CN114543303A - Operation optimization method and system of central air-conditioning refrigeration station based on operation big data - Google Patents
Operation optimization method and system of central air-conditioning refrigeration station based on operation big data Download PDFInfo
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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- F24F—AIR-CONDITIONING; AIR-HUMIDIFICATION; VENTILATION; USE OF AIR CURRENTS FOR SCREENING
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- F—MECHANICAL ENGINEERING; LIGHTING; HEATING; WEAPONS; BLASTING
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Abstract
The invention relates to a central air-conditioning refrigeration station operation optimization method and system based on operation big data, comprising the following steps: acquiring operation data and environmental data of a central air-conditioning refrigeration station; training based on a predictive training algorithm model according to the startup operation data and the environmental data to output predicted cold quantity; determining a starting strategy of the refrigerating unit according to the starting operation data, the environmental data and the predicted cold quantity; obtaining a plus-minus machine mark through plus-minus machine judgment logic according to the adjusted operation data, the environment data and the predicted cold quantity; determining an adjusting strategy of the refrigerating unit according to the cold quantity meter, the predicted cold quantity and the plus and minus machine mark; and determining a shutdown strategy of the refrigerating unit according to the shutdown operation data and the environmental data. The invention optimizes the operation strategy based on the operation big data, needs less end hardware facilities, has low investment and operation and maintenance cost, can adapt to the individualized cooling demand and achieves the cooling-as-needed energy-saving operation.
Description
Technical Field
The invention relates to the field of central air-conditioning operation management, in particular to a method and a system for optimizing operation of a central air-conditioning refrigeration station based on operation big data.
Background
The central air-conditioning system of the building is a big household of building energy consumption, wherein a part of the important reason that the energy consumption is not high is caused by poor operation management of the central air-conditioning system, at present, the operation control strategy is mostly unreasonable, and a lot of energy-saving spaces exist in the operation management.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method and a system for optimizing the operation of a central air-conditioning refrigeration station based on operation big data aiming at the defects of the prior art.
The technical scheme adopted by the invention for solving the technical problems is as follows: the method for optimizing the operation of the central air-conditioning refrigeration station based on the operation big data comprises the following steps:
acquiring operation data and environmental data of a central air-conditioning refrigeration station; the operational data includes: startup operation data, adjustment operation data and shutdown operation data;
training based on a predictive training algorithm model according to the starting operation data and the environmental data to output predicted cold quantity;
determining a starting strategy of the refrigerating unit according to the starting operation data, the environment data and the predicted cold quantity;
obtaining a plus-minus machine sign through plus-minus machine judgment logic according to the adjusting operation data, the environment data and the predicted cold quantity;
determining an adjusting strategy of the refrigerating unit according to a cold quantity meter, the predicted cold quantity and the plus and minus machine sign;
and determining a shutdown strategy of the refrigerating unit according to the shutdown operation data and the environment data.
In the operation optimization method of the central air-conditioning refrigeration station based on the operation big data, the startup operation data comprises: number of boot hours; the environmental data includes: current indoor temperature, indoor target temperature, current outdoor wet bulb temperature;
the step of training based on the predictive training algorithm model to output the predicted cold quantity according to the startup operation data and the environmental data comprises the following steps:
and training based on the predictive training algorithm model according to the startup hours, the current indoor temperature, the indoor target temperature and the current outdoor wet bulb temperature to obtain the predicted cold quantity.
In the operation optimization method for a central air conditioning refrigeration station based on big operation data, the startup operation data further includes: target business hours, set earliest boot time, and set latest boot time
The step of determining the starting strategy of the refrigerating unit according to the starting operation data, the environment data and the predicted cold quantity comprises the following steps:
predicting the starting time of the refrigerating unit according to the current indoor temperature, the indoor target temperature, the target business time, the set earliest starting time and the set latest starting time;
determining the starting combination of the refrigerating unit according to the predicted cold quantity and the cold quantity meter;
and obtaining a starting-up strategy of the refrigerating unit according to the starting-up time and the starting-up combination of the refrigerating unit.
In the operation optimization method of the central air conditioning refrigeration station based on the big operation data, the determining the starting combination of the refrigeration unit according to the predicted cold capacity and the cold capacity meter comprises the following steps:
obtaining all permutation combinations started by the refrigerating unit and corresponding cooling capacity according to the cooling capacity meter;
determining the permutation combination which meets a cold quantity threshold value in all the permutation combinations based on the predicted cold quantity;
acquiring the starting combination with the largest system COP in the permutation combinations meeting the cold quantity threshold value according to the permutation combinations meeting the cold quantity threshold value;
and the starting combination with the largest system COP in the permutation combination meeting the cold quantity threshold value is the starting combination of the refrigerating unit.
In the operation optimization method of the central air-conditioning refrigeration station based on the operation big data, the adjusting operation data comprises the following steps: actual cold quantity, outlet water temperature of the refrigerator and outlet water temperature set value of the refrigerator; the environmental data includes: current indoor temperature, current outdoor temperature and outdoor forecast temperature;
the step of obtaining the plus-minus machine sign through the plus-minus machine judgment logic according to the adjustment operation data, the environment data and the predicted cold quantity comprises the following steps:
judging whether the current indoor temperature is greater than an indoor temperature upper limit value or not;
if yes, determining the plus-minus machine mark based on the predicted cold quantity and the actual cold quantity;
if not, determining the plus-minus machine sign based on the predicted cold quantity, the actual cold quantity, the current indoor temperature, the current outdoor temperature, the outdoor forecast temperature, the outlet water temperature of the refrigeration host machine and the outlet water temperature set value of the refrigeration host machine.
In the operation optimization method of the central air conditioning refrigeration station based on the big operation data, the determining the plus-minus machine sign based on the predicted cold capacity and the actual cold capacity includes:
judging whether the actual cold quantity is less than or equal to the predicted cold quantity;
and if so, determining the plus-minus machine mark as plus machine.
In the operation optimization method of a central air conditioning refrigeration station based on big operation data according to the present invention, the determining the plus-minus machine flag based on the predicted refrigeration capacity, the actual refrigeration capacity, the current indoor temperature, the current outdoor temperature, and the outdoor forecast temperature includes:
judging whether the current indoor temperature is within a preset temperature range or not;
if so, judging whether the actual cold quantity is less than or equal to the predicted cold quantity;
if the actual cold quantity is less than or equal to the predicted cold quantity, determining the plus-minus machine mark as a plus machine;
if the actual cold quantity is larger than the predicted cold quantity, determining the plus-minus machine sign according to the current outdoor temperature and the outdoor forecast temperature;
and if the current indoor temperature is out of the preset temperature range, determining the plus-minus machine mark as a minus machine.
In the operation optimization method of the central air-conditioning refrigeration station based on the big operation data, the determining the adjustment strategy of the refrigeration unit according to the cold quantity meter, the predicted cold quantity and the plus-minus machine sign comprises the following steps:
obtaining all permutation combinations started by the refrigerating unit and corresponding cooling capacity according to the cooling capacity meter;
determining the permutation combination which meets a cold quantity threshold value in all the permutation combinations based on the predicted cold quantity;
determining a pre-selected permutation and combination according to the addition and subtraction machine mark and the permutation and combination meeting the cold quantity threshold;
according to the preselection permutation combination, obtaining an opening combination with the largest system COP in the preselection permutation combination;
and the opening combination with the largest system COP in the preselection arrangement combination is the adjusting strategy of the refrigerating unit.
In the operation optimization method for a central air conditioning refrigeration station based on operation big data, the shutdown operation data includes: business ending time, set earliest shutdown time and set latest shutdown time; the environmental data includes: a current indoor temperature and an indoor target temperature;
the determining a shutdown strategy of the refrigeration unit according to the shutdown operation data and the environmental data includes:
predicting the shutdown time of the refrigerating unit according to the current indoor temperature, the indoor target temperature, the business ending time, the set earliest shutdown time and the set latest shutdown time;
and determining a shutdown strategy of the refrigerating unit based on the shutdown time of the refrigerating unit and in combination with the current state of the refrigerating unit.
The invention also provides a central air-conditioning refrigeration station operation optimization system based on operation big data, which comprises:
the system comprises an acquisition unit, a storage unit and a control unit, wherein the acquisition unit is used for acquiring operation data and environment data of a central air-conditioning refrigeration station; the operational data includes: startup operation data, adjustment operation data and shutdown operation data;
the prediction unit is used for training based on a prediction training algorithm model according to the starting operation data and the environmental data so as to output prediction cold quantity;
the starting strategy making unit is used for determining a starting strategy of the refrigerating unit according to the starting operation data, the environment data and the predicted cold quantity;
the plus-minus machine unit is used for obtaining plus-minus machine marks through plus-minus machine judgment logic according to the adjusting operation data, the environment data and the predicted cold quantity;
the adjusting strategy making unit is used for determining the adjusting strategy of the refrigerating unit according to a cold quantity meter, the predicted cold quantity and the addition and subtraction mark;
and the shutdown strategy making unit is used for determining the shutdown strategy of the refrigerating unit according to the shutdown operation data and the environment data.
The operation optimization method and the system of the central air-conditioning refrigeration station based on the operation big data have the following beneficial effects that: the method comprises the following steps: acquiring operation data and environmental data of a central air-conditioning refrigeration station; training based on a predictive training algorithm model according to the startup operation data and the environmental data to output predicted cold quantity; determining a starting strategy of the refrigerating unit according to the starting operation data, the environmental data and the predicted cold quantity; obtaining a plus-minus machine mark through plus-minus machine judgment logic according to the adjusted operation data, the environment data and the predicted cold quantity; determining an adjusting strategy of the refrigerating unit according to the cold quantity meter, the predicted cold quantity and the plus and minus machine mark; and determining a shutdown strategy of the refrigerating unit according to the shutdown operation data and the environmental data. The invention optimizes the operation strategy based on the operation big data, requires less hardware facilities at the tail end, has low investment and operation and maintenance cost, can adapt to the individualized cooling demand and achieves the cooling energy-saving operation according to the demand.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a schematic flow chart diagram of a method for optimizing operation of a central air conditioning refrigeration station based on operation big data according to an embodiment of the present invention;
FIG. 2 is a diagram illustrating predicted cold provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of a predicted boot time according to an embodiment of the present invention;
FIG. 4 is a schematic diagram illustrating a method for determining a start-up combination for a refrigeration unit in accordance with an embodiment of the present invention;
FIG. 5 is a schematic diagram of the determination of the sign of the add/subtract machine provided by the embodiment of the present invention;
FIG. 6 is a schematic diagram of determining a conditioning strategy for a refrigeration unit provided by an embodiment of the present invention;
FIG. 7 is a schematic diagram of a predicted shutdown time according to an embodiment of the present invention;
fig. 8 is a schematic diagram for determining a shutdown strategy for a refrigeration unit according to an embodiment of the present invention.
Fig. 9 is a schematic structural diagram of a central air conditioning refrigeration station operation optimization system based on operation big data according to an embodiment of the present invention.
Detailed Description
For a more clear understanding of the technical features, objects and effects of the present invention, embodiments of the present invention will now be described in detail with reference to the accompanying drawings.
The operation optimization method of the central air-conditioning refrigeration station based on the operation big data can be applied to the operation control system of the central air-conditioning refrigeration station. According to the operation optimization method, a complex algorithm for processing mass data is combined into data analysis, the incidence relation between data and the change rule of the data are mined, a refrigeration station operation adjustment strategy is constructed, the operation energy-saving requirement is met, and the energy-saving potential in the operation process is further mined.
The operation optimization method of the central air-conditioning refrigeration station based on the operation big data, provided by the embodiment of the invention, has the advantages of less required end hardware facilities, investment and operation and maintenance costs compared with group control, and can adapt to the cooling demand of the individualized refrigeration station by comprehensively analyzing and mining the data acquired in real time, so as to achieve cooling-on-demand energy-saving operation. And moreover, an operation strategy of the central air-conditioning refrigeration station is formulated based on a big data mining algorithm, so that the obtained operation strategy is more accurate, and the project pertinence is strong.
Further, the operation optimization method for the central air-conditioning refrigeration station based on the operation big data, provided by the embodiment of the invention, can be used for making an efficient starting strategy, an efficient adjusting strategy and an efficient shutdown strategy of the refrigeration station according to daily data samples of the refrigeration station, realizing full-cycle energy-saving control of the refrigeration station, realizing accurate supply according to requirements, improving energy efficiency, guiding the making of low-cost operation control parameters of the refrigeration station and the evaluation of energy-saving transformation potential, and has strong universality and wide engineering applicability.
Specifically, as shown in fig. 1, the operation optimization method for the central air conditioning refrigeration station based on the operation big data includes the following steps:
and S101, acquiring operation data and environment data of the central air-conditioning refrigeration station. Wherein the operational data includes: startup operation data, adjustment operation data, and shutdown operation data.
And S102, training based on the predictive training algorithm model according to the starting operation data and the environmental data to output the predicted cold quantity.
In some embodiments, the boot-up data includes: number of boot hours; the environmental data includes: current indoor temperature, indoor target temperature, current outdoor wet bulb temperature.
Specifically, as shown in fig. 2, the training based on the predictive training algorithm model to output the predicted cold amount according to the startup operation data and the environmental data includes: and training based on a predictive training algorithm model according to the startup hours, the current indoor temperature, the indoor target temperature and the current outdoor wet bulb temperature to obtain the predicted cold quantity.
Optionally, in the embodiment of the present invention, the number of startup hours may be set manually (for example, the number of startup hours may be set to 8 hours, 9 hours, and the like). Or, in some other embodiments, the number of startup hours may also be counted according to historical data to obtain a relationship between the outdoor temperature and the number of startup hours, and then the number of startup hours is derived.
Optionally, in the embodiment of the present invention, the predictive training algorithm model may include, but is not limited to, an SVM algorithm model, a neural network algorithm model, an LSTM algorithm model, and the like.
The prediction training algorithm model provided by the embodiment of the invention can be obtained by training the processing of historical data. For example, historical one year day data may be used: historical startup hours, historical 24-hour hourly indoor temperature, historical 24-hour hourly outdoor temperature, historical indoor target temperature and historical 24-hour hourly actual refrigerating capacity are trained to obtain a predictive training algorithm model. After the predictive training algorithm model is obtained, the current monitoring data (i.e., the number of hours to start, the current indoor temperature, the indoor target temperature, and the current outdoor wet bulb temperature) can be imported into the predictive training algorithm model for training, so that the current predicted cold capacity at the current day can be predicted.
And step S103, determining a starting strategy of the refrigerating unit according to the starting operation data, the environmental data and the predicted cold quantity.
In some embodiments, the boot execution data further comprises: target business hours, set earliest boot time, and set latest boot time.
Optionally, determining the start-up strategy of the refrigerating unit according to the start-up operation data, the environmental data and the predicted cold quantity includes: predicting the starting time of the refrigerating unit according to the current indoor temperature, the indoor target temperature, the target business time, the set earliest starting time and the set latest starting time; determining the starting combination of the refrigerating unit according to the predicted cold quantity and the cold quantity meter; and obtaining the starting strategy of the refrigerating unit according to the starting time and the starting combination of the refrigerating unit.
Fig. 3 is a schematic diagram of predicting boot time according to an embodiment of the present invention.
Specifically, first, an indoor temperature difference is obtained from an indoor target temperature (which is an indoor temperature required during business hours) and a current indoor temperature (i.e., an indoor temperature 2 hours (or 1 hour) before the business hours); and then, the starting time of the refrigerating unit can be obtained according to a calculation formula of the room temperature change rate. Wherein, the room temperature change rate can be specifically expressed as:
room temperature change rate is room temperature difference/time length.
Wherein, the duration is the target business time-TOpening device(1)。
The indoor temperature difference is the current indoor temperature — the indoor target temperature (2).
Therefore, the starting time T of the refrigerating unit can be obtained by combining the formulas (1) and (2)Opening device. Wherein, TOpening deviceThe final determination is performed by combining the set earliest startup time and the set latest startup time, for example, if the earliest startup time is 7 am and the latest startup time is 8 am, the calculated T isOpening deviceBetween 7 and 8 points, the calculated T can be directly takenOpening deviceIf T isOpening deviceOut of 7 points to 8 points, the neighboring value is taken, i.e. if 7 points are neighboring, 7 points are taken (e.g. calculated TOpening device6 points 50, then TOpening deviceTaking 7 points); if the point is adjacent to 8 points, 8 points are taken (such as T obtained by calculation)Opening deviceAt 8 points and 20, TOpening deviceTake 8 points).
In some embodiments, as shown in fig. 4, determining the start-up combination of the refrigeration unit based on the predicted capacity and the capacity table includes:
and S401, acquiring all permutation and combination started by the refrigerating unit and corresponding cooling capacity according to the cooling capacity meter.
Optionally, in the embodiment of the present invention, the refrigeration capacity meter includes all permutation and combination for starting the refrigeration unit and corresponding refrigeration capacity. Specifically, it can be expressed as follows:
combination 1: starting a No. 1 host and a No. 2 host, wherein the outlet water temperature of a header pipe is 7 ℃, and the balanced load rate of the system is 80%; the cooling capacity of the system is 800KW, and the COP of the system is 5.0.
And (3) combination 2: starting a No. 1 host and a No. 2 host, wherein the outlet water temperature of a header pipe is 7.5 ℃, and the balanced load rate of the system is 80%; the cooling capacity of the system is 750KW, and the COP of the system is 5.6.
And S402, determining the permutation and combination which meets the cold quantity threshold value in all the permutation and combination based on the predicted cold quantity.
Optionally, in the embodiment of the present invention, the cold amount threshold may be determined based on the predicted cold amount. For example, the determination may be made using 0.9 times the predicted coldness and 1.1 times the predicted coldness as the coldness threshold. That is, if the corresponding combinations that the system cooling capacity is 0.9 times to 1.1 times of the predicted cooling capacity are extracted from the obtained permutation combinations, the combinations are the permutation combinations meeting the cooling capacity threshold. It will be appreciated that the threshold value of the cold quantity can also be determined in other ways and is not limited to the examples of the invention.
And S403, acquiring the starting combination with the largest system COP in the permutation combinations meeting the cold quantity threshold value according to the permutation combinations meeting the cold quantity threshold value.
And S404, the starting combination with the largest system COP in the permutation combinations meeting the cold quantity threshold is the starting combination of the refrigerating unit.
And step S104, obtaining a plus-minus machine sign through plus-minus machine judgment logic according to the adjustment operation data, the environment data and the prediction cold quantity.
In some embodiments, adjusting the operational data comprises: actual cold quantity, outlet water temperature of the refrigerator and outlet water temperature set value of the refrigerator; the environmental data includes: a current indoor temperature, a current outdoor temperature, and an outdoor forecast temperature.
In some embodiments, obtaining the add/subtract machine flag according to the adjusted operation data, the environmental data, the predicted cold amount and the add/subtract machine judgment logic includes: judging whether the current indoor temperature is greater than an indoor temperature upper limit value or not; if yes, determining an adding and subtracting mark based on the predicted cold quantity and the actual cold quantity; if not, determining an adding and subtracting sign based on the predicted cold quantity, the actual cold quantity, the current indoor temperature, the current outdoor temperature, the outdoor forecast temperature, the water outlet temperature of the refrigeration host and the water outlet temperature set value of the refrigeration host.
In some embodiments, determining the add-subtract flag based on the predicted cold and the actual cold comprises: judging whether the actual cold quantity is less than or equal to the predicted cold quantity or not; if yes, determining the plus-minus machine mark as plus machine.
In some embodiments, determining the add-subtract flag based on the predicted cold, the actual cold, the current indoor temperature, the current outdoor temperature, and the outdoor forecasted temperature comprises: judging whether the current indoor temperature is within a preset temperature range; if so, judging whether the actual cold quantity is less than or equal to the predicted cold quantity; if the actual cold quantity is less than or equal to the predicted cold quantity, determining the plus-minus machine mark as plus machine; if the actual cold quantity is larger than the predicted cold quantity, determining an adding and subtracting mark according to the current outdoor temperature and the outdoor predicted temperature; and if the current indoor temperature is out of the preset temperature range, determining the plus-minus machine mark as the minus machine.
As shown in fig. 5, the determining of the add/subtract machine identifier provided by the embodiment of the present invention specifically includes the following steps:
step S501, initialization is carried out first, and the position of the plus-minus machine mark is 0.
And step S502, judging whether the current indoor temperature is greater than the indoor temperature upper limit value.
And step S503, if yes, judging whether the actual cold quantity is less than or equal to the predicted cold quantity.
And step S504, if the actual cold quantity is less than or equal to the predicted cold quantity, setting the plus-minus sign to be 1 (namely determining the plus-minus sign to be plus).
Step S505, if the current indoor temperature is lower than the upper limit value of the indoor temperature, determining whether the current indoor temperature is within a preset temperature range (for example, a predicted temperature range or an indoor temperature upper limit value of-0.8 to the upper limit value of the indoor temperature).
Step S506, if the current indoor temperature is within the preset temperature range, whether the actual cold quantity is smaller than or equal to the predicted cold quantity is judged.
And step S507, if the actual cold is less than or equal to the predicted cold, setting the plus-minus sign to be 1 (namely determining the plus-minus sign to be plus).
And step S508, if the actual cold quantity is larger than the predicted cold quantity, judging whether the difference value between the outdoor forecast temperature and the current outdoor temperature is in a preset range.
Wherein the preset range may be 1 or less. That is, the difference between the outdoor predicted temperature and the current outdoor temperature determines whether the outdoor predicted temperature is within 1 degree of the current outdoor temperature. Of course, it is understood that the preset value is not limited to within 1 degree.
Step S509, if the difference between the outdoor predicted temperature and the current outdoor temperature is within the preset value, the plus-minus machine flag is: the current state is maintained.
Step S510, if the difference between the outdoor predicted temperature and the current outdoor temperature is not within the preset range, determining whether the outdoor predicted temperature is greater than the current outdoor temperature. Optionally, in the embodiment of the present invention, the outdoor predicted temperature is an outdoor temperature after two hours in the future.
And step S511, if the outdoor forecast temperature is greater than the current outdoor temperature, setting the plus-minus sign to 1 (namely determining the plus-minus sign to be plus).
And S512, if the outdoor forecast temperature is lower than the current outdoor temperature, setting the plus-minus sign to be 3 (namely determining the plus-minus sign to be the minus sign).
Step S513, if the current indoor temperature is not within the preset temperature range, setting the plus-minus flag to 3 (i.e., determining the plus-minus flag to be minus). The current indoor temperature is not within the preset temperature range, which means that the current indoor temperature is out of the preset range.
Further, after step S512, the following steps are also included:
and step S514, judging whether the minimum value of the outlet water temperature of the refrigeration host is greater than the outlet water temperature threshold of the refrigeration host.
Optionally, in the embodiment of the present invention, the outlet water temperature threshold of the refrigeration host may be +1.5 degrees of the outlet water temperature set value of the refrigeration host.
Step S515; and if the minimum value of the water outlet temperature of the refrigeration host is greater than the water outlet temperature threshold, judging whether the minimum value of the water outlet temperature of the refrigeration host continuously decreases within a preset time period.
Optionally, in the embodiment of the present invention, the continuous decrease of the minimum value of the outlet water temperature of the refrigeration host in the preset time period may be: at least 0.2 degrees of decline was observed at each of the past three time points (e.g., every 15 minutes).
In step S516, if the operation is continuously performed, the plus-minus flag is set to 3 (i.e., the plus-minus flag is determined to be the minus operation).
And S517, outputting the current plus-minus machine mark.
And S105, determining an adjusting strategy of the refrigerating unit according to the cold quantity meter, the predicted cold quantity and the machine adding and subtracting mark.
In some embodiments, as shown in fig. 6, determining the adjustment strategy of the refrigeration unit according to the cold quantity meter, the predicted cold quantity and the plus-minus machine sign includes:
step S601, all permutation and combination started by the refrigerating unit and corresponding cooling capacity are obtained according to the cooling capacity meter.
And step S602, determining the permutation and combination which meets the cold quantity threshold value in all the permutation and combination based on the predicted cold quantity.
Optionally, in the embodiment of the present invention, the cold amount threshold may be determined based on the predicted cold amount. For example, the determination may be made using 0.9 times the predicted coldness and 1.1 times the predicted coldness as the coldness threshold. That is, if the corresponding combinations that the system cooling capacity is 0.9 times to 1.1 times of the predicted cooling capacity are extracted from the obtained permutation combinations, the combinations are the permutation combinations meeting the cooling capacity threshold. It will be appreciated that the threshold value of the cold quantity can also be determined in other ways and is not limited to the examples of the invention.
And step S603, determining a pre-selected permutation and combination according to the addition and subtraction machine mark and the permutation and combination meeting the cold quantity threshold value.
Optionally, in the embodiment of the present invention, after obtaining the permutation and combination satisfying the cold quantity threshold, all the permutation and combinations satisfying need to be determined by combining the add/subtract flag, that is, if the permutation and combination satisfying the cold quantity threshold is obtained, the method further includes: and starting the #1 host, the #2 host and the #3 host, if the current plus-minus machine mark is minus machine, closing one or two of the #1 host, the #2 host and the #3 host, and specifically, subtracting the machine according to the number of the minus machines and the corresponding hosts.
And step S604, acquiring the maximum system COP opening combination in the preselection permutation combination according to the preselection permutation combination.
And step S605, the opening combination with the maximum system COP in the pre-selected permutation combination is used as the adjusting strategy of the refrigerating unit.
And S106, determining a shutdown strategy of the refrigerating unit according to the shutdown operation data and the environmental data.
In some embodiments, the shutdown operation data comprises: business ending time, set earliest shutdown time and set latest shutdown time; the environmental data includes: a current indoor temperature and an indoor target temperature.
Optionally, determining a shutdown strategy of the refrigeration unit according to the shutdown operation data and the environmental data includes: predicting the shutdown time of the refrigerating unit according to the current indoor temperature, the indoor target temperature, the business ending time, the set earliest shutdown time and the set latest shutdown time; and determining a shutdown strategy of the refrigerating unit based on the shutdown time of the refrigerating unit and in combination with the current state of the refrigerating unit.
Fig. 7 is a schematic diagram of predicting the shutdown time according to an embodiment of the present invention.
Specifically, first, an indoor temperature difference is obtained from an indoor target temperature (which is an indoor temperature required at the end of business) and a current indoor temperature (i.e., an indoor temperature 2 hours (or 1 hour) before the end of business); and then, the shutdown time of the refrigerating unit can be obtained according to a calculation formula of the room temperature change rate. Wherein, the room temperature change rate can be specifically expressed as:
room temperature change rate is room temperature difference/time length.
Wherein, the duration is the target business time-TClosing device(3)。
The indoor temperature difference is the current indoor temperature — the indoor target temperature (4).
Therefore, the shutdown time T of the refrigerating unit can be obtained by combining the expressions (3) and (4)Closing device. Wherein, TClosing deviceAnd finally determining the latest startup time by combining the set earliest startup time and the set latest startup time, for example, if the earliest shutdown time is 6 pm and the latest shutdown time is 8 pm, the calculated T is obtainedClosing deviceBetween 6 and 8 points, the calculated T can be directly takenClosing deviceIf T isClosing deviceOut of 6-8 points, the neighboring value is taken, i.e. if 6 points are neighboring, 6 points are taken (e.g. calculated TOpening device5 points and 50 points, then TClosing deviceTaking 6 points); if the point is close to 8 points, then 8 points are taken (such as T obtained by calculation)Closing deviceIs 9, then TClosing deviceTake 8 points).
Fig. 8 is a schematic diagram for determining a shutdown strategy of a refrigeration unit according to an embodiment of the present invention.
As shown in fig. 8, the method may specifically include the following steps:
and step S801, acquiring the current state of the refrigerating unit.
Step S802, judging whether the current state of the refrigerating unit is shut down.
Step S803, if the current state of the refrigeration unit is shut down, outputting the current shutdown time, where the current shutdown time is the shutdown strategy of the refrigeration unit.
And step S804, if the current state of the refrigerating unit is not shut down, predicting the shutdown time of the refrigerating unit by referring to the method of FIG. 7.
In step S805, the shutdown policy (i.e., the shutdown time obtained in step S803 or step S804) is output.
Referring to fig. 9, a schematic structural diagram of an operation optimization system of a central air conditioning refrigeration station based on operation big data according to an embodiment of the present invention is provided. The operation optimization system of the central air-conditioning refrigeration station based on the operation big data can be used for realizing the operation optimization method of the central air-conditioning refrigeration station based on the operation big data disclosed by the embodiment of the invention.
Specifically, as shown in fig. 9, the operation optimization system for the central air conditioning refrigeration station based on the operation big data includes:
an obtaining unit 901, configured to obtain operation data and environmental data of the central air-conditioning refrigeration station. The operational data includes: startup operation data, adjustment operation data, and shutdown operation data.
And the prediction unit 902 is used for training based on the prediction training algorithm model according to the startup operation data and the environment data to output the predicted refrigeration capacity.
And the starting-up strategy formulation unit 903 is used for determining the starting-up strategy of the refrigerating unit according to the starting-up operation data, the environmental data and the predicted cold quantity.
And the adding and subtracting unit 904 is used for obtaining an adding and subtracting sign through adding and subtracting judgment logic according to the adjusting operation data, the environment data and the prediction cold quantity.
And the adjusting strategy making unit 905 is used for determining the adjusting strategy of the refrigerating unit according to the cold quantity meter, the predicted cold quantity and the addition and subtraction mark.
A shutdown strategy making unit 906, configured to determine a shutdown strategy of the refrigeration unit according to the shutdown operation data and the environmental data.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other. The device disclosed by the embodiment corresponds to the method disclosed by the embodiment, so that the description is simple, and the relevant points can be referred to the method part for description.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative components and steps have been described above generally in terms of their functionality in order to clearly illustrate this interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the implementation. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. A software module may reside in Random Access Memory (RAM), memory, Read Only Memory (ROM), electrically programmable ROM, electrically erasable programmable ROM, registers, hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.
The above embodiments are merely illustrative of the technical ideas and features of the present invention, and are intended to enable those skilled in the art to understand the contents of the present invention and implement the present invention, and not to limit the scope of the present invention. All equivalent changes and modifications made within the scope of the claims of the present invention should be covered by the claims of the present invention.
Claims (10)
1. A central air-conditioning refrigeration station operation optimization method based on operation big data is characterized by comprising the following steps:
acquiring operation data and environmental data of a central air-conditioning refrigeration station; the operational data includes: startup operation data, adjustment operation data and shutdown operation data;
training based on a predictive training algorithm model according to the starting operation data and the environmental data to output predicted cold quantity;
determining a starting strategy of the refrigerating unit according to the starting operation data, the environment data and the predicted cold quantity;
obtaining a plus-minus machine sign through plus-minus machine judgment logic according to the adjusting operation data, the environment data and the predicted cold quantity;
determining an adjusting strategy of the refrigerating unit according to a cold quantity meter, the predicted cold quantity and the plus and minus machine sign;
and determining a shutdown strategy of the refrigerating unit according to the shutdown operation data and the environment data.
2. The operation optimization method for the central air-conditioning refrigeration station based on the operation big data as claimed in claim 1, wherein the startup operation data comprises: number of boot hours; the environmental data includes: current indoor temperature, indoor target temperature, current outdoor wet bulb temperature;
the step of training based on the predictive training algorithm model to output the predicted cold quantity according to the startup operation data and the environmental data comprises the following steps:
and training based on the predictive training algorithm model according to the startup hours, the current indoor temperature, the indoor target temperature and the current outdoor wet bulb temperature to obtain the predicted cold quantity.
3. The operation optimization method for the central air-conditioning refrigeration station based on the operation big data as claimed in claim 2, wherein the startup operation data further comprises: target business hours, set earliest boot time, and set latest boot time
The step of determining the starting strategy of the refrigerating unit according to the starting operation data, the environment data and the predicted cold quantity comprises the following steps:
predicting the starting time of the refrigerating unit according to the current indoor temperature, the indoor target temperature, the target business time, the set earliest starting time and the set latest starting time;
determining the starting combination of the refrigerating unit according to the predicted cold quantity and the cold quantity meter;
and obtaining a starting-up strategy of the refrigerating unit according to the starting-up time and the starting-up combination of the refrigerating unit.
4. The operation optimization method for the central air-conditioning refrigerating station based on the operation big data as claimed in claim 1, wherein the determining the startup combination of the refrigerating unit according to the predicted cold capacity and the cold capacity meter comprises:
obtaining all permutation combinations started by the refrigerating unit and corresponding cooling capacity according to the cooling capacity meter;
determining the permutation combination which meets a cold quantity threshold value in all the permutation combinations based on the predicted cold quantity;
acquiring the starting combination with the largest system COP in the permutation combinations meeting the cold quantity threshold value according to the permutation combinations meeting the cold quantity threshold value;
and the starting combination with the largest system COP in the permutation combination meeting the cold quantity threshold value is the starting combination of the refrigerating unit.
5. The operation optimization method for the central air-conditioning refrigerating station based on the operation big data as claimed in claim 1, wherein the adjusting the operation data comprises: actual cold quantity, outlet water temperature of the refrigerator and outlet water temperature set value of the refrigerator; the environmental data includes: current indoor temperature, current outdoor temperature and outdoor forecast temperature;
the step of obtaining the plus-minus machine sign through the plus-minus machine judgment logic according to the adjustment operation data, the environment data and the predicted cold quantity comprises the following steps:
judging whether the current indoor temperature is greater than an indoor temperature upper limit value or not;
if yes, determining the plus-minus machine mark based on the predicted cold quantity and the actual cold quantity;
if not, determining the plus-minus machine sign based on the predicted cold quantity, the actual cold quantity, the current indoor temperature, the current outdoor temperature, the outdoor forecast temperature, the outlet water temperature of the refrigeration host machine and the outlet water temperature set value of the refrigeration host machine.
6. The operation optimization method for the central air-conditioning refrigerating station based on the operation big data as claimed in claim 5, wherein the determining the plus-minus machine sign based on the predicted cold capacity and the actual cold capacity comprises:
judging whether the actual cold quantity is less than or equal to the predicted cold quantity;
and if so, determining the plus-minus machine mark as plus machine.
7. The central air conditioning refrigeration station operation optimization method based on operation big data as claimed in claim 5, wherein the determining the plus or minus machine flag based on the predicted refrigeration capacity, the actual refrigeration capacity, the current indoor temperature, the current outdoor temperature and the outdoor forecast temperature comprises:
judging whether the current indoor temperature is within a preset temperature range or not;
if so, judging whether the actual cold quantity is less than or equal to the predicted cold quantity;
if the actual cold quantity is less than or equal to the predicted cold quantity, determining the plus-minus machine mark as a plus machine;
if the actual cold quantity is larger than the predicted cold quantity, determining the plus-minus machine sign according to the current outdoor temperature and the outdoor forecast temperature;
and if the current indoor temperature is out of the preset temperature range, determining the plus-minus machine mark as a minus machine.
8. The operation optimization method for the central air-conditioning refrigerating station based on the operation big data as claimed in claim 1, wherein the determining the adjustment strategy of the refrigerating unit according to the cold quantity meter, the predicted cold quantity and the plus-minus machine sign comprises the following steps:
obtaining all permutation combinations started by the refrigerating unit and corresponding cooling capacity according to the cooling capacity meter;
determining the permutation combination which meets a cold quantity threshold value in all the permutation combinations based on the predicted cold quantity;
determining a pre-selected permutation and combination according to the addition and subtraction machine mark and the permutation and combination meeting the cold quantity threshold;
according to the preselection permutation combination, obtaining an opening combination with the largest system COP in the preselection permutation combination;
and the opening combination with the largest system COP in the preselection arrangement combination is the adjusting strategy of the refrigerating unit.
9. The operation optimization method for the central air conditioning refrigeration station based on the operation big data as claimed in claim 1, wherein the shutdown operation data comprises: business ending time, set earliest shutdown time and set latest shutdown time; the environmental data includes: a current indoor temperature and an indoor target temperature;
the determining a shutdown strategy of the refrigeration unit according to the shutdown operation data and the environmental data includes:
predicting the shutdown time of the refrigerating unit according to the current indoor temperature, the indoor target temperature, the business ending time, the set earliest shutdown time and the set latest shutdown time;
and determining a shutdown strategy of the refrigerating unit based on the shutdown time of the refrigerating unit and in combination with the current state of the refrigerating unit.
10. A central air conditioning refrigeration station operation optimization system based on big operation data is characterized by comprising:
the system comprises an acquisition unit, a storage unit and a control unit, wherein the acquisition unit is used for acquiring operation data and environment data of a central air-conditioning refrigeration station; the operational data includes: startup operation data, adjustment operation data and shutdown operation data;
the prediction unit is used for training based on a prediction training algorithm model according to the starting operation data and the environmental data so as to output prediction cold quantity;
the starting strategy making unit is used for determining a starting strategy of the refrigerating unit according to the starting operation data, the environment data and the predicted cold quantity;
the plus-minus machine unit is used for obtaining plus-minus machine marks through plus-minus machine judgment logic according to the adjusting operation data, the environment data and the predicted cold quantity;
the adjusting strategy making unit is used for determining the adjusting strategy of the refrigerating unit according to a cold quantity meter, the predicted cold quantity and the addition and subtraction mark;
and the shutdown strategy making unit is used for determining the shutdown strategy of the refrigerating unit according to the shutdown operation data and the environment data.
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