Energy-saving control method and system for centralized cooling of combined air cabinets

Abstract

The invention relates to the technical field of centralized cooling energy-saving control, and particularly discloses an energy-saving control method and system for centralized cooling of a combined air cabinet, which are suitable for a central air-conditioning system of a large building group, firstly, an isochronous interval division method is utilized to collect outdoor temperature and humidity data, and time sequence characteristics are formed through a sliding window technology; an ARIMA model is trained, after model training is completed, the future outdoor environment is predicted, the indoor temperature set value, the air supply amount and the humidity control threshold value are dynamically adjusted accordingly, meanwhile, the system adopts a closed-loop control strategy to monitor indoor temperature changes in real time, the air amount is automatically adjusted by calculating the error between the current environment state and the target state, and the indoor temperature is monitored in real time. And in order to prevent overload or underload of equipment, the system is further provided with an air volume adjustment mapping and amplitude limiting processing mechanism, and in conclusion, the purposes of energy conservation and emission reduction are achieved, and the living quality of residents is further improved.

Description

Technical Field

[0001] The present invention relates to the technical field of centralized cooling energy-saving control, and in particular to an energy-saving control method and system for centralized cooling with a combined air cabinet. Background Art

[0002] With global climate change and people's growing demand for a comfortable living environment, the construction industry is facing a major challenge of how to achieve energy conservation and emission reduction while ensuring the quality of the indoor environment. Traditional centralized cooling systems usually rely on fixed thresholds to control indoor temperature and humidity, and cannot flexibly respond to dynamic changes in outdoor environmental conditions, resulting in serious energy waste. Especially during the transition between seasons, due to the drastic fluctuations in external temperature and humidity, traditional systems are often difficult to quickly adjust to the optimal working state, affecting user experience and increasing unnecessary energy consumption. Therefore, it is particularly important to explore a centralized cooling energy-saving control method that can perceive changes in the external environment in real time and dynamically adjust the control strategy accordingly.

[0003] There are many control technologies for building environment regulation on the market, such as constant temperature control systems based on sensor feedback, automatic adjustment systems based on schedules, etc. However, these technologies generally have the following shortcomings: First, most of them rely on preset thresholds or schedules to decide when to turn on or off air conditioning equipment, and lack sensitivity to changes in the external environment; second, when encountering extreme weather conditions, due to the lack of sufficient prediction capabilities, these systems often cannot respond in time, resulting in unstable indoor environments or excessive cooling / heating; finally, due to the lack of an effective closed-loop control mechanism, existing technologies are difficult to achieve maximum energy saving while ensuring indoor comfort. Therefore, it is urgent to develop a new type of centralized cooling energy-saving control system to overcome the above defects.

[0004] The present invention proposes an innovative energy-saving control method for centralized cooling of combined wind cabinets. By introducing the ARIMA prediction model, the trend of outdoor environmental changes is accurately predicted, and the indoor temperature and humidity thresholds are dynamically adjusted based on the prediction results, which solves the problem of energy waste caused by the inability to respond to external environmental changes in a timely manner in the prior art. The outdoor temperature and humidity in the future are predicted by the prediction model. When the prediction results show that the future temperature is low, the indoor set temperature can be appropriately increased to reduce the cooling demand; otherwise, the set temperature is lowered. In addition, the present invention also combines PID closed-loop control technology to adjust the air volume in real time according to the error between the currently measured indoor temperature and the target temperature, ensuring that the indoor temperature is always maintained within the ideal range, while avoiding damage to the equipment caused by frequent start and stop, further improving the reliability and stability of the system. Summary of the invention

[0005] The purpose of the present invention is to provide an energy-saving control method and system for centralized cooling with a combined wind cabinet, by constructing an outdoor environmental state prediction model, using the ARIMA model to process and extract features from the collected temperature and humidity data, and determining the model parameters through the autocorrelation function (ACF) and the partial autocorrelation function (PACF), and finally realizing accurate prediction of the future outdoor environmental state. Based on the prediction results, the present invention formulates a dynamic threshold adjustment strategy, and adjusts the indoor temperature setting value and air volume in real time according to the change trend of the predicted temperature and humidity values ​​to ensure that the indoor environment is always in a comfortable state. In addition, through the application of real-time monitoring and closed-loop control strategies, the present invention realizes accurate control of indoor temperature, and through the air volume adjustment mapping and limiting processing mechanism, ensures that the system can operate efficiently under different environmental conditions and avoids equipment overload or underload, thereby achieving the goal of energy saving and emission reduction.

[0006] The purpose of the present invention can be achieved through the following technical solutions:

[0007] An energy-saving control method and system for centralized cooling of a combined air cabinet comprises the following steps:

[0008] Collect outdoor environmental status data to obtain an outdoor temperature group and an outdoor air humidity group;

[0009] Process each set of data to form time series features;

[0010] Use time series features to train the model and obtain a prediction model;

[0011] The prediction model is used to predict the outdoor environmental state to obtain the predicted outdoor temperature value and the predicted outdoor air humidity value;

[0012] Develop a threshold adjustment strategy based on the comparison between the prediction results and the mean of the current environmental status;

[0013] Obtain the current indoor temperature value in real time, and calculate it with the new indoor temperature setting value to obtain the error value;

[0014] Based on the error value, a closed-loop control strategy is constructed to obtain the air volume adjustment control value;

[0015] Based on the air volume adjustment control amount, the maximum and minimum air volume ranges of the air cabinet are obtained, and the air volume adjustment coefficient is preset;

[0016] The fresh air volume is calculated by combining the basic air volume and the air volume adjustment control volume;

[0017] Limit the fresh air volume.

[0018] As a further solution of the present invention: the monitoring period is divided into a number of time sub-units of equal length, and outdoor environmental status data is collected at the middle time point of each time sub-unit, which is recorded as outdoor environmental status data;

[0019] All outdoor environmental status data within the monitoring period are integrated and processed to obtain two outdoor environmental status groups, including an outdoor temperature group and an outdoor air humidity group;

[0020] The sliding window technique is applied to each outdoor environment state group to calculate the average value and obtain the time series characteristics, namely the sliding window average value;

[0021] Construct an ARIMA model based on time series characteristics.

[0022] As a further solution of the present invention: outdoor environmental data is predicted by a prediction model to obtain predicted outdoor environmental state data for a certain period of time in the future, including predicted outdoor temperature values ​​and predicted outdoor air humidity values;

[0023] The outdoor environment state group is processed according to the mean calculation formula to obtain the mean outdoor temperature and the mean outdoor air humidity;

[0024] comparing the predicted outdoor environment state data with the mean of the outdoor environment state data;

[0025] If the predicted outdoor temperature is higher or lower than the outdoor temperature average, a new indoor temperature setting value is calculated.

[0026] As a further solution of the present invention: when the predicted outdoor air humidity value is higher than the outdoor air humidity average value, the start-up conditions of the dehumidification mode of the device are adjusted;

[0027] Based on the new humidity threshold, the dehumidification mode is started when the indoor air humidity exceeds the new humidity threshold, and is stopped when the humidity drops below the threshold.

[0028] As a further solution of the present invention: after dynamically adjusting the threshold according to the environmental change trend, a closed-loop control strategy is constructed to adjust the operating parameters of the system according to the current environmental status data and the preset target values.

[0029] As a further solution of the present invention: based on the output value u(t), the maximum and minimum air volume ranges of the wind cabinet are obtained;

[0030] Based on the current basic air volume and output value, the fresh air volume is calculated.

[0031] As a further solution of the present invention: based on the fresh air volume F(t), the fresh air volume is limited;

[0032] As a further solution of the present invention: the equipment is regulated based on the amount of fresh air after treatment;

[0033] Continuously monitor the indoor environmental status data after adjustment, and collect the latest current indoor and outdoor environmental status data;

[0034] Calculate the difference between the latest indoor and outdoor environmental status data and the set target status to obtain the latest error value e(t);

[0035] Based on the latest error value, the latest output value u(t) and the latest air volume F(t) are obtained;

[0036] Based on the latest air volume F(t), the equipment is regulated;

[0037] When the indoor environment status data reaches the set target state, the operation will stop immediately.

[0038] As a further solution of the present invention: an energy-saving control system for centralized cooling of a combined wind cabinet, comprising a data acquisition and processing module, a model building module, a threshold dynamic adjustment module and a closed-loop control module;

[0039] The data acquisition module is used to collect outdoor environmental status data to obtain an outdoor temperature group and an outdoor air humidity group;

[0040] Process each set of data to form time series features;

[0041] The model building module is used to train the model using time series features to obtain a prediction model;

[0042] The prediction model is used to predict the outdoor environmental state to obtain the predicted outdoor temperature value and the predicted outdoor air humidity value;

[0043] The threshold dynamic adjustment module is used to formulate a threshold adjustment strategy based on the comparison between the prediction results and the mean of the current environmental state;

[0044] The closed-loop control module obtains the current indoor temperature value in real time, and calculates it with the new indoor temperature setting value to obtain the error value;

[0045] Based on the error value, a closed-loop control strategy is constructed to obtain the air volume adjustment control value;

[0046] Based on the air volume adjustment control amount, the maximum and minimum air volume ranges of the air cabinet are obtained, and the air volume adjustment coefficient is preset;

[0047] The fresh air volume is calculated by combining the basic air volume and the air volume adjustment control volume;

[0048] Limit the fresh air volume.

[0049] Beneficial effects of the present invention:

[0050] (1) When constructing an outdoor environment state prediction model, the present invention processes and extracts features from data collected from multiple equal-length time subunits within a monitoring period, and uses the ARIMA model for prediction. This technical feature enables the system to accurately predict future outdoor environment change trends based on historical data, solving the problem of response lag caused by the lack of perception of dynamic changes in the external environment in the prior art. By applying the sliding window technology to calculate the average value to form time series features, and then combining the autocorrelation function (ACF) and the partial autocorrelation function (PACF) to determine the model parameters, the present invention can effectively capture the law of environmental changes and provide a scientific basis for subsequent dynamic threshold adjustment. Therefore, when facing seasonal changes or sudden climate events, the system can respond in advance to avoid discomfort in the indoor environment caused by sudden changes in temperature or humidity, thereby achieving the purpose of energy saving and emission reduction;

[0051] (2) The present invention proposes a strategy for dynamically adjusting the threshold based on the prediction results. When the predicted outdoor temperature is lower or higher than the outdoor temperature average, the system will adjust the indoor temperature setting accordingly and change the air volume setting in a timely manner according to the outdoor temperature change trend to adapt to the new environmental conditions. This flexible threshold adjustment mechanism overcomes the energy waste problem caused by the inability to cope with environmental fluctuations under the traditional fixed threshold control method. Especially for commercial buildings or large public facilities, this dynamic adjustment not only helps to maintain a constant indoor comfort level, but also significantly reduces operating costs and promotes the development of green buildings. In addition, when the outdoor air humidity is predicted to be high, the present invention will automatically start the dehumidification mode to further improve the air quality and create a healthy and livable space;

[0052] (3) The present invention uses a closed-loop control strategy to monitor the indoor temperature in real time and compare it with the set value. Based on the error between the current state and the target state, the air volume is dynamically adjusted to ensure that the indoor temperature is always maintained within the ideal range. Compared with the traditional open-loop control method, the closed-loop control has stronger robustness and adaptability. Even when the external conditions change rapidly, it can maintain good control accuracy and continue to optimize on this basis. The present invention can achieve the maximum energy saving effect while ensuring the comfort of the indoor environment. At the same time, in order to prevent overload or underload during the air volume adjustment process, the present invention also specially sets a limiting processing mechanism to ensure the long-term stable operation of the system, extend the life of the equipment, and provide users with long-lasting and reliable air-conditioning services. In short, through the application of a series of innovative technologies, the present invention has successfully constructed a highly intelligent, energy-saving and environmentally friendly combined wind cabinet centralized cooling energy-saving control system with excellent user experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The present invention will be further described below in conjunction with the accompanying drawings.

[0054] Figure 1 It is a flow chart without steps of an energy-saving control method and system of a combined wind cabinet centralized cooling of the present invention;

[0055] Figure 2 is a flow chart of dynamically adjusting the threshold value in the present invention;

[0056] Figure 3 It is a flow chart of constructing a closed-loop control strategy in the present invention. DETAILED DESCRIPTION

[0057] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0058] Example 1

[0059] See also Figure 1 As shown, the present invention is an energy-saving control method for centralized cooling of a combined wind cabinet, comprising the following steps:

[0060] Collect outdoor environmental status data to obtain an outdoor temperature group and an outdoor air humidity group;

[0061] Process each set of data to form time series features;

[0062] Use time series features to train the model and obtain a prediction model;

[0063] The prediction model is used to predict the outdoor environmental state to obtain the predicted outdoor temperature value and the predicted outdoor air humidity value;

[0064] Develop a threshold adjustment strategy based on the comparison between the prediction results and the mean of the current environmental status;

[0065] Obtain the current indoor temperature value in real time, and calculate it with the new indoor temperature setting value to obtain the error value;

[0066] Based on the error value, a closed-loop control strategy is constructed to obtain the air volume adjustment control value;

[0067] Based on the air volume adjustment control amount, the maximum and minimum air volume ranges of the air cabinet are obtained, and the air volume adjustment coefficient is preset;

[0068] The fresh air volume is calculated by combining the basic air volume and the air volume adjustment control volume;

[0069] Limit the fresh air volume.

[0070] Example 2

[0071] The monitoring period is divided into a number of time sub-units of equal length, and outdoor environmental status data is collected at the middle time point of each time sub-unit, which is recorded as outdoor environmental status data;

[0072] The monitoring cycle is a complete cycle of sensor data collection, including but not limited to 1 day or 1 week. The equal-length time subunits are equal time periods obtained by dividing the equal time zone division method, including but not limited to 1 minute, 10 minutes, etc. The middle time point of the time subunit is the time point when the time subunit is in the middle time. For example, if the time subunit is 1 minute, the middle time point of the time subunit is the 30th second. The environmental status data includes temperature value and air humidity value.

[0073] It should be noted that the state of the environmental state data tends to be stable and will not change instantaneously. Therefore, taking the intermediate moment data of the time subunit is representative and real-time and efficient;

[0074] Based on all the outdoor environmental status data within the monitoring period, a prediction model is constructed to predict future environmental change trends. Specifically:

[0075] All outdoor environmental status data within the monitoring period are integrated and processed to obtain two outdoor environmental status groups, including an outdoor temperature group and an outdoor air humidity group;

[0076] The sliding window technique is applied to each outdoor environment state group to calculate the average value and obtain the time series characteristics, namely the sliding window average value;

[0077] Build ARIMA model based on time series characteristics;

[0078] It should be noted that the ARIMA model is an autoregressive integrated moving average model, which is suitable for the prediction of stable time series data. The general form of the ARIMA model is ARIMA (p, d, q), which consists of three parameters, where p is the order of the autoregressive term, d is the number of differences, which is used to make the time series stable, and q is the order of the moving average term. The values ​​of p and q are obtained through the autocorrelation function (ACF) and partial autocorrelation function (PACF) charts, and the d value is obtained through the unit root test (ADF);

[0079] The general form of the ARIMA(p,d,q) model is Among them, YC t is the observed value at time t, φ 1 ,φ 2 ,...,φ p is the autoregressive coefficient, ∈ t is the white noise error term at time t, θ 1 ,θ 2,...,θ q is the moving average coefficient, is d times difference operation;

[0080] It should be noted that the ARIMA (p, d, q) model includes an autoregressive part (AR), a moving average part (MA) and an integration part (I). The autoregressive part captures the linear relationship of the time series itself, the integration part removes the trend and seasonality through the difference operation to make the time series stable, and the moving average part captures the correlation between random error terms.

[0081] Each outdoor environment state group is divided into a training set and a validation set, with a ratio of 80% training set and 20% validation set;

[0082] By using the training set data to train the ARIMA model, using the validation set data to adjust the model's p, d, and q parameters, and using the test set data to evaluate the model performance, a complete prediction model is obtained.

[0083] Example 3

[0084] See also Figure 2 As shown, the outdoor environment data is predicted by the prediction model to obtain the predicted outdoor environment state data for a certain period of time in the future, including the predicted outdoor temperature value and the predicted outdoor air humidity value;

[0085] It should be noted that after obtaining the predicted outdoor environment state data through the prediction model, a threshold adjustment strategy is formulated based on the predicted outdoor environment state data and the actual use needs of the building, including, first, ensuring that the indoor environment is always maintained within a comfortable range, for example, setting the indoor temperature between 22°C and 26°C, and then saving energy as much as possible under the premise of meeting the comfort level. For example, when the predicted outdoor temperature is low, the indoor temperature setting value is appropriately increased to reduce the cooling demand. Finally, avoid damage to the equipment due to frequent start and stop, for example, set a reasonable temperature change range to avoid drastically adjusting the air conditioner setting temperature in a short period of time. This method can enable the combined wind cabinet centralized cooling energy-saving control system to automatically adjust the threshold according to the prediction results to adapt to the changing environmental conditions, thereby more intelligently controlling the refrigeration system, maintaining the comfort of the indoor environment, and saving energy as much as possible;

[0086] The specific process is:

[0087] Obtaining outdoor environment status data at the middle moment of each time subunit within the monitoring period;

[0088] All outdoor environmental status data are processed according to the mean calculation formula to obtain the mean outdoor temperature and the mean outdoor air humidity;

[0089] It should be noted that by comparing the predicted outdoor environment state data with the mean value of the outdoor environment state data, if the predicted outdoor environment state data differs from the mean value of the outdoor environment state data by a certain value, it is considered that the predicted outdoor environment state data is higher or lower than the mean value of the outdoor environment state data. For example, if the predicted outdoor temperature is lower than the mean value of the outdoor temperature by a certain percentage (for example, lower than 50%-10%=40%), it can be considered that the predicted outdoor temperature is lower than the mean value of the outdoor temperature, that is, the predicted temperature is low, and the outdoor temperature will drop in the future. If the predicted outdoor temperature is higher than the mean value of the outdoor temperature by a certain percentage (for example, higher than 50%+10%=60%), it can be considered that the predicted temperature is high, and the outdoor temperature will rise in the future.

[0090] If the predicted outdoor temperature is higher than the average outdoor temperature, it means that the outdoor temperature will rise in the future. set =T set -kT×(T out -T out,base ) obtain a new indoor temperature setting value;

[0091] Among them, T' set is the new indoor temperature setting value, T set is the indoor temperature measurement value, kT is the preset temperature sensitivity coefficient, T out is the predicted outdoor temperature, T out,base is the average outdoor temperature;

[0092] At the same time, in order to speed up the reaching of the new set temperature, the air volume can be increased;

[0093] The formula for increasing air volume is F'=F+ΔF z , where F' is the new air volume setting value, F is the current air volume setting value, ΔF z It is a preset increment, which can be a fixed value or a proportional factor related to temperature change;

[0094] If the predicted outdoor temperature is lower than the average outdoor temperature, it means that the outdoor temperature will drop in the future. set =T set +kT×(T out,base -T out ) to make adjustments;

[0095] At the same time, in order to save energy, the air volume can be reduced;

[0096] The formula for reducing air volume is F'=F-ΔF j , where ΔF j For preset decrements, this can be a fixed value or a proportional factor related to temperature change;

[0097] When the predicted outdoor air humidity value is higher than the average outdoor air humidity, it means that the outdoor air will be higher in the future, and the system can adjust the start-up conditions of the dehumidification mode;

[0098] Adjustment formula is H' th =H th +kH×(H out -H out,base );

[0099] Among them, H' th is the new humidity threshold, H th is the current humidity threshold, H out is the predicted outdoor air humidity, H out,base is the average outdoor air humidity, kH is the preset humidity sensitivity coefficient;

[0100] Based on the new humidity threshold, when the indoor air humidity exceeds the new humidity threshold, the dehumidification mode is started and stops when the humidity drops below the threshold;

[0101] It should be noted that automatically adjusting the threshold according to the prediction results is of great significance and role for the energy-saving control system of centralized cooling. By predicting the trend of future environmental changes in advance, the system can respond faster and adjust the ventilation status in time to cope with the upcoming environmental changes, thereby improving the responsiveness and adaptability of the system. This method of dynamically adjusting the threshold enhances the flexibility and robustness of the system, enabling it to maintain good performance under various conditions. In addition, by optimizing energy utilization, the system can reduce the use of refrigeration and air conditioning when necessary, thereby saving energy. More importantly, dynamically adjusting the threshold helps to improve the quality of the indoor environment, ensuring that the temperature and humidity are always within the ideal range, thereby improving the comfort of the living or office environment and protecting people's health. In this way, the system not only helps to promote sustainable development and reduce the consumption of natural resources, but also improves user satisfaction and quality of life, while reducing maintenance costs, and has significant economic value. Therefore, automatically adjusting the threshold according to the prediction results is an important part of the intelligent and sustainable development of modern buildings, and is of great significance for building green buildings and promoting environmental protection.

[0102] Example 4

[0103] See also Figure 3 As shown, after dynamically adjusting the threshold according to the environmental change trend, a closed-loop control strategy is constructed to adjust the system's operating parameters according to the current environmental status data and the preset target value. Specifically:

[0104] Get the current real-time indoor temperature value T in (t);

[0105] Define the error e(t) as the current indoor temperature value Tin (t) and the new indoor temperature setting value T' set The difference between

[0106] The error formula is e(t) = T' set -T in (t);

[0107] The formula for the closed-loop control strategy is

[0108] Among them, u(t) is the output value of the closed-loop control formula, that is, the air volume adjustment control amount, K p , K i , K d is the coefficient of the formula, K p e(t) is a proportional term used to quickly respond to errors. is the integral term, which accumulates past errors to eliminate steady-state errors. is the differential term, predicting the error change rate, used to reduce overshoot, dτ is a small increment of the time variable τ, t is the current time, is the rate of change of the error value e(t) with time t, that is, the rate of change of the error;

[0109] Coefficient K p , K i , K d The acquisition process is:

[0110] By formula Calculate the coefficient K p , where Kl is the coefficient value when the adjusted indoor temperature starts to fluctuate continuously, i.e., the critical gain, and Tr is the time required for the adjusted indoor temperature to reach the final stable value from the initial value for the first time, i.e., the rise time;

[0111] It should be noted that the critical gain Kl refers to the coefficient K when the indoor temperature after adjustment produces continuous fluctuations under the control ratio. p value, when the coefficient K p Above this value, the indoor temperature becomes unstable and exhibits a continuously fluctuating behavior;

[0112] The process of obtaining the critical gain Kl is:

[0113] Preset an initial coefficient K p1 , initial coefficient K i1 and the initial coefficient K d1 ;

[0114] Based on the initial coefficient K p1 , initial coefficient K i1 and the initial coefficient K d1 , obtain the initial air volume adjustment control amount;

[0115] Based on the initial air volume adjustment control amount, the air volume is adjusted and the adjusted indoor temperature is monitored in real time;

[0116] Keeping the initial coefficient K i1 and the initial coefficient K d1 Under the condition of no change, gradually increase the initial coefficient K p1 When the indoor temperature starts to fluctuate continuously, stop and record the initial coefficient K when the indoor temperature starts to fluctuate continuously. p1 Value, denoted as critical gain Kl;

[0117] The process of obtaining the rise time Tr is:

[0118] Monitor the changes in the air volume when it is adjusted based on the initial air volume adjustment control amount, obtain the temperature change over time, and draw a temperature response curve;

[0119] Based on the temperature response curve, the time required for the temperature to change from the initial value to the final stable value is obtained, that is, the rise time Tr;

[0120] By formula Calculate the coefficient K i1 ;

[0121] By formula K d1 =Kl*Tr to calculate the coefficient K d1 ;

[0122] It should be noted that the closed-loop control strategy is to regulate the air volume through a PID controller. In this case, the PID controller adjusts the air volume according to the error between the currently measured indoor temperature and the set temperature to achieve the required indoor temperature.

[0123] It should be noted that the process of calculating the air volume control amount required to adjust the indoor temperature from the current temperature to the target temperature by the error value between the actual temperature and the target temperature is a closed-loop control. The error e(t) is used to determine whether the current air volume is appropriate. If the current indoor temperature T in (t) is lower than the new indoor temperature setting value T' set , then the error e(t) is positive and u(t) is positive, which means that the cooling intensity needs to be increased, that is, the air volume needs to be increased. If the current indoor temperature T in (t) is higher than the new indoor temperature setting value T' set , then the error e(t) is negative and u(t) is a negative value, which means that the cooling intensity needs to be reduced, that is, the air volume needs to be reduced;

[0124] By adjusting the air volume in real time to ensure that the indoor temperature reaches the expected target temperature, closed-loop control can automatically adjust the air volume. This method not only realizes closed-loop control to ensure that the indoor temperature is always close to the set value, but also enhances the stability and reliability of the system. By adjusting the air volume in real time, the indoor temperature can be kept stable even when external conditions change, while saving energy consumption as much as possible.

[0125] Example 5

[0126] Based on the output value u(t), it needs to be mapped to the actual air volume adjustment value. This process needs to be carried out according to the specific air cabinet involved and the control range. Specifically:

[0127] Get the maximum and minimum air volume range of the wind cabinet, the maximum air volume of the wind cabinet is recorded as Fmax, and the minimum air volume is recorded as Fmin;

[0128] Preset air volume adjustment factor KF;

[0129] It should be noted that the size of the air volume adjustment coefficient KF needs to be determined according to the actual response characteristics of the wind cabinet to ensure the effectiveness and rationality of the air volume adjustment;

[0130] Based on the current base air volume Fbase and the output value u(t), the fresh air volume F(t) is calculated;

[0131] The formula for calculating the fresh air volume is F(t) = F base +KF×u(t);

[0132] It should be noted that in order to prevent the air volume from exceeding the control range of the actual equipment, the calculated fresh air volume F(t) needs to be limited;

[0133] Specifically: F(t) = max(F min ,min(F max ,F(t)));

[0134] It should be noted that by limiting the fresh air volume F(t), F(t) is always kept between Fmin and Fmax to ensure that the adjusted air volume does not exceed the actual working range of the wind cabinet, thus preventing overload or underload.

[0135] Control the equipment based on the amount of fresh air after treatment;

[0136] After the equipment is regulated for a certain period of time, the current indoor and outdoor environmental status data is obtained;

[0137] It should be noted that the system can adjust the control signal, that is, the output value u(t), according to the error between the current state (data obtained through the sensor) and the set target state (set value), and then adjust the actuator (such as the air volume of the wind cabinet), and continuously correct the control signal by continuously monitoring the actual effect.

[0138] Example 6

[0139] The present invention is an energy-saving control system for centralized cooling of a combined wind cabinet, comprising a data acquisition and processing module, a model building module, a threshold dynamic adjustment module and a closed-loop control module;

[0140] The data acquisition module is used to collect outdoor environmental status data to obtain an outdoor temperature group and an outdoor air humidity group;

[0141] Process each set of data to form time series features;

[0142] The model building module is used to train the model using time series features to obtain a prediction model;

[0143] The prediction model is used to predict the outdoor environmental state to obtain the predicted outdoor temperature value and the predicted outdoor air humidity value;

[0144] The threshold dynamic adjustment module is used to formulate a threshold adjustment strategy based on the comparison between the prediction results and the mean of the current environmental state;

[0145] The closed-loop control module obtains the current indoor temperature value in real time, and calculates it with the new indoor temperature setting value to obtain the error value;

[0146] Based on the error value, a closed-loop control strategy is constructed to obtain the air volume adjustment control value;

[0147] Based on the air volume adjustment control amount, the maximum and minimum air volume ranges of the air cabinet are obtained, and the air volume adjustment coefficient is preset;

[0148] The fresh air volume is calculated by combining the basic air volume and the air volume adjustment control volume;

[0149] Limit the fresh air volume.

[0150] Working principle of the present invention: The purpose of the present invention is to provide an energy-saving control method and system for centralized cooling with a combined wind cabinet. By constructing an outdoor environmental state prediction model, the collected temperature and humidity data are processed and feature extracted using the ARIMA model, and the model parameters are determined by the autocorrelation function (ACF) and the partial autocorrelation function (PACF), ultimately achieving accurate prediction of future outdoor environmental states. Based on the prediction results, the present invention formulates a dynamic threshold adjustment strategy, which adjusts the indoor temperature setting value and air volume in real time according to the changing trend of the predicted temperature and humidity values ​​to ensure that the indoor environment is always in a comfortable state. In addition, through the application of real-time monitoring and closed-loop control strategies, the present invention achieves accurate control of the indoor temperature, and through the air volume adjustment mapping and limiting processing mechanism, ensures that the system can operate efficiently under different environmental conditions and avoids equipment overload or underload, thereby achieving the goal of energy saving and emission reduction.

[0151] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. An energy-saving control method for centralized cooling of a combined wind cabinet, characterized in that: The following steps are involved: Collect outdoor environmental status data to obtain an outdoor temperature group and an outdoor air humidity group; Process each set of data to form time series features; Use time series features to train the model and obtain a prediction model; The prediction model is used to predict the outdoor environmental state to obtain the predicted outdoor temperature value and the predicted outdoor air humidity value; Develop a threshold adjustment strategy based on the comparison between the prediction results and the mean of the current environmental status; Obtain the current indoor temperature value in real time, and calculate it with the new indoor temperature setting value to obtain the error value; Based on the error value, a closed-loop control strategy is constructed to obtain the air volume adjustment control value; Based on the air volume adjustment control amount, the maximum and minimum air volume ranges of the air cabinet are obtained, and the air volume adjustment coefficient is preset; The fresh air volume is calculated by combining the basic air volume and the air volume adjustment control volume; Limit the fresh air volume.

2. The energy-saving control method for centralized cooling of a combined wind cabinet according to claim 1 is characterized in that: The monitoring period is divided into a number of time sub-units of equal length, and outdoor environmental status data is collected at the middle time point of each time sub-unit, which is recorded as outdoor environmental status data; All outdoor environmental status data within the monitoring period are integrated and processed to obtain two outdoor environmental status groups, including an outdoor temperature group and an outdoor air humidity group; The sliding window technique is applied to each outdoor environment state group to calculate the average value and obtain the time series characteristics, namely the sliding window average value; Construct an ARIMA model based on time series characteristics.

3. The energy-saving control method for centralized cooling of a combined wind cabinet according to claim 2 is characterized in that: The outdoor environment data is predicted by the prediction model to obtain the predicted outdoor environment state data for a certain period of time in the future, including the predicted outdoor temperature value and the predicted outdoor air humidity value; The outdoor environment state group is processed according to the mean calculation formula to obtain the mean outdoor temperature and the mean outdoor air humidity; comparing the predicted outdoor environment state data with the mean of the outdoor environment state data; If the predicted outdoor temperature is higher or lower than the outdoor temperature average, a new indoor temperature setting value is calculated.

4. The energy-saving control method for centralized cooling of a combined wind cabinet according to claim 3 is characterized in that: When the predicted outdoor air humidity value is higher than the outdoor air humidity average, the start-up conditions of the dehumidification mode of the equipment are adjusted; Based on the new humidity threshold, the dehumidification mode is started when the indoor air humidity exceeds the new humidity threshold, and is stopped when the humidity drops below the threshold.

5. The energy-saving control method for centralized cooling of combined wind cabinets according to claim 4 is characterized in that: After dynamically adjusting the threshold according to the environmental change trend, a closed-loop control strategy is constructed to adjust the system's operating parameters according to the current environmental status data and the preset target values.

6. The energy-saving control method for centralized cooling of combined wind cabinets according to claim 5 is characterized in that: Based on the output value u(t), the maximum and minimum air volume range of the wind cabinet is obtained; Based on the current basic air volume and output value, the fresh air volume is calculated.

7. The energy-saving control method for centralized cooling of combined wind cabinets according to claim 6 is characterized in that: Based on the fresh air volume F(t), the fresh air volume is limited.

8. The energy-saving control method for centralized cooling of combined wind cabinets according to claim 7 is characterized in that: Control the equipment based on the amount of fresh air after treatment; Continuously monitor the indoor environmental status data after adjustment, and collect the latest current indoor and outdoor environmental status data; Calculate the difference between the latest indoor and outdoor environmental status data and the set target status to obtain the latest error value e(t); Based on the latest error value, the latest output value u(t) and the latest air volume F(t) are obtained; Based on the latest air volume F(t), the equipment is regulated; When the indoor environment status data reaches the set target state, the operation will stop immediately.

9. An energy-saving control system for centralized cooling of combined wind cabinets, the system realizing the method according to claim 1, comprising a data acquisition and processing module, a model building module, a threshold dynamic adjustment module and a closed-loop control module; The data acquisition module is used to collect outdoor environmental status data to obtain an outdoor temperature group and an outdoor air humidity group; Process each set of data to form time series features; The model building module is used to train the model using time series features to obtain a prediction model; The prediction model is used to predict the outdoor environmental state to obtain the predicted outdoor temperature value and the predicted outdoor air humidity value; The threshold dynamic adjustment module is used to formulate a threshold adjustment strategy based on the comparison between the prediction results and the mean of the current environmental state; The closed-loop control module obtains the current indoor temperature value in real time, and calculates it with the new indoor temperature setting value to obtain the error value; Based on the error value, a closed-loop control strategy is constructed to obtain the air volume adjustment control value; Based on the air volume adjustment control amount, the maximum and minimum air volume ranges of the air cabinet are obtained, and the air volume adjustment coefficient is preset; The fresh air volume is calculated by combining the basic air volume and the air volume adjustment control volume; Limit the fresh air volume.

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