CN111928450A - Building energy consumption optimization control method - Google Patents

Building energy consumption optimization control method Download PDF

Info

Publication number
CN111928450A
CN111928450A CN202010714652.7A CN202010714652A CN111928450A CN 111928450 A CN111928450 A CN 111928450A CN 202010714652 A CN202010714652 A CN 202010714652A CN 111928450 A CN111928450 A CN 111928450A
Authority
CN
China
Prior art keywords
water
air
temperature
air conditioner
energy consumption
Prior art date
Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
Pending
Application number
CN202010714652.7A
Other languages
Chinese (zh)
Inventor
戴安
邱泽晶
吴凯槟
Current Assignee (The listed assignees may be inaccurate. Google has not performed a legal analysis and makes no representation or warranty as to the accuracy of the list.)
Wuhan Energy Efficiency Evaluation Co Ltd Of State Grid Electric Power Research Institute
Original Assignee
Wuhan Energy Efficiency Evaluation Co Ltd Of State Grid Electric Power Research Institute
Priority date (The priority date is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the date listed.)
Filing date
Publication date
Application filed by Wuhan Energy Efficiency Evaluation Co Ltd Of State Grid Electric Power Research Institute filed Critical Wuhan Energy Efficiency Evaluation Co Ltd Of State Grid Electric Power Research Institute
Priority to CN202010714652.7A priority Critical patent/CN111928450A/en
Publication of CN111928450A publication Critical patent/CN111928450A/en
Pending legal-status Critical Current

Links

Classifications

    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/62—Control or safety arrangements characterised by the type of control or by internal processing, e.g. using fuzzy logic, adaptive control or estimation of values
    • F24F11/63—Electronic processing
    • F24F11/64—Electronic processing using pre-stored data
    • F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
    • F24—HEATING; RANGES; VENTILATING
    • F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
    • F24F11/00—Control or safety arrangements
    • F24F11/70—Control systems characterised by their outputs; Constructional details thereof
    • F24F11/80—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air
    • F24F11/83—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling the supply of heat-exchange fluids to heat-exchangers
    • F24F11/85—Control systems characterised by their outputs; Constructional details thereof for controlling the temperature of the supplied air by controlling the supply of heat-exchange fluids to heat-exchangers using variable-flow pumps

Abstract

The invention discloses a building energy consumption optimization control method, which comprises an air-conditioning refrigeration host control strategy, wherein the air-conditioning refrigeration host control strategy adopts a support vector machine algorithm to perform sample classification on supply and return water temperature, water flow and historical load data of an air-conditioning cold water host, and predicts a user load QPredictionAnd according to QPredictionDetermining the optimal operation condition of the air conditioner host, dynamically increasing or decreasing the number of the turned-on air conditioner hosts, and if Q is greaterCold>QPredictionIncreasing the load of the air conditioner host and increasing the number of the air conditioner hosts; if QCold<QPredictionThe load of the air conditioner main unit is reduced, and the number of the air conditioner main units is reduced. The invention adopts a dynamic optimization method to search the point with the lowest energy consumption of the air conditioning system, thereby realizing the system-level global optimization control.

Description

Building energy consumption optimization control method
Technical Field
The invention belongs to the technical field of comprehensive energy efficiency service, and particularly relates to an energy consumption optimization control method for a building.
Technical Field
At present, the building energy consumption accounts for about one third of the energy consumption of the whole society in China, along with the rapid development of economy, the total amount of buildings and the energy consumption intensity in China are continuously increased, wherein the unit energy consumption of commercial buildings is more than 2 times of that of other buildings, and the energy consumption of cold and heat sources and air conditioning systems accounts for 40-60% of the building energy consumption, so that the building energy consumption optimization control method is a key object for building energy consumption optimization control. The current commercial building can only have the problems of monitoring without control and approximate. More than 90% of buildings only realize the functions of energy consumption monitoring and remote operation, and only 7% of buildings use an automatic system to realize the cooperative optimization control of cooling, heating and end equipment. The manual adjustment of the operation state of the equipment to realize energy-saving control is still a common phenomenon, and is difficult to adapt to the energy-saving control requirements of complex and variable air conditioners. In addition, most energy-saving optimization control systems in the market only carry out local optimization on 'source' equipment or tail-end equipment, and cross coupling mutual influence among all equipment of the air conditioning system is not considered. Aiming at the pain point problems of low automation level, insufficient energy utilization and high energy consumption of commercial buildings, an energy utilization optimization control method for the buildings is urgently needed, an overall solution for inhibiting unreasonable energy consumption on the demand side is provided for customers, ubiquitous connection between energy utilization equipment and systems of the commercial buildings can be promoted, the comfort level and the intelligent control capability of the commercial buildings are improved, the energy utilization cost is reduced, and the comprehensive energy efficiency level is comprehensively improved.
Disclosure of Invention
The invention aims to provide a building energy consumption optimization control method aiming at the technical problems, and the invention starts from the largest energy consumption system-air conditioning system in the building energy consumption system, comprehensively considers the coupling relation and mutual influence among all equipment of the air conditioning system, and adopts a dynamic optimization method to search the point with the lowest energy consumption of the air conditioning system under different outdoor conditions and system loads so as to realize the system-level global optimization control.
In order to achieve the purpose, the building energy consumption optimization control method comprises an air-conditioning refrigeration host control strategy, wherein the air-conditioning refrigeration host control strategy adopts a support vector machine algorithm shown in formulas 1 and 2 to carry out sample classification on supply and return water temperature, water flow and historical load data of an air-conditioning refrigeration host, and the user load Q is predictedPredictionAnd according to QPredictionDetermining the optimal operation condition of the air conditioner host, dynamically increasing or decreasing the number of the turned-on air conditioner hosts, and if Q is greaterCold>QPredictionIncreasing the load of the air conditioner host and increasing the number of the air conditioner hosts; if QCold<QPredictionReducing the load of the air conditioner main unit and the number of the air conditioner main units to be started, so that Q isCold＝QPrediction；
QPrediction＝SVM(TGo back to，TFor supplying to，Q，QH) (1)
QCold＝Q·ρ·c·(TGo back to-TFor supplying to) (2)
Wherein Q isPredictionTo predict the refrigeration load, QHFor historical refrigeration loads, QColdFor actual refrigerating capacity, SVM represents a support vector machine algorithm, Q is the flow rate of chilled water, rho is the specific gravity of water, c is the specific heat of water, and T isGo back toAnd TFor supplying toThe return water temperature and the supply water temperature of the chilled water are respectively.
The building energy consumption optimization control method provided by the invention starts from the largest energy consumption system in the building energy consumption system, namely the air conditioning system, comprehensively considers the coupling relationship and mutual influence among all equipment of the air conditioning system, and adopts a dynamic optimization method to search the point with the lowest energy consumption of the air conditioning system under different outdoor conditions and system loads, thereby realizing the system-level global optimization control.
The invention can also give an alarm in time when some operation fault occurs in the system or the equipment, and judge whether the operation has the fault or not by comparing the acquired data with the normal operation data of each equipment.
Detailed Description
The present invention is further illustrated in detail by the following examples:
the method for optimizing and controlling energy consumption of the building is characterized in that the whole air conditioning system is optimized and controlled by collected air conditioning system data through an air conditioning energy consumption optimization control strategy. The air conditioner energy consumption optimization control strategy comprises an air conditioner refrigeration host control strategy, a water pump control strategy, a cooling tower control strategy, an air conditioner tail end control strategy and an air conditioner system cooperative control strategy.
The control strategy of the air-conditioning refrigeration host adopts the support vector machine algorithm shown in formulas 1 and 2 to perform sample classification on supply and return water temperature, water flow and historical load data of the air-conditioning refrigeration host, and predict user load QPredictionAnd according to QPredictionDetermining the optimal operation condition of the air conditioner host, dynamically increasing or decreasing the number of the turned-on air conditioner hosts, and if Q is greaterCold>QPredictionIncreasing the load of the air conditioner main unit and increasing the opening of the air conditioner main unitThe number of the devices; if QCold<QPredictionReducing the load of the air conditioner main unit and the number of the air conditioner main units to be started, so that Q isCold＝QPrediction，QCold>QPredictionIt is said that the actual cooling load demand is greater than the predicted cooling load demand, requiring more hosts to be turned on to meet the actual cooling load demand, QCold<QPredictionThe actual cold load demand is smaller than the predicted cold load demand, the number of the main machines needs to be reduced to meet the actual cold load demand, and the comfort level and the energy-saving effect of an air conditioner user are ensured;
Qprediction＝SVM(TGo back to，TFor supplying to，Q，QH) (1)
QCold＝Q·ρ·c·(TGo back to-TFor supplying to) (2)
Wherein Q isPredictionTo predict the refrigeration load, QHFor historical refrigeration loads, QColdFor actual refrigerating capacity, according to QColdThe number of running machines and the running load of the host are adjusted to ensure that the host runs under the optimal working condition, the SVM represents a support vector machine algorithm, Q is the flow rate of chilled water, rho is the specific gravity of water, c is the specific heat of water, and T is the specific heat of waterGo back toAnd TFor supplying toThe return water temperature and the supply water temperature of the chilled water are respectively.
The water pump control strategy adopts formulas 3-6, and according to the characteristic curve of the water pump, the flow, the lift and the active power of the water pump control strategy are synchronously regulated through frequency conversion regulation of the water pump, so that the water flow adapts to the requirement of load change, the power consumption of a water pump motor is greatly reduced, and effective energy conservation is realized;
Q∝n (3)
Pshaft＝Q·H·ρ·g/1000η (4)
PShaft∝n3 (5)
n＝60f(1-s)/p (6)
Wherein, a is a proportional sign, n is the rotation speed of the water pump, H is the lift of the water pump, PShaftThe method is characterized in that the power of a water pump shaft is adopted, eta is the efficiency of the water pump, f is the power supply frequency of a water pump motor, s is the slip ratio of the water pump motor, p is the pole pair number of a stator winding of the motor, and s and p are constants. Detailed description of the inventionWhen the energy-saving water pump is used, n can be reduced and Q can be reduced by reducing f, so that the power of the water pump can be obviously reduced, the energy saving is realized, the temperature difference between the water supply and the water return is compared with 5 ℃ during specific calculation, whether the frequency of the water pump is adjustable or not is detected by comparing the temperature difference between the water supply and the water return with the temperature of 5 ℃, and the running frequency of the water pump is reduced by adjusting the frequency of the water pump so as to; when the temperature difference between the supplied water and the returned water is more than 5 ℃, the running frequency of the water pump is improved, and the energy-saving effect cannot be achieved.
The cooling tower control strategy is characterized in that outdoor temperature and humidity are collected, an enthalpy value (the enthalpy value in the air refers to total heat contained in the air) and a wet bulb temperature (the wet bulb temperature is the lowest temperature which can be achieved only by evaporating moisture in the current environment) are calculated by using a formula 7-9, the dry bulb temperature and the relative humidity are known, the wet bulb temperature is found by using an enthalpy diagram (i-d) of the wet air, and the start-stop or rotating speed adjustment of a fan of the cooling tower is controlled by comparing a temperature value obtained by adding a cold amplitude value to the wet bulb temperature with the actual outlet water temperature of the cooling tower; if the value of the wet bulb temperature value plus the cold amplitude is smaller than the actual outlet water temperature of the cooling tower, the fan of the cooling tower is started or the rotating speed is adjusted to be fast, otherwise, the fan of the cooling tower is closed or the rotating speed is adjusted to be slow; the cooling effect is brought by the temperature difference, the exchange medium area and the medium flow rate to the maximum extent, the number of the opening cooling towers is reduced, the running time is shortened, the temperature of the return water is ensured to be converged to the optimal condenser temperature of the refrigeration host, the efficiency of the refrigeration host is higher, and the more the air conditioning system saves energy
H＝1.005t+w·(2500+1.84t) (7)
lgP＝7.07406-(1657.46/(T+227.02))) (8)
Figure BDA0002595361380000041
Wherein H is enthalpy, T is dry-bulb temperature, w is outdoor moisture content, P is saturated vapor pressure of water at T temperature, T is water temperature, H is vapor pressure of water at T temperaturerIs the relative humidity in the air, and the numerical range is 0-100%.
The air conditioner terminal control strategy utilizes a group control algorithm, the fan rotating speed, the number of running machines, the running frequency and the running load of terminal air conditioners (various fan coil pipes, air conditioning units or air processing units, fresh air units, air conditioning boxes, fan coil pipes and other equipment) are regulated according to the room temperature and a set value, the room relative humidity, the cooling/heating mode identification, the water valve switch state, the water valve duty cycle, the water valve control period and the air conditioner fan rotating speed, so that the room temperature and the room humidity are in preset intervals, the terminal air conditioners perform self-regulation, and the terminal is guaranteed to meet the space requirement with the optimal setting, and therefore the system energy consumption is reduced.
The air conditioning system combines an air conditioning refrigeration host, a water pump control strategy, a cooling tower control strategy and an air conditioning tail end control strategy, and the total energy consumption P of the air conditioning system is ensured by adjusting (adjusting according to a dynamic optimization method) the number of the air conditioning refrigeration host, the water pump, the cooling tower and the air conditioning tail end of the whole air conditioning system and the operating frequency of the water pump, the cooling tower and the air conditioning tail end in a linkage manner through an energy consumption collaborative optimization algorithm of formulas 10 to 16General assemblyThe energy efficiency ratio COP of the system is improved, the overall energy consumption of the air conditioning system is reduced, and the minimum overall energy consumption of the air conditioning system is dynamically searched;
Pgeneral assembly＝P1+P2+P3+P4+P5 (10)
P1＝QCold/(COPe·X) (11)
X＝1+a(Tc1-Tce) (12)
P2＝η1Q3·He/Qe 2 (13)
QCold＝Q·ρ·c/(TGo back to-TFor supplying to) (14)
P4＝k·ρ·c·ΔT/(·Δh) (15)
P5＝0.016(Tg-TFor supplying to)/R (16)
Wherein, PGeneral assemblyFor the total energy consumption of the air conditioning system, P1-P5 respectively refer to the energy consumption of a main machine of the air conditioner, a chilled water pump, a cooling tower and a fan coil at the tail end of the air conditioner, COPe refers to the rated refrigeration efficiency of the main machine, X refers to a proportionality coefficient, and T refers to the ratio coefficientc1And TceRespectively the actual inlet water temperature of the cooling water and the rated inlet water temperature of the cooling water, aThe performance constant of the host provided for the manufacturer, He is the rated lift of the water pump, QeFor rated flow, η1Is the performance parameter (including flow, lift, rotating speed, efficiency, shaft power and necessary cavitation allowance, here is a constant) of the water pump, k is the operation energy consumption system of the cooling tower fan, rho.c.delta.T represents the heat lost by the cooling water, is the heat exchange power of the cooling tower, delta h is the difference between the enthalpy value of saturated air and the enthalpy value of inlet air, TgFor the temperature of the dry bulb of air before entering the fan coil, TFor supplying toThe temperature of the water supplied for the chilled water, R is the heat resistance in the heat transfer process, QColdQ is the heat dissipation capacity of the cooling medium, TGo back toThe temperature is the return water temperature of the chilled water.
The objective of the cooperative optimization control of the air conditioning system is to minimize the value of P on the premise of meeting the constraint of the refrigerating capacity requirement, namely, to solve a min (P) function.
Details not described in this specification are within the skill of the art that are well known to those skilled in the art.

Claims (5)

1. A building energy consumption optimization control method is characterized by comprising the following steps: the method comprises an air-conditioning refrigeration host control strategy, wherein the air-conditioning refrigeration host control strategy adopts a support vector machine algorithm shown in formulas 1 and 2 to perform sample classification on supply and return water temperature, water flow and historical load data of an air-conditioning cold water host and predict user load QPredictionAnd according to QPredictionDetermining the optimal operation condition of the air conditioner host, dynamically increasing or decreasing the number of the turned-on air conditioner hosts, and if Q is greaterCold>QPredictionIncreasing the load of the air conditioner host and increasing the number of the air conditioner hosts; if QCold<QPredictionReducing the load of the air conditioner main unit and the number of the air conditioner main units to be started, so that Q isCold＝QPrediction；
QPrediction＝SVM(TGo back to，TFor supplying to，Q，QH) (1)
QCold＝Q·ρ·c·(TGo back to-TFor supplying to) (2)
Wherein Q isPredictionTo predict the refrigeration load, QHFor historical refrigeration loads, QColdFor actual refrigerating capacity, SVM represents a support vector machine algorithm, Q is the flow rate of chilled water, rho is the specific gravity of water, c is the specific heat of water, and T isGo back toAnd TFor supplying toThe return water temperature and the supply water temperature of the chilled water are respectively.
2. The building energy consumption optimization control method according to claim 1, wherein: the air conditioner further comprises a water pump control strategy of the air conditioner, wherein the water pump control strategy adopts a formula of 3-6, and the flow, the lift and the active power of the water pump control strategy are synchronously adjusted by carrying out variable frequency adjustment on the water pump according to a characteristic curve of the water pump, so that the water flow adapts to the load change requirement;
Q∝n (3)
Pshaft＝Q·H·ρ·g/1000η (4)
PShaft∝n3 (5)
n＝60f(1-s)/p (6)
Wherein, a is a proportional sign, n is the rotation speed of the water pump, H is the lift of the water pump, PShaftThe method comprises the following steps of calculating the power of a water pump shaft, wherein eta is the efficiency of the water pump, f is the power supply frequency of a water pump motor, s is the slip ratio of the water pump motor, and p is the number of pole pairs of a stator winding of the motor.
3. The building energy consumption optimization control method according to claim 1 or 2, wherein: the cooling tower control strategy is characterized by comprising a cooling tower control strategy, wherein the cooling tower control strategy is used for calculating an enthalpy value and a wet bulb temperature by acquiring outdoor temperature and humidity and utilizing a formula 7-9, knowing the dry bulb temperature and the relative humidity, finding out the wet bulb temperature by utilizing an enthalpy diagram of wet air, and comparing a temperature value obtained by adding a cold amplitude value to the wet bulb temperature value with the actual outlet water temperature of the cooling tower to control the starting and stopping or rotating speed adjustment of a fan of the cooling tower;
H＝1.005t+w·(2500+1.84t) (7)
lgP＝7.07406-(1657.46/(T+227.02))) (8)
Figure FDA0002595361370000021
wherein H is enthalpy, T is dry-bulb temperature, w is outdoor moisture content, P is saturated vapor pressure of water at T temperature, T is water temperature, H is vapor pressure of water at T temperaturerIs the relative humidity in the air.
4. The building energy consumption optimization control method according to claim 1 or 2, wherein: the air conditioner terminal control strategy utilizes a group control algorithm, and according to the room temperature and a set value, the room relative humidity, the refrigerating/heating mode identification, the water valve switching state, the water valve duty ratio, the water valve control period and the air conditioner fan rotating speed, the number of running air conditioners at the terminal, the running frequency and the running load are regulated and controlled to enable the room temperature and the room humidity to be in a preset interval.
5. The building energy consumption optimization control method according to claim 1 or 2, wherein: the energy consumption cooperative optimization method further comprises an air conditioning system cooperative control strategy, the air conditioning system combines an air conditioning refrigeration host, a water pump control strategy, a cooling tower control strategy and an air conditioning tail end control strategy, the running numbers of the air conditioning refrigeration host, the water pump, the cooling tower and the air conditioning tail end of the whole air conditioning system and the running frequencies of the water pump, the cooling tower and the air conditioning tail end are adjusted in a linkage mode through an energy consumption cooperative optimization algorithm of formulas 10-16, and therefore the total energy consumption P of the air conditioning system is enabled to be PGeneral assemblyThe lowest;
Pgeneral assembly＝P1+P2+P3+P4+P5 (10)
P1＝QCold/(COPe·X) (11)
X＝1+a(Tc1-Tce) (12)
P2＝η1Q3·He/Qe 2 (13)
QCold＝Q·ρ·c/(TGo back to-TFor supplying to) (14)
P4＝k·ρ·c·ΔT/(·Δh) (15)
P5＝0.016(Tg-TFor supplying to)/R (16)
Wherein the content of the first and second substances,Pgeneral assemblyFor the total energy consumption of the air conditioning system, P1-P5 respectively refer to the energy consumption of a main machine of the air conditioner, a chilled water pump, a cooling tower and a fan coil at the tail end of the air conditioner, COPe refers to the rated refrigeration efficiency of the main machine, X refers to a proportionality coefficient, and T refers to the ratio coefficientc1And TceThe actual inlet water temperature and the rated inlet water temperature of the cooling water are respectively, a is a host performance constant provided by a manufacturer, He is the rated lift of the water pump, and Q iseFor rated flow, η1The performance parameter of the water pump, k is the operation energy consumption system of a fan of the cooling tower, rho.c.delta.T represents the heat lost by the cooling water, the heat exchange power of the cooling tower, delta h is the difference between the enthalpy value of saturated air and the enthalpy value of inlet air, and T is the temperature difference between saturated air and the enthalpy value of inlet airgFor the temperature of the dry bulb of air before entering the fan coil, TFor supplying toThe temperature of the water supplied for the chilled water, R is the heat resistance in the heat transfer process, QColdQ is the heat dissipation capacity of the cooling medium, TGo back toThe temperature is the return water temperature of the chilled water.
CN202010714652.7A 2020-07-21 2020-07-21 Building energy consumption optimization control method Pending CN111928450A (en)

Priority Applications (1)

Application Number Priority Date Filing Date Title
CN202010714652.7A CN111928450A (en) 2020-07-21 2020-07-21 Building energy consumption optimization control method

Applications Claiming Priority (1)

Application Number Priority Date Filing Date Title
CN202010714652.7A CN111928450A (en) 2020-07-21 2020-07-21 Building energy consumption optimization control method

Publications (1)

Publication Number Publication Date
CN111928450A true CN111928450A (en) 2020-11-13

Family

ID=73315276

Family Applications (1)

Application Number Title Priority Date Filing Date
CN202010714652.7A Pending CN111928450A (en) 2020-07-21 2020-07-21 Building energy consumption optimization control method

Country Status (1)

Country Link
CN (1) CN111928450A (en)

Cited By (3)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112923536A (en) * 2021-02-22 2021-06-08 上海旭尔网络科技有限公司 Temperature and humidity control system and control method for building
CN113432268A (en) * 2021-06-28 2021-09-24 广东美的白色家电技术创新中心有限公司 Control method of module unit and related equipment
CN116379655A (en) * 2023-04-19 2023-07-04 广州施杰节能科技有限公司 Cold machine optimizing control system and method

Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN201463177U (en) * 2009-01-16 2010-05-12 深圳职业技术学院 Central air conditioner optimized energy-saving control system based on load forecasting
CN202216363U (en) * 2011-08-12 2012-05-09 东莞市中科机电安装工程有限公司 Energy conservation control device of cooling water tower of central air conditioner
CN103375878A (en) * 2012-04-26 2013-10-30 上海宝信软件股份有限公司 Group control method for central air conditioning refrigerating unit
US20140000300A1 (en) * 2012-06-28 2014-01-02 Hitachi, Ltd. Cooling system and cooling method
CN106403207A (en) * 2016-10-24 2017-02-15 珠海格力电器股份有限公司 Load-prediction-based control system and method for heating ventilation air-conditioning system
CN108917103A (en) * 2018-05-03 2018-11-30 广东美的暖通设备有限公司 The cold water main unit control method of central air conditioner system, apparatus and system
CN110288164A (en) * 2019-07-02 2019-09-27 广州市特沃能源管理有限公司 A kind of building air conditioning refrigeration station system forecast Control Algorithm

Patent Citations (7)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN201463177U (en) * 2009-01-16 2010-05-12 深圳职业技术学院 Central air conditioner optimized energy-saving control system based on load forecasting
CN202216363U (en) * 2011-08-12 2012-05-09 东莞市中科机电安装工程有限公司 Energy conservation control device of cooling water tower of central air conditioner
CN103375878A (en) * 2012-04-26 2013-10-30 上海宝信软件股份有限公司 Group control method for central air conditioning refrigerating unit
US20140000300A1 (en) * 2012-06-28 2014-01-02 Hitachi, Ltd. Cooling system and cooling method
CN106403207A (en) * 2016-10-24 2017-02-15 珠海格力电器股份有限公司 Load-prediction-based control system and method for heating ventilation air-conditioning system
CN108917103A (en) * 2018-05-03 2018-11-30 广东美的暖通设备有限公司 The cold water main unit control method of central air conditioner system, apparatus and system
CN110288164A (en) * 2019-07-02 2019-09-27 广州市特沃能源管理有限公司 A kind of building air conditioning refrigeration station system forecast Control Algorithm

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
范同顺等: "《基于智能化工程的建筑能效管理策略研究》", 31 May 2015, 中国建材工业出版社 *

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN112923536A (en) * 2021-02-22 2021-06-08 上海旭尔网络科技有限公司 Temperature and humidity control system and control method for building
CN113432268A (en) * 2021-06-28 2021-09-24 广东美的白色家电技术创新中心有限公司 Control method of module unit and related equipment
CN116379655A (en) * 2023-04-19 2023-07-04 广州施杰节能科技有限公司 Cold machine optimizing control system and method
CN116379655B (en) * 2023-04-19 2023-09-05 广州施杰节能科技有限公司 Cold machine optimizing control system and method

Similar Documents

Publication Publication Date Title
CN111928450A (en) Building energy consumption optimization control method
US10101043B2 (en) HVAC system and method of operation
CN102455093A (en) Energy efficiency controlling method for refrigerating system
CN103175276A (en) Air-cooling direct-current frequency-conversion magnetic levitation nature cold source water chilling unit
CN113739371B (en) Central air conditioning system based on cloud cooperation and control method thereof
CN111140950A (en) Data center indirect evaporative cooling system and control method
CN101769576B (en) Total energy-saving environmental base station air-conditioning
CN105240958A (en) Dual-cold-source three-pipe-system air conditioner system
CN105222241A (en) Two low-temperature receiver four-pipe system air-conditioning system
CN102889650A (en) Integral combination type computer room air conditioning unit and control method thereof
CN113790479B (en) Heat pump air conditioner with dehumidification and hot water function
CN202216363U (en) Energy conservation control device of cooling water tower of central air conditioner
CN114611288A (en) Intelligent efficient computer room energy-saving algorithm model of central air-conditioning system
CN114459133A (en) Energy-saving control method and energy-saving control system for central air-conditioning system
CN114576806A (en) Central air-conditioning cooling water system energy-saving optimization method based on variable frequency control
CN202835950U (en) Air source heat pump water chilling unit provided with all-season refrigeration function
CN204404419U (en) Realize the combined air-conditioning system of independent temperature-humidity control
CN206514574U (en) A kind of data center&#39;s refrigeration system
CN102748892A (en) Movable-type heat pump device for partial heating/refrigerating
CN215637696U (en) Air-cooled heat pump air conditioning unit condenser re-cooling system
CN205174627U (en) Four control air conditioning system of double -cold -source
CN110017563B (en) Dehumidification control method for double-cold-source fresh air unit
CN201615559U (en) Integral energy-saving and environment-friendly air conditioner for base station
CN203501386U (en) Frequency conversion driving system of central air conditioner
CN112628985A (en) Air conditioning unit control method and device and air conditioning unit

Legal Events

Date Code Title Description
PB01 Publication
PB01 Publication
SE01 Entry into force of request for substantive examination
SE01 Entry into force of request for substantive examination