CN103093284A - Hourly water consumption forecasting method of island water supply system - Google Patents

Hourly water consumption forecasting method of island water supply system Download PDF

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Publication number
CN103093284A
CN103093284A CN2013100181229A CN201310018122A CN103093284A CN 103093284 A CN103093284 A CN 103093284A CN 2013100181229 A CN2013100181229 A CN 2013100181229A CN 201310018122 A CN201310018122 A CN 201310018122A CN 103093284 A CN103093284 A CN 103093284A
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water consumption
data
days
pattern
water
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Inventor
江爱朋
姜周曙
王剑
黄国辉
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Hangzhou Dianzi University
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Hangzhou Dianzi University
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Abstract

The invention discloses an hourly water consumption forecasting method of an island water supply system. Utilizing historical data of past N days, the hourly water consumption forecasting method of the island water supply system creates a historical trend pattern. On the basis, hourly water consumption is forecasted by comparing the water consumption data and the pattern data of the last hours of the previous day. In order to improve precision, the historical trend pattern is combined with a linear shift average method. Final water consumption forecasting data can be obtained by combining the forecasting water consumption obtained by the pattern and the forecasting water consumption obtained by the linear shift average method. The hourly water consumption forecasting method of the island water supply system is high in forecasting accuracy and has a good effect in a situation, wherein the situation is obvious in hour trend law and unobvious in day trend law.

Description

A kind of freshwater supply to sea island system hourly water consumption Forecasting Methodology
Technical field
The invention belongs to the water resource technical field, especially a kind of freshwater supply to sea island system hourly water consumption Forecasting Methodology.
Background technology
Water consumption is predicted at aspects such as water-supply systems scheduling, physical construction planning, urban industrial structure adjustment all important roles.The water consumption prediction divides by predetermined period can be divided into short-term forecasting and medium-and long-term forecasting.General short-term water consumption prediction can be divided into hourly water consumption prediction and daily water consumption prediction etc.; Medium-term and long-term water consumption prediction can be divided into water consumption per month prediction and year water consumption prediction etc.From prediction principle, can be divided into cause and effect type predicted method and trend extrapolation again.See also from processing observation data angle and can be divided into grey method, time series analysis method etc.
The prediction of short-term water consumption is mainly used in the water supply scheduling of public supply mains.Its accuracy is directly connected to reliability and the superiority of scheduling result.For seawater desalination system, its short-term water consumption prediction used is different on target with the urban water consumption prediction, water consumption predicts the outcome as the initial conditions of desalinization water processed, water supply unit Optimized Operation, in order to reasonably water-making machine group plan for start-up and shut-down, product pond water supply plan in the formulation cycle.
The area of adopting desalination technology to prepare fresh water is mainly the island, although this area's town water can be divided into town water and resident living water, but adopt identical transmission pipeline network, it is less that daily water consumption is subject to the impact of the aspects such as festivals or holidays, water consumption has comparatively significantly trend feature, show take 24 hours as one-period, present periodically variable rule.In one-period (24 hours), water consumption in the morning 9 o'clock to 10 o'clock and at 7 o'clock in evening to 8 o'clock two periods reach water use peak, touch the bottom in 3:00 AM to 5 water consumption, the water consumption of every day presents " M " type and changes.
Summary of the invention
The present invention realizes water processed and for water integrated, needs predict hourly water consumption in order to satisfy desalinization scheduling requirement, instructs seawater desalination system to dispatch, and reaches energy-saving and cost-reducing target and has proposed a kind of freshwater supply to sea island system hourly water consumption Forecasting Methodology.
The present invention adopts historical data to set up historical trend pattern, utilizes on this basis water usage data and the pattern data of last several hours of the previous day relatively to predict hourly water consumption.In order to improve precision, the historical trend pattern that proposes combines with the Linear-moving averaging method, and the prediction water consumption that the prediction water consumption that pattern is obtained and Linear-moving averaging method obtain merges, and obtains final water consumption predicted data.The method precision of prediction is high, for the time trend rule obviously and day unconspicuous occasion of trend has better effect.
The concrete steps of the inventive method are as follows:
Step (1). build initial historical trend pattern
Choose over
Figure 2013100181229100002DEST_PATH_IMAGE002
It each water usage data constantly, wherein L is the number between 10～15.Each water consumption observed reading constantly is
Figure 2013100181229100002DEST_PATH_IMAGE004
, here Be constantly, unit is hour,
Figure 2013100181229100002DEST_PATH_IMAGE008
, Be number of days, unit is the sky,
Figure 2013100181229100002DEST_PATH_IMAGE012
By finding the solution the least square objective function Obtain
Figure 2013100181229100002DEST_PATH_IMAGE016
,
Figure 111520DEST_PATH_IMAGE016
Expression the
Figure 592180DEST_PATH_IMAGE006
Hour the water consumption predicted value.Obtain the water consumption predicted value in 24 moment
Figure 71571DEST_PATH_IMAGE016
After, with 24 Couple together, form by these 24 p i The initial historical trend pattern that value consists of.
Step (2). the correction of historical trend pattern and renewal
After obtaining initial historical trend pattern, in order to ensure the real-time of pattern, need every day and upgrade pattern.For this reason, after the water consumption observed reading that obtains the same day, need to add the water consumption predicted value on the same day, then pick out old historical data, step (1) be participated in the water consumption observed reading of each day of calculating for this reason
Figure 560639DEST_PATH_IMAGE004
With obtain
Figure 212200DEST_PATH_IMAGE016
Sequence is asked its degree of correlation, obtains
Figure 943002DEST_PATH_IMAGE002
Individual relevance degree sequence
Figure 2013100181229100002DEST_PATH_IMAGE018
, reject degree of correlation minimum value corresponding that day of historical data (namely deleting oscillation on large scale, to get rid of the water consumption observation data under large interference), then add the new historical data of a day, recomputate new historical trend pattern, obtain new
Figure 275894DEST_PATH_IMAGE016
Sequence.The degree of correlation
Figure 2013100181229100002DEST_PATH_IMAGE020
Computing formula is as follows:
Figure 2013100181229100002DEST_PATH_IMAGE022
Step (3). obtain based on water consumption predicted value under historical trend pattern
After acquisition the historical water usage data and historical trend pattern of Q days, in order to predict each water consumption constantly in Q+1 days, with Q days In the data of hourly consumption and pattern, corresponding data relatively, obtains the water consumption predicted value of each hour in Q+1 days.Wherein
Figure 949321DEST_PATH_IMAGE024
Represent the 19th～24 hour.Circular is as follows:
If Q days
Figure 37363DEST_PATH_IMAGE024
Hour the water consumption observed reading be
Figure 2013100181229100002DEST_PATH_IMAGE026
,
Figure 2013100181229100002DEST_PATH_IMAGE028
, order
Figure 2013100181229100002DEST_PATH_IMAGE030
,
Figure 2013100181229100002DEST_PATH_IMAGE032
,
Figure 2013100181229100002DEST_PATH_IMAGE034
Expression is last
Figure 117446DEST_PATH_IMAGE024
The mean absolute deviation of hour data,
Figure 2013100181229100002DEST_PATH_IMAGE036
Expression is last The mean relative deviation of hour data.Adopt the water consumption predicted value of each hour in Q+1 days that historical trend pattern obtains to be
Figure 2013100181229100002DEST_PATH_IMAGE038
Step (4). adopt linear averaging method prediction hourly water consumption
Concrete grammar is:
Figure 2013100181229100002DEST_PATH_IMAGE040
Q represents the current water consumption cycle; Expression
Figure 280497DEST_PATH_IMAGE006
The hourly water consumption in the Q+1 days moment; Expression
Figure 726391DEST_PATH_IMAGE006
The water consumption observed reading in the Q days moment; nFor the moving window size, generally get 5～7.
Step (5). step (3) and step (4) are obtained
Figure 2013100181229100002DEST_PATH_IMAGE046
With
Figure 418403DEST_PATH_IMAGE042
Carry out linear data and merge, obtain final water usage data predicted value.The method that adopts linear data to merge, i.e. final water usage data predicted value
Figure 2013100181229100002DEST_PATH_IMAGE048
,
Figure 843831DEST_PATH_IMAGE008
,
Figure 2013100181229100002DEST_PATH_IMAGE050
Represent final The water usage data predicted value in the Q+1 days moment, wherein
Figure 2013100181229100002DEST_PATH_IMAGE052
Value adopts following rule: if
Figure 859377DEST_PATH_IMAGE036
More than or equal to 0.20,
Figure 2013100181229100002DEST_PATH_IMAGE056
If
Figure 520909DEST_PATH_IMAGE036
Less than 0.20,
Adopt beneficial effect of the present invention to be: the precision of hourly water consumption prediction obviously improves, and has directive significance for industry and domestic water scheduling on the island.Be applicable to hourly water consumption and have obvious trend feature, relatively large, the regular not strong situation of amplitude of variation before and after its daily water consumption.The method has the advantages such as precision of prediction is high, applicability is strong.
Embodiment:
Step (1). build initial historical trend pattern
Choose over
Figure 999295DEST_PATH_IMAGE002
It each water usage data constantly, wherein L is the number between 10～15.Each water consumption observed reading constantly is
Figure 829717DEST_PATH_IMAGE004
, here
Figure 102566DEST_PATH_IMAGE006
Be constantly, unit is hour, ,
Figure 598718DEST_PATH_IMAGE010
Be number of days, unit is the sky,
Figure 96695DEST_PATH_IMAGE012
In order to obtain best historical trend pattern, at first by finding the solution the least square objective function
Figure 540446DEST_PATH_IMAGE014
Obtain , here
Figure 826120DEST_PATH_IMAGE016
Expression the
Figure 178604DEST_PATH_IMAGE006
Hour the water consumption predicted value.Obtain the water consumption predicted value in 24 moment
Figure 58835DEST_PATH_IMAGE016
After, with 24
Figure 496770DEST_PATH_IMAGE016
Couple together, form by these 24 p i The initial historical trend pattern that value consists of.
Step (2). the correction of historical trend pattern and renewal
After obtaining initial historical trend pattern, in order to ensure the real-time of pattern, need every day and upgrade pattern.For this reason, after the water consumption observed reading that obtains the same day, need to add the water consumption predicted value on the same day, then pick out old historical data, step (1) be participated in the water consumption observed reading of each day of calculating for this reason With obtain
Figure 75442DEST_PATH_IMAGE016
Sequence is asked its degree of correlation, obtains
Figure 860996DEST_PATH_IMAGE002
Individual relevance degree sequence
Figure 786226DEST_PATH_IMAGE018
, reject degree of correlation minimum value corresponding that day of historical data (namely deleting oscillation on large scale, to get rid of the water consumption observation data under large interference), then add the new historical data of a day, recomputate new historical trend pattern, obtain new
Figure 197485DEST_PATH_IMAGE016
Sequence.The degree of correlation
Figure 462244DEST_PATH_IMAGE020
Computing formula is as follows:
Figure 746595DEST_PATH_IMAGE022
Step (3). obtain based on water consumption predicted value under historical trend pattern
After acquisition the historical water usage data and historical trend pattern of Q days, in order to predict each water consumption constantly in Q+1 days, with Q days
Figure 847537DEST_PATH_IMAGE024
In the data of hourly consumption and pattern, corresponding data relatively, obtains the water consumption predicted value of each hour in Q+1 days.Wherein Represent the 19th～24 hour.Circular is as follows:
If Q days
Figure 729223DEST_PATH_IMAGE024
Hour the water consumption observed reading be
Figure 184475DEST_PATH_IMAGE026
,
Figure 271248DEST_PATH_IMAGE028
, order
Figure 775042DEST_PATH_IMAGE030
,
Figure 811131DEST_PATH_IMAGE032
,
Figure 122770DEST_PATH_IMAGE034
Expression is last
Figure 509889DEST_PATH_IMAGE024
The mean absolute deviation of hour data,
Figure 551795DEST_PATH_IMAGE036
Expression is last The mean relative deviation of hour data.Adopt the water consumption predicted value of each hour in Q+1 days that historical trend pattern obtains to be
Figure 691975DEST_PATH_IMAGE038
Step (4). adopt linear averaging method prediction hourly water consumption
Concrete grammar is:
Figure 238494DEST_PATH_IMAGE040
Q represents the current water consumption cycle;
Figure 100402DEST_PATH_IMAGE042
Expression
Figure 111083DEST_PATH_IMAGE006
The hourly water consumption in the Q+1 days moment;
Figure 16722DEST_PATH_IMAGE044
Expression
Figure 299805DEST_PATH_IMAGE006
The water consumption observed reading in the Q days moment; nFor the moving window size, generally get 5～7.
Step (5). step (3) and step (4) are obtained
Figure 745830DEST_PATH_IMAGE046
With
Figure 814280DEST_PATH_IMAGE042
Carry out linear data and merge, obtain final water usage data predicted value.The method that adopts linear data to merge, i.e. final water usage data predicted value
Figure 638623DEST_PATH_IMAGE048
,
Figure 222051DEST_PATH_IMAGE008
,
Figure 409450DEST_PATH_IMAGE050
Represent final
Figure 581674DEST_PATH_IMAGE006
The water usage data predicted value in the Q+1 days moment, wherein
Figure 891433DEST_PATH_IMAGE052
Figure 899840DEST_PATH_IMAGE054
Value adopts following rule: if
Figure 641662DEST_PATH_IMAGE036
More than or equal to 0.20,
Figure 419125DEST_PATH_IMAGE056
If
Figure 899785DEST_PATH_IMAGE036
Less than 0.20,
Figure 379177DEST_PATH_IMAGE058

Claims (1)

1. freshwater supply to sea island system hourly water consumption Forecasting Methodology is characterized in that the method comprises the following steps:
Step (1). build initial historical trend pattern
Choose over
Figure 2013100181229100001DEST_PATH_IMAGE002
It each water usage data constantly, wherein L is the number between 10～15; Each water consumption observed reading constantly is ,
Figure 2013100181229100001DEST_PATH_IMAGE006
Be constantly, unit is hour,
Figure 2013100181229100001DEST_PATH_IMAGE008
,
Figure 2013100181229100001DEST_PATH_IMAGE010
Be number of days, unit is the sky,
Figure 2013100181229100001DEST_PATH_IMAGE012
By finding the solution the least square objective function
Figure 2013100181229100001DEST_PATH_IMAGE014
Obtain
Figure 2013100181229100001DEST_PATH_IMAGE016
, here
Figure 504036DEST_PATH_IMAGE016
Expression the
Figure 883808DEST_PATH_IMAGE006
Hour the water consumption predicted value; Obtain the water consumption predicted value in 24 moment
Figure 535370DEST_PATH_IMAGE016
After, with 24
Figure 518369DEST_PATH_IMAGE016
Couple together, form by these 24 p i The initial historical trend pattern that value consists of;
Step (2). the correction of historical trend pattern and renewal
After obtaining initial historical trend pattern, in order to ensure the real-time of pattern, need every day and upgrade pattern; After the water consumption observed reading that obtains the same day, need to add the water consumption predicted value on the same day, then pick out old historical data, step (1) is participated in the water consumption observed reading of each day of calculating for this reason With obtain
Figure 462371DEST_PATH_IMAGE016
Sequence is asked its degree of correlation, obtains Individual relevance degree sequence
Figure 2013100181229100001DEST_PATH_IMAGE018
, reject degree of correlation minimum value corresponding that day of historical data, then add the new historical data of a day, recomputate new historical trend pattern, obtain new
Figure 942080DEST_PATH_IMAGE016
Sequence; The degree of correlation
Figure 2013100181229100001DEST_PATH_IMAGE020
Computing formula is as follows:
Figure 2013100181229100001DEST_PATH_IMAGE022
Step (3). obtain based on water consumption predicted value under historical trend pattern
After acquisition the historical water usage data and historical trend pattern of Q days, in order to predict each water consumption constantly in Q+1 days, with Q days
Figure 2013100181229100001DEST_PATH_IMAGE024
In the data of hourly consumption and pattern, corresponding data relatively, obtains the water consumption predicted value of each hour in Q+1 days; Wherein
Figure 501500DEST_PATH_IMAGE024
Represent the 19th～24 hour; Circular is as follows:
If Q days Hour the water consumption observed reading be
Figure 2013100181229100001DEST_PATH_IMAGE026
,
Figure 2013100181229100001DEST_PATH_IMAGE028
, order
Figure 2013100181229100001DEST_PATH_IMAGE030
,
Figure 2013100181229100001DEST_PATH_IMAGE032
,
Figure 2013100181229100001DEST_PATH_IMAGE034
Expression is last
Figure 177124DEST_PATH_IMAGE024
The mean absolute deviation of hour data,
Figure 2013100181229100001DEST_PATH_IMAGE036
Expression is last
Figure 852825DEST_PATH_IMAGE024
The mean relative deviation of hour data; Adopt the water consumption predicted value of each hour in Q+1 days that historical trend pattern obtains to be
Figure 2013100181229100001DEST_PATH_IMAGE038
Step (4). adopt linear averaging method prediction hourly water consumption
Concrete grammar is:
Figure 2013100181229100001DEST_PATH_IMAGE040
Q represents the current water consumption cycle; Expression
Figure 481515DEST_PATH_IMAGE006
The hourly water consumption in the Q+1 days moment;
Figure DEST_PATH_IMAGE044
Expression
Figure 942583DEST_PATH_IMAGE006
The water consumption observed reading in the Q days moment; nFor the moving window size, generally get 5～7;
Step (5). step (3) and step (4) are obtained
Figure DEST_PATH_IMAGE046
With
Figure 293799DEST_PATH_IMAGE042
Carry out linear data and merge, obtain final water usage data predicted value; The method that adopts linear data to merge, i.e. final water usage data predicted value
Figure DEST_PATH_IMAGE048
,
Figure 473107DEST_PATH_IMAGE008
,
Figure DEST_PATH_IMAGE050
The water usage data predicted value that represents the final Q+1 days moment of i, wherein
Figure DEST_PATH_IMAGE052
Figure DEST_PATH_IMAGE054
Value adopts following rule: if
Figure 636979DEST_PATH_IMAGE036
More than or equal to 0.20,
Figure DEST_PATH_IMAGE056
If Less than 0.20,
Figure DEST_PATH_IMAGE058
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CN106228460A (en) * 2015-06-02 2016-12-14 Ls产电株式会社 Method of supplying water
CN106839468A (en) * 2017-04-14 2017-06-13 广州机智云物联网科技有限公司 A kind of solar water heater runoff investigation method and system
CN108302768A (en) * 2017-12-29 2018-07-20 深圳和而泰数据资源与云技术有限公司 Based on the real time checking method, device and storage medium with water behavior
CN111259334A (en) * 2020-01-14 2020-06-09 杭州电子科技大学 Monitoring and early warning method for water use abnormity of large users of industrial enterprises
CN113551296A (en) * 2021-06-21 2021-10-26 顺德职业技术学院 Daily water consumption adjusting method based on periodic variation

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Cited By (8)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN104715292A (en) * 2015-03-27 2015-06-17 上海交通大学 City short-term water consumption prediction method based on least square support vector machine model
CN106228460A (en) * 2015-06-02 2016-12-14 Ls产电株式会社 Method of supplying water
CN106839468A (en) * 2017-04-14 2017-06-13 广州机智云物联网科技有限公司 A kind of solar water heater runoff investigation method and system
CN108302768A (en) * 2017-12-29 2018-07-20 深圳和而泰数据资源与云技术有限公司 Based on the real time checking method, device and storage medium with water behavior
CN111259334A (en) * 2020-01-14 2020-06-09 杭州电子科技大学 Monitoring and early warning method for water use abnormity of large users of industrial enterprises
CN111259334B (en) * 2020-01-14 2023-06-23 杭州电子科技大学 Abnormal water consumption monitoring and early warning method for large users of industrial enterprises
CN113551296A (en) * 2021-06-21 2021-10-26 顺德职业技术学院 Daily water consumption adjusting method based on periodic variation
CN113551296B (en) * 2021-06-21 2022-06-07 顺德职业技术学院 Daily water consumption adjusting method based on periodic variation

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Application publication date: 20130508