CN104751233A - Contract capacity optimization system and optimization method - Google Patents

Abstract

A contract capacity optimization system includes a processing unit, an input unit and a database. The processing unit reads historical data relating to past electricity usage of the building from the database and receives future planning data relating to future policies of the building from the input unit. And the processing unit predicts the maximum demand prediction value of each time period in each month of the next year of the building according to the historical data and the future planning data. Then, the processing unit receives the user requirement from the input unit, and calculates an optimal contract capacity according with the user requirement according to the predicted maximum requirement predicted value. Therefore, the system can obtain the contract capacity which can meet the requirements of users for the building and simultaneously enable the total electric charge of the building to be the lowest so as to facilitate the users to sign contracts with the power company. The invention optimizes contract capacity in next year.

Description

Contract capacity optimization system and optimization method

technical field

The present invention relates to an optimization system and an optimization method, in particular to an optimization system and an optimization method for calculating the optimal contract capacity.

Background technique

Today's companies, factories, department stores and other construction companies with high electricity demand usually sign a contract with the power company, requiring that the instantaneous wattage (that is, the maximum immediate demand) or the total power consumption should not exceed a certain value , otherwise the operator needs to pay an additional punitive electricity fee (or called over-contract electricity fee), which is the so-called contracted capacity. Therefore, in the technical field of the present invention, there are some related technologies that can assist the industry to calculate a better and more reasonable contract capacity, so as to facilitate the industry to sign the contract with the power company for the next year.

Most of these related technologies are only calculated by algorithms, analyzing the power consumption information of a building based on the historical data of power consumption in the past year, and then calculating a contracted capacity recommended for use in the next year. However, there are many factors that affect the electricity consumption of a building, such as the number of people indoors, the outdoor temperature, etc. As an analysis basis, it is really difficult to accurately predict the possible electricity consumption and the maximum immediate demand in the next year. In this way, of course, it is impossible to calculate the precise contract capacity. If the operator signs a contract with the power company based on this imprecise contract capacity, it may cause waste because the actual electricity consumption in the next year does not reach the contract capacity, or because the electricity consumption exceeds the contract capacity too much and needs to pay Substantial over-approximate electricity bills.

Contents of the invention

The main purpose of the present invention is to provide a contract capacity optimization system and optimization method, in order to predict the possible maximum demand forecast value of the building in the next year based on the historical electricity consumption data of the building and the future strategy, In order to enable the system to more accurately calculate the optimal contract capacity for the next year, it is convenient for users to sign contracts with power companies.

Another main purpose of the present invention is to provide a contract capacity optimization system and optimization method, which can accept the user's demand, so that the system can calculate the capacity without violating the user's demand. The contract capacity that the electricity rate of the building next year is the lowest.

To achieve the above purpose, the present invention discloses a contract capacity optimization system including a processing unit, an input unit and a database, and an optimization method used in the optimization system. Wherein, the processing unit reads historical data related to the past electricity consumption of the building from the database, and receives future planning data related to the future strategy of the building from the input unit. The processing unit predicts the maximum demand forecast value of the building at various time periods in each month of the next year based on historical data and future planning data. Next, the processing unit receives the user demand from the input unit, and calculates an optimal contract capacity that meets the user demand according to the predicted maximum demand forecast value.

The present invention uses historical data and future strategies of the building at the same time, first predicts the possible maximum demand forecast value of next year, and then calculates the contract capacity of next year according to the maximum demand forecast value. The invention can improve the problem that the prior art only uses historical data to estimate and calculate the contract capacity of the next year, and the result obtained is not accurate enough.

Furthermore, the purpose of optimizing the contracted capacity is to minimize the total electricity bill to be paid in the next year through planning. However, the total electricity fee generally includes the basic electricity fee and the over-contract electricity fee. Therefore, in the mode where the basic electricity fee is greatly reduced after planning, even if the overage electricity fee needs to be paid, the total electricity fee may still be higher than that without the overage electricity fee, but the basic electricity fee Very high patterns come lower. In this way, although the total electricity bill that needs to be paid can be saved, it may cause a bad perception of the manager due to too much over-contraction fees, and may even cause a large increase in the number of over-contracted months due to calculation errors, and finally lead to actual The total electricity bill on the network does not decrease but increases. Therefore, when the present invention calculates the contract capacity for the next year, it also adopts the user's demand at the same time, so that the contract capacity for the next year can be optimized on the premise of meeting the user's demand.

Description of drawings

FIG. 1 is a system block diagram of the first embodiment of the present invention.

FIG. 2 is a schematic diagram of an optimized architecture of the first embodiment of the present invention.

Fig. 3 is a schematic diagram of historical data of the first specific embodiment of the present invention.

Fig. 4 is a schematic diagram of future planning data according to the first specific embodiment of the present invention.

FIG. 5 is an optimization flowchart of the first embodiment of the present invention.

FIG. 6 is a schematic diagram of an optimized architecture of a second specific embodiment of the present invention.

Wherein, the reference signs are explained as follows:

1…optimize the system

2…processing unit

21...Data Prediction Module

22…Contract Optimization Module

3…database

31…historical data

31A...Historical data of the first period

31B...Historical data of the second period

31C...Historical data of the third period

311…Date

312…time

313…Maximum demand value

314…Number of people data

315…Outdoor temperature data

316...Equipment enabling status data

317...Other information

4…Input unit

5…Output unit

6…future planning data

61...Production increase and decrease planning

62...Equipment replacement plan

63...Planning for the increase or decrease of the number of people

64…other factors

7…Maximum demand forecast

71...The maximum demand forecast value in the first period

72...Maximum demand forecast value in the second period

73...Maximum demand forecast value in the third period

8…User needs

9… Optimizing Contract Capacity

S10～S24...optimization steps

Detailed ways

A preferred embodiment of the present invention will now be described in detail with reference to the accompanying drawings.

First please refer to FIG. 1 and FIG. 2 , which are respectively a system block diagram and an optimized architecture diagram of the first embodiment of the present invention. The present invention discloses a contract capacity optimization system (abbreviated as the system 1 in the specification), the system 1 mainly includes a processing unit 2, a database 3, an input unit 4 and an output unit 5, wherein the The processing unit 2 is electrically connected to the database 3 , the input unit 4 and the output unit 5 .

In this embodiment, the system 1 is mainly installed in a building (not shown in the figure), and can be a building energy management system (Building Energy Management System, BEMS) of the building, or it can be connected with the building Some BEMS are integrated into one without limitation.

Record the historical data 31 related to electricity consumption of the building in the past in the database 3. In a preferred embodiment, the database 3 mainly records the historical data 31 related to the electricity consumption of the building in the past year; however, in other In the embodiment, the database 3 can also completely record the historical data 31 related to electricity consumption of the building in the past several years, but it is not limited thereto. The processing unit 2 can mainly obtain the historical data 31 from the database 3 , and receive external (user) input data related to the future operation strategy of the building from the input unit 4 . In this way, the processing unit 2 can predict the possible maximum electricity demand of the building in the future, and then calculate an optimal contract capacity, and display or output it externally through the output unit 5 . In a preferred embodiment, the processing unit mainly receives the data related to the operation strategy of the building in the next year through the input unit 4, and predicts the possible maximum electricity demand of the building in the next year based on the input unit 4, but Not limited. The present invention uses the processing unit 2 to predict the large demand of electricity consumption and calculate the optimal contract capacity, which is helpful for the user of the building to sign a contract with the power company (mainly for next year's contract).

As shown in FIG. 2 , the processing unit 2 in this embodiment mainly includes a data prediction module 21 and a contract optimization module 22 , wherein the contract optimization module 22 is connected to the data prediction module 21 . The data prediction module 21 can obtain the historical data 31 related to electricity consumption from the database 3 , and the data prediction module 21 can receive a future planning data 6 of the building from the input unit. The data prediction module 21 can predict a future maximum demand forecast value 7 of the building according to the historical data 31 and the future planning data 6 .

It is worth mentioning that, in this embodiment, the quantity and corresponding period of the maximum demand forecast value 7 correspond to the quantity and corresponding period of the historical data 31 . For example, if the historical data 31 includes all the data of one or more years in the past, then the data prediction module 21 can predict the maximum demand forecast value 7 in different periods in each month in the whole year of next year

The contract optimization module 22 can obtain the predicted maximum demand value 7 from the data prediction module 21 , and calculate an optimal contract capacity 9 through algorithm calculation. The optimized contract capacity 9 is output and displayed through the output unit 5 , so that the user can sign a contract with the power company according to the content of the optimized contract capacity 9 .

In another embodiment, the contract optimization module 22 can receive an externally input user demand 8 through the input unit 4, so as to exclude those that do not meet the user demand when calculating the optimal contract capacity 9 8, and among the one or more contracted capacities that meet the user’s demand 8, select one or more contracted capacities with the lowest electricity cost as the optimized contracted capacity 9.

Specifically, when the contract optimization module 22 calculates the optimal contract capacity 9 , whether it meets the user's demand 8 is regarded as the first requirement, and the level of electricity charges is regarded as the second requirement. For example, even if the calculated contract capacity A can save more electricity charges than contract capacity B, if the contract capacity A does not meet the user's demand8 and the contract capacity B meets the user's demand8, the contract is the most The optimization module 22 regards the contract capacity B as the optimized contract capacity 9 . In this embodiment, the user requirement 8 is the maximum number of months acceptable to the user (details will be described below), but it is not limited.

Generally speaking, the total electricity fee includes the basic electricity fee and the over-contract electricity fee, and only when the maximum demand exceeds the contracted capacity, the over-contract electricity fee needs to be paid. In other words, even if you have to pay an over-contracted electricity bill due to over-contraction, as long as the basic electricity bill is reduced, the total electricity bill may still reach the minimum. Therefore, although the optimized contract capacity can generally achieve the lowest total electricity charge, the reduction of the total electricity charge may be achieved by compressing the basic electricity charge. In order to reduce the basic electricity charge for certain months, other One or more months are overdue. In this case, although the total electricity bill is reduced, the excessive number of months may cause the user to feel bad, or make a third party think that the user has poor control over the electricity consumption. Therefore, the present invention provides the user to set the user demand 8 (that is, the maximum acceptable number of over-contract months), whereby the system 1 can calculate the optimal contract capacity on the premise of the user demand 8 9.

Referring to FIG. 3 , it is a schematic diagram of historical data of the first specific embodiment of the present invention. As shown in Figure 3, in this embodiment, the historical data 31 can mainly include date 311, time 312, maximum demand value 313, number of people data 314, outdoor temperature data 315, equipment activation status data 316 and other information (such as humidity) 317 et al. In the present invention, the data prediction module 21 is mainly based on the maximum demand value 313 in the historical data 31, combined with the future planning data 6 input by the user, to predict the maximum demand forecast value of the building next year 7. Wherein, the size of the maximum demand value 313 depends on the power consumption of the building, and the power consumption depends on the current data of the building (such as the above-mentioned number of people data 314, outdoor temperature data 315, equipment activation status data 316 and other information 317, etc.). Therefore, in the present invention, the system 1 simultaneously records these data through the database 3 , and the data forecasting module 21 takes these data into consideration when predicting the maximum demand forecast value 7 .

For example, if the same indoor temperature is to be maintained, when the number of people in the building increases, the air conditioner needs to balance and maintain the indoor temperature to cope with the increase in the number of people, so the electricity bill will increase, and the maximum demand value 313 will increase . For another example, when the outdoor temperature of the building drops, the indoor temperature can be lowered through the outside air temperature, so the operating temperature of the air-conditioning equipment can be lowered, or part of the air-conditioning equipment can be turned off, or even all the air-conditioning equipment can be turned off, so the electricity bill is reduced. will decrease, and the maximum demand value 313 decreases. According to Table 1 below, the relationship between these data can be seen more clearly.

Table 1

As shown in the example in Table 1 above, due to the high outdoor temperature in August, the power consumption of air-conditioning equipment is higher than that in other months, which leads to a higher maximum demand value in this month than in other months. In addition, the electricity consumption values in September and December are similar, but in September, there are more people in the building, the outdoor temperature is higher, and the humidity is higher, which also causes the maximum demand value of the month to be higher than December The month comes high. However, the above description is only a preferred specific example of the present invention and is not limited thereto.

What is shown in Table 1 is an example of a maximum demand value 313 every month. However, different countries and regions have different calculation methods for electricity charges. For example, in Taiwan, China, each month is also divided into peak hours (including summer peak hours and non-summer peak hours), Saturday half-peak hours, and off-peak hours. Various time periods. Therefore, the historical data 31 may include a plurality of maximum demand values 313, which are distinguished according to the date 311 and the time 312, and the multiple maximum demand values 313 are respectively corresponding to each of the past months. kind of time period. In this embodiment, the data forecasting module 21 can predict multiple maximum demand forecast values 7 based on the multiple maximum demand values 313 combined with the future planning data 6, and the multiple maximum demand forecasts The value 7 corresponds to various periods in each month in the future (generally next year). Moreover, in this embodiment, the contract optimization module 22 can calculate the multiple optimized contract capacities 9 that meet the user's demand 8 according to the multiple maximum demand forecast values 7, and the multiple The optimized contract capacity is applicable to contracts in different time periods to achieve the goal of the lowest basic electricity charge.

For example, if the historical data 31 records a plurality of maximum demand values 313 during the peak hours and off-peak periods of each month in the past five years (that is, a total of 120 maximum demand values 313), Then the data prediction module 21 can predict a plurality of the maximum demand forecast values 7 (that is, a total of 24 maximum demand forecast values 7) in the peak hours and off-peak periods of each month of the building in the next year. ). Finally, the contract optimization module 22 can calculate a total of two optimized contract capacities 9 that meet the user's needs 8, and the two optimized contract capacities 9 are respectively applicable to contracts during peak hours and off-peak time period contract.

As shown in the above embodiment and Table 1, the multiple maximum demand values 313 in the historical data 31 are accompanied by the number of people data 314 and the outdoor temperature data at the date and time when the multiple maximum demand values 313 occurred. 315 . The device activates the status data 316 and the other information 317 . In this way, when the data forecasting module 21 predicts the maximum demand forecast value 7 of the building at a certain time period in a certain month next year, the forecasted data can actually be more accurate.

Referring to FIG. 4 , it is a schematic diagram of future planning data according to the first specific embodiment of the present invention. As shown in the figure, the future planning data 6 in this embodiment mainly includes a production increase/decrease plan 61 , an equipment replacement plan 62 , headcount increase/decrease plan 63 and other factors 64 . The production increase/decrease plan 61 mainly corresponds to the future equipment utilization rate of the building. For example, if the building is a factory, if the output of the next year increases/decreases, the equipment utilization rate of the building next year will increase/decrease. Therefore, the data forecasting module 21 can use the current equipment utilization rate (according to the equipment activation status data 316) and the future utilization rate (according to the production increase and decrease plan 61) are compared, and as one of the parameters of prediction

.

The equipment replacement plan 62 mainly corresponds to the future equipment quantity and equipment performance of the building. For example, if the factory purchases/discards multiple devices next year, the number of devices in the factory will increase/decrease next year. Therefore, the data forecasting module 21 can calculate the current number of devices (according to the status of the device in use) Data 316) is compared with the future quantity (according to the equipment replacement plan 62), and used as one of the predicted parameters. Furthermore, if the factory replaces multiple low-efficiency equipment with high-efficiency equipment next year, the equipment efficiency of the factory will increase next year. Therefore, the data prediction module 21 can compare the current performance and future performance of the equipment. The effectiveness of the comparison, and as one of the parameters of the prediction

.

The number increase/decrease plan 63 mainly corresponds to the total number of people in the building in the future. For example, if the factory will increase n people/decrease m people next year, then the total number of people in the factory will increase/decrease next year. Therefore, the data prediction module 21 can use the current total number of people in the equipment (according to the number of people data 314) is compared with the total number of people in the future (according to the number increase or decrease plan 63), and it is used as one of the parameters for prediction.

The other factors 64 can be, for example, forecasts of future environmental factors such as temperature and humidity, and the data prediction module 21 can also compare the current environmental factors (based on the other information 317) with future environmental factors (based on the other factors 64) , and used as one of the prediction parameters. However, the above descriptions are only preferred specific examples of the present invention and are not limited thereto.

To sum up, in a preferred embodiment of the present invention, the data prediction module 21 can mainly be based on the multiple maximum demand values 313, the multiple number of people data 314, the multiple outdoor temperature data 315, the multiple Parameters such as the equipment activation status data 316, the multiple other information 317, the output increase and decrease plan 61, the equipment replacement plan 62, the number of people increase and decrease plan 63, and other factors 64, etc., jointly predict one or more The maximum demand forecast value7. Furthermore, the contract optimization module 22 calculates the optimized contract capacity 9 that meets the user's demand 8 and corresponds to one or more time periods based on the one or more predicted maximum demand values 7 .

It is worth mentioning that the calculation methods of electricity charges in different countries or regions are not the same. If a country or region adopts a single electricity price without distinguishing between time periods, the present invention calculates an optimal sum that meets the needs of the user8. Contract capacity9. Conversely, if another country or region differentiates multiple time periods for electricity prices (for example, Taiwan, China is divided into four time periods), then the present invention calculates that the four time periods that meet the needs of the user8 correspond to the four time periods. Optimizing contract capacity9. The following list may represent the objective function of one or more contract capacity optimizations of the present invention:

$\mathrm{<span\; class="notranslate">\; z</span>}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; )</span><span\; class="notranslate">\; =</span>\underset{\mathrm{<span\; class="notranslate">\; j</span>}<span\; class="notranslate">\; -</span>\mathrm{<span\; class="notranslate">\; 1</span>}}{\overset{\mathrm{<span\; class="notranslate">\; m</span>}}{\mathrm{<span\; class="notranslate">\; \&Sigma;</span>}}}{\mathrm{<span\; class="notranslate">\; the\; y</span>}}_{\mathrm{<span\; class="notranslate">\; j</span>}}<span\; class="notranslate">\; (</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 1</span>}}<span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; 2</span>}}<span\; class="notranslate">\; ,</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&CenterDot;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; \&Center\; Dot;</span><span\; class="notranslate">\; ,</span>{\mathrm{<span\; class="notranslate">\; x</span>}}_{\mathrm{<span\; class="notranslate">\; no</span>}}<span\; class="notranslate">\; )</span>$

As shown in the above function, where x _i is the contract capacity of the i-th time period; n is the number of time periods in a month (for example, if there is no time period, then n is 1; if it is divided into four time periods, then n is 4 ); yj is the basic electricity fee + over-contract fee for the jth month; m is the number of months used to evaluate the optimal contract capacity; z is the total basic electricity fee + total over-contract fee for m months.

The main purpose of the present invention is to find a set of make And the number of months overdue in time period i≤C ₁ , where c _i is the maximum number of overdue months acceptable for the i-th time period set by the user.

Referring to FIG. 5 , it is an optimization flowchart of the first specific embodiment of the present invention. Figure 5 discloses the contract capacity optimization method of the present invention. To implement the method of the present invention, firstly, the data forecasting module 21 obtains the historical data 31 related to the electricity consumption of the building from the database 3 (step S10 ). In this embodiment, the historical data 31 mainly refers to data related to electricity consumption of the building in the past year, but it is not limited thereto. At the same time, the data prediction module 21 obtains the future planning data 6 of the building (step S12). Wherein, the future planning data 6 can mainly be input by the user through the input unit 4 , or be pre-stored in the database 3 in other ways, without limitation. Moreover, in this embodiment, the future planning data 6 mainly refers to various operating strategies expected to be implemented by the building next year, but it is not limited thereto.

Next, the data prediction module 21 can predict the maximum demand forecast value 7 of the building in different time periods in each month of the next year according to the historical data 31 and the future planning data 6 . More specifically, if the historical data 31 includes multiple maximum demand values 313, the processing unit 2 can calculate the multiple maximum demand values according to the date 311 and the time 312 of the historical data 31. The values 313 respectively correspond to various periods in the past months (step S14 ). Thereby, the data prediction module 21 can predict a plurality of the maximum demand forecast values 7, wherein the multiple maximum demand forecast values 7 respectively correspond to various time periods in each future month of the building (step S16) .

After the data forecasting module 21 predicts one or more items of the maximum demand forecast value 7 , it sends the one or more items of the maximum demand forecast value 7 to the contract optimization module 22 . Thereby, the contract optimization module 22 can calculate the maximum demand that meets the user demand 8 and corresponds to different time periods according to the one or more predicted maximum demand values 7 after receiving the externally input user demand 8 . Optimal contract capacity9.

More specifically, the contract optimization module 22 can first judge whether there is the user demand 8 received from the external input (step S18), if the user demand 8 is not received, the contract optimization module 22 Calculating and generating one or more optimized contract capacities 9 directly based on the plurality of predicted maximum demand values 7 (step S20). Conversely, if the user demand 8 is received, the contract optimization module 22 calculates and generates one or more optimized orders that meet the user demand 8 based on the one or more maximum demand forecast values 7 Contract capacity 9 (step S22). Finally, the system 1 outputs or displays the one or more optimized contract volumes 9 through the output unit 5 (step S24).

Referring to FIG. 6 , it is a schematic diagram of an optimized architecture of a second specific embodiment of the present invention. As mentioned above, the historical data 31 in the database 3 can mainly correspond to various periods in the past months, and the data forecasting module 21 can cooperate with the future according to the historical data 31 of various periods. The planning data 6 respectively predicts the maximum demand forecast value 7 in various time periods in the future.

Taking the one shown in FIG. 6 as an example, the database 3 records the historical data 31A of the first time period, the historical data 31B of the second time period, the historical data 31C of the third time period of the building, etc., wherein the historical data of the first time period 31A includes data such as the maximum demand value 313, the number of people data 314, the outdoor temperature data 315, the equipment activation status data 316, and the other information 317 in the first period of a certain month in the past; the second period historical data 31B includes Data such as the maximum demand value 313 , the number of people data 314 , the outdoor temperature data 315 , the equipment activation status data 316 and the other information 317 in the second period of a past month, and so on.

At the same time, the data forecasting module 21 can receive the future planning data 6 through the input unit 4 , thereby predicting a plurality of maximum demand forecast values 7 together with the historical data 31A, 31B, and 31C. In this embodiment, the data prediction module 21 can respectively predict a maximum demand forecast value 71 in the first period, a maximum demand forecast value 72 in the second period, and a maximum demand forecast value 73 in the third period. Wherein, the maximum demand forecast value 71 of the first period corresponds to the first period of a certain month in the next year; the maximum demand forecast value 72 of the second period corresponds to the second period of a certain month in the next year, and so on. Table 2 below reveals an example implementation of this maximum demand forecast 7:

Table 2

As shown in Table 2 above, in this embodiment, the maximum demand forecast value 71 in the first period can mainly correspond to the peak hours of each month in the next year; the maximum demand forecast value 72 in the second period can mainly correspond to the peak periods in each month in the next year. The half-peak period on Saturday of the month; and the maximum demand forecast value 73 in the third period can mainly correspond to the off-peak period of each month in the next year. However, each country or region calculates electricity in a different way. If a specific country or region does not distinguish between time periods, then the database 3 can only record the historical data of 12 months in the past year, and the data forecasting module 21 is used for forecasting The maximum demand forecast value for the 12 months of next year does not need to divide the historical data 31 and the maximum demand forecast value 7 into multiple time periods according to date and time. It can be seen from this that the system and method of the present invention can be widely used in countries or regions with different calculation methods of electricity charges.

Finally, the contract optimization module 22 can receive one or more of the maximum demand forecast values 71-73 predicted by the data forecast module 21, and after calculation, one or more items that meet the user demand 8 can be obtained. Multiple sums of the optimized contract capacity9. More specifically, if the data prediction module 21 only predicts the maximum demand forecast value of one type (that is, without distinguishing time periods), the contract optimization module 22 will only calculate the optimal contract capacity in the end 9. However, if the data prediction module 21 respectively predicts the maximum demand forecast values in various time periods, the contract optimization module 22 will finally calculate a plurality of the optimized contract capacities 9, and the multiple optimal The contract capacity 9 is applicable to the contracts of each time period respectively. For example, if the prediction result of the data prediction module 21 is as shown in Table 2, the contract optimization module 22 will finally calculate three optimized contract capacities 9, wherein the first optimized The contracted capacity is applicable to the contract during the peak period of next year, the second optimal contract capacity is applicable to the contract during the half peak period on Saturday next year, and the third optimized contract capacity is applicable to the contract during the off-peak period of the next year. Moreover, these three optimized contract capacities 9 can make the total basic electricity charge for a certain period of time in the future be the lowest under the condition of meeting the user's demand. However, the above descriptions are only preferred specific examples of the present invention and are not intended to be limiting.

The above descriptions are only preferred specific examples of the present invention, and are not intended to limit the scope of the present invention. Therefore, all equivalent changes made by using the content of the present invention are all included in the scope of protection of the claims of the present invention. Inside, together with 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.