Energy-saving control algorithm for energy control system of central air conditioner

Abstract

The invention discloses an energy-saving control algorithm for a central air-conditioning energy control system. The energy-saving control algorithm comprises the steps that the total power of a central air-conditioning system in a target building space is predicted according to historical meteorological data; establishing a heat transfer model of a single space of each partition according to the partition condition of the target building; establishing a power control model of the central air-conditioning system; respective energy consumption models are established through the physical models of the components in the central air conditioning system; and constraint conditions of all the components of the central air-conditioning system are established, and all the components are controlled by combining the constraint conditions of all the components of the central air-conditioning system. An updated prediction model is obtained by establishing a heat transfer formula of a partition space and energy consumption modeling of a central air-conditioning system, an updated model prediction value is compared with an actual interpolation and then serves as a correction reference to be introduced into a preliminary prediction model, and then the air conditioner is controlled according to constraint conditions.

Description

Technical Field

[0001] The present invention relates to the technical field of energy control, and in particular to an energy-saving control algorithm for a central air-conditioning energy control system. Background Art

[0002] In the current field of energy-saving control of central air-conditioning systems in buildings, although there are many technical means dedicated to optimizing energy consumption, most technologies mainly focus on energy-saving control by calculating the energy consumption of various components in the central air-conditioning system (such as chillers, water pumps, fans, etc.) during operation. This control strategy often ignores the complexity and diversity of the heat distribution of the building itself, especially in super high-rise buildings and specific industrial buildings (such as high-rise industrial pig farming buildings), where the difference in heat distribution is particularly significant.

[0003] Chinese patent document CN 114508784A records a multi-source complementary heating system and its optimization control method. The control system uses the optimization control algorithm in the core controller to optimize the data detected by the sensor, and the calculated data is used as the set value of the bottom controller, so that the bottom controller automatically controls the power output of the solar collector, electric boiler, and ground source heat pump and the opening of the outlet valve of the hot water storage tank according to the set value. CN116734404A records an energy-saving optimization control system and optimization method for a central air-conditioning system, including: a physical model of a chiller, which is used to reflect the basic operating characteristics of the actual equipment and to calculate the upper machine energy efficiency COP of the central air-conditioning system; a load prediction model, which is constructed based on a neural network algorithm; a cold source energy efficiency model, which is used to obtain the optimal state point data of energy consumption; a water pump performance model, which is used to calculate the water pump energy consumption under operating conditions.

[0004] The existing technology has the following limitations: 1. Ignoring the heat differences between building partitions: Most of the existing energy-saving control technologies do not fully consider the heat zoning characteristics inside the building. For example, in super high-rise buildings, rooms of different heights and orientations have significant differences in heat distribution due to factors such as solar radiation, wind direction and wind speed. At the same time, the top and bottom floors of high-rise buildings will also form different thermal environments due to different meteorological conditions such as atmospheric pressure and temperature stratification. However, existing control systems often adopt a unified control strategy and cannot accurately adjust the water system supply according to the actual heat demand of each partition, resulting in excess or insufficient cooling or heating capacity.

[0005] 2. Mismatch between target distribution and heat demand: In large public buildings or special industrial buildings (such as high-rise industrial pig farms), target distribution is often uneven and changes over time. For example, in pig farms, the density and activity intensity of pigs in different areas are different, resulting in uneven heat generation and distribution. Existing energy-saving control systems are usually based on fixed load setpoints and cannot perceive and respond to such dynamic changes in real time, resulting in energy waste.

[0006] 3. Uneven energy distribution in the water system: Central air conditioning systems generally include air systems and water systems, of which the water system accounts for a large proportion of energy consumption. However, existing water system control is often based on the prediction and adjustment of the average load of the entire system, ignoring the differences in the demand for hot and cold water in various partitions within the building. This results in excessive or insufficient water distribution in some areas during actual operation, resulting in unsatisfactory cooling or heating effects, and even energy waste and low energy efficiency.

[0007] 4. Lack of intelligent monitoring and regulation: Existing energy-saving control systems still have deficiencies in monitoring and regulation. Many systems lack real-time and accurate sensor networks to monitor parameters such as temperature, humidity, and flow of people inside the building, resulting in the inability to adjust the control strategy in a timely manner according to actual conditions. In addition, the adjustment methods of some systems are relatively simple and cannot achieve refined control of complex thermal environments.

[0008] In summary, although the existing energy-saving control technology of building central air-conditioning system has achieved certain results in reducing energy consumption, it still has many shortcomings in practical application due to ignoring the complexity and diversity of building partition heat distribution. Therefore, it is particularly important to develop a central air-conditioning energy-saving control algorithm based on building partition heat distribution. The algorithm should be able to perceive the heat distribution of each partition inside the building in real time, and make precise adjustments according to actual needs, so as to achieve the goal of efficient energy utilization and energy saving and emission reduction. Summary of the invention

[0009] The technical problem to be solved by the present invention is to provide an energy-saving control algorithm for a central air-conditioning energy control system, which overcomes the shortcomings of the prior art, meets the heat demand of the target building, and improves the utilization efficiency of the energy consumption of the building's central air-conditioning.

[0010] In order to solve the above technical problems, the technical solution adopted by the present invention is: A central air conditioning energy control system energy saving control algorithm comprises the following steps: Step 1: partition the target building space according to the structure of the target building, including dividing it according to height, location and lighting; Step 2: Set up a weather monitoring station at the target building to monitor the weather parameters of the target building area and save the corresponding data; Step 3: Install light intensity sensors in the zones that are accessible to the outside light of the target building zone, install temperature and humidity sensors in each zone divided in Step 1, establish communication between each sensor and the central air-conditioning control system, and record and store the historical temperature control conditions of each zone and the total power of the central control system; Step 4, pre-processing the historical meteorological data in Step 2 and the corresponding historical data of the total power of the central air-conditioning system in Step 3; Step 5, perform correlation analysis on the historical meteorological data preprocessed in Step 4 and the historical data of the total power of the central air-conditioning system, extract the meteorological features that have an impact on the total power of the central air-conditioning system greater than the set value, and eliminate invalid meteorological features. Step 6, use variational mode decomposition to decompose the historical data of the total power of the central air-conditioning system into different inherent mode functions IMF, and reconstruct all IMFs into high-frequency components and low-frequency components by calculating the approximate entropy AE of each IMF; Step 7, using a gated neural network to predict the total power of the central air-conditioning system at a future time point based on the historical meteorological data obtained in Step 5 and the historical high-frequency power components and historical low-frequency power components in Step 6; Step 8, according to the target building partition situation in Step 1, establish the heat transfer model of each partition single space; Step 9, establish a power control model of the central air-conditioning system; establish the energy consumption model of each component through the physical model of the central air-conditioning system; Step 10, establish the constraints of each component of the central air-conditioning system, and combine the heat transfer of each partition in Step 8 to obtain the actual power model of the central air-conditioning system; Step 11, compare the difference between the total power value of the central control system predicted in Step 7 and the actual total power value, and introduce the power model of the central air-conditioning system obtained in Step 10 as a reference for correction, to obtain a corrected total power prediction model of the central control system; Step 12: According to the power prediction value obtained by the revised total power prediction model of the central control system, each component of the central air-conditioning system is controlled in combination with the constraint conditions of each component.

[0011] The meteorological parameters in the above-mentioned Step 2 include light intensity, light direction, air pressure and temperature.

[0012] The preprocessing of Step 4 above includes filling the missing values ​​of the collected historical meteorological data and the historical data of the total power of the central air-conditioning system with the mean of the records of the set time interval before and after, and using the isolation forest algorithm to detect and eliminate the abnormal values ​​according to the following steps: The isolation forest algorithm continuously segments the data samples until each sample space contains only one data point. The segmentation process is as follows: For a data set X, a binary tree T with N nodes is used to describe it. or The data of is a subset of X, where i represents the number of layers of the tree, j represents the jth node in the previous layer, and r and l are used to distinguish the right node and left node in the same layer; For the data set contained in a certain layer , randomly select the sample attribute q and its value range space value p to divide and , corresponding to the node set and , data less than or equal to p is divided into Node, others are divided into Node, where j* represents the j*th node in the i+1th layer; When the following situation occurs, a complete binary tree is obtained and the partitioning is completed: A. The depth of the data tree reaches the set maximum value; B. Node Contains only one data point or contains identical data points.

[0013] In the above Step 5, the Pearson correlation coefficient is used to analyze the correlation.

[0014] The heat transfer model of a single space in each partition in Step 8 above is: = ; ; ; ; ; In the formula, i represents the moment, C is the heat capacity of the building space, is the dimensionless distribution coefficient of air conditioning cooling output in space: ; F is the thermal diffusivity, t is the cooling time, and S is the space area; It is the space between the object space and its neighbors. i The thermal resistance between To open space from the object space to its neighbors k The thermal induction between For space i The temperature of the moment, For k and k 1 The net heat gain during the time between is the length of the time interval, is the thermal resistance between the target space and the outdoor air, M is the number of spaces that communicate with the target space, It is an adjacent space that communicates with the object space. k exist i The relative humidity at the time, It is an adjacent space that communicates with the object space. k exist i Net heat gain at any time.

[0015] The objective function of the power control model of the central air conditioning system in Step 9 above is: ; Energy consumption in central air conditioning system Energy consumption of fan coil units and fresh air units , Chilled water pump energy consumption , Refrigeration unit energy consumption , Cooling water pump energy consumption And cooling tower energy consumption composition.

[0016] The energy consumption of the above fan coil units and fresh air units It is composed of multiple chillers, and its mathematical model is: ; in: ; ; The total cooling load of the system is the sum of the cooling loads of each room, and the total cooling load is distributed to each chiller in proportion; the actual cooling capacity of each unit, the number of units, and the unit capacity satisfy the following formula: ; In the above formula, is the total energy consumption of multiple chillers, For the i The cooling capacity of each unit, For the i The efficiency of a unit at a certain load rate, For the i The load factor of the unit, For the i The temperature regulation coefficient of each unit is For the i The actual cooling capacity of a unit under a certain load, is the actual cooling load of a circuit in the building, is the chilled water supply temperature of the chiller, is the return water temperature of the chiller cooling water, is the number of refrigeration units, is the number of fans, is the number of air conditioners; The energy consumption mathematical model of the chilled water pump is as follows: ; In the formula, is the total efficiency of the variable speed pump, which is affected by the water flow rate and the pump head; N2 is the number of chilled water pumps; The water flow of the chilled water pump is the sum of the flow rates of the chilled water flowing through each circuit. The distribution relationship between the total chilled water flow rate and the flow rates of each chilled water pump is determined by the following formula: ; When the speed of the variable speed water pump is n, the pressure and flow rate satisfy the following curve equation relationship: ; The speed ratio , is the rated speed, ~ is the model parameter, then the energy consumption of the chilled water pump is Converted into a relationship only related to the speed ratio and the chilled water pump flow: ; Energy consumption of fan coil units and fresh air units The mathematical model is as follows:

[0017] In the formula, is the air flow rate, is the air pressure, For variable speed fans, medium efficiency; Air volume per circuit is the air volume supplied to all air-conditioned rooms The relationship between the total air volume and the air volume of each air conditioning unit is determined by the following formula: ; When the speed of the variable speed fan is n, the pressure and air volume satisfy the following curve equation relationship: ; The speed ratio , is the rated speed, ~ is the model parameter, then the energy consumption of fan coil unit and fresh air unit is Transformed into a relationship only related to the speed ratio and the air volume of each fan coil unit:

[0018] In the above formula, represents a constant, and its value varies depending on the dimension unit of total pressure or flow rate; if the dimension unit of air flow rate and water flow rate is the same, and the dimension unit of pump head and air pressure is the same, then for the above formulas It will be the same; Cooling pump energy consumption And cooling tower energy consumption The mathematical model is expressed as follows: ; ; ; ; In the system modeling process, parameters , , , , , The value of can be obtained from the sample data of the corresponding manufacturer. The cooling load at the end of each air conditioning circuit Available from DDC Systems.

[0019] The constraints of the power control model of the central air conditioning system in Step 10 above are: The range of the inlet water temperature and outlet water temperature of the chiller is as follows: ; ; The water flow through the refrigeration pump and the cooling pump is subject to the following constraints: ; ; The air volume of air conditioning units and cooling towers is subject to the following constraints: ; ; The pressure that the air conditioning unit and refrigeration pump can provide must be within the operating range of the variable speed control: ; ; The variation range of fresh air volume of each fan coil unit: .

[0020] In the above-mentioned Step 11, the total power value of the central control system predicted in Step 7 is compared with the actual total power value to obtain the difference between multiple time intervals, and the multiple differences are introduced into the power model of the central air-conditioning system obtained in Step 10, and the difference is multiplied by the set weight and added to the power model of the central air-conditioning system to obtain the corrected power model of the central air-conditioning system, and then the prediction is repeated until the difference is less than the set threshold.

[0021] In the above Step 1, the area that can receive light and the area that cannot receive light are distinguished.

[0022] The present invention provides an energy-saving control algorithm for a central air-conditioning energy control system. The algorithm divides the building into zones according to its structure, models the weather in the area where the current building is located and the historical power data of the central air-conditioning of the building to obtain a preliminary prediction model. The algorithm establishes a heat transfer formula for the zoned space and an energy consumption model for the central air-conditioning system to obtain an updated prediction model. The updated model prediction value is compared with the actual interpolation value and introduced into the preliminary prediction model as a correction reference, and the air-conditioning is then controlled according to the constraint conditions. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The present invention will be further described below in conjunction with the accompanying drawings and embodiments: Figure 1 is a schematic diagram of vertical area division of a high-rise building in an embodiment of the present invention; Figure 2 It is a schematic diagram of cross-sectional area division in an embodiment of the present invention. DETAILED DESCRIPTION

[0024] The technical solution of the present invention is described in detail below in conjunction with the accompanying drawings and embodiments.

[0025] A central air conditioning energy control system energy saving control algorithm comprises the following steps: Step 1, such as Figure 1 and 2As shown in , the target building space is zoned according to the structure of the target building. The zoning includes division according to height, location and lighting. In super high-rise buildings, the surrounding area around the core support part is divided into areas that can directly receive sunlight and areas that cannot directly receive sunlight. At the same time, different heights are also divided to cope with changes in meteorological conditions at different heights, such as changes in air pressure, cloud cover and wind speed; Similarly, in large-scale building complexes, such as high-rise industrial breeding buildings, only the outer layer receives sunlight, while many of the interior cannot directly receive sunlight. These areas are divided and the heat source in each area, that is, the number of people or breeding animals, is monitored; Step 2: Set up a weather monitoring station at the target building to monitor the weather parameters of the target building area and save the corresponding data; Step 3: Install light intensity sensors in the zones that are accessible to the outside light of the target building zone, install temperature and humidity sensors in each zone divided in Step 1, establish communication between each sensor and the central air-conditioning control system, and record and store the historical temperature control conditions of each zone and the total power of the central control system; Step 4, pre-processing the historical meteorological data in Step 2 and the corresponding historical data of the total power of the central air-conditioning system in Step 3; Step 5, perform correlation analysis on the historical meteorological data preprocessed in Step 4 and the historical data of the total power of the central air-conditioning system, extract the meteorological features that have an impact on the total power of the central air-conditioning system greater than the set value, and eliminate invalid meteorological features. Step 6, use variational mode decomposition to decompose the historical data of the total power of the central air-conditioning system into different inherent mode functions IMF, and reconstruct all IMFs into high-frequency components and low-frequency components by calculating the approximate entropy AE of each IMF; Step 7, using a gated neural network to predict the total power of the central air-conditioning system at a future time point based on the historical meteorological data obtained in Step 5 and the historical high-frequency power components and historical low-frequency power components in Step 6; Step 8, according to the target building partition situation in Step 1, establish the heat transfer model of each partition single space; Step 9, establish a power control model of the central air-conditioning system; establish the energy consumption model of each component through the physical model of the central air-conditioning system; Step 10, establish the constraints of each component of the central air-conditioning system, and combine the heat transfer of each partition in Step 8 to obtain the actual power model of the central air-conditioning system; Step 11, compare the difference between the total power value of the central control system predicted in Step 7 and the actual total power value, and introduce the power model of the central air-conditioning system obtained in Step 10 as a reference for correction, to obtain a corrected total power prediction model of the central control system; Step 12: According to the power prediction value obtained by the revised total power prediction model of the central control system, each component of the central air-conditioning system is controlled in combination with the constraint conditions of each component.

[0026] The meteorological parameters in the above-mentioned Step 2 include light intensity, light direction, air pressure and temperature.

[0027] The preprocessing of Step 4 above includes filling the missing values ​​of the collected historical meteorological data and the historical data of the total power of the central air-conditioning system with the mean of the records of the set time interval before and after, and using the isolation forest algorithm to detect and eliminate the abnormal values ​​according to the following steps: The isolation forest algorithm continuously segments the data samples until each sample space contains only one data point. The segmentation process is as follows: For a data set X, a binary tree T with N nodes is used to describe it. or The data of is a subset of X, where i represents the number of layers of the tree, j represents the jth node in the previous layer, and r and l are used to distinguish the right node and left node in the same layer; For the data set contained in a certain layer , randomly select the sample attribute q and its value range space value p to divide and , corresponding to the node set and , data less than or equal to p is divided into Node, others are divided into Node, where j* represents the j*th node in the i+1th layer; When the following situation occurs, a complete binary tree is obtained and the partitioning is completed: A. The depth of the data tree reaches the set maximum value; B. Node Contains only one data point or contains identical data points.

[0028] In the above Step 5, the Pearson correlation coefficient is used to analyze the correlation. The specific process is as follows: The Pearson correlation coefficient is used to calculate the correlation coefficient between each meteorological characteristic and the power of the central air conditioning system. The formula is: ; in the formula Indicates meteorological characteristics. represents photovoltaic power generation power, for and The covariance of and They are and The variance of According to the results of correlation analysis, invalid meteorological features with correlation coefficients lower than the set value or negative correlation are eliminated, and the remaining meteorological features are retained; the multidimensional meteorological features are reduced in dimension to obtain multiple components and corresponding contribution rates, and each component is weighted and summed according to its contribution rate to obtain a comprehensive meteorological factor and its contribution rate.

[0029] In Step 6, the variational mode decomposition (VMD) is used to decompose the photovoltaic power into a series of intrinsic mode functions with specific bandwidths. By calculating the approximate entropy AE of each IMF, all IMFs are reconstructed into high-frequency components and low-frequency components. The high-frequency component reflects the fluctuation characteristics of the power, and the low-frequency component reflects the change trend of the power. In Step 7, a gated recurrent neural network is used to add an attention mechanism to obtain different prediction models. Different prediction models receive the high-frequency components and low-frequency components in Step 6, and the prediction results under the two frequency modes are superimposed and reconstructed to obtain the final prediction results.

[0030] The heat transfer model of a single space in each partition in Step 8 above is: = ; ; ; ; ; In the formula, i represents the moment, C is the heat capacity of the building space, is the dimensionless distribution coefficient of air conditioning cooling output in space: ; F is the thermal diffusivity, t is the cooling time, and S is the space area; It is the space between the object space and its neighbors. i The thermal resistance between To open space from the object space to its neighbors k The thermal induction between For space i The temperature of the moment, For k and k 1The net heat gain during the time between is the length of the time interval, is the thermal resistance between the target space and the outdoor air, M is the number of spaces that communicate with the target space, It is an adjacent space that communicates with the object space. k exist i The relative humidity at the time, It is an adjacent space that communicates with the object space. k exist i Net heat gain at any time.

[0031] The spatial heat transfer here includes the heat transfer between areas that can directly receive light and areas that cannot directly receive light. At the same time, the longitude and latitude information can be used to obtain the light direction of different areas that can directly receive light at different times and when the light is lost (as the earth rotates, the light direction changes, and the light conditions are lost).

[0032] The objective function of the power control model of the central air conditioning system in Step 9 above is: ; Energy consumption in central air conditioning system Energy consumption of fan coil units and fresh air units , Chilled water pump energy consumption , Refrigeration unit energy consumption , Cooling water pump energy consumption And cooling tower energy consumption composition.

[0033] The energy consumption of the above fan coil units and fresh air units It is composed of multiple chillers, and its mathematical model is: ; in: ; ; The total cooling load of the system is the sum of the cooling loads of each room, and the total cooling load is distributed to each chiller in proportion; the actual cooling capacity of each unit, the number of units, and the unit capacity satisfy the following formula: ; In the above formula, is the total energy consumption of multiple chillers, For the i The cooling capacity of each unit, For the i The efficiency of a unit at a certain load rate, For the i The load factor of the unit, For the i The temperature regulation coefficient of each unit is For the i The actual cooling capacity of a unit under a certain load, is the actual cooling load of a circuit in the building, is the chilled water supply temperature of the chiller, is the return water temperature of the chiller cooling water, is the number of refrigeration units, is the number of fans, is the number of air conditioners; The energy consumption mathematical model of the chilled water pump is as follows: ; In the formula, is the total efficiency of the variable speed pump, which is affected by the water flow rate and the pump head; N2 is the number of chilled water pumps; The water flow of the chilled water pump is the sum of the flow rates of the chilled water flowing through each circuit. The distribution relationship between the total chilled water flow rate and the flow rates of each chilled water pump is determined by the following formula: ; When the speed of the variable speed water pump is n, the pressure and flow rate satisfy the following curve equation relationship: ; The speed ratio , is the rated speed, ~ is the model parameter, then the energy consumption of the chilled water pump is Converted into a relationship only related to the speed ratio and the chilled water pump flow: ; Energy consumption of fan coil units and fresh air units The mathematical model is as follows:

[0034] In the formula, is the air flow rate, is the air pressure, For variable speed fans, medium efficiency; Air volume per circuit is the air volume supplied to all air-conditioned rooms The relationship between the total air volume and the air volume of each air conditioning unit is determined by the following formula: ; When the speed of the variable speed fan is n, the pressure and air volume satisfy the following curve equation relationship: ; The speed ratio , is the rated speed, ~ is the model parameter, then the energy consumption of fan coil unit and fresh air unit is Transformed into a relationship only related to the speed ratio and the air volume of each fan coil unit:

[0035] In the above formula, represents a constant, and its value varies depending on the dimension unit of total pressure or flow rate; if the dimension unit of air flow rate and water flow rate is the same, and the dimension unit of pump head and air pressure is the same, then for the above formulas It will be the same; Cooling pump energy consumption And cooling tower energy consumption The mathematical model is expressed as follows: ; ; ; ; In the system modeling process, parameters , , , , , The value of can be obtained from the sample data of the corresponding manufacturer. The cooling load at the end of each air conditioning circuit Available from DDC Systems.

[0036] The constraints of the power control model of the central air conditioning system in Step 10 above are: The range of the inlet water temperature and outlet water temperature of the chiller is as follows: ; ; Chilled water outlet temperature Cannot be too low, otherwise it will cause ice and blockage of the unit; chilled water outlet temperature The upper limit of the cooling water return temperature is mainly determined by two factors: whether the maximum temperature can meet the cooling load requirements of the air-conditioning terminal; and the other is the comfort requirements of the air-conditioned place. For example, its lower limit is the wet-bulb temperature of the outdoor environment, which is also the minimum limit for realizing the heat conduction process; its upper limit is mainly from the perspective of product manufacturers, to ensure that the pressure is maintained within a certain acceptable range while ensuring the safety standard requirements to achieve safe and reliable operation of the equipment.

[0037] The water flow through the refrigeration pump and the cooling pump is subject to the following constraints: ; ; If the water flow rate is too small, the cooling load of the chilled water circuit and the cooling water circuit will not be met in time. The increase in water temperature will aggravate the impact of the above constraints. Therefore, the minimum setting value of the flow rate should meet the requirements of the water temperature of the chilled water circuit and the cooling water circuit to remain within the normal range. The maximum water flow rate is mainly limited by the motor capacity equipped with the water pump.

[0038] The air volume of air conditioning units and cooling towers is subject to the following constraints: ; ; The minimum air volume of air conditioning units and cooling towers is mainly determined by the end user or operator. The load will not be met in time. The increase in water temperature will aggravate the impact of the above constraints. Similar to constraint 2, the maximum air volume is mainly limited by the motor capacity equipped with the fan.

[0039] The pressure that the air conditioning unit and refrigeration pump can provide must be within the operating range of the variable speed control: ; ; Theoretically, the lowest pressure boundary that the air conditioning unit and refrigeration pump can provide occurs when the variable speed control is running at the minimum constraint limit and the assumed losses through the ventilation ducts and water pump network are the largest. The maximum pressure boundary occurs when the variable speed control is running at full speed and the dampers and valves in the network are open.

[0040] The variation range of fresh air volume of each fan coil unit: .

[0041] In the above-mentioned Step 11, the total power value of the central control system predicted in Step 7 is compared with the actual total power value to obtain the difference between multiple time intervals, and the multiple differences are introduced into the power model of the central air-conditioning system obtained in Step 10, and the difference is multiplied by the set weight and added to the power model of the central air-conditioning system to obtain the corrected power model of the central air-conditioning system, and then the prediction is repeated until the difference is less than the set threshold.

[0042] After obtaining the above control model, a stable basic power part can be provided for the building to use based on the prediction, which can meet the basic needs of the building. In case of emergency, such as drastic changes in climate conditions causing rapid changes in demand, the power can be increased or decreased based on the basic power, and fuzzy PID can be used for adjustment.

[0043] The above-mentioned embodiments are only preferred technical solutions of the present invention and should not be regarded as limitations of the present invention. The embodiments and features in the embodiments of the present application can be arbitrarily combined with each other without conflict. The protection scope of the present invention shall be the technical solutions recorded in the claims, including the equivalent replacement solutions of the technical features in the technical solutions recorded in the claims. That is, equivalent replacement improvements within this scope are also within the protection scope of the present invention.

Claims

1. A central air conditioning energy control system energy saving control algorithm, characterized in that: The following steps are involved: Step 1: partition the target building space according to the structure of the target building, including dividing it according to height, location and lighting; Step 2: Set up a weather monitoring station at the target building to monitor the weather parameters of the target building area and save the corresponding data; Step 3: Install light intensity sensors in the zones that are accessible to the outside light of the target building zone, install temperature and humidity sensors in each zone divided in Step 1, establish communication between each sensor and the central air-conditioning control system, and record and store the historical temperature control conditions of each zone and the total power of the central control system; Step 4, pre-processing the historical meteorological data in Step 2 and the corresponding historical data of the total power of the central air-conditioning system in Step 3; Step 5, perform correlation analysis on the historical meteorological data preprocessed in Step 4 and the historical data of the total power of the central air-conditioning system, extract the meteorological features that have an impact on the total power of the central air-conditioning system greater than the set value, and eliminate invalid meteorological features. Step 6, use variational mode decomposition to decompose the historical data of the total power of the central air-conditioning system into different inherent mode functions IMF, and reconstruct all IMFs into high-frequency components and low-frequency components by calculating the approximate entropy AE of each IMF; Step 7, using a gated neural network to predict the total power of the central air-conditioning system at a future time point based on the historical meteorological data obtained in Step 5 and the historical high-frequency power components and historical low-frequency power components in Step 6; Step 8, according to the target building partition situation in Step 1, establish the heat transfer model of each partition single space; Step 9, establish a power control model of the central air-conditioning system; establish the energy consumption model of each component through the physical model of the central air-conditioning system; Step 10, establish the constraints of each component of the central air-conditioning system, and combine the heat transfer of each partition in Step 8 to obtain the actual power model of the central air-conditioning system; Step 11, compare the difference between the total power value of the central control system predicted in Step 7 and the actual total power value, and introduce the power model of the central air-conditioning system obtained in Step 10 as a reference for correction, to obtain a corrected total power prediction model of the central control system; Step 12: According to the power prediction value obtained by the revised total power prediction model of the central control system, each component of the central air-conditioning system is controlled in combination with the constraint conditions of each component.

2. According to the energy-saving control algorithm of a central air-conditioning energy control system described in claim 1, it is characterized in that: The meteorological parameters in Step 2 include light intensity, light direction, air pressure and temperature.

3. According to the energy-saving control algorithm of a central air-conditioning energy control system described in claim 2, it is characterized in that: The preprocessing of Step 4 includes filling the missing values ​​of the collected historical meteorological data and the historical data of the total power of the central air-conditioning system with the mean of the records of the set time interval before and after, and using the isolation forest algorithm to detect and eliminate the abnormal values ​​according to the following steps: The isolation forest algorithm continuously segments the data samples until each sample space contains only one data point. The segmentation process is as follows: For a data set X, a binary tree T with N nodes is used to describe it. or The data of is a subset of X, where i represents the number of layers of the tree, j represents the jth node in the previous layer, and r and l are used to distinguish the right node and left node in the same layer; For the data set contained in a certain layer , randomly select the sample attribute q and its value range space value p to divide and , corresponding to the node set and , data less than or equal to p is divided into Node, others are divided into Node, where j* represents the j*th node in the i+1th layer; When the following situation occurs, a complete binary tree is obtained and the partitioning is completed: A. The depth of the data tree reaches the set maximum value; B. Node Contains only one data point or contains identical data points.

4. According to the energy-saving control algorithm of a central air-conditioning energy control system described in claim 3, it is characterized in that: In the Step 5, the Pearson correlation coefficient is used to analyze the correlation.

5. According to the energy-saving control algorithm of a central air-conditioning energy control system described in claim 4, it is characterized in that: The heat transfer model of each partition single space in Step 8 is as follows: = ； ； ； ； ； In the formula, i represents the moment, C is the heat capacity of the building space, is the dimensionless distribution coefficient of air conditioning cooling output in space: ; F is the thermal diffusivity, t is the cooling time, and S is the space area; It is the space between the object space and its neighbors. i The thermal resistance between To open space from the object space to its neighbors k The thermal induction between For space i The temperature of the moment, For k and k 1 The net heat gain during the time between is the length of the time interval, is the thermal resistance between the target space and the outdoor air, M is the number of spaces that communicate with the target space, It is an adjacent space that communicates with the object space k exist i The relative humidity at the time, It is an adjacent space that communicates with the object space k exist i Net heat gain at any time.

6. According to the energy-saving control algorithm of a central air-conditioning energy control system described in claim 5, it is characterized in that: The objective function of the power control model of the central air conditioning system in Step 9 is: ； Energy consumption in central air conditioning system Energy consumption of fan coil units and fresh air units , Chilled water pump energy consumption , Refrigeration unit energy consumption , Cooling water pump energy consumption And cooling tower energy consumption composition.

7. According to the energy-saving control algorithm of a central air-conditioning energy control system described in claim 6, it is characterized in that: The fan coil unit and fresh air unit energy consumption It is composed of multiple chillers, and its mathematical model is: ； in: ； ； The total cooling load of the system is the sum of the cooling loads of each room, and the total cooling load is distributed to each chiller in proportion; the actual cooling capacity of each unit, the number of units, and the unit capacity satisfy the following formula: ; In the above formula, is the total energy consumption of multiple chillers, For the i The cooling capacity of each unit, For the i The efficiency of a unit at a certain load rate, For the i The load factor of the unit, For the i The temperature regulation coefficient of each unit is For the i The actual cooling capacity of a unit under a certain load, is the actual cooling load of a circuit in the building, is the chilled water supply temperature of the chiller, is the return water temperature of the chiller cooling water, is the number of refrigeration units, is the number of fans, is the number of air conditioners; The energy consumption mathematical model of the chilled water pump is as follows: ； In the formula, is the total efficiency of the variable speed pump, which is affected by the water flow rate and the pump head; N2 is the number of chilled water pumps; The water flow of the chilled water pump is the sum of the flow rates of the chilled water flowing through each circuit. The distribution relationship between the total chilled water flow rate and the flow rates of each chilled water pump is determined by the following formula: ； When the speed of the variable speed water pump is n, the pressure and flow rate satisfy the following curve equation relationship: ； The speed ratio , is the rated speed, ~ is the model parameter, then the energy consumption of the chilled water pump is Converted into a relationship only related to the speed ratio and the chilled water pump flow: ； Energy consumption of fan coil units and fresh air units The mathematical model is as follows: In the formula, is the air flow rate, is the air pressure, For variable speed fans, medium efficiency; Air volume per circuit is the air volume supplied to all air-conditioned rooms The relationship between the total air volume and the air volume of each air conditioning unit is determined by the following formula: ； When the speed of the variable speed fan is n, the pressure and air volume satisfy the following curve equation relationship: ； The speed ratio , is the rated speed, ~ is the model parameter, then the energy consumption of fan coil unit and fresh air unit is Transformed into a relationship only related to the speed ratio and the air volume of each fan coil unit: In the above formula, represents a constant, and its value varies depending on the dimension unit of total pressure or flow rate; if the dimension unit of air flow rate and water flow rate is the same, and the dimension unit of pump head and air pressure is the same, then for the above formulas It will be the same; Cooling pump energy consumption And cooling tower energy consumption The mathematical model is expressed as follows: ； ； ； ； In the system modeling process, parameters , , , , , The value of can be obtained from the sample data of the corresponding manufacturer. The cooling load at the end of each air conditioning circuit Available from DDC Systems.

8. According to the energy-saving control algorithm of a central air-conditioning energy control system described in claim 7, it is characterized in that: The constraints of the power control model of the central air conditioning system in Step 10 are: The range of the inlet water temperature and outlet water temperature of the chiller is as follows: ； ； The water flow through the refrigeration pump and the cooling pump is subject to the following constraints: ； ； The air volume of air conditioning units and cooling towers is subject to the following constraints: ； ； The pressure that the air conditioning unit and refrigeration pump can provide must be within the operating range of the variable speed control: ； ； The range of fresh air volume for each fan coil unit: 。 9. According to the energy-saving control algorithm of a central air-conditioning energy control system described in claim 8, it is characterized in that: In the Step 11, the total power value of the central control system predicted in Step 7 is compared with the actual total power value to obtain the difference between multiple time intervals, and the multiple differences are introduced into the power model of the central air-conditioning system obtained in Step 10, and the difference is multiplied by the set weight and added to the power model of the central air-conditioning system to obtain the corrected power model of the central air-conditioning system, and then the prediction is repeated until the difference is less than the set threshold.

10. According to the energy-saving control algorithm of a central air-conditioning energy control system described in claim 9, it is characterized in that: In the above-mentioned Step 1, the area that can receive light and the area that cannot receive light are distinguished.

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