Refrigerating capacity prediction method and control system for centralized cooling system
阅读说明：本技术 用于集中供冷系统的制冷量预测方法及控制系统 (Refrigerating capacity prediction method and control system for centralized cooling system ) 是由 滕林 古林平 朱清华 杨日贵 于 2021-09-09 设计创作，主要内容包括：本发明公开一种用于集中供冷系统的制冷量预测方法及控制系统。用于集中供冷系统的制冷量预测方法,包括以下步骤：构建周系数表,周系数记为M；构建变温系数,变温系数记为N；获取昨日售冷量,昨日售冷量记为H-(s)；计算预制冷量H-(b)。控制系统应用上述制冷量预测方法。本发明通过构建周系数表、变温系数,从而获得预测制冷量计算公式的模型架构。将预测结果运用集中供冷系统中,能够制定合理的制冷方案,优化调整能量供应量,进一步提高了集中供冷系统运行的稳定性和能源的利用效率；实现节能、高效的系统制冷方案；能够提高系统效率,实现节能减排。(The invention discloses a refrigerating capacity prediction method and a control system for a centralized cooling system. The refrigerating capacity prediction method for the concentrated cooling system comprises the following steps: constructing a week coefficient table, and recording the week coefficient as M; constructing a temperature change coefficient, and recording the temperature change coefficient as N; acquiring yesterday selling cold quantity, and recording the yesterday selling cold quantity as H s (ii) a Calculating prefabricated cold quantity H b . The control system applies the refrigerating capacity prediction method. According to the invention, the model framework of the calculation formula for predicting the refrigerating capacity is obtained by constructing the cycle coefficient table and the temperature change coefficient. The prediction result is applied to the centralized cooling system, a reasonable refrigeration scheme can be formulated, the energy supply amount is optimized and adjusted, and the centralized cooling system is further improvedThe operation stability and the energy utilization efficiency; an energy-saving and efficient system refrigeration scheme is realized; the system efficiency can be improved, and energy conservation and emission reduction are realized.)
1. The method for predicting the refrigerating capacity of the centralized cooling system is characterized by comprising the following steps of:
constructing a week coefficient table, and recording the week coefficient as M;
constructing a temperature change coefficient, and recording the temperature change coefficient as N;
acquiring yesterday selling cold quantity, and recording the yesterday selling cold quantity as Hs；
Calculating prefabricated cold quantity HbWherein, in the step (A),
formula (1): hb＝Hs·M·N
Obtaining the prefabricated cold quantity H through the formula (1)b。
2. The cooling capacity prediction method for a concentrated cooling system as claimed in claim 1, further comprising: constructing a cold loss comparison table, and recording the cold loss as Hl(ii) a The prefabricated cold quantity H is calculatedbIn the step (a), the step (b),
formula (1): hb＝Hs·M·N+Hl
Obtaining the prefabricated cold quantity H by the formulab。
3. The cooling capacity prediction method for a concentrated cooling system as claimed in claim 2, further comprising: acquiring the residual cold capacity of yesterday, and recording the residual cold capacity of yesterday as Hr(ii) a The prefabricated cold quantity H is calculatedbIn the step (a), the step (b),
formula (1): hb＝Hs·M·N+Hl-Hr
Obtaining the prefabricated cold quantity H by the formulab。
4. The refrigeration capacity prediction method for a concentrated cooling system according to claim 3, wherein the step of obtaining yesterday's remaining refrigeration capacity is:
get yesteryThe daily refrigerating capacity and yesterday refrigerating capacity are recorded as Hb2；
Acquiring yesterday cold loss according to the cold loss comparison table, wherein yesterday cold loss is recorded as Hl2；
Formula (2): hr＝Hb2-Hs-Hl2
Obtaining yesterday residual cold quantity H through the formula (2)r。
5. The refrigeration capacity prediction method for a concentrated cooling system according to claim 4, wherein the step of obtaining the yesterday residual refrigeration capacity comprises:
the safety number is also set and marked as S;
formula (2): hr＝Hb2-Hs-Hl2-S
Obtaining yesterday residual cold quantity H through the formula (2)r。
6. The refrigeration capacity prediction method for a concentrated cooling system according to claim 5, wherein the step of obtaining the yesterday residual refrigeration capacity comprises: when H is presentrWhen pi 0, default yesterday remains 0.
7. The refrigerating capacity prediction method for a concentrated cooling system according to any one of claims 1 to 6, wherein in the step of constructing the temperature change coefficient,
measuring temperature difference T, wherein T is the difference value between the environment temperature of yesterday and the environment temperature of today,
formula (3):
and (4) obtaining the temperature change coefficient N according to the formula (3).
8. The cooling capacity prediction method for a concentrated cooling system according to any one of claims 1 to 6, wherein in the step of constructing the week coefficient table,
the cycle coefficient M is respectively M0-0.7403, M1-1.8604, M2-0.9981, M3-1.0163, M4-1.0028, M5-1.0164 and M6-0.8299 which are cyclically changed in one cycle.
9. The refrigerating capacity prediction method for a concentrated cooling system according to claim 8, wherein the prefabricated refrigerating capacity H is obtained by the formula (1)bIn the above description, the first day-to-week coefficient M on a specific day is M6-0.8299, and the first day-to-week coefficient M on a specific day is M1-1.8604.
10. The control system, characterized by comprising the refrigerating capacity prediction method for a concentrated cooling system according to any one of claims 1 to 9, further comprising,
a storage module configured to store data;
a measurement module configured to measure a temperature;
the computing module is configured to perform computing processing on the data;
and the control module is configured to control the centralized cooling system.
Technical Field
The invention relates to the technical field of cold quantity supply control, in particular to a refrigerating capacity prediction method for a centralized cold supply system and a control system.
Background
With the development of cities, the increase of high-grade communities, office buildings and urban complexes, more and more centralized cooling systems such as central air conditioners are provided, the market is larger and larger, and the demand of multiple energy sources is increased. The cooling energy consumption accounts for 60% of the building energy consumption, so the energy-saving management of the centralized cooling system is particularly important. Currently, the general main control method of the centralized cooling system is as follows: the energy supply end cooling unit operates according to the fixed water outlet temperature, calculates the temperature difference value according to the return water temperature and the water temperature, loads or unloads the supply quantity according to the temperature difference value, and controls the supply quantity of the refrigerant medium. For example, in a refrigeration mode, the temperature difference is large, which indicates that the indoor temperature is high, the system load is large, and the supply amount should be increased by loading; otherwise, if the temperature difference is small, the indoor temperature is low, the system load is small, and the supply amount should be reduced.
The existing solutions for common energy supply end control systems (such as "central air conditioning automatic control system design" of hokkaido, and "central air conditioning energy saving and automatic control system design" of zhao literature), generally use programmable controller PLC or DDC as the core control system, the control mode includes that the system adopts closed-loop control, unit start/stop control, unit protection control, water supply pump control, unit operation mode control, circulating water pump control, cooling tower control, corresponding system interlock control, water supply and return pressure difference control, system interlock control, etc., and the control center of gravity is limited to the energy consumption supply end only. The energy output control mode of the energy supply end is as follows: the internal operation control of the cooling unit is automatically completed by the inside of the unit, and the external control system only controls the start and stop of the unit.
The control method has various defects, such as that environmental factors, human factors and the like are not considered, so that a larger difference value is generated between the predicted refrigerating capacity and the actual refrigerating capacity sold in the day, excessive refrigerating capacity is remained, and even the situation of short supply and short demand is generated.
Disclosure of Invention
According to one aspect of the present invention, there is provided a cooling capacity prediction method for a concentrated cooling system, comprising the steps of:
constructing a week coefficient table, and recording the week coefficient as M;
constructing a temperature change coefficient, and recording the temperature change coefficient as N;
acquiring yesterday selling cold quantity, and recording the yesterday selling cold quantity as Hs；
Calculating prefabricated cold quantity HbWherein, in the step (A),
formula (1): hb＝Hs·M·N
Obtaining the prefabricated cold quantity H through the formula (1)b。
The invention provides a method for predicting the refrigerating capacity of a centralized cooling system. In the invention, a model framework of a calculation formula is obtained by constructing a cycle coefficient table and a temperature change coefficient, and the prefabricated refrigeration capacity H is calculated by the formula (1)bAnd calculating to obtain the cold quantity to be manufactured by the system, so that the system can reasonably distribute the refrigerating time, avoid the refrigerating capacity being greater than the output quantity, save energy and reduce the operating cost of enterprises.
In some embodiments, the method for predicting cooling capacity of a centralized cooling system further comprises: constructing a cold loss comparison table, and recording the cold loss as Hl(ii) a Calculating prefabricated cold quantity HbIn the step, formula (1) is corrected, formula (1): hb＝Hs·M·N+Hl
Obtaining the prefabricated cold quantity H by the formulab。
Therefore, the cold energy loss generated during the cold energy transmission is reduced; in the method, a cold loss comparison table is also constructed according to the loss, the prediction method is modified, and the condition that the refrigerating capacity is less than the output capacity is avoided.
In some embodiments, the cooling capacity for a concentrated cooling systemThe prediction method further comprises the following steps: acquiring the residual cold capacity of yesterday, and recording the residual cold capacity of yesterday as Hr(ii) a Calculating prefabricated cold quantity HbIn the step, the formula (1) is corrected,
formula (1): hb＝Hs·M·N+Hl-Hr
Obtaining the prefabricated cold quantity H by the formulab。
Therefore, the method also enables the residual cooling capacity H of yesterdayrAnd the method is incorporated into a prediction method, so that the refrigeration prediction result is more accurate, and excessive surplus is avoided.
In some embodiments, in the step of obtaining the yesterday remaining cold:
obtaining yesterday refrigerating capacity which is recorded as Hb2；
Acquiring yesterday cold loss according to the cold loss comparison table, wherein yesterday cold loss is recorded as Hl2；
Formula (2): hr＝Hb2-Hs-Hl2
Obtaining the yesterday residual cold quantity H by the formula (2)r。
Therefore, the residual cold is obtained through the formula (2), and the accuracy of the prefabricated cold is ensured.
In some embodiments, in the step of obtaining the yesterday remaining cold:
the safety number is also set and marked as S; the formula (2) is corrected,
formula (2): hr＝Hb2-Hs-Hl2-S
Obtaining the yesterday residual cold quantity H by the formula (2)r。
Therefore, in order to avoid errors in calculation of the residual cold quantity in the formula, the insurance number S is arranged in the formula (2), and the condition that the prefabricated cold quantity obtained through the formula (1) is insufficient is avoided.
In some embodiments, in the step of obtaining the yesterday remaining cold: when H is presentrWhen pi 0, default yesterday remains 0.
Thus, when the remaining cold quantity H obtained by the formula (2)rWhen pi 0, the remaining cold capacity is defaultedIs 0, the situation that the prefabricated cold quantity obtained by the formula (1) is insufficient is avoided.
In some embodiments, in the step of constructing the temperature change coefficient,
measuring temperature difference T, wherein T is the difference value between the environment temperature of yesterday and the environment temperature of today,
formula (3):
and (4) constructing a temperature change coefficient N according to the formula (3).
Therefore, the temperature change coefficient N is constructed by the formula and needs to be substituted into the temperature difference T for operation.
In some embodiments, in the step of constructing the week coefficient table,
the cycle coefficient M is respectively M0-0.7403, M1-1.8604, M2-0.9981, M3-1.0163, M4-1.0028, M5-1.0164 and M6-0.8299 which are cyclically changed in one cycle.
Thus, the week coefficient is constructed with the working day of one week as the cycle, and M0 is 0.7403 for the week day, and so on.
In some embodiments, the prefabricated cold quantity H is obtained by formula (1)bIn the above description, the first day-to-week coefficient M on a specific day is M6-0.8299, and the first day-to-week coefficient M on a specific day is M1-1.8604.
Thus, a particular date, i.e., a holiday. On the first day of the holiday, the coefficient M is M6-0.8299, and the other M is MtWhen the first day cycle coefficient M after a specific day is equal to 1, M1 is equal to 1.8604.
According to an aspect of the present invention, there is provided a control system applying the above-mentioned refrigerating capacity prediction method for a concentrated cooling system, including,
a storage module configured to store data;
a measurement module configured to measure a temperature;
the computing module is configured to perform computing processing on the data;
and the control module is configured to control the centralized cooling system.
In the control system applying the prediction method, the storage module can store data, the calculation module calculates and processes the data so as to obtain the predicted refrigerating capacity, and the control module reasonably distributes the refrigerating time and the refrigerating plan of the centralized cold supply system according to the predicted refrigerating capacity.
The invention has the following beneficial effects: according to the invention, the model framework of the calculation formula for predicting the refrigerating capacity is obtained by constructing the cycle coefficient table and the temperature change coefficient. The prediction result is applied to the centralized cooling system, a reasonable refrigeration scheme can be formulated, the energy supply amount is optimized and adjusted, and the operation stability of the centralized cooling system and the utilization efficiency of energy are further improved; an energy-saving and efficient system refrigeration scheme is realized; the system efficiency can be improved, and energy conservation and emission reduction are realized.
Drawings
Fig. 1 is a schematic structural diagram of a concentrated cooling system according to an embodiment of the present invention.
Fig. 2 is a schematic structural diagram of a control system according to an embodiment of the present invention.
Fig. 3 is a schematic structural diagram of an automatic control method according to an embodiment of the present invention.
Detailed Description
The present invention will be described in further detail with reference to the accompanying drawings.
Fig. 1 schematically shows a relationship structure between a concentrated cooling system and a user.
With reference to fig. 3, the refrigeration prediction method applied to the centralized cooling system provided by the present invention includes the following steps:
constructing a week coefficient table, and recording the week coefficient as M;
constructing a temperature change coefficient, and recording the temperature change coefficient as N;
acquiring yesterday selling cold quantity, and recording the yesterday selling cold quantity as Hs；
Calculating prefabricated cold quantity HbWherein, in the step (A),
formula (1): hb＝Hs·M·N
Obtaining the prefabricated cold quantity H through the formula (1)b。
The invention provides a method for predicting the refrigerating capacity of a centralized cooling system. In the invention, a model framework of a calculation formula is obtained by constructing a cycle coefficient table and a temperature change coefficient, and the prefabricated refrigeration capacity H is calculated by the formula (1)bAnd calculating to obtain the cold quantity to be manufactured by the system, so that the system can reasonably distribute the refrigerating time, avoid the refrigerating capacity being greater than the output quantity, save energy and reduce the operating cost of enterprises.
The refrigerating capacity prediction method for the concentrated cooling system further comprises the following steps: constructing a cold loss comparison table, and recording the cold loss as Hl(ii) a Calculating prefabricated cold quantity HbIn the step (a), the step (b),
formula (1): hb＝Hs·M·N+Hl
Obtaining the prefabricated cold quantity H by the formulab. The cold energy loss generated during the cold energy transmission is reduced; in the method, a cold loss comparison table is also constructed according to the loss, the prediction method is modified, and the condition that the refrigerating capacity is less than the output capacity is avoided.
The actual refrigerating capacity and the actual refrigerating capacity sold every day are led out from a historical database, and the refrigerating loss is recorded as HlThe actual refrigerating capacity-the actual refrigerating capacity sold. Therefore, a cold loss amount comparison table as shown in table 1 was constructed. In application, the data of the reference table of the accessory 2 is integrated into an automatic control system module, and the system can output the cold loss according to the predicted cold sale and temperature conditions.
Table 1: the data are shown above when T is greater than 28 ℃ and the data below when T is less than or equal to 28 ℃. (column zone temperature difference when T is 23: 30)
When the cold storage warehouse still has no cold sold in yesterday, the method for predicting the refrigerating capacity of the concentrated cold supply system further comprises the following steps: obtaining yesterday residual coldVolume, yesterday residual cold volume is recorded as Hr(ii) a Calculating prefabricated cold quantity HbIn the step, the formula (1) is corrected,
formula (1): hb＝Hs·M·N+Hl-Hr
Obtaining the prefabricated cold quantity H by the formulab. The method also compares the residual cooling capacity H of yesterdayrAnd the method is incorporated into a prediction method, so that the refrigeration prediction result is more accurate, and excessive surplus is avoided.
Further, in the step of obtaining the residual cooling capacity of yesterday:
obtaining yesterday refrigerating capacity which is recorded as Hb2；
Acquiring yesterday cold loss according to the cold loss comparison table, wherein yesterday cold loss is recorded as Hl2；
Formula (2): hr＝Hb2-Hs-Hl2
Obtaining the yesterday residual cold quantity H by the formula (2)r. The residual cold quantity is obtained through the formula (2), and the accuracy of the prefabricated cold quantity is ensured.
Further, in the step of obtaining the residual cooling capacity of yesterday:
the safety number is also set and marked as S; the formula (2) is corrected,
formula (2): hr＝Hb2-Hs-Hl2-S
Obtaining the yesterday residual cold quantity H by the formula (2)r. The method comprises the following steps of obtaining yesterday residual cold quantity: when H is presentrWhen pi 0, default yesterday remains 0. When the residual cold quantity H obtained by the formula (2)rAnd when the temperature is pi 0, the default residual cold quantity is 0, so that the condition that the prefabricated cold quantity obtained by the formula (1) is insufficient is avoided.
Further, in the step of constructing the temperature variation coefficient, the temperature difference T is measured, wherein T is the difference value between the environment temperature of yesterday and the environment temperature of this day, T is the temperature difference of the tower area when T is 23:30,
formula (3):
and (4) constructing a temperature change coefficient N according to the formula (3). The temperature change coefficient N is constructed by the above formula, and needs to be substituted into the temperature difference T for operation, as shown in table 2.
Table 2: the temperature-changing coefficient (N) can be obtained by directly substituting T into the formula (3).
Further, in the step of constructing the cycle coefficient table, the cycle coefficients are cyclically changed according to a cycle of one week, and the cycle coefficients M are respectively M0-0.7403, M1-1.8604, M2-0.9981, M3-1.0163, M4-1.0028, M5-1.0164, and M6-0.8299. The week coefficient is constructed by taking the working day of one week as a period, and M0 is 0.7403 on the day of the week, and so on.
Further, the prefabricated cold quantity H is obtained through the formula (1)bIn the above description, the first day-to-week coefficient M on a specific day is M6-0.8299, and the first day-to-week coefficient M on a specific day is M1-1.8604. A particular date, i.e., a holiday. On the first day of the holiday, the coefficient M is M6-0.8299, and the other M is MtWhen the first day cycle coefficient M after a specific day is equal to 1, M1 is equal to 1.8604.
For example, the week coefficient is calculated as follows according to the normal working day:
day of the week: hb＝Hs·M·N+Hl-Hr(M is M0, M0 ═ 0.7403)
And B, Monday: hb＝Hs·M·N+Hl-Hr(M is M1, M1 ═ 1.8604)
And B, Tuesday: hb＝Hs·M·N+Hl-Hr(M is M2, M2 ═ 0.9981)
And D, three weeks: hb＝Hs·M·N+Hl-Hr(M is M3, M3 ═ 1.0163)
B, B: hb＝Hs·M·N+Hl-Hr(M is M4, M4 ═ 1.0028)
Friday: hb＝Hs·M·N+Hl-Hr(M is M5, M5 ═ 1.0164)
Saturday: hb＝Hs·M·N+Hl-Hr(M is M6, M6 is 0.8299).
If the national public holiday is met, if the holiday is three days, the public holiday (holiday day) and the first day after the public holiday are calculated as follows:
the first day of public holiday: hb＝Hs·M·N+Hl-Hr(M is M6, M6 ═ 0.8299)
B, second holiday: hb＝Hs·M·N+Hl-Hr(M is Mt, Mt＝1)
B, three days of public rest: hb＝Hs·M·N+Hl-Hr(M is Mt, Mt＝1)
After the section: hb＝Hs·M·N+Hl-Hr(M is M1, M1 is 1.8604).
With reference to fig. 3, a control system for applying the above method for predicting cooling capacity of a centralized cooling system includes:
a storage module configured to store data;
a measurement module configured to measure a temperature;
the computing module is configured to perform computing processing on the data;
and the control module is configured to control the centralized cooling system.
In the control system applying the prediction method, the storage module can store data, the calculation module calculates and processes the data so as to obtain the predicted refrigerating capacity, and the control module reasonably distributes the refrigerating time and the refrigerating plan of the centralized cold supply system according to the predicted refrigerating capacity.
By using prefabricated cold energy HbIn developing a refrigeration plan, the following controls are included, but are not limited to:
according to the prefabricated cold quantity HbThe opening number of the refrigerating unit is controlled, so that refrigeration is avoidedThe unit has large load; will prefabricate cold energy HbOn average to an on refrigeration unit. For example, the daily average refrigerating capacity load of each refrigerating unit is 10 kilo-watts (KWH), and the prefabricated refrigerating capacity is HbIf the refrigerating capacity is 58 ten thousand KWH, six refrigerating units are required to be started, and the refrigerating capacity of each refrigerating unit is 58/6 ten thousand KWH;
and controlling the time-interval frequency refrigeration of the refrigerating unit. For example, the temperature at night is low, the cooling tower has good heat dissipation effect, and the refrigerating frequency of the refrigerating unit distributed at night is high;
and a plurality of refrigerating units alternately work. For example, the concentrated cooling system is provided with seven groups of refrigeration units, respectively identified as A, B, C, D, E, F, G; the starting unit for Monday is A, B, C, D, E, F, the starting unit for Tuesday is B, C, D, E, F, G, the starting unit for Wednesday is C, D, E, F, G, A, and so on. If, the prefabricated cold quantity H of a certain daybIf the number of the started refrigerating units is less than the specific value, judging that the number of the started refrigerating units is less than six, and preferentially letting the refrigerating unit with the largest starting times in the sequence starting setting have a rest;
other control items such as a conveying channel and the like are also included.
The refrigerating plans of the refrigerating units can be reasonably distributed according to the prediction method, and reasonable refrigerating plans such as refrigerating time, the number of the units and the like are formulated. According to the prefabricated cold quantity HbThe value of the preset value is one of the most important links in the field of system refrigeration scheme formulation, and the preset refrigeration capacity H can be improved by the methodbThe accuracy of (2).
According to the invention, the model framework of the calculation formula for predicting the refrigerating capacity is obtained by constructing the cycle coefficient table and the temperature change coefficient. The prediction result is applied to the centralized cooling system, a reasonable refrigeration scheme can be formulated, the energy supply amount is optimized and adjusted, and the operation stability of the centralized cooling system and the utilization efficiency of energy are further improved; an energy-saving and efficient system refrigeration scheme is realized; the system efficiency can be improved, and energy conservation and emission reduction are realized.
What has been described above are merely some embodiments of the present invention. It will be apparent to those skilled in the art that various changes and modifications can be made without departing from the inventive concept thereof, and these changes and modifications can be made without departing from the spirit and scope of the invention.
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