CN104751233A - Contract capacity optimization system and optimization method - Google Patents
Contract capacity optimization system and optimization method Download PDFInfo
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- CN104751233A CN104751233A CN201310739512.5A CN201310739512A CN104751233A CN 104751233 A CN104751233 A CN 104751233A CN 201310739512 A CN201310739512 A CN 201310739512A CN 104751233 A CN104751233 A CN 104751233A
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Abstract
A contract capacity optimization system comprises a processing unit, an input unit, and a database. The processing unit reads historical data related to the electricity consumption of a building in the past from the database, and receives future planning data related to a future strategy of the building from the input unit. The processing unit predicts the maximum demand predicted value of the building among all time periods of all months of next year. Then, the processing unit receives a requirement of a user from the input unit, and works out an optimal contract capacity according with the requirement of the user based on the obtained maximum demand predicted value. Thus, the system can obtain a contract capacity which can meet the requirement of the user and minimize the total electric charge of the building for the building to facilitate contract signing between the user and a power company. The system of the invention is used for optimizing the contract capacity of next year.
Description
Technical field
The present invention relates to optimization system and optimization method, particularly relate to the optimization system and optimization method that calculate best contract capacity.
Background technology
Company now, factory, department store etc. have the building dealer of high need for electricity, usually all can with Utilities Electric Co.'s signing contract, require that wattage (namely instantaneously, maximum instant requirement) or total electricity consumption must not exceed some definite values, otherwise dealer needs to pay the extra punitive electricity charge (or being called the super about electricity charge), and this is so-called contract capacity.Therefore in the technical field of the invention, have part correlation technique and dealer can be assisted to calculate better, more rational contract capacity, sign the contract in next year in order to dealer and Utilities Electric Co..
Those correlation techniques are many to be calculated by means of only algorithm, analyzes its power consumption information, and then calculate a contract capacity of suggestion use in next year according to the buildings electricity consumption historical data of a year in the past.But, the reason of ringing shadow one buildings power consumption has a lot, such as occupancy number, outdoor temperature height etc., if why do not know the high/low reason of last year power consumption, and only come as analysis foundation with last power consumption information, the power consumption being difficult in fact to predict that next year is possible exactly and maximum instant requirement.Thus, certainly also contract capacity accurately cannot just be calculated.If dealer according to this not accurately contract capacity come with Utilities Electric Co.'s sign a contract, may not reach this contract capacity because of actual power consumption in next year and cause waste, or because power consumption exceedes this contract capacity need pay the considerable super about electricity charge too much.
Summary of the invention
Fundamental purpose of the present invention, be to provide a kind of contract capacity optimization system and optimization method, for can according to the history electricity consumption data of buildings and Future tactics, dope possible maximum demand predicted value in buildings next year, so as to the optimization contract capacity making system more adequately can calculate next year, in order to user and Utilities Electric Co.'s sign a contract.
Another fundamental purpose of the present invention, be to provide a kind of contract capacity optimization system and optimization method, for the demand of user can be received, so as to making system can under the prerequisite not violating user's demand, calculate the contract capacity that the electricity charge in buildings next year can be made minimum.
For reaching above-mentioned purpose, the invention discloses the contract capacity optimization system comprising processing unit, input block and database, and the optimization method that this optimization system uses.Wherein, processing unit reads the historical data relevant to the power consumption in buildings past by database, and receives the policy-related (noun) future plan data with buildings future by input block.Processing unit foundation historical data and future plan data, the maximum demand predicted value of the various periods in predict good each month in next year.Then, processing unit receives user's demand by input block again, and according to predicting the maximum demand predicted value drawn, calculates the optimization contract capacity meeting user's demand.
The present invention is the historical data and the Future tactics that use buildings simultaneously, first dopes possible maximum demand predicted value in next year, and calculates the contract capacity in next year according to maximum demand predicted value again.The present invention can improve prior art and only use historical data to estimate and the way calculating the contract capacity in next year, and the result drawn is problem not accurately.
Moreover the object of optimization contract capacity wishes to pass through planning, and the total electricity charge making to pay next year are minimum.But, total electricity charge have generally comprised basic charge as per installed capacity and the super about electricity charge, therefore at basic charge as per installed capacity under planning the pattern be greatly reduced, even if need to pay the super about electricity charge, its total electricity charge still may pay the super about electricity charge than not needing, but the very high pattern of basic charge as per installed capacity is come lower.Thus, although total electricity charge that need pay can be saved, gerentocratic perception may be caused not good because super about expense is too many, even may because the error on calculating, cause super moon number about significantly to increase, finally cause the phenomenon that the actual total electricity charge not anti-reflection increases.Be with the present invention when calculating the contract capacity in next year, also adopt user's demand simultaneously, by this can under the prerequisite meeting user's demand, for the contract capacity in next year carries out optimization.
Accompanying drawing explanation
Fig. 1 is the system block diagram of the first specific embodiment of the present invention.
Fig. 2 is the optimization configuration diagram of the first specific embodiment of the present invention.
Fig. 3 is the historical data schematic diagram of the first specific embodiment of the present invention.
Fig. 4 is the future plan schematic diagram data of the first specific embodiment of the present invention.
Fig. 5 is the optimization process flow diagram of the first specific embodiment of the present invention.
Fig. 6 is the optimization configuration diagram of the second specific embodiment of the present invention.
Wherein, description of reference numerals is as follows:
1 ... optimization system
2 ... processing unit
21 ... data prediction module
22 ... contract optimization module
3 ... database
31 ... historical data
31A ... first period historical data
31B ... second period historical data
31C ... 3rd period historical data
311 ... date
312 ... time
313 ... maximum demand value
314 ... people's logarithmic data
315 ... outdoor temperature data
316 ... equipment enables status data
317 ... other information
4 ... input block
5 ... output unit
6 ... future plan data
61 ... output increase and decrease planning
62 ... equipment is eliminated and is changed planning
63 ... number increase and decrease planning
64 ... other factors
7 ... maximum demand predicted value
71 ... first period maximum demand predicted value
72 ... second period maximum demand predicted value
73 ... 3rd period maximum demand predicted value
8 ... user's demand
9 ... optimization contract capacity
S10 ~ S24 ... optimization step
Embodiment
A now just preferred embodiment of the present invention, coordinates accompanying drawing, is described in detail as follows.
Head refers to Fig. 1 and Fig. 2, is respectively system block diagram and the optimization configuration diagram of the first specific embodiment of the present invention.The invention discloses a kind of contract capacity optimization system (in instructions in literary composition referred to as this system 1), this system 1 mainly comprises processing unit 2, database 3, input block 4 and an output unit 5, and wherein this processing unit 2 is electrically connected this database 3, this input block 4 and this output unit 5.
In the present embodiment, this system 1 is mainly arranged in a buildings (figure does not indicate), and can be an architectural resource management system (the Building Energy Management System of this buildings, BEMS), or the BEMS existing with this buildings integrates, and is not limited.
This buildings past historical data 31 relevant to power consumption is recorded in this database 3, in a preferred embodiment, the historical data 31 that this this buildings last year of database 3 essential record is relevant to power consumption; But in other embodiments, this database 3 also can this buildings of complete documentation passing multiple year the historical data 31 relevant to power consumption, but not to be limited.This processing unit 2 mainly can obtain this historical data 31 from this database 3, and receives that outside (user) inputs, relevant with the operation strategy in this buildings future data from this input block 4.By this, this processing unit 2 can dope the following possible electricity consumption maximum demand of this buildings, and then the optimization contract capacity of calculating, and is externally shown by this output unit 5 or exported.In a preferred embodiment, this processing unit receives the data relevant with the operation strategy in this buildings next year mainly through this input block 4, and dopes possible electricity consumption maximum demand in this buildings next year according to this, but is not limited.The present invention predicts the large requirement of this power consumption by this processing unit 2, and calculates this optimization contract capacity, and the user and the Utilities Electric Co. that contribute to this buildings sign contract (being mainly the contract in next year).
As shown in Figure 2, in the present embodiment, this processing unit 2 mainly comprises data prediction module 21 and a contract optimization module 22, and wherein this contract optimization module 22 connects this data prediction module 21.This data prediction module 21 can by obtaining this historical data 31 relevant to power consumption in this database 3, and this data prediction module 21 can be received future plan data 6 of this buildings of outside input by this input block.This data prediction module 21 according to this historical data 31 and this future plan data 6, can predict a maximum demand predicted value 7 in this buildings future.
It is worth mentioning that, in the present embodiment, the quantity of this maximum demand predicted value 7 and corresponding period, correspond to the quantity of this historical data 31 and corresponding period.Such as, if this historical data 31 comprises in the past one or all data for many years, then Different periods maximum demand predicted value 7 in each month in this data prediction module 21 measurable a year and a day in next year
This contract optimization module 22 can obtain it from this data prediction module 21 and predict this maximum demand predicted value 7 drawn, and calculates via algorithm, draws an optimization contract capacity 9.This optimization contract capacity 9 externally exports via this output unit 5, shows, and comes and Utilities Electric Co.'s sign a contract according to the content of this optimization contract capacity 9 in order to user.
In another embodiment, this contract optimization module 22 is by this input block 4, receive user's demand 8 of outside input, so as to when calculating this optimization contract capacity 9, get rid of one or more contract capacity not meeting this user's demand 8, and in one or more contract capacity meeting this user's demand 8, choose one or more contract capacity that the electricity charge are minimum, as this optimization contract capacity 9.
Specifically, whether this contract optimization module 22, when calculating this optimization contract capacity 9, will meet this user's demand 8 and be considered as the first important document, and the height of the electricity charge will be considered as the second important document.For example, even if the contract capacity A calculated can save the electricity charge more more than contract capacity B, if but contract capacity A does not meet this user's demand 8 and contract capacity B meets this user's demand 8, then contract capacity B can be considered as this optimization contract capacity 9 by this contract optimization module 22.In the present embodiment, this user's demand 8 is user's acceptable maximum super about moon number (holding detailed description), but is not limited.
In general, contain basic charge as per installed capacity and the super about electricity charge in total electricity charge, and only when maximum demand exceedes contract capacity, just need pay the super about electricity charge.In other words, even if the super about electricity charge need be paid, as long as but make basic charge as per installed capacity reduce, Zong then the electricity charge still likely reach minimum because of surpassing about.Therefore, although general contract capacity after optimization can reach minimum total electricity charge, but the reduction of its total electricity charge may be reached via compression basic charge as per installed capacity, and in order to reduce the basic charge as per installed capacity in certain several month, may deliberately allow other one or more months super about.In in the case, although total electricity charge reduce, because super moon number is about too much, the perception of user may be caused not good, or user is not good for the management and control of power consumption to make the 3rd people think.Therefore the invention provides user to set this user's demand 8 (that is, acceptable maximum the super about moon number), by this, this system 1 premised on this user's demand 8, can calculate this optimization contract capacity 9.
Consulting Fig. 3, is the historical data schematic diagram of the first specific embodiment of the present invention.As shown in Figure 3, in the present embodiment, this historical data 31 mainly can comprise the date 311, time 312, maximum demand value 313, people's logarithmic data 314, outdoor temperature data 315, equipment enables status data 316 and other information (such as humidity) 317 etc.In the present invention, this data prediction module 21 is mainly according to this maximum demand value 313 in this historical data 31, and these future plan data 6 of the person's of being combined input, dope this maximum demand predicted value 7 in this buildings next year.Wherein, the size of this maximum demand value 313 depends on the power consumption of this buildings, and power consumption depends on this buildings every data instantly (people's logarithmic data 314 described above, outdoor temperature data 315, equipment enable status data 316 and other information 317 etc.).Therefore in the present invention, simultaneously this system 1 records those data by this database 3, and this data prediction module 21 is when predicting this maximum demand predicted value 7, considers these data simultaneously.
For example, to maintain identical indoor temperature, then when the number in this buildings increases, air-conditioning equipment needs balance to increase to deal with number with maintenance indoor temperature, therefore the electricity charge will improve, and this maximum demand value 313 rises.Again such as, when the outdoor temperature of this buildings declines, because reducing indoor temperature by outer temperature degree, therefore the operational temperature of air-conditioning equipment can be reduced, or closed portion air-conditioning equipment, even can by air-conditioning equipment Close All, therefore the electricity charge will reduce, and this maximum demand value 313 declines.According to following table 1, the relation of those data more clearly can be found out.
Table 1
Example as shown in Table 1 above, because the outdoor temperature of August is higher, thus the power consumption of air-conditioning equipment is high compared with other months, and then cause the maximum demand value of this month compared with other months high.Separately, the electricity consumption numerical value of September and December is suitable, but during the September, the number in this buildings is more, and outdoor temperature is higher, and humidity is higher, thus also cause the maximum demand value of this month comparatively the December high.But the above is all only a preferred embodiments of the present invention, is not limited.
Person shown in table 1 is for maximum demand value 313 of each month.But, different countries and regions have different electricity charge account forms, such as at TaiWan, China, be also distinguished in each month the spike period (spike period in summer and non-spike period in summer can be comprised), spike period Saturday half, from various periods such as peak periods.Therefore, this maximum demand value 313 of many can be comprised in this historical data 31, and distinguish according to this date 311 and this time 312, by the various periods that these many maximum demand values 313 are corresponded to in each month respectively.In this embodiment, this data prediction module 21 can according to these many maximum demand values 313, dope this maximum demand predicted value 7 of many in conjunction with these future plan data 6, and these many maximum demand predicted values 7 correspond to the various periods in future (being generally next year) each month respectively.And, in this embodiment, this contract optimization module 22 can according to these many maximum demand predicted values 7, calculate many these optimization contract capacity 9 meeting this user's demand 8, and these many optimization contract capacity are applicable to the contract of Different periods respectively, to reach the target of minimum basic charge as per installed capacity.
For example, if have recorded in this historical data 31 each month in 5 years the spike period and from the peak period many these maximum demand values 313 (namely, have this maximum demand value 313 of 120), then this data prediction module 21 can dope the spike period in this buildings each month in next year and many these maximum demand predicted values 7 (that is, having this maximum demand predicted value 7 of 24) from the peak period according to this.Finally, this contract optimization module 22 can calculate this optimization contract capacity 9 that two meet this user's demand 8 altogether, and these two optimization contract capacity 9 are applicable to the contract of spike period and the contract from the peak period respectively.
Shown in above-described embodiment and table 1, these many maximum demand values 313 in this historical data 31 enable status data 316 and these other information 317 with this people's logarithmic data 314 when there is the date and time of these many maximum demand values 313, these outdoor temperature data 315, this equipment simultaneously.By this, this data prediction module 21, when predicting this maximum demand predicted value 7 of this buildings in month in next year certain period, can make in fact and predict that the data drawn are more accurate.
Consulting Fig. 4, is the future plan schematic diagram data of the first specific embodiment of the present invention.As shown in the figure, in the present embodiment, these future plan data 6 mainly can comprise output increase and decrease planning 61, one equipment and eliminate and change planning 62, number increase and decrease planning 63 and other factors 64 etc.This output increase and decrease planning 61 mainly may correspond to the capacity utilization to this buildings future.For example, if this buildings is factory, if then the output in next year increases/reduces, the capacity utilization in this buildings next year will improve/reduce, therefore, utilization rate (enabling status data 316 according to this equipment) present for equipment and following utilization rate (according to this output increase and decrease planning 61) can contrast by this data prediction module 21, and as one of parameter predicted
。
This equipment is eliminated and is changed planning 62 and mainly may correspond to number of devices to this buildings future and equipment effectiveness.For example, if this factory will buy more/will have scrapped many equipment next year, then the number of devices in this factory next year will increase/reduce, therefore, quantity (enabling status data 316 according to this equipment) present for equipment and following quantity (eliminate according to this equipment and change planning 62) can contrast by this data prediction module 21, and as one of parameter predicted.Moreover, if the equipment of low for multiple stage usefulness will be eliminated next year by this factory be changed to dynamical equipment, then the equipment effectiveness in this factory next year will improve, therefore, usefulness present for equipment and following usefulness can contrast by this data prediction module 21, and as one of parameter predicted
。
This number increase and decrease planning 63 mainly may correspond to the total number of persons to this buildings future.For example, if this factory will increase n people/minimizing m people next year, then the total number of persons in this factory next year will increase/reduce, therefore, total number of persons (according to this people's logarithmic data 314) present for equipment and following total number of persons (according to this number increase and decrease planning 63) can contrast by this data prediction module 21, and as one of parameter predicted.
These other factors 64 can be such as following environmental factor such as temperature, the humidity forecast, present environmental factor (according to these other information 317) and following environmental factor (according to these other factors 64) also can contrast by this data prediction module 21, and as one of parameter predicted.But the above is all only preferred embodiments of the present invention, is not limited.
In sum, in a preferred embodiments of the present invention, this data prediction module 21 mainly can according to these many maximum demand values 313, these many people's logarithmic datas 314, these many outdoor temperature data 315, these many equipment enable status data 316, these many other information 317, this output increase and decrease planning 61, these equipment are eliminated and changed planning 62, the increase and decrease of this number planning 63 and these other factors 64 etc. parameter, jointly dope one or many this maximum demand predicted value 7.And then this contract optimization module 22 according to this one or many maximum demand predicted value 7, calculates and meets this user's demand 8 again, and correspond to this optimization contract capacity 9 of one or more period.
It is worth mentioning that, the electricity charge account form in various countries or area is not identical, if a certain state or area adopt single price and do not distinguish the period, then the present invention calculates this optimization contract capacity 9 meeting this user's demand 8.Otherwise if multiple period (such as TaiWan, China divides into four periods) has been distinguished for electricity price in another state or area, then the present invention calculates and meets this user's demand 8, and correspond to four these optimization contract capacity 9 of these four periods.List the objective function that can represent one or many contract capacity optimization of the present invention below:
As shown in above-mentioned function, wherein, x
iit is the contract capacity of the i-th period; N for distinguish in the month time hop count (if such as do not distinguish the period, then n is 1; If point four periods, then n is 4); Yj is the basic charge as per installed capacity+super about expense in a jth month; M is the moon number for assessment optimization contract capacity; Z is total basic charge as per installed capacity+total super about expense of m month.
Fundamental purpose of the present invention, is to find one group
make
and the super about moon number≤C of period i
1, wherein c
ifor user's setting, i-th the period acceptable maximum super about moon number.
Consulting Fig. 5, is the optimization process flow diagram of the first specific embodiment of the present invention.Fig. 5 discloses contract capacity optimization method of the present invention.Realize method of the present invention, first in this database 3, obtain this historical data 31 (step S10) relevant to power consumption of this buildings etc. by this data prediction module 21.In the present embodiment, this historical data 31 mainly refers to the data that this buildings last year is relevant to power consumption etc., but is not limited.Meanwhile, this data prediction module 21 obtains these future plan data 6 (step S12) of this buildings again.Wherein, these future plan data 6 mainly can be inputted by this input block 4 by user, or are pre-stored within this database 3 via other modes, are not limited.Further, in the present embodiment, these future plan data 6 mainly refer to that next year estimated that the every operation carried out is tactful by this buildings, but are not limited.
Then, this data prediction module 21 according to this historical data 31 and this future plan data 6, can predict this maximum demand predicted value 7 of Different periods in this buildings each month in next year.More specifically, if contain this maximum demand value 313 of many in this historical data 31, then this processing unit 2 can according to this date 311 of this historical data 31 and this time 312, the various periods (step S14) of these many maximum demand values 313 being corresponded to over respectively in each month.By this, this data prediction module 21 can dope this maximum demand predicted value 7 of many, and wherein these many maximum demand predicted values 7 correspond to the various periods (step S16) in this buildings each month following respectively.
After this data prediction module 21 dopes one or many this maximum demand predicted value 7, send this one or many maximum demand predicted value 7 to this contract optimization module 22.So as to, this contract optimization module 22 after this user's demand 8 receiving outside input, according to this one or many maximum demand predicted value 7, can calculate and meet this user's demand 8 and this optimization contract capacity 9 of corresponding Different periods.
More specifically, this contract optimization module 22 first can judge whether this user's demand 8 (step S18) receiving outside input, if do not receive this user's demand 8, then this this more than one maximum demand predicted value 7 of contract optimization module 22 direct basis, calculates this optimization contract capacity 9 (step S20) of generation one or many.Otherwise if receive this user's demand 8, then this contract optimization module 22 is according to this one or many maximum demand predicted value 7, calculate one or many this optimization contract capacity 9 (step S22) producing and meet this user's demand 8.Finally, this system 1 is exported by this output unit 5 or shows this one or many optimization contract capacity 9 (step S24).
Consulting Fig. 6, is the optimization configuration diagram of the second specific embodiment of the present invention.As described in the text, this historical data 31 in this database 3, mainly can correspond to the various periods in each middle of the month in the past respectively, this data prediction module 21 can according to this historical data 31 of various period, coordinate this future plan data 6, dope the maximum demand predicted value 7 of following various period respectively.
For that shown in Figure 6, the first period historical data 31A of this buildings, the second period historical data 31B, the 3rd period historical data 31C is have recorded in this database 3 ... Deng, wherein, this first period historical data 31A comprises this maximum demand value 313 of first period in the past certain month, this people's logarithmic data 314, these outdoor temperature data 315, this equipment enables the data such as status data 316 and this other information 317; This second period historical data 31B comprises this maximum demand value 313 of second period in the past certain month, this people's logarithmic data 314, these outdoor temperature data 315, this equipment enable the data such as status data 316 and this other information 317, by that analogy.
Meanwhile, this data prediction module 21 can receive this future plan data 6 via this input block 4, by this, jointly dopes this maximum demand predicted value 7 of many with those historical data 31A, 31B, 31C.In the present embodiment, this data prediction module 21 can dope one first period maximum demand predicted value 71,1 second period maximum demand predicted value 72, the 3rd period maximum demand predicted value 73 respectively.Wherein, this first period maximum demand predicted value 71 corresponds to first period in month in next year; This second period maximum demand predicted value 72 corresponds to second period in month in next year, by that analogy.Following table 2 discloses the enforcement example of this maximum demand predicted value 7:
Table 2
As above, shown in table 2, in the present embodiment, this first period maximum demand predicted value 71 mainly may correspond to the spike period to each month in next year; This second period maximum demand predicted value 72 mainly may correspond to spike period Saturday half to each month in next year; And the 3rd period maximum demand predicted value 73 mainly may correspond to each month in next year from the peak period.But, the mode that each country calculates the electricity charge is different, if particular country or area are not distinguished the period, then this database 3 only can record over the historical data in 12 month an of year, and this data prediction module 21 is for predicting the maximum demand predicted value in 12 month of next year, and does not need this historical data 31 and this maximum demand predicted value 7 to be divided into the multiple period according to date, time zone.Can find out thus, System and method for of the present invention is real be can be widely used among the neither identical country of each electricity charge account form.
Finally, this contract optimization module 22 can receive this data prediction module 21 and predict one or many this maximum demand predicted value 71-73 drawn, and as calculated after, draw one or many this optimization contract capacity 9 meeting this user's demand 8.More specifically, if this data prediction module 21 only predicts the maximum demand predicted value (that is, not distinguishing the period) of a kind, then this contract optimization module 22 finally only can calculate this optimization contract capacity 9.But, if this data prediction module 21 predicts the maximum demand predicted value of multiple period respectively, then this contract optimization module 22 finally will calculate many these optimization contract capacity 9, and these many optimization contract capacity 9 are applicable to the contract of each period respectively.Such as, if the person as shown in table 2 that predicts the outcome of this data prediction module 21, then this contract optimization module 22 finally will calculate three these optimization contract capacity 9, wherein, the first stroke optimization contract capacity is applicable to the contract of spike period in next year, second optimization contract capacity is applicable to the contract of spike period Saturday half in next year and the 3rd optimization contract capacity is applicable to the contract of next year from the peak period.Further, these three optimization contract capacity 9 are meeting under user's demand condition, and total basic charge as per installed capacity that can make following a period of time is minimum.But the above is all only preferred embodiments of the present invention, is not limited.
The foregoing is only preferred embodiments of the present invention, non-ly therefore namely limit to scope of the present invention, therefore the equivalence change of such as using content of the present invention to do, be all in like manner all contained in scope that claim of the present invention protects, close and give Chen Ming.
Claims (20)
1. a contract capacity optimization system, comprising:
One database, records a historical data relevant to buildings power consumption;
One input block, receives outside user's demand of input and future plan data of this buildings;
One processing unit, be electrically connected this database and this input block, this processing unit comprises:
One data prediction module, receives this historical data and this future plan data, predicts a maximum demand predicted value in this buildings future according to this; And
One contract optimization module, connects this data prediction module, receives this maximum demand predicted value, calculates the optimization contract capacity meeting this user's demand according to this.
2. contract capacity optimization system as claimed in claim 1, wherein also comprises an output unit, is electrically connected this processing unit, in order to export this optimization contract capacity.
3. contract capacity optimization system as claimed in claim 1, wherein this historical data comprises many maximum demand values, the various periods that these many maximum demand values are corresponded to in each month respectively.
4. contract capacity optimization system as claimed in claim 3, wherein this data prediction module is according to these many maximum demand values, in conjunction with these future plan data, predict many these maximum demand predicted values, wherein these many maximum demand predicted values correspond to the various periods in each month following respectively.
5. contract capacity optimization system as claimed in claim 4, wherein this contract optimization module is according to these many maximum demand predicted values, calculate many these optimization contract capacity meeting this user's demand respectively, wherein these many optimization contract capacity are applicable to the contract of Different periods respectively.
6. contract capacity optimization system as claimed in claim 3, wherein this user's demand be user acceptable one maximum surpass about the moon number.
7. contract capacity optimization system as claimed in claim 3, wherein this historical data also comprises many people's logarithmic datas, the various periods that these many people's logarithmic datas are corresponded to in each month respectively.
8. contract capacity optimization system as claimed in claim 3, wherein this historical data also comprises many outdoor temperature data, the various periods that these many outdoor temperature data are corresponded to in each month respectively.
9. contract capacity optimization system as claimed in claim 1, wherein these future plan data comprise an output increase and decrease planning, and this output increase and decrease planning corresponds to a capacity utilization in this buildings future.
10. contract capacity optimization system as claimed in claim 1, wherein these future plan data comprise an equipment and eliminate and change planning, and this equipment is eliminated and changed a number of devices and the equipment effectiveness that planning corresponds to this buildings future.
11. contract capacity optimization systems as claimed in claim 1, wherein these future plan data comprise a number increase and decrease planning, and this number increase and decrease planning corresponds to a total number of persons in this buildings future.
12. 1 kinds of contract capacity optimization methods, comprising:
A) historical data relevant to buildings power consumption is obtained;
B) future plan data of this buildings are obtained;
C) according to a maximum demand predicted value in this buildings of this historical data and this future plan data prediction future;
D) user's demand of outside input is received; And
E) according to this maximum demand predicted value, the optimization contract capacity meeting this user's demand is calculated.
13. contract capacity optimization methods as claimed in claim 12, wherein also comprise a step f: if do not receive this user's demand, this optimization contract capacity of this maximum demand predictor calculation of direct basis.
14. contract capacity optimization methods as claimed in claim 12, wherein this user's demand be user acceptable one the maximum super about moon number.
15. contract capacity optimization methods as claimed in claim 12, wherein this historical data comprises many maximum demand values, and this contract capacity optimization method also comprises a step g: the various periods of these many maximum demand values being corresponded to over respectively in each month according to date and time; This step c is according to these many maximum demand values, and in conjunction with this maximum demand predicted value of this future plan data prediction many, wherein these many maximum demand predicted values correspond to the various periods in each month following respectively; This step e, according to these many maximum demand predicted values, calculates many these optimization contract capacity meeting this user's demand respectively, wherein changes most the contract that contract capacity is applicable to Different periods respectively for these many.
16. contract capacity optimization methods as claimed in claim 12, wherein these future plan data comprise one output increase and decrease planning, an equipment eliminate change planning and a number increase and decrease planning, this output increase and decrease planning corresponds to a capacity utilization in this buildings future, this equipment is eliminated and is changed a number of devices and the equipment effectiveness that planning corresponds to this buildings future, and this number increase and decrease planning corresponds to a total number of persons in this buildings future.
17. 1 kinds of contract capacity optimization methods, comprising:
A) obtain a historical data relevant to buildings power consumption, wherein this historical data at least comprises many maximum demand values;
B) future plan data of this buildings are obtained;
C) according to the various periods that these many maximum demand values are corresponded to in each month by date and time respectively;
D) according to many maximum demand values of this in this historical data, in conjunction with this future plan data prediction many maximum demand predicted values, wherein these many maximum demand predicted values correspond to the various periods in each month following respectively;
E) the user's demand receiving outside input is judged whether;
If f) receive this user's demand, calculate many optimization contract capacity meeting this user's demand according to these many maximum demand predicted values respectively, wherein these many optimization contract capacity are applicable to the contract of Different periods respectively; And
If g) do not receive this user's demand, these many maximum demand predicted values of direct basis calculate many these optimization contract capacity respectively, and wherein these many optimization contract capacity are applicable to the contract of Different periods respectively.
18. contract capacity optimization methods as claimed in claim 17, wherein this user's demand be user acceptable one the maximum super about moon number.
19. contract capacity optimization methods as claimed in claim 17, wherein this historical data also comprises many people's logarithmic datas and many outdoor temperature data, the various periods that these many people's logarithmic datas and this many outdoor temperature data are corresponded to in each month respectively, wherein in this steps d, according to many maximum demand values of this in this historical data, these many people's logarithmic datas and this many outdoor temperature data, in conjunction with these many maximum demand predicted values of this future plan data prediction.
20. contract capacity optimization methods as claimed in claim 19, wherein these future plan data comprise one output increase and decrease planning, an equipment eliminate change planning and a number increase and decrease planning, this output increase and decrease planning corresponds to a capacity utilization in this buildings future, this equipment is eliminated and is changed a number of devices and the equipment effectiveness that planning corresponds to this buildings future, and this number increase and decrease planning corresponds to a total number of persons in this buildings future.
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| CN201310739512.5A CN104751233A (en) | 2013-12-27 | 2013-12-27 | Contract capacity optimization system and optimization method |
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| CN111160611A (en) * | 2019-12-02 | 2020-05-15 | 万翼科技有限公司 | Energy consumption estimation method and device, computer equipment and storage medium |
| CN111492552A (en) * | 2018-11-21 | 2020-08-04 | 亿可能源科技（上海）有限公司 | Energy storage management and control method, system, computer equipment and storage medium |
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