Area is occupied / unoccupied, lighting is sufficient / insufficient; AI execution strategy: PLC carrier drive for automatic stepless dimming, automatically switching brightness curves according to time periods; Control targets: maintain illuminance of 300-500 lx, and save 3.2-5.6 kWh of electricity per lamp per year.

[0058] 3.3 AI-powered flexible control of the fresh air system: AI automatically adjusts the air volume based on CO2, VOC, temperature, and humidity. Air quality linkage regulation, monitoring parameters: CO2, VOC, temperature and humidity; Judgment conditions: i. CO2 < 800ppm → 20%–30% fresh air intake ii. 800ppm≤CO2<1000ppm → 50% fresh air intake iii. 1000ppm≤CO2<1200ppm → 80% fresh air intake iv. CO2 ≥ 1200 ppm or VOC > 0.6 mg / m³ → 100% fresh air intake Air quality assessment: Excellent / Good / Poor / Very poor AI executes strategies; adjusts the stepless speed of the DC fresh air fan to maintain optimal air exchange rate; Control targets: CO2 ≤ 1000 ppm, VOC ≤ 0.6 mg / m³, energy saving 25%~50%.

[0059] 3.4 Energy Storage: AI-powered peak shaving and valley filling, charging during off-peak hours and discharging during peak hours, can reduce electricity costs by 12%–25% per transaction. Peak shaving and valley filling control monitoring parameters: total load P_load, time period, electricity price Judgment conditions: Peak electricity period: Load > threshold → Energy storage discharge; Off-peak electricity period: Load < threshold → Energy storage charging; Status determination: Peak electricity consumption / Off-peak consumption; AI execution strategy: discharge during peak hours to suppress load, and charge and store energy during off-peak hours; Regulation targets: reduce electricity prices by 12% to 25% and reduce peak power by 15% to 30%.

[0060] 3.5 DC Microgrid: AI coordinates photovoltaic output and load demand, increasing self-consumption rate to 85%–92%. Parameters for monitoring and regulating photovoltaic power output fluctuations: irradiance, photovoltaic power, and bus voltage; Judgment criteria: Sudden increase / decrease in sunlight → Photovoltaic power fluctuation > ±10%; Status determination: DC bus is unstable; AI executes strategies to quickly charge and discharge energy storage and adjust load power to match photovoltaic output; Control target: Stable bus voltage DC750V±5%.

[0061] The air conditioning energy-saving control method and its effects of the present invention are as follows: (1) The terminal AC fan was replaced with a DC brushless stepless speed-regulating fan. Original AC fan: rated efficiency 65%~75%, operating noise 58~65dB (A) After modification, the DC fan has a rated efficiency of 88%–92%, energy consumption is reduced by 68W / unit to 145W / unit, and noise is reduced by 8–12dB(A). (2) Detection of air system filter blockage Normal temperature difference: 3-5℃; Temperature difference threshold: ≥8℃ indicates blockage; Heat exchange efficiency decreases by ≥18% after blockage; Efficiency can be restored to over 96% after maintenance following the warning.

[0062] (3) Detection of water filter blockage in water purifier Normal inlet and outlet water temperature difference: 1.5~2.5℃; Temperature difference threshold: ≥4℃ indicates blockage; After blockage, the main unit power consumption increases by 1.2kW~2.8kW / unit.

[0063] (4) The water turbine circulating pump was replaced with a DC stepless speed regulating pump. Original AC pump: rated efficiency 72%~78%, rated power consumption 1.5kW~4.0kW; The modified DC pump has an efficiency of 86%–91%, a power consumption reduction of 0.45kW–1.2kW per unit, and an adjustment accuracy of ±1%.

[0064] (5) Ground source energy coupled air conditioning system Ground source temperature: 16-18℃ in summer, 14-16℃ in winter; Reduced power consumption of air conditioning unit: 2.5kW-6.8kW / unit; Improved seasonal energy efficiency ratio (SEER): 1.8-2.6.

[0065] (6) Precise temperature adjustment at multiple altitudes Detection height: 0.3m, 1.2m, 2.5m (three-point temperature); Control accuracy: ±0.5℃; Over-adjustment loss reduction: 0.6kW~1.3kW / terminal.

[0066] The energy-saving lighting control method and its effects of the present invention are as follows: (1) PLC carrier centralized drive Original distributed drive: annual failure rate 8%~12%; after centralized drive: annual failure rate ≤1.8%; number of lamps driven per loop: 30~60; maintenance cost reduced: 3.5~6.2 yuan / lamp / year.

[0067] (2) Intelligent dimming control Illuminance threshold: adjustable in increments of 200lx, 300lx, and 500lx; dimming range: 10%–100%; power consumption reduction per lamp: 8W–22W; overall energy saving: 3.2–5.6 kWh / lamp / year.

[0068] (3) DC power supply loss DC750V main line loss: ≤1.2%; DC375V branch line loss: ≤2.1%; the line loss is reduced by 1.8 to 3.2 percentage points compared to the AC system.

[0069] The fresh air energy-saving control method and its effects of the present invention are as follows: (1) DC fresh air fan Original AC fan: efficiency 66%~74%, power consumption 0.75kW~2.2kW; After modification, the DC fan has an efficiency of 87%–91% and a power consumption reduction of 0.21kW–0.73kW per unit. (2) Air quality linkage regulation CO2 control thresholds: 800ppm, 1000ppm, 1200ppm; VOC control threshold: ≤0.6mg / m³; Fresh air volume adjustment range: 20%~100%.

[0070] The elevator energy-saving control method and its effects of the present invention are as follows: With the addition of an energy feedback device, the braking energy recovery efficiency is 82%–89%; the feedback current harmonics are THDi ≤5%. PLC carrier communication: Communication rate: 2400bps~9600bps; Communication distance: ≤1200m; Reduced cabling cost: 35~55 yuan / m; Shortened construction period: 40%~60%.

[0071] This invention relates to a complex system with deep integration of source, grid, load, storage, and control. The system's overall energy saving rate, air conditioning energy saving rate, SEER (Self-Energy Efficiency Ratio), main unit power consumption, and indoor temperature control accuracy are not independent; rather, these parameters are strongly coupled, mutually restrictive, and inversely related. The mutual influence relationships are as follows: 1. If you simply reduce the power consumption of the air conditioner unit and increase the energy efficiency, it will lead to insufficient heat exchange capacity, indoor temperature deviation exceeding ±0.5℃, and decreased environmental comfort. 2. If the temperature control accuracy (±0.5℃) is strictly maintained, the air conditioner fan and circulating pump need to be dynamically adjusted at high frequency, which will increase the power consumption of auxiliary equipment and reduce the overall energy saving rate and SEER; 3. Deep coupling of three parameters: ground source coupling ratio, fan / pump speed, and three-layer temperature measurement threshold: excessive speed → increased energy consumption; excessive speed → insufficient heat exchange and excessive temperature difference; improper ground source coupling ratio → imbalance between main unit load and auxiliary unit load.

[0072] The preferred parameters and balancing scheme of this invention are as follows: 1. Temperature measurement points are fixed at 0.3m, 1.2m, and 2.5m (preferred ranges are 0.25~0.35m, 1.1~1.3m, and 2.4~2.6m), and the interlayer temperature difference threshold is set at 1.5℃ as the control trigger boundary; 2. The upper limit of the circulating pump speed is limited to 10% of the rated speed, and fine-tuned stepwise from 5% to 10% to avoid excessive flow compensation causing a surge in energy consumption; 3. The ground source energy coupling adopts AI adaptive matching. In summer / winter, the intervention depth is dynamically adjusted according to the real-time load of the host, ensuring that the host power consumption is reduced by 2.5 to 6.8 kW and the SEER is increased by 1.8 to 2.6, while the indoor temperature is kept stable within the accuracy range of ±0.5℃, achieving a two-way balance between energy saving and comfort.

[0073] The flexible regulation (peak power, electricity cost reduction), photovoltaic self-consumption rate, AI prediction accuracy, and DC bus stability of this invention have the following interrelationships: 1. Photovoltaic output and load fluctuate randomly. If the accuracy of AI load / photovoltaic prediction is low, the coordinated scheduling of energy storage, ACDC modules, and load will be inaccurate, and the photovoltaic self-consumption rate will not be able to reach 85% to 92%. At the same time, the peak power reduction effect of the system will be worse (it will be difficult to achieve a reduction of 15% to 30%), and the reduction in electricity costs will be reduced. 2. To improve prediction accuracy, the data sampling frequency and algorithm computation will be increased. If the parameters are not configured properly, it will lead to EMS data transmission delay and DC bus voltage fluctuation, thereby increasing AC-DC conversion loss. 3. Bus voltage fluctuations will in turn affect the operating efficiency of all DC loads (lighting, fans, sensors), creating a chain reaction.

[0074] Therefore, the preferred parameters and balancing scheme of the present invention are as follows: 1. Set the data acquisition frequency to 1-5 times / second, data delay ≤200ms, and abnormal response ≤1 second, while ensuring full-area monitoring of 1024 measuring points and data accuracy ≥99.5%, and avoid computational overload; 2. The AI ​​algorithm adopts an adaptive weighted multi-objective optimization model, which dynamically allocates the weights of "energy consumption, electricity cost, and reliability" based on peak and off-peak electricity prices, production periods, and lighting conditions. 3. The bidirectional AC / DC module, in conjunction with the energy storage system, smooths power fluctuations at the millisecond level, controlling AC / DC conversion losses within the optimal range of 5-11 percentage points, ultimately achieving: prediction accuracy ≥92% / 88%, self-consumption rate 85%-92%, peak power reduction of 15%-30%, and electricity cost reduction of 12%-25%.

[0075] The filter heat exchange efficiency, fresh air / air conditioning energy saving rate, PLC carrier communication reliability, and construction cost of this invention are influenced by the following factors: 1. Filter clogging will cause a decrease in heat exchange efficiency (maximum decrease of 25%), directly offsetting the 26% to 41% energy-saving effect of air conditioning; relying on traditional differential pressure sensor monitoring will increase wiring and hardware costs, offsetting the advantages of PLC carrier wave “reduced construction costs by 35 to 55 yuan / m and shortened construction period by 40% to 60%”. 2. PLC carrier communication is susceptible to electromagnetic interference from motors such as fans and water pumps. Communication abnormalities can lead to failure in acquiring filter temperature difference data, misjudgment of filter status, and loss of control over heat exchange efficiency.

[0076] Therefore, the preferred parameters and balancing scheme of the present invention are as follows: 1. The solution of adding an additional differential pressure sensor is abandoned. Instead, the existing temperature sensor and temperature difference threshold (≥8℃ for air system and ≥4℃ for water system) are reused to achieve status identification without adding wiring. 2. The DC PLC carrier wave is optimized for anti-interference in industrial electromagnetic environments, ensuring stable transmission across 1024 measurement points; 3. By combining the pump body with a stepped speed regulation strategy, the flow rate is flexibly compensated when the filter is slightly clogged, and the heat exchange efficiency is stabilized at over 90%. This retains the advantages of low cost and short construction period of PLC carrier while ensuring long-term energy-saving effects.

[0077] The lighting energy saving rate, brightness control accuracy, and human body / light sensor linkage logic of this invention have the following interrelationships: Excessive dimming range (aiming for 20% to 50% energy savings) will cause the brightness deviation to exceed ±1%, reducing visual comfort; overly sensitive dimming response, with frequent starting, stopping, and dimming of the lamps, will increase the failure rate by 8% to 12%.

[0078] Therefore, the preferred parameters and balancing scheme of the present invention are as follows: 1. Set illumination thresholds of 200lx, 300lx, and 500lx for phased dimming, with a dimming range of 10% to 100% and brightness control precision locked at ±1%; 2. Add delay anti-shake logic to avoid frequent operating condition switching, reduce the lamp failure rate from 8% to 12% to ≤1.8%, and balance energy saving, temperature control accuracy and equipment reliability.

[0079] The elevator energy recovery efficiency and DC bus load matching of this invention have the following interrelationships: Elevator braking energy is instantaneous pulse electrical energy, and the recovery efficiency of 82% to 89% depends on the real-time absorption capacity of the DC bus. If the bus load is insufficient, the excess electrical energy will cause voltage rise, increase conversion loss, and even affect the operation of other equipment.

[0080] Therefore, the present invention optimizes the parameters and balance scheme: AI real-time linkage between elevator feedback device and DC load and energy storage system in the plant area, pulse energy is consumed on-site and stored instantly, ensuring that the recovery efficiency is stable at 82% to 89%, while the bus voltage is stable and the conversion loss is controllable.

[0081] This invention achieves a long-term stable heat exchange efficiency of ≥90% for the filter without the need for additional sensors, avoiding the 15%–25% annual efficiency degradation. The specific challenges are analyzed below: 1. Shortcomings of traditional solutions: The industry's conventional approach is to install differential pressure sensors to monitor filter blockage, which has high hardware costs, large wiring requirements, and is susceptible to failure due to dust and moisture interference; manual inspection is lagging, and problems are often only discovered after heat exchange efficiency has significantly decreased.

[0082] 2. Significant operational interference: Changes in air conditioning load, fluctuations in inlet water temperature, and adjustments in fan speed can all cause normal fluctuations in the temperature difference between inlet and outlet air / inlet and outlet water, making it easy to misjudge the blockage status; if the threshold is set too high, the warning will not be timely, and if it is set too low, there will be frequent false alarms, making it difficult to accurately determine the blockage critical point.

[0083] 3. Inconsistent compensation logic: Simply increasing the speed of the fan / pump after the filter is clogged can restore the flow rate, but it will significantly increase the energy consumption of auxiliary equipment, offsetting the energy-saving benefits of the system.

[0084] Therefore, the present invention adopts the following targeted technical measures: 1. Reusing the existing temperature sensing network, the innovative temperature difference recognition mechanism relies entirely on the system's original PT100 / NTC temperature sensor. For the air system, the inlet and outlet air temperature difference is ≥8℃, and for the water system, the **water temperature difference before and after the filter is ≥4℃** is used as the blockage judgment threshold. No new hardware or wiring is added, and it is compatible with the PLC carrier wiring-free architecture.

[0085] 2. The dynamic threshold correction algorithm AI combines real-time air conditioning load, ambient temperature, and unit operating time to make small dynamic corrections to the temperature difference threshold, offsetting the interference caused by fluctuations in normal operating conditions. The blockage identification accuracy is ≥95%, eliminating false judgments and missed judgments.

[0086] 3. The stepped flexible flow compensation strategy limits the upper limit of the circulating pump speed adjustment to 10% of the rated speed, and makes two-level fine adjustments (first +5%, observe for 1-3 minutes, and if ineffective, increase to +10%) to compensate for flow loss with the minimum auxiliary power consumption increase; at the same time, it pushes operation and maintenance alarms, shifting from "passive compensation" to a combination of "early warning + operation and maintenance".

[0087] 4. The full-domain data correlation verification combines the air conditioner host power and the equipment health HI value to cross-verify the filter status, further improving the reliability of the judgment.

[0088] Ultimately, the heat exchange efficiency is kept stable at over 90% for a long period, effectively avoiding the natural efficiency decay of 15% to 25% per year, while retaining the advantages of low cost and less construction of PLC carrier.

[0089] Although the present invention has been disclosed above with reference to preferred embodiments, it is not intended to limit the present invention. Any person skilled in the art can make some modifications and improvements without departing from the spirit and scope of the present invention. Therefore, the scope of protection of the present invention shall be defined by the claims.

Claims

1. A method for intelligent flexible control of an air conditioning system based on temperature difference detection, characterized in that, Includes the following steps: S1. Collect the inlet and outlet temperatures of the air conditioning system and calculate the inlet and outlet temperature difference; S2. Collect the water temperature before and after the filter of the air conditioning water system and calculate the temperature difference between the inlet and outlet water; S3. Set the blockage threshold for the air system and the blockage threshold for the water system, replace the AC fans of the air conditioning terminal and the fresh air system with DC fans, and change the AC circulation pump of the water system to a DC circulation pump. S4. When the temperature difference between the inlet and outlet air exceeds the blockage threshold of the air system, it is determined that the filter is blocked, and a filter cleaning warning is issued. The DC fan of the air conditioning terminal and the fresh air system is steplessly speed-regulated to temporarily increase the fan speed to compensate for the heat exchange efficiency. S5. When the temperature difference between the inlet and outlet water exceeds the water system blockage threshold, it is determined that the water machine water circuit is blocked, a water circuit maintenance warning is issued, and the DC circulation pump of the water system is infinitely speed-regulated to compensate for the flow by temporarily increasing the speed of the DC circulation pump.

2. The intelligent flexible control method for an air conditioning system according to claim 1, characterized in that, Step S3: Set the blockage threshold ΔTwind of the air system to ≥8℃ and the blockage threshold ΔTwater of the water system to ≥4℃. Step 4: Temporarily increase the fan speed by 10% to 15% until the temperature difference between the inlet and outlet water ΔT_wind is controlled within 3 to 5℃. Step S5: Temporarily increase the speed of the DC circulating pump by 5-10% until ΔTwater is controlled within 3 to 5℃.

3. The intelligent flexible control method for an air conditioning system according to claim 1, characterized in that, Step S3 involves the following replacement control for the air conditioning terminal fans: replacing the AC fans with a rated efficiency of 65%–75% with DC fans with a rated efficiency of 88%–92%, reducing the power consumption per unit by 68W–145W; and the following replacement control for the fresh air system fans: replacing the AC fans with a rated efficiency of 66%–74% with DC fans with a rated efficiency of 87%–91%, reducing the energy consumption per unit by 0.21kW–0.73kW, and adjusting the air volume according to the CO2 concentration levels.

4. The intelligent flexible control method for an air conditioning system according to claim 3, characterized in that, The fresh air system fan control is as follows: Monitor indoor CO2 concentration and VOC levels; The following tiered adjustment strategy is set: when CO2 < 800 ppm, the fresh air volume is controlled at 20%–30%; when 800 ppm ≤ CO2 < 1000 ppm, the fresh air volume is controlled at 50%; when CO2 ≥ 1200 ppm or VOC > 0.6 mg / m³, the fresh air volume is controlled at 30%. 3 The fresh air volume is controlled at 100%.

5. The intelligent flexible control method for an air conditioning system according to claim 1, characterized in that, Step S3 replaces the AC circulating pump with a rated efficiency of 72% to 78% with a DC circulating pump with a rated efficiency of 86% to 91%, reducing the power consumption of a single unit by 0.45kW to 1.2kW.

6. The intelligent flexible control method for an air conditioning system according to claim 2, characterized in that, In step S5, first increase the speed of the DC circulation pump by 5%, observe and wait for 1 to 3 minutes. If ΔTwater is still not controlled within 3 to 5℃, then increase the speed of the DC circulation pump by 10%.

7. The intelligent flexible control method for an air conditioning system according to claim 1, characterized in that, This also includes the following ground-source energy coupled air conditioning control method: Monitor the ground source temperature and the load of the air conditioning unit; When the ground source side temperature is 16-18℃ in summer and the main unit load is greater than 70%, or when the ground source side temperature is 14-16℃ in winter and the main unit load is greater than 70%, the ground source side heat exchange circuit is turned on. By reducing the output of the air conditioning unit by 20% to 35% and using ground-source cooling / heating for auxiliary regulation, the seasonal energy efficiency ratio (SEER) can be increased by 1.8 to 2.

6.

8. The intelligent flexible control method for an air conditioning system according to claim 1, characterized in that, It also includes arranging temperature sensors at multiple height levels indoors to finely regulate the indoor temperature; when the temperature difference between different height levels is greater than 1.5℃, adjusting the DC fan speed and the opening of the air conditioning water valve until the temperature difference between different height levels is controlled within 0.5℃.

9. The intelligent flexible control method for an air conditioning system according to claim 8, characterized in that, The multiple height layers include a low-level temperature measurement layer, an intermediate reference layer, and an upper-level temperature measurement layer. The height of the low-level temperature measurement layer is 0.25m to 0.35m, the height of the intermediate reference layer is 1.1m to 1.3m, and the height of the upper-level temperature measurement layer is 2.4m to 2.6m.

10. The intelligent flexible control method for an air conditioning system according to claim 9, characterized in that, The height of the lower temperature measuring layer is 0.3m, the height of the intermediate reference layer is 1.2m, and the height of the upper temperature measuring layer is 2.5m.

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