CN114543303B - Operation optimization method and system for central air-conditioning refrigeration station based on operation big data - Google Patents
Operation optimization method and system for 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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Abstract
The invention relates to a central air-conditioning refrigerating station operation optimization method and system based on operation big data, comprising the following steps: acquiring operation data and environment data of a central air-conditioning refrigeration station; training based on the prediction training algorithm model according to the starting operation data and the environment data to output predicted cold energy; determining a starting strategy of the refrigerating unit according to starting operation data and environment data and predicted cold quantity; predicting cold energy according to the regulating operation data and the environment data, and obtaining an add-drop machine mark through an add-drop machine judging logic; determining an adjusting strategy of the refrigerating unit according to the cold quantity table, the predicted cold quantity and the adding and subtracting machine mark; and determining a shutdown strategy of the refrigerating unit according to the shutdown operation data and the environment data. The invention optimizes the operation strategy based on the operation big data, has less needed terminal hardware facilities and low investment and operation and maintenance cost, can adapt to the personalized cooling requirement, and achieves the cooling energy-saving operation according to the requirement.
Description
Technical Field
The invention relates to the field of central air conditioner operation management, in particular to a central air conditioner refrigerating station operation optimization method and system based on operation big data.
Background
The central air conditioning system of the building is a large household of building energy consumption, a part of important reasons for high energy consumption are that the central air conditioning operation management is poor, most of the current reasons are that the operation control strategy is unreasonable, a plurality of energy-saving spaces are also reserved in the operation management, the traditional central air conditioning group control strategy is used for judging the opening and closing of equipment based on more monitoring data, the strict control strategy is not used for optimizing operation parameters, the actual characteristics of different central air conditioning systems and functional objects are different, and how to conduct operation in the whole life cycle is always one of the difficulties which afflict the operation management staff of the central air conditioning system.
Disclosure of Invention
Aiming at the defects of the prior art, the invention provides a central air-conditioning refrigerating station operation optimization method and system based on operation big data.
The technical scheme adopted for solving the technical problems is as follows: a central air-conditioning refrigerating station operation optimization method based on operation big data is constructed, which comprises the following steps:
acquiring operation data and environment data of a central air-conditioning refrigeration station; the operation data includes: startup operation data, adjustment operation data, and shutdown operation data;
training based on the prediction training algorithm model according to the starting operation data and the environment data to output predicted cold energy;
determining a starting strategy of the refrigerating unit according to the starting operation data, the environment data and the predicted cold quantity;
obtaining an adder-adder sign through an adder-adder judging logic according to the adjustment operation data, the environment data and the predicted cold quantity;
determining an adjusting strategy of the refrigerating unit according to the cold quantity table, the predicted cold quantity and the adding and subtracting machine mark;
and determining a shutdown strategy of the refrigerating unit according to the shutdown operation data and the environment data.
In the central air-conditioning refrigerating station operation optimization method based on operation big data, the starting operation data comprise: the number of hours of starting up; the environmental data includes: current indoor temperature, indoor target temperature, current outdoor wet bulb temperature;
the training based on the start-up operation data and the environment data and based on the predictive training algorithm model to output the predicted cold energy comprises:
and training based on the predictive training algorithm model according to the starting hours, the current indoor temperature, the indoor target temperature and the current outdoor wet bulb temperature to obtain the predictive cooling capacity.
In the central air-conditioning refrigerating station operation optimization method based on operation big data, the starting operation data further comprises: target business hours, set earliest start-up time, and set latest start-up time
The 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 hours, the set earliest starting time and the set latest starting time;
determining a starting combination of the refrigerating unit according to the predicted cold quantity and the cold quantity table;
and obtaining a starting strategy of the refrigerating unit according to the starting time and 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 determining the starting combination of the refrigeration unit according to the predicted cold quantity and the cold quantity table comprises the following steps:
according to the cold meter, all arrangement combinations of the starting of the refrigerating unit and corresponding cold supply quantity are obtained;
determining the permutation and combination meeting the cold quantity threshold value in all permutation and combination based on the predicted cold quantity;
acquiring an opening combination with the maximum system COP in the arrangement combination meeting the cold quantity threshold according to the arrangement combination meeting the cold quantity threshold;
and the starting combination with the largest system COP in the arrangement combination meeting the cold energy threshold is the starting combination of the refrigerating unit.
In the central air-conditioning refrigerating station operation optimizing method based on operation big data, the operation data adjustment comprises the following steps: the actual cold quantity, the outlet water temperature of the refrigerator and the outlet water temperature set value of the refrigerator; the environmental data includes: current indoor temperature, current outdoor temperature, and outdoor forecast temperature;
the obtaining the addition and subtraction machine mark according to the adjustment operation data, the environment data and the predicted cold quantity and through the addition and subtraction machine judgment logic comprises the following steps:
judging whether the current indoor temperature is greater than an indoor temperature upper limit value;
if yes, determining the addition and subtraction machine mark based on the predicted cold quantity and the actual cold quantity;
if not, determining the addition and subtraction machine mark based on the predicted cooling capacity, the actual cooling capacity, the current indoor temperature, the current outdoor temperature, the outdoor predicted temperature, the outlet water temperature of the refrigeration host and the outlet water temperature set value of the refrigeration host.
In the central air-conditioning refrigerating station operation optimization method based on operation big data of the present invention, the determining the adder-subtractor flag based on the predicted cooling capacity and the actual cooling capacity includes:
judging whether the actual cooling capacity is smaller than or equal to the predicted cooling capacity;
if yes, determining the add-subtract machine mark as an add machine.
In the central air-conditioning refrigerating station operation optimization method based on operation big data of the present invention, the determining the adder-subtractor flag based on the predicted cooling capacity, the actual cooling capacity, the current indoor temperature, the current outdoor temperature and the outdoor predicted temperature includes:
judging whether the current indoor temperature is within a preset temperature range or not;
if yes, judging whether the actual cooling capacity is smaller than or equal to the predicted cooling capacity;
if the actual cooling capacity is smaller than or equal to the predicted cooling capacity, determining that the add-subtract machine mark is an add machine;
if the actual cooling capacity is larger than the predicted cooling capacity, determining the adder-adder sign 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 that the machine adding and subtracting mark is a machine subtracting mark.
In the operation optimization method of the central air-conditioning refrigeration station based on the operation big data, the determining the adjustment strategy of the refrigeration unit according to the cold scale, the predicted cold quantity and the adding and subtracting machine mark comprises the following steps:
according to the cold meter, all arrangement combinations of the starting of the refrigerating unit and corresponding cold supply quantity are obtained;
determining the permutation and combination meeting the cold quantity threshold value in all permutation and combination based on the predicted cold quantity;
determining a preselected permutation and combination according to the addition and subtraction machine mark and the permutation and combination meeting the cold quantity threshold value;
acquiring an opening combination with the maximum system COP in the preselected permutation and combination according to the preselected permutation and combination;
and the starting combination with the largest system COP in the pre-selected permutation combination is the regulating strategy of the refrigerating unit.
In the central air-conditioning refrigerating station operation optimization method based on operation big data, the shutdown operation data comprise: business ending time, set earliest shutdown time and set latest shutdown time; the environmental data includes: current indoor temperature and indoor target temperature;
the determining the shutdown strategy of the refrigerating unit according to the shutdown operation data and the environment data comprises the following steps:
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 refrigerating station operation optimizing system based on the operation big data, which comprises:
the acquisition unit is used for acquiring the operation data and the environment data of the central air-conditioning refrigeration station; the operation 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 environment data so as to output predicted cold energy;
the starting strategy making unit is used for determining the starting strategy of the refrigerating unit according to the starting operation data, the environment data and the predicted cold quantity;
the addition and subtraction unit is used for obtaining addition and subtraction marks through addition and subtraction judgment logic according to the adjustment operation data, the environment data and the predicted cold quantity;
the regulation strategy making unit is used for determining the regulation strategy of the refrigerating unit according to the cold scale, the predicted cold quantity and the addition and subtraction machine 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 running optimization method and the running optimization system for the central air-conditioning refrigerating station based on the running big data have the following beneficial effects: comprising the following steps: acquiring operation data and environment data of a central air-conditioning refrigeration station; training based on the prediction training algorithm model according to the starting operation data and the environment data to output predicted cold energy; determining a starting strategy of the refrigerating unit according to starting operation data and environment data and predicted cold quantity; predicting cold energy according to the regulating operation data and the environment data, and obtaining an add-drop machine mark through an add-drop machine judging logic; determining an adjusting strategy of the refrigerating unit according to the cold quantity table, the predicted cold quantity and the adding and subtracting machine mark; and determining a shutdown strategy of the refrigerating unit according to the shutdown operation data and the environment data. The invention optimizes the operation strategy based on the operation big data, has less needed terminal hardware facilities and low investment and operation and maintenance cost, can adapt to the personalized cooling requirement, and achieves the cooling energy-saving operation according to the requirement.
Drawings
The invention will be further described with reference to the accompanying drawings and examples, in which:
fig. 1 is a schematic flow chart of a central air-conditioning refrigerating station operation optimizing method based on operation big data according to an embodiment of the present invention;
FIG. 2 is a schematic diagram of predicted cooling capacity provided by an embodiment of the present invention;
FIG. 3 is a schematic diagram of predicting boot time according to an embodiment of the present invention;
FIG. 4 is a schematic diagram of determining a start-up combination of a refrigeration unit according to an embodiment of the present invention;
FIG. 5 is a schematic diagram of an add-subtract machine flag determination provided by an embodiment of the present invention;
FIG. 6 is a schematic diagram of a determination of 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 provided by an embodiment of the present invention;
fig. 8 is a schematic diagram of determining a shutdown strategy of a refrigeration unit according to an embodiment of the present invention.
Fig. 9 is a schematic structural diagram of a central air-conditioning refrigerating station operation optimizing system based on operation big data according to an embodiment of the present invention.
Detailed Description
For a clearer understanding of technical features, objects and effects of the present invention, a detailed description of embodiments of the present invention will be made with reference to the accompanying drawings.
The central air-conditioning refrigerating station operation optimization method based on the operation big data can be applied to a central air-conditioning refrigerating station operation control system. According to the operation optimization method, complex algorithms for processing mass data are combined into data analysis, association relations among data and change rules of the data are mined, an operation adjustment strategy of the refrigerating station is constructed, the requirement of operation energy conservation is met, and energy conservation potential in the operation process is further mined.
The central air-conditioning refrigerating station operation optimization method based on the big data is less in terminal hardware facilities, more advantageous in investment and operation and maintenance cost than group control, and can adapt to the cooling requirement of a personalized refrigerating station by comprehensively analyzing and mining data acquired in real time, so that the cooling energy-saving operation according to the requirement is achieved. Moreover, the 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.
Furthermore, the central air-conditioning refrigerating station operation optimization method based on the big operation data provided by the embodiment of the invention can formulate an efficient starting strategy, an adjusting strategy and a shutdown strategy of the refrigerating station according to daily data samples of the refrigerating station, realize full-period energy-saving control of the refrigerating station, realize accurate supply as required, improve energy efficiency, guide low-cost operation control parameter formulation and energy-saving transformation potential evaluation of the refrigerating station, and has strong universality and wide engineering application.
Specifically, as shown in fig. 1, the operation optimization method of the central air-conditioning refrigeration station based on the operation big data comprises the following steps:
and step S101, acquiring operation data and environment data of a central air-conditioning refrigeration station. Wherein the operation data includes: startup operation data, adjustment operation data, and shutdown operation data.
Step S102, training is carried out based on the prediction training algorithm model according to the starting operation data and the environment data so as to output the predicted cold quantity.
In some embodiments, the boot-up operational data includes: the number of hours of starting up; the environmental data includes: current indoor temperature, indoor target temperature, current outdoor wet bulb temperature.
Specifically, as shown in fig. 2, training based on the prediction training algorithm model to output the predicted cooling capacity according to the startup operation data and the environmental data includes: and training based on a predictive training algorithm model according to the starting hours, the current indoor temperature, the indoor target temperature and the current outdoor wet bulb temperature to obtain the predictive cooling capacity.
Alternatively, in the embodiment of the present invention, the number of power-on hours may be manually set (for example, the number of power-on hours may be set to 8 hours, 9 hours, etc.). Alternatively, in other embodiments, the number of power-on hours may be counted according to the historical data, so as to obtain the relationship between the outdoor temperature and the number of power-on hours, and further derive the number of power-on hours.
Alternatively, in embodiments 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 predictive training algorithm model of the embodiment of the invention can be obtained through training of the processing of the historical data. For example, daily data for one year can be used: the historical starting hours, the historical 24-hour indoor temperature, the historical 24-hour outdoor temperature, the historical indoor target temperature and the historical 24-hour actual cooling capacity are trained to obtain a predictive training algorithm model. After the predictive training algorithm model is obtained, the current monitoring data (namely the starting-up hours, 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 based on the current monitoring data, so that the current predictive cooling capacity in the current day can be predicted.
And step 103, determining a starting strategy of the refrigerating unit according to the starting operation data and the environment data and the predicted cold quantity.
In some embodiments, the boot-up operation data further includes: target business hours, set earliest start-up time, and set latest start-up time.
Optionally, determining the starting policy of the refrigeration unit according to the starting operation data and 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 hours, the set earliest starting time and the set latest starting time; determining a starting combination of the refrigerating unit according to the predicted cold quantity and the cold quantity table; 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 a startup 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 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 = room temperature difference/duration.
Wherein duration = target business hours-T Opening device (1)。
Indoor temperature difference = current indoor temperature-indoor target temperature (2).
Therefore, the start-up time T of the refrigerating unit can be obtained by combining the expression (1) and the expression (2) Opening device . Wherein T is Opening device It is also necessary to combine the earliest set start-up time and the latest set start-up time to perform final determination, for example, the earliest set start-up time is 7 a.m. and the latest set start-up time is 8 a.m., the calculated T Opening device Between 7 and 8 points, the calculated T can be directly taken Opening device If T Opening device Outside 7-8 points, the adjacent value is taken, i.e. if 7 points are adjacent, 7 points are taken (e.g. calculated T Opening device At 6 point 50, T Opening device Taking 7 points); if 8 points are adjacent, 8 points (e.g. calculated T Opening device 8 points 20, T Opening device Taking 8 points).
In some embodiments, as shown in fig. 4, determining the start-up combination of the refrigeration unit based on the predicted cooling capacity and the cooling capacity table includes:
and S401, obtaining all arrangement combinations of the starting of the refrigerating unit and corresponding cold supply quantity according to the cold meter.
Optionally, in the embodiment of the present invention, the cold gauge includes all the permutations and combinations of the opening of the refrigerating unit and the corresponding cold supply. Specifically, the method can be expressed as follows:
combination 1: starting a No. 1 host, starting a No. 2 host, wherein the outlet water temperature of a main 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.
Combination 2: starting a No. 1 host, starting a No. 2 host, wherein the outlet water temperature of a main 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.
Step S402, based on the predicted cold quantity, determining the permutation combination meeting the cold quantity threshold value in all permutation combinations.
Alternatively, in embodiments of the present invention, the cooling capacity threshold may be determined based on a predicted cooling capacity. For example, the determination may be made using 0.9 times the predicted cooling capacity and 1.1 times the predicted cooling capacity as the cooling capacity threshold. If the obtained permutation and combination is the combination corresponding to the system cooling capacity which is 0.9 times to 1.1 times of the predicted cooling capacity, the combination is the permutation and combination meeting the cooling capacity threshold. It will be appreciated that the cold threshold may also be determined in other ways, not limiting to the examples of the invention.
Step S403, acquiring an opening combination with the maximum system COP in the arrangement combination meeting the cold energy threshold according to the arrangement combination meeting the cold energy threshold.
And step S404, the starting combination with the largest system COP in the arrangement combination meeting the cold energy threshold is the starting combination of the refrigerating unit.
And step S104, predicting the cold quantity according to the operation data and the environment data, and obtaining an add-subtract machine mark through an add-subtract machine judging logic.
In some embodiments, adjusting the operational data includes: the actual cold quantity, the outlet water temperature of the refrigerator and the outlet water temperature set value of the refrigerator; the environmental data includes: current indoor temperature, current outdoor temperature, and outdoor predicted temperature.
In some embodiments, predicting the cold amount according to the adjusted operation data and the environment data, and obtaining the adder-adder flag through the adder-adder judgment logic includes: judging whether the current indoor temperature is greater than an indoor temperature upper limit value; if yes, determining an add-subtract machine mark based on the predicted cold quantity and the actual cold quantity; if not, determining an addition and subtraction sign based on the predicted cold quantity, the actual cold quantity, the current indoor temperature, the current outdoor temperature, the outdoor predicted temperature, the outlet water temperature of the refrigeration host machine and the outlet water temperature set value of the refrigeration host machine.
In some embodiments, determining the adder-adder flag based on the predicted cold amount and the actual cold amount includes: judging whether the actual cold quantity is smaller than or equal to the predicted cold quantity; if yes, determining the add-subtract machine mark as an add machine.
In some embodiments, determining the adder flag based on the predicted cooling capacity, the actual cooling capacity, the current indoor temperature, the current outdoor temperature, and the outdoor predicted temperature includes: judging whether the current indoor temperature is within a preset temperature range or not; if yes, judging whether the actual cold quantity is smaller than or equal to the predicted cold quantity; if the actual cooling capacity is smaller than or equal to the predicted cooling capacity, determining an add-subtract machine mark as an add machine; if the actual cooling capacity is larger than the predicted cooling capacity, determining an add-subtract machine 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 that the machine adding and subtracting mark is machine subtracting.
As shown in fig. 5, the determination of the add-subtract machine flag provided by the embodiment of the invention specifically includes the following steps:
step S501, initializing is carried out first, and the addition and subtraction machine mark position 0 is obtained.
Step S502, judging whether the current indoor temperature is larger than the upper limit value of the indoor temperature.
And S503, if yes, judging whether the actual cooling capacity is smaller than or equal to the predicted cooling capacity.
And step S504, if the actual cooling capacity is smaller than or equal to the predicted cooling capacity, setting an add-subtract flag to be 1 (namely determining the add-subtract flag to be an add-subtract).
Step S505, if the current indoor temperature is less than the upper limit value of the indoor temperature, judging whether the current indoor temperature is within the preset temperature range (for example, the predicted temperature range or the upper limit value of the indoor temperature-0.8-the upper limit value of the indoor temperature).
And step S506, judging whether the actual cooling capacity is smaller than or equal to the predicted cooling capacity or not if the current indoor temperature is within the preset temperature range.
And S507, if the actual cooling capacity is smaller than or equal to the predicted cooling capacity, setting an add-subtract flag to 1 (namely determining the add-subtract flag to be an add-subtract).
And step S508, if the actual cooling capacity is larger than the predicted cooling capacity, judging whether the difference between the outdoor predicted temperature and the current outdoor temperature is in a preset range.
Wherein, the preset range can be within 1. I.e. here 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 add-subtract 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, judging whether the outdoor predicted temperature is greater than the current outdoor temperature. Optionally, in the embodiment of the present invention, the outdoor forecast temperature is an outdoor temperature after two hours in the future.
And S511, if the outdoor forecast temperature is greater than the current outdoor temperature, setting an add-subtract machine flag to be 1 (namely determining the add-subtract machine flag to be an add machine).
And S512, if the outdoor forecast temperature is smaller than the current outdoor temperature, setting an add-subtract machine flag to 3 (namely determining the add-subtract machine flag to be the subtract machine).
Step S513, if the current indoor temperature is not within the preset temperature range, the add-subtract flag is set to 3 (i.e. the add-subtract flag is determined to be the subtract). Wherein, the current indoor temperature is not within the preset temperature range means that the current indoor temperature is out of the preset range.
Further, after step S512, the following steps are further included:
step S514, judging whether the minimum value of the outlet water temperature of the refrigeration host is larger than the outlet water temperature threshold value of the refrigeration host.
Optionally, in the embodiment of the present invention, the outlet water temperature threshold of the refrigeration host may be a set value of the outlet water temperature of the refrigeration host+1.5 degrees.
Step S515; if the minimum value of the water outlet temperature of the refrigeration host is larger than the water outlet temperature threshold, judging whether the minimum value of the water outlet temperature of the refrigeration host continuously drops in 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 a 0.2 degree drop in each time point (e.g., every 15 minutes) was observed over the past three time points.
In step S516, if the operation is continuously performed, the add-subtract flag is set to 3 (i.e., the add-subtract flag is determined to be the subtract).
Step S517, outputting the current adder-adder flag.
And step 105, determining the regulation strategy of the refrigerating unit according to the cold quantity table, the predicted cold quantity and the addition and subtraction marks.
In some embodiments, as shown in fig. 6, determining the adjustment strategy for the refrigeration unit based on the cold scale, the predicted cold and the add-subtract flag includes:
and step S601, all the arrangement combinations of the starting of the refrigerating unit and the corresponding cold supply quantity are obtained according to the cold meter.
Step S602, based on the predicted cold quantity, determining the permutation combination meeting the cold quantity threshold value in all permutation combinations.
Alternatively, in embodiments of the present invention, the cooling capacity threshold may be determined based on a predicted cooling capacity. For example, the determination may be made using 0.9 times the predicted cooling capacity and 1.1 times the predicted cooling capacity as the cooling capacity threshold. If the obtained permutation and combination is the combination corresponding to the system cooling capacity which is 0.9 times to 1.1 times of the predicted cooling capacity, the combination is the permutation and combination meeting the cooling capacity threshold. It will be appreciated that the cold threshold may also be determined in other ways, not limiting to the examples of the invention.
Step S603, determining a preselected 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 that satisfies the cold energy threshold, all permutation and combination that is satisfied by combining the adder-subtractor sign is further required to be determined, that is, if the permutation and combination that satisfies the cold energy threshold is obtained: if the current add-subtract mark is the subtracting machine, one or two of the #1 host, the #2 host and the #3 host are required to be closed, and the subtracting machine is specifically required to be performed according to the subtracting machine number and the corresponding host.
Step S604, acquiring an opening combination with the maximum system COP in the pre-selected permutation and combination according to the pre-selected permutation and combination.
In step S605, the starting combination with the largest system COP in the pre-selected permutation and combination is the adjustment strategy of the refrigerating unit.
And step S106, determining a shutdown strategy of the refrigerating unit according to the shutdown operation data and the environment data.
In some embodiments, 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.
Optionally, determining the shutdown strategy of the refrigeration unit according to the shutdown operation data and the environment 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 predicted 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 = room temperature difference/duration.
Wherein duration = target business hours-T Switch for closing (3)。
Indoor temperature difference = current indoor temperature-indoor target temperature (4).
Therefore, the shutdown time T of the refrigerating unit can be obtained by combining the expression (3) and the expression (4) Switch for closing . Wherein T is Switch for closing The final determination is also performed by combining the set earliest start-up time and the set latest start-up time, for example, the earliest start-up time is 6 pm, the latest start-up time is 8 pm, and the calculated T Switch for closing Between 6 and 8 points, the calculated T can be directly taken Switch for closing If T Switch for closing Outside 6-8 points, the adjacent value is taken, i.e. if 6 points are adjacent, 6 points are taken (e.g. calculated T Opening device 5 points 50, T Switch for closing Taking 6 points); if 8 points are adjacent, 8 points are taken (e.g. calculatedT to Switch for closing Is 9, T Switch for closing Taking 8 points).
Fig. 8 is a schematic diagram of determining a shutdown strategy of a refrigeration unit according to an embodiment of the present invention.
As shown in fig. 8, the method specifically includes the following steps:
step S801, the current state of a refrigerating unit is obtained.
Step S802, judging whether the current state of the refrigerating unit is shut down.
Step 803, if the current state of the refrigeration unit is shutdown, outputting the current shutdown time, where the current shutdown time is the shutdown strategy of the refrigeration unit.
In step S804, if the current state of the refrigeration unit is not shutdown, the shutdown time of the refrigeration unit is predicted according to the method of fig. 7.
Step S805, outputting a shutdown policy (i.e., the shutdown time obtained in step S803 or step S804).
Referring to fig. 9, a schematic structural diagram of a central air-conditioning refrigerating station operation optimizing system based on operation big data according to an embodiment of the present invention is provided. The central air-conditioning refrigerating station operation optimization system based on the operation big data can be used for realizing the central air-conditioning refrigerating station operation optimization method based on the operation big data disclosed by the embodiment of the invention.
Specifically, as shown in fig. 9, the central air-conditioning refrigerating station operation optimizing system based on the operation big data comprises:
an acquisition unit 901 for acquiring operation data and environment data of the central air-conditioning refrigeration station. The operation data includes: startup operation data, adjustment operation data, and shutdown operation data.
The prediction unit 902 is configured to perform training based on the startup operation data and the environmental data and the prediction training algorithm model to output a predicted cooling capacity.
The starting-up policy making unit 903 is configured to determine a starting-up policy of the refrigeration unit according to the starting-up operation data, the environmental data, and the predicted cooling capacity.
The adder-adder unit 904 is configured to predict the cooling capacity according to the adjustment operation data and the environment data, and obtain an adder-adder flag through an adder-adder judgment logic.
The adjustment policy making unit 905 is configured to determine an adjustment policy of the refrigeration unit according to the cold scale, the predicted cold quantity, and the add-subtract flag.
The shutdown policy making unit 906 is configured to determine a shutdown policy of the refrigeration unit according to the shutdown operation data and the environmental data.
In the present specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, and identical and similar parts between the embodiments are all enough to refer to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
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 elements and steps are described above generally in terms of functionality in order to clearly illustrate the 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 solution. 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. The software modules may be disposed 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 provided to illustrate the technical concept and features of the present invention and are intended to enable those skilled in the art to understand the content of the present invention and implement the same according to the content of the present invention, and not to limit the scope of the present invention. All equivalent changes and modifications made with the scope of the claims should be covered by the claims.
Claims (9)
1. The central air-conditioning refrigerating station operation optimization method based on the operation big data is characterized by comprising the following steps of:
acquiring operation data and environment data of a central air-conditioning refrigeration station; the operation data includes: startup operation data, adjustment operation data, and shutdown operation data;
training based on the prediction training algorithm model according to the starting operation data and the environment data to output predicted cold energy;
determining a starting strategy of the refrigerating unit according to the starting operation data, the environment data and the predicted cold quantity;
obtaining an adder-adder sign through an adder-adder judging logic according to the adjustment operation data, the environment data and the predicted cold quantity;
determining an adjusting strategy of the refrigerating unit according to the cold quantity table, the predicted cold quantity and the adding and subtracting machine mark;
determining a shutdown strategy of the refrigerating unit according to the shutdown operation data and the environment data;
wherein the adjustment operation data includes: the actual cold quantity, the outlet water temperature of the refrigerator and the outlet water temperature set value of the refrigerator; the environmental data includes: current indoor temperature, current outdoor temperature, and outdoor forecast temperature;
the obtaining the addition and subtraction machine mark according to the adjustment operation data, the environment data and the predicted cold quantity and through the addition and subtraction machine judgment logic comprises the following steps:
judging whether the current indoor temperature is greater than an indoor temperature upper limit value;
if yes, determining the addition and subtraction machine mark based on the predicted cold quantity and the actual cold quantity;
if not, determining the addition and subtraction machine mark based on the predicted cooling capacity, the actual cooling capacity, the current indoor temperature, the current outdoor temperature, the outdoor predicted temperature, the outlet water temperature of the refrigerator and the outlet water temperature set value of the refrigerator.
2. The operation optimization method for a central air-conditioning refrigeration station based on operation big data according to claim 1, wherein the startup operation data comprises: the number of hours of starting up; the environmental data includes: current indoor temperature, indoor target temperature, current outdoor wet bulb temperature;
the training based on the start-up operation data and the environment data and based on the predictive training algorithm model to output the predicted cold energy comprises:
and training based on the predictive training algorithm model according to the starting hours, the current indoor temperature, the indoor target temperature and the current outdoor wet bulb temperature to obtain the predictive cooling capacity.
3. The operation optimization method for a central air-conditioning refrigeration station based on operation big data according to claim 2, wherein the startup operation data further comprises: target business hours, set earliest start-up time and set latest start-up time;
the 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 hours, the set earliest starting time and the set latest starting time;
determining a starting combination of the refrigerating unit according to the predicted cold quantity and the cold quantity table;
and obtaining a starting strategy of the refrigerating unit according to the starting time and the starting combination of the refrigerating unit.
4. The method for optimizing operation of a central air conditioning refrigeration station based on operational big data as set forth in claim 3, wherein said determining a start-up combination of said refrigeration unit based on said predicted cold amount and said cold amount table comprises:
according to the cold meter, all arrangement combinations of the starting of the refrigerating unit and corresponding cold supply quantity are obtained;
determining the permutation and combination meeting the cold quantity threshold value in all permutation and combination based on the predicted cold quantity;
acquiring an opening combination with the maximum system COP in the arrangement combination meeting the cold quantity threshold according to the arrangement combination meeting the cold quantity threshold;
and the starting combination with the largest system COP in the arrangement combination meeting the cold energy threshold is the starting combination of the refrigerating unit.
5. The method for optimizing operation of a central air conditioning and cooling station based on operational big data according to claim 1, wherein the determining the adder-adder flag based on the predicted cooling capacity and the actual cooling capacity includes:
judging whether the actual cooling capacity is smaller than or equal to the predicted cooling capacity;
if yes, determining the add-subtract machine mark as an add machine.
6. The method of optimizing operation of a central air conditioning and cooling station based on operational big data according to claim 1, wherein the determining the adder-adder flag based on the predicted cooling capacity, the actual cooling 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 yes, judging whether the actual cooling capacity is smaller than or equal to the predicted cooling capacity;
if the actual cooling capacity is smaller than or equal to the predicted cooling capacity, determining that the add-subtract machine mark is an add machine;
if the actual cooling capacity is larger than the predicted cooling capacity, determining the adder-adder sign 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 that the machine adding and subtracting mark is a machine subtracting mark.
7. The method for optimizing operation of a central air conditioning refrigeration station based on operational big data according to claim 1, wherein determining the adjustment strategy of the refrigeration unit according to the cold scale, the predicted cold quantity and the adder-adder flag comprises:
according to the cold meter, all arrangement combinations of the starting of the refrigerating unit and corresponding cold supply quantity are obtained;
determining the permutation and combination meeting the cold quantity threshold value in all permutation and combination based on the predicted cold quantity;
determining a preselected permutation and combination according to the addition and subtraction machine mark and the permutation and combination meeting the cold quantity threshold value;
acquiring an opening combination with the maximum system COP in the preselected permutation and combination according to the preselected permutation and combination;
and the starting combination with the largest system COP in the pre-selected permutation combination is the regulating strategy of the refrigerating unit.
8. The operation optimization method for a central air-conditioning refrigeration station based on operation big data according to claim 1, wherein the shutdown operation data comprises: business ending time, set earliest shutdown time and set latest shutdown time; the environmental data includes: current indoor temperature and indoor target temperature;
the determining the shutdown strategy of the refrigerating unit according to the shutdown operation data and the environment data comprises the following steps:
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.
9. A central air conditioning refrigeration station operation optimization system based on operational big data, comprising:
the acquisition unit is used for acquiring the operation data and the environment data of the central air-conditioning refrigeration station; the operation 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 environment data so as to output predicted cold energy;
the starting strategy making unit is used for determining the starting strategy of the refrigerating unit according to the starting operation data, the environment data and the predicted cold quantity;
the addition and subtraction unit is used for obtaining addition and subtraction marks through addition and subtraction judgment logic according to the adjustment operation data, the environment data and the predicted cold quantity;
the regulation strategy making unit is used for determining the regulation strategy of the refrigerating unit according to the cold scale, the predicted cold quantity and the addition and subtraction machine mark;
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;
wherein the adjustment operation data includes: the actual cold quantity, the outlet water temperature of the refrigerator and the outlet water temperature set value of the refrigerator; the environmental data includes: current indoor temperature, current outdoor temperature, and outdoor forecast temperature;
the adder-adder unit is further configured to:
judging whether the current indoor temperature is greater than an indoor temperature upper limit value;
if yes, determining the addition and subtraction machine mark based on the predicted cold quantity and the actual cold quantity;
if not, determining the addition and subtraction machine mark based on the predicted cooling capacity, the actual cooling capacity, the current indoor temperature, the current outdoor temperature, the outdoor predicted temperature, the outlet water temperature of the refrigerator and the outlet water temperature set value of the refrigerator.
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