CN114322634A - Data screening method and device for refrigerating system strategy model - Google Patents
Data screening method and device for refrigerating system strategy model Download PDFInfo
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Abstract
The invention discloses a data screening method and device for a refrigerating system strategy model. The method comprises the following steps: acquiring a historical data set of a refrigeration system in a preset time period, wherein the historical data set comprises a plurality of parameters of the refrigeration system corresponding to different moments; deleting data corresponding to the moment when the preset parameter value is absent from the historical data set; deleting abnormal data in the historical data set; and deleting data corresponding to the equipment unopened time and the equipment switching time in the historical data set. Therefore, the data screening logic suitable for the refrigerating system is established, the data set meeting the modeling requirement is obtained by preprocessing and screening the historical data set of the refrigerating system in the preset time period, the modeling is carried out based on the screened data, and the accuracy of the model is greatly improved.
Description
Technical Field
The invention relates to the field of cooling tower control, in particular to a data screening method and device of a refrigerating system strategy model, electronic equipment and a computer readable storage medium.
Background
The cooling tower is used as general equipment for a thermodynamic system to discharge waste heat, the control of the cooling tower is often ignored in the operation and maintenance process of an actual heating, ventilation and air conditioning system, the cooling tower of 100 commercial squares researched and researched is taken as a basis, the number of the cooling towers in most squares is unchanged and unchanged, and the situations that the full frequency is fully opened or the amplitude of the variable frequency variable tower of the cooling tower is overlarge exist. But the cooling performance of the cooling tower is important to the operational performance of the refrigeration unit. The performance test and prediction model of the cooling tower is the key point of a plurality of researches, different scholars make a series of researches on the performance test and prediction model based on different models, such as an epsilon-NTU method (efficiency-heat transfer unit number method), but the method needs more physical parameters (mass transfer coefficient and the like) which are difficult to measure; with the continuous accumulation of the public building subentry measurement, the black box-based cooling tower performance model gradually becomes a research hotspot, but the black box-based cooling tower model is not like a cold machine experience model, and the input parameters of the model and the selection of the model are not consistent: the multivariate polynomial used by simulation software such as EnergyPlus and Modelica is a regression model, and other different students adopt Support Vector Machines (SVM), Random Forest (RANDOM Forest) and the like. Different projects are based on actually acquired data to fit a cooling tower model, are not strong in universality, and currently, a fitting model and design input parameters still need to be selected based on actual data conditions.
In the operation of a cooling system, a computer learning model is established by utilizing a heating ventilation heat transfer theory, partial experimental data, field actual measurement data and the like, but the quality of model data is influenced by technical development, acquisition conditions, hardware quality and communication conditions, so that direct modeling cannot be performed frequently, and how to reject and screen out data interfering modeling through a certain heating ventilation rule and logic becomes a problem to be solved urgently.
Disclosure of Invention
In view of the above, the present invention has been made to provide a data screening method, apparatus, electronic device, computer readable storage medium for a refrigerant system policy model that overcomes or at least partially solves the above mentioned problems.
One embodiment of the present invention provides a data screening method for a policy model of a refrigeration system, the method including:
acquiring a historical data set of a refrigeration system in a preset time period, wherein the historical data set comprises a plurality of parameters of the refrigeration system corresponding to different moments;
deleting data corresponding to the moment when the preset parameter value is absent from the historical data set;
deleting abnormal data in the historical data set;
and deleting data corresponding to the equipment unopened time and the equipment switching time in the historical data set.
Optionally, the plurality of parameters of the refrigeration system include: the system comprises a refrigeration capacity load rate, a refrigeration capacity, a rated refrigeration capacity, a heat dissipation capacity load rate, a cooling tower fan frequency average value, a chilled water supply temperature, a chilled water return temperature, a chilled water flow rate, an outdoor wet-bulb temperature, the number of opened cooling towers, the number of opened cooling pumps, the number of opened coolers, the energy consumption of cooling towers, the energy consumption of freezing pumps, a current load rate, the total energy consumption of the cold towers of the coolers, the number of opened large machines and the number of opened small machines.
Optionally, the deleting abnormal data in the historical data set includes:
deleting data of the historical data set, wherein the energy consumption of the cooling tower is greater than a first preset threshold value;
deleting data of which the heat consumption is greater than a second preset threshold value in the historical data set;
deleting the data of which the mean value of the fan frequency of the cooling tower is greater than a third preset threshold value in the historical data set;
deleting the data of which the refrigerating capacity load rate is greater than a fourth preset threshold value in the historical data set;
and deleting the data of which the supply water temperature of the frozen water is greater than a fifth preset threshold value in the historical data set.
Optionally, the deleting data corresponding to the device unopened time and the device switching time in the historical data set includes:
when the number of the opened cooling towers, the number of the opened refrigerating pumps and the number of the opened cold machines are all larger than 0, starting equipment, and deleting data corresponding to the moment when the equipment is not started in the historical data set;
and deleting data corresponding to preset time before and after equipment switching in the historical data set.
Optionally, the method further comprises: and deleting the number of cards in the historical data set according to the energy consumption of the refrigerator, the energy consumption of the cooling tower, the flow rate of the chilled water, the return water temperature of the chilled water and the outdoor wet bulb temperature.
Optionally, the method further comprises: and deleting data which are not synchronous among different parameters in the historical data set.
Optionally, after the filtering the historical data set, the method further includes: and carrying out modeling feasibility analysis according to the data quantity and the data interval distribution. Another embodiment of the present invention provides a data screening apparatus for a policy model of a refrigeration system, including:
the historical data set acquisition unit is used for acquiring a historical data set of the refrigeration system in a preset time period, and the historical data set comprises a plurality of parameters of the refrigeration system corresponding to different moments;
the defective data deleting unit is used for deleting data corresponding to the moment when the preset parameter value is lacked in the historical data set;
the abnormal data deleting unit is used for deleting the abnormal data in the historical data set;
and the device unsteady state operation data deleting unit is used for deleting the data corresponding to the device unopened time and the device switching time in the historical data set.
Optionally, the plurality of parameters of the refrigeration system include: the system comprises a refrigeration capacity load rate, a refrigeration capacity, a rated refrigeration capacity, a heat dissipation capacity load rate, a cooling tower fan frequency average value, a chilled water supply temperature, a chilled water return temperature, a chilled water flow rate, an outdoor wet-bulb temperature, the number of opened cooling towers, the number of opened cooling pumps, the number of opened coolers, the energy consumption of cooling towers, the energy consumption of freezing pumps, a current load rate, the total energy consumption of the cold towers of the coolers, the number of opened large machines and the number of opened small machines.
Optionally, the abnormal data deleting unit is further configured to:
deleting data of the historical data set, wherein the energy consumption of the cooling tower is greater than a first preset threshold value;
deleting data of which the heat consumption is greater than a second preset threshold value in the historical data set;
deleting the data of which the mean value of the fan frequency of the cooling tower is greater than a third preset threshold value in the historical data set;
deleting the data of which the refrigerating capacity load rate is greater than a fourth preset threshold value in the historical data set;
and deleting the data of which the supply water temperature of the frozen water is greater than a fifth preset threshold value in the historical data set.
Optionally, the device unsteady-state operation data deletion unit is further configured to:
when the number of the opened cooling towers, the number of the opened refrigerating pumps and the number of the opened cold machines are all larger than 0, starting equipment, and deleting data corresponding to the moment when the equipment is not started in the historical data set;
and deleting data corresponding to preset time before and after equipment switching in the historical data set.
Optionally, the apparatus further comprises: and the card number deleting unit is used for deleting the card number in the historical data set according to the energy consumption of the refrigerator, the energy consumption of the cooling tower, the flow rate of the freezing water, the return water temperature of the freezing water and the outdoor wet bulb temperature.
Optionally, the apparatus further comprises: and the asynchronous data deleting unit is used for deleting asynchronous data among different parameters in the historical data set.
Optionally, the apparatus further includes a feasibility analysis unit, configured to perform modeling feasibility analysis according to data amount and data interval distribution after the historical data set is screened.
Another embodiment of the present invention provides an electronic device, wherein the electronic device includes:
a processor; and the number of the first and second groups,
a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the method described above.
Another embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores one or more programs which, when executed by a processor, implement the above-described method.
The method has the advantages that the data screening logic suitable for the refrigerating system is established, the data set meeting the modeling requirement is obtained by preprocessing and screening the historical data set of the refrigerating system in the preset time period, the modeling is carried out based on the screened data, and the accuracy of the model is greatly improved.
Drawings
FIG. 1 is a schematic flow chart of a data screening method for a refrigerant system policy model according to an embodiment of the present invention;
FIG. 2 is a schematic structural diagram of a data screening device of a refrigerant system strategy model according to an embodiment of the invention;
FIG. 3 shows a schematic structural diagram of an electronic device according to one embodiment of the invention;
fig. 4 shows a schematic structural diagram of a computer-readable storage medium according to an embodiment of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, embodiments of the present invention will be described in detail with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart of a data screening method for a refrigerant system policy model according to an embodiment of the present invention. As shown in fig. 1, the method includes:
s11: acquiring a historical data set of a refrigeration system in a preset time period, wherein the historical data set comprises a plurality of parameters of the refrigeration system corresponding to different moments;
in practical application, the data related to the cold season of the refrigeration systems of the commercial squares 2020.5.1-10.31 are used as a historical data set, and the time granularity can be 15 minutes. The policy model algorithm may be an XGBoost (tree model) that requires normalization processing on the data set, or may be a linear regression (regression model) that does not require normalization processing on the data set.
The embodiment of the invention adopts the historical data set to replace the real-time data, has low requirement on the real-time performance of the data, has less number shortage phenomenon, and avoids the serious number shortage problem possibly existing in the real-time data transmission.
S12: deleting data corresponding to the moment when the preset parameter value is absent from the historical data set;
in practical application, if a parameter value is lacked at a certain time, the data corresponding to the time is deleted. Or if the data corresponding to the preset parameter value is lacking at a certain time, deleting the data corresponding to the certain time, for example, if the key parameter values such as energy consumption, temperature, flow and the like are lacking at a certain time, deleting the data corresponding to the certain time. S13: deleting abnormal data in the historical data set;
s14: and deleting data corresponding to the equipment unopened time and the equipment switching time in the historical data set.
It can be understood that, since data corresponding to the time lacking the preset parameter value, abnormal data, and data corresponding to the time when the device is not turned on and the time when the device is switched cannot be used for modeling, the data screening logic according to the embodiment of the present invention deletes the data from the historical data set.
According to the data screening method of the refrigeration system strategy model, the data screening logic suitable for the refrigeration system is established, the data set meeting the modeling requirement is obtained by preprocessing and screening the historical data set of the refrigeration system in the preset time period, the modeling is carried out based on the screened data, and the accuracy of the model is greatly improved.
Specifically, the plurality of parameters of the refrigeration system include: the system comprises a refrigeration capacity load rate, a refrigeration capacity, a rated refrigeration capacity, a heat dissipation capacity load rate, a cooling tower fan frequency average value, a chilled water supply temperature, a chilled water return temperature, a chilled water flow rate, an outdoor wet-bulb temperature, the number of opened cooling towers, the number of opened cooling pumps, the number of opened coolers, the energy consumption of cooling towers, the energy consumption of freezing pumps, a current load rate, the total energy consumption of the cold towers of the coolers, the number of opened large machines and the number of opened small machines.
In an optional implementation manner of the embodiment of the present invention, the deleting abnormal data in the historical data set includes:
deleting data of the historical data set, wherein the energy consumption of the cooling tower is greater than a first preset threshold value;
deleting data of which the heat consumption is greater than a second preset threshold value in the historical data set;
deleting the data of which the mean value of the fan frequency of the cooling tower is greater than a third preset threshold value in the historical data set;
deleting the data of which the refrigerating capacity load rate is greater than a fourth preset threshold value in the historical data set;
and deleting the data of which the supply water temperature of the frozen water is greater than a fifth preset threshold value in the historical data set.
In practical application, the first preset threshold is 100, the second preset threshold is 600, the third preset threshold is 55, the fourth preset threshold is 1.05, and the fifth preset threshold is 13. The preset threshold value can be adjusted according to actual needs.
In addition, if the supply water temperature of the chilled water is higher than the return water temperature of the chilled water, the supply water temperature is also abnormal data, and the supply water temperature needs to be deleted from the historical data set. Specifically, the deleting data corresponding to the device unopened time and the device switching time in the history data set includes:
when the number of the opened cooling towers, the number of the opened refrigerating pumps and the number of the opened cold machines are all larger than 0, starting equipment, and deleting data corresponding to the moment when the equipment is not started in the historical data set;
and deleting data corresponding to preset time before and after equipment switching in the historical data set. Specifically, data corresponding to 3 points after the start of the refrigerator, the refrigerating pump, the plus-minus refrigerator, the refrigerating pump and the shut-down refrigerator and the refrigerating pump and 1 point before the start of the refrigerator and the refrigerating pump are deleted.
Further, the method further comprises: and deleting the number of cards in the historical data set according to the energy consumption of the refrigerator, the energy consumption of the cooling tower, the flow rate of the chilled water, the return water temperature of the chilled water and the outdoor wet bulb temperature.
It can be understood that the energy consumption, the flow and the temperature should be dynamically changed, and if the energy consumption of the refrigerator, the energy consumption of the cooling tower, the flow of the chilled water, the return water temperature of the chilled water and the outdoor wet bulb temperature are constant, namely the parameters are equal to the card number in the historical data set, the card number is deleted from the historical data set.
In practical application, deleting data corresponding to the moment when the energy consumption of the cold machine, the energy consumption of the cooling tower and the flow of the chilled water are completely the same; deleting data of the return water temperature of the chilled water with continuous calorie number exceeding 4 points; and deleting data of the outdoor wet bulb temperature continuous card number exceeding 9 points.
Under the condition that the refrigerating system operates in a steady state, the difference between the return water temperature of the chilled water and the water supply temperature of the chilled water is greater than 1, the refrigerating capacity is greater than 400, the flow rate of the chilled water is greater than 100, and data that the difference between the return water temperature of the chilled water and the water supply temperature of the chilled water, the refrigerating capacity and the flow rate of the chilled water do not accord with the conditions are deleted from the historical data set.
The temperature of the freezing water is under the working condition of cooling: chilled water main supply temperature >6 and <16
The cooling side state has stabilized: the temperature difference between the water supply and the water return of the cooling header pipe is more than 1; cooling water manifold flow > 100. Under the condition of steady-state operation of the refrigerating system, the load factor (the total refrigerating capacity of the current refrigerator/the total rated refrigerating capacity of all started refrigerators) is more than 0.3 and less than 1.05; the current load rate (total power of the current cooler/total rated power of all started coolers) is more than 0.3 and less than 1.05, and data of which the load rate and the current load rate do not meet the conditions are deleted from the historical data set.
Further, the method further comprises: and deleting data which are not synchronous among different parameters in the historical data set.
In practical application, COP is the ratio of refrigerating capacity to power consumption, and under the condition of normal operation of the refrigerating system, 2 < COP < 15, if COP < 2 or COP > 15, corresponding data are deleted from the historical data set.
Under normal operation of the refrigeration system, the energy consumption of the refrigerating pump is proportional to the flow of the chilled water. And if the energy consumption of the refrigerating pump is less than 5 and the flow rate of the refrigerating water is more than 100, the energy consumption of the refrigerating pump and the flow rate are asynchronous, and corresponding data are deleted from the historical data set.
Before modeling, the refrigerating capacity can be smoothed; and equally dividing the frequency data, and solving the median of the power corresponding to the frequency in the interval to be used as a training set.
Further, after the filtering the historical data set, the method further comprises: and carrying out modeling feasibility analysis according to the data quantity and the data interval distribution.
And if the data volume in the historical data set is greater than 1000 and the data interval distribution is wide enough, such as the frequency clustering number is greater than 2 after data screening, the modeling feasibility is considered.
After the model is built based on the screened data, the accuracy of the model can be evaluated according to the measured value and the predicted value, and the evaluation index can be the Root Mean Square Error (RMSE) of the measured value and the predicted value. If the model is the XGboost algorithm, cross validation can be adopted, and the average value of the model evaluation indexes is calculated according to the model evaluation indexes R2 of the measured value and the predicted value.
In practical applications, the screening can be performed on the historical data set of the refrigeration system by using the screening order and the screening conditions shown in the following table.
Fig. 2 is a schematic structural diagram of a data screening apparatus of a refrigerant system policy model according to an embodiment of the present invention. As shown in fig. 2, the apparatus includes:
a historical data set obtaining unit 21, configured to obtain a historical data set of the refrigeration system in a preset time period, where the historical data set includes multiple parameters of the refrigeration system corresponding to different times;
a defective data deleting unit 22, configured to delete data corresponding to a moment when a preset parameter value is absent from the historical data set;
an abnormal data deleting unit 23 configured to delete abnormal data in the historical data set;
and the device unsteady state operation data deleting unit 24 is configured to delete data corresponding to the device unopened time and the device switching time in the historical data set.
The data screening device of the strategy model of the refrigeration system, provided by the embodiment of the invention, establishes data screening logic suitable for the refrigeration system, obtains a data set meeting the modeling requirement by preprocessing and screening the historical data set of the refrigeration system in a preset time period, and performs modeling based on the screened data, thereby greatly improving the accuracy of the model.
Specifically, the plurality of parameters of the refrigeration system include: the system comprises a refrigeration capacity load rate, a refrigeration capacity, a rated refrigeration capacity, a heat dissipation capacity load rate, a cooling tower fan frequency average value, a chilled water supply temperature, a chilled water return temperature, a chilled water flow rate, an outdoor wet-bulb temperature, the number of opened cooling towers, the number of opened cooling pumps, the number of opened coolers, the energy consumption of cooling towers, the energy consumption of freezing pumps, a current load rate, the total energy consumption of the cold towers of the coolers, the number of opened large machines and the number of opened small machines.
Optionally, the abnormal data deleting unit 23 is further configured to:
deleting data of the historical data set, wherein the energy consumption of the cooling tower is greater than a first preset threshold value;
deleting data of which the heat consumption is greater than a second preset threshold value in the historical data set;
deleting the data of which the mean value of the fan frequency of the cooling tower is greater than a third preset threshold value in the historical data set;
deleting the data of which the refrigerating capacity load rate is greater than a fourth preset threshold value in the historical data set;
and deleting the data of which the supply water temperature of the frozen water is greater than a fifth preset threshold value in the historical data set.
Optionally, the device unsteady-state operation data deletion unit is further configured to:
when the number of the opened cooling towers, the number of the opened refrigerating pumps and the number of the opened cold machines are all larger than 0, starting equipment, and deleting data corresponding to the moment when the equipment is not started in the historical data set;
and deleting data corresponding to preset time before and after equipment switching in the historical data set.
Optionally, the apparatus further comprises: and the card number deleting unit is used for deleting the card number in the historical data set according to the energy consumption of the refrigerator, the energy consumption of the cooling tower, the flow rate of the freezing water, the return water temperature of the freezing water and the outdoor wet bulb temperature.
Optionally, the apparatus further comprises: and the asynchronous data deleting unit is used for deleting asynchronous data among different parameters in the historical data set.
Optionally, the apparatus further includes a feasibility analysis unit, configured to perform modeling feasibility analysis according to data amount and data interval distribution after the historical data set is screened.
It should be noted that the data filtering apparatus of the refrigerant system policy model in the above embodiments can be respectively used for executing the methods in the foregoing embodiments, and therefore, the detailed description thereof is omitted.
In conclusion, the data screening logic suitable for the refrigeration system is established, the data set meeting the modeling requirement is obtained by preprocessing and screening the historical data set of the refrigeration system in the preset time period, the modeling is carried out based on the screened data, and the accuracy of the model is greatly improved.
As will be appreciated by one skilled in the art, embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
It should be noted that:
the algorithms and displays presented herein are not inherently related to any particular computer, virtual machine, or other apparatus. Various general purpose devices may be used with the teachings herein. The required structure for constructing such a device will be apparent from the description above. Moreover, the present invention is not directed to any particular programming language. It is appreciated that a variety of programming languages may be used to implement the teachings of the present invention as described herein, and any descriptions of specific languages are provided above to disclose the best mode of the invention.
In the description provided herein, numerous specific details are set forth. It is understood, however, that embodiments of the invention may be practiced without these specific details. In some instances, well-known methods, structures and techniques have not been shown in detail in order not to obscure an understanding of this description.
Similarly, it should be appreciated that in the foregoing description of exemplary embodiments of the invention, various features of the invention are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the invention and aiding in the understanding of one or more of the various inventive aspects. However, the disclosed method should not be interpreted as reflecting an intention that: that the invention as claimed requires more features than are expressly recited in each claim. Rather, as the following claims reflect, inventive aspects lie in less than all features of a single foregoing disclosed embodiment. Thus, the claims following the detailed description are hereby expressly incorporated into this detailed description, with each claim standing on its own as a separate embodiment of this invention.
Those skilled in the art will appreciate that the modules in the device in an embodiment may be adaptively changed and disposed in one or more devices different from the embodiment. The modules or units or components of the embodiments may be combined into one module or unit or component, and furthermore they may be divided into a plurality of sub-modules or sub-units or sub-components. All of the features disclosed in this specification (including any accompanying claims, abstract and drawings), and all of the processes or elements of any method or apparatus so disclosed, may be combined in any combination, except combinations where at least some of such features and/or processes or elements are mutually exclusive. Each feature disclosed in this specification (including any accompanying claims, abstract and drawings) may be replaced by alternative features serving the same, equivalent or similar purpose, unless expressly stated otherwise.
Furthermore, those skilled in the art will appreciate that while some embodiments described herein include some features included in other embodiments, rather than other features, combinations of features of different embodiments are meant to be within the scope of the invention and form different embodiments. For example, in the following claims, any of the claimed embodiments may be used in any combination.
The various component embodiments of the invention may be implemented in hardware, or in software modules running on one or more processors, or in a combination thereof. It will be appreciated by those skilled in the art that a microprocessor or Digital Signal Processor (DSP) may be used in practice to implement some or all of the functions of some or all of the components of the apparatus for detecting a wearing state of an electronic device according to embodiments of the present invention. The present invention may also be embodied as apparatus or device programs (e.g., computer programs and computer program products) for performing a portion or all of the methods described herein. Such programs implementing the present invention may be stored on computer-readable media or may be in the form of one or more signals. Such a signal may be downloaded from an internet website or provided on a carrier signal or in any other form.
For example, fig. 3 shows a schematic structural diagram of an electronic device according to an embodiment of the invention. The electronic device conventionally comprises a processor 31 and a memory 32 arranged to store computer-executable instructions (program code). The memory 32 may be an electronic memory such as a flash memory, an EEPROM (electrically erasable programmable read only memory), an EPROM, a hard disk, or a ROM. The memory 32 has a storage space 33 storing program code 34 for performing the method steps shown in fig. 1 and in any of the embodiments. For example, the storage space 33 for storing the program code may comprise respective program codes 34 for implementing the various steps in the above method, respectively. The program code can be read from or written to one or more computer program products. These computer program products comprise a program code carrier such as a hard disk, a Compact Disc (CD), a memory card or a floppy disk. Such a computer program product is typically a computer readable storage medium such as described in fig. 4. The computer readable storage medium may have memory segments, memory spaces, etc. arranged similarly to the memory 32 in the electronic device of fig. 3. The program code may be compressed, for example, in a suitable form. In general, the memory space stores program code 41 for performing the steps of the method according to the invention, i.e. there may be program code, such as read by the processor 31, which, when run by the electronic device, causes the electronic device to perform the steps of the method described above.
While the foregoing is directed to embodiments of the present invention, other modifications and variations of the present invention may be devised by those skilled in the art in light of the above teachings. It should be understood by those skilled in the art that the foregoing detailed description is for the purpose of better explaining the present invention, and the scope of the present invention should be determined by the scope of the appended claims.
Claims (10)
1. A data screening method of a refrigerating system strategy model is characterized by comprising the following steps:
acquiring a historical data set of a refrigeration system in a preset time period, wherein the historical data set comprises a plurality of parameters of the refrigeration system corresponding to different moments;
deleting data corresponding to the moment when the preset parameter value is absent from the historical data set;
deleting abnormal data in the historical data set;
and deleting data corresponding to the equipment unopened time and the equipment switching time in the historical data set.
2. The method of claim 1, wherein the plurality of parameters of the refrigeration system comprise: the system comprises a refrigeration capacity load rate, a refrigeration capacity, a rated refrigeration capacity, a heat dissipation capacity load rate, a cooling tower fan frequency average value, a chilled water supply temperature, a chilled water return temperature, a chilled water flow rate, an outdoor wet-bulb temperature, the number of opened cooling towers, the number of opened cooling pumps, the number of opened coolers, the energy consumption of cooling towers, the energy consumption of freezing pumps, a current load rate, the total energy consumption of the cold towers of the coolers, the number of opened large machines and the number of opened small machines.
3. The method of claim 2, wherein the deleting anomalous data in the historical data set comprises:
deleting data of the historical data set, wherein the energy consumption of the cooling tower is greater than a first preset threshold value;
deleting data of which the heat consumption is greater than a second preset threshold value in the historical data set;
deleting the data of which the mean value of the fan frequency of the cooling tower is greater than a third preset threshold value in the historical data set;
deleting the data of which the refrigerating capacity load rate is greater than a fourth preset threshold value in the historical data set;
and deleting the data of which the supply water temperature of the frozen water is greater than a fifth preset threshold value in the historical data set.
4. The method according to claim 2, wherein the deleting data corresponding to the device unopened time and the device switching time in the historical data set comprises:
when the number of the opened cooling towers, the number of the opened refrigerating pumps and the number of the opened cold machines are all larger than 0, starting equipment, and deleting data corresponding to the moment when the equipment is not started in the historical data set;
and deleting data corresponding to preset time before and after equipment switching in the historical data set.
5. The method of claim 2, further comprising: and deleting the number of cards in the historical data set according to the energy consumption of the refrigerator, the energy consumption of the cooling tower, the flow rate of the chilled water, the return water temperature of the chilled water and the outdoor wet bulb temperature.
6. The method of claim 1, further comprising: and deleting data which are not synchronous among different parameters in the historical data set.
7. The method of claim 1, wherein after filtering the historical data set, the method further comprises: and carrying out modeling feasibility analysis according to the data quantity and the data interval distribution.
8. A data screening apparatus for a policy model of a refrigeration system, comprising:
the historical data set acquisition unit is used for acquiring a historical data set of the refrigeration system in a preset time period, and the historical data set comprises a plurality of parameters of the refrigeration system corresponding to different moments;
the defective data deleting unit is used for deleting data corresponding to the moment when the preset parameter value is lacked in the historical data set;
the abnormal data deleting unit is used for deleting the abnormal data in the historical data set;
and the device unsteady state operation data deleting unit is used for deleting the data corresponding to the device unopened time and the device switching time in the historical data set.
9. An electronic device, comprising:
a processor; and the number of the first and second groups,
a memory arranged to store computer executable instructions that, when executed, cause the processor to perform the method of any one of claims 1-7.
10. A computer readable storage medium, characterized in that the computer readable storage medium stores one or more programs which, when executed by a processor, implement the method of any of claims 1-7.
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