Water demand prediction system

Abstract

To control water to be sent to a water distribution area on the basis of an accurately predicted water demand amount by improving accuracy of prediction of the water demand amount more than before.SOLUTION: The water demand prediction system 1 includes an information acquisition unit 71, a prediction unit 73, and a pipeline control unit 77. The information acquirer 71 acquires sequential information D1 that is information updated a plurality of times a day, the sequential information D1 including information on movement of a water consumption entity. The predictor 73 predicts the water demand for each of the plurality of water distribution areas 30 based on the sequential information D1 acquired by the information acquirer 71. The pipeline control unit 77 controls the pipeline device 43 that controls water to be sent to the water distribution area 30 based on the water demand predicted by the prediction unit 73.SELECTED DRAWING: Figure 2

Description

[Technical Field]

[0001] The present invention relates to a water demand prediction system. [Background technology]

[0002] For example, Patent Document 1 describes predicting water demand for each water distribution area based on past statistical information. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] Japanese Patent Application Publication No. 2020-201609 Summary of the Invention [Problem to be solved by the invention]

[0004] However, water demand predictions based on past statistical information have insufficient prediction accuracy. For example, when an unexpected event occurs that is not reflected in past statistical information, the accuracy of the water demand prediction becomes insufficient. As a result, water supplied to the water distribution area is controlled based on an inaccurate water demand prediction. Therefore, it is desirable to improve the accuracy of water demand predictions and control water supplied to the water distribution area based on accurately predicted water demand.

[0005] Therefore, the present invention aims to provide a water demand prediction system that can improve the accuracy of water demand prediction compared to conventional systems and can control the water sent to a distribution area based on the accurately predicted water demand. [Means for solving the problem]

[0006] The water demand prediction system includes an information acquisition unit, a prediction unit, and a pipeline control unit. The information acquisition unit acquires sequential information, which is information updated multiple times a day and includes information on the movements of water consumers. The prediction unit predicts water demand for each of multiple water distribution areas based on the sequential information acquired by the information acquisition unit. The pipeline control unit controls water flowing through pipelines that deliver water to the water distribution areas based on the water demand predicted by the prediction unit. [Effects of the Invention]

[0007] With the above configuration, the accuracy of water demand prediction can be improved compared to the conventional method, and the water supplied to the water distribution area can be controlled based on the accurately predicted water demand. [Brief explanation of the drawings]

[0008] [Figure 1] 1 is a diagram showing a water supply system 10 and the like of a water demand prediction system 1. FIG. [Figure 2] FIG. 2 is a diagram showing a computer 70 and other components of the water demand prediction system 1. [Figure 3] FIG. 1 is a diagram showing the movement of people under normal circumstances. [Figure 4] 2 is a graph showing the number of people in a residential area 30r and an office area 30o shown in FIG. 1 when an unexpected event occurs and when no unexpected event occurs. DETAILED DESCRIPTION OF THE INVENTION

[0009] The water demand prediction system 1 will be described with reference to FIGS.

[0010] The water demand prediction system 1 is a system that predicts the demand for purified water (water demand) based on sequential information D1 (described later) shown in Figure 2. The water demand prediction system 1 is a pipeline control system that performs pipeline control (described later) based on the predicted water demand. The water demand prediction system 1 includes a water supply system 10 (see Figure 1) and a computer 70.

[0011] As shown in Figure 1, water supply system 10 is a system for distributing purified water to a water distribution area 30. Water supply system 10 includes purified water distribution facilities 20, water distribution area 30, pipelines 41, pipeline equipment 43, and pipeline sensors 45 (see Figure 2).

[0012] The water purification and distribution facility 20 is a facility for producing purified water and distributing the purified water to the water distribution area 30. The water purification and distribution facility 20 includes a water purification plant 21 and a water supply station 23.

[0013] The water purification plant 21 is a facility that purifies water and produces purified water. The water purification plant 21 takes in water from a water source through a water conduit (not shown) and purifies the water. Chlorine is added to the water at the water purification plant 21. In the following, unless otherwise specified, "water" refers to purified water distributed to the water distribution area 30.

[0014] The water supply station 23 is a facility for distributing water to the water distribution area 30. The water supply station 23 includes a distributing reservoir 23a and a pumping station 23b. The distributing reservoir 23a is a facility for temporarily storing water. Because water tends to flow from high ground to low ground, the distributing reservoir 23a is located (installed) at a higher position than the water distribution area 30. The distributing reservoir 23a is located, for example, in high ground such as a mountain. The pumping station 23b is a facility for transporting water. The pumping station 23b includes a pump 43p, which will be described later. The pumping station 23b may, for example, transport water from the distributing reservoir 23a to the water distribution area 30, or may transport water from the distributing reservoir 23a to another distributing reservoir 23a. Note that water may be distributed from the water purification plant 21 to the water distribution area 30 without passing through one or both of the distributing reservoir 23a and the pumping station 23b. One or both of the reservoir 23a and the pumping station 23b may not be installed.

[0015] The water distribution area 30 is an area (block) to which water is distributed from the water purification and distribution facility 20 via a pipeline 41. The water distribution area 30 is the unit for managing water distribution (water distribution management is performed for each water distribution area 30). Multiple water distribution areas 30 are set. The water distribution areas 30 are set (divided) from various perspectives. For example, the water distribution areas 30 are set from the perspective of location, area, type (residential area 30r, business area 30o, etc.), upstream side, downstream side, each building, etc. The water distribution areas 30 may be set in stages (hierarchically). For example, the water distribution area 30 may have multiple (two in FIG. 1) large areas 31 (large blocks). One large area 31 may have multiple (two in FIG. 1) medium areas 33. One medium area 33 may have multiple (two in FIG. 1) small areas 35.

[0016] In the example shown in FIG. 1, a residential area 30r and an office area 30o are set as the water distribution area 30. Each of the residential area 30r and the office area 30o has a large area 31, a medium area 33, and a small area 35. The medium area 33 of the residential area 30r is set (divided) into an upstream side and a downstream side. The small areas 35 of the residential area 30r are set, for example, for each house. Similar to the large area 31, medium area 33, and small area 35 of the residential area 30r, the large area 31, medium area 33, and small area 35 of the office area 30o are set. The small areas 35 of the office area 30o are set, for example, for each office.

[0017] The water distribution area 30 may be set in various ways. When the water distribution area 30 is set in stages, the number of stages can be set in various ways. In the example shown in FIG. 1, the water distribution area 30 has three stages: large area 31, medium area 33, and small area 35, but it may also have two stages, or four or more stages. The water distribution area 30 does not have to be set in stages (the number of stages may be one). Furthermore, when the water distribution area 30 is set by type, the types can be set in various ways. In the example shown in FIG. 1, residential areas 30r and business areas 30o are set as the types of water distribution area 30. For example, factories, schools, commercial facilities, stadiums (e.g., baseball stadiums, soccer stadiums, etc.), event venues, theme parks, etc. may be set as the types of water distribution area 30.

[0018] The pipeline 41 is a pipe (water pipe) that transports water. The pipeline 41 is a pipe for transporting water to the water distribution area 30. For example, the pipeline 41 includes a water transmission pipe 41a, a water distribution pipe 41b, and a water supply pipe (not shown). The water transmission pipe 41a (water transmission pipeline) is a pipe installed in the water purification and distribution facility 20. For example, the water transmission pipe 41a transports water from the water purification plant 21 to the water supply station 23. The water transmission pipe 41a transports water from the water distribution reservoir 23a to the pumping station 23b. The distribution pipe 41b is a pipe that distributes water from the water purification and distribution facility 20 to the water distribution area 30. The distribution pipe 41b may distribute water from the water purification plant 21 to the water distribution area 30, or may distribute water from the water supply station 23 to the water distribution area 30. The distribution pipes 41b are arranged in a mesh pattern (forming a water distribution pipe network). The water distribution pipes 41b connect multiple water distribution areas 30. For example, the water distribution pipes 41b connect adjacent water distribution areas 30 (for example, adjacent medium areas 33 or adjacent small areas 35). The water supply pipes (not shown) are pipes for drawing water from the water distribution pipes 41b to each building.

[0019] The pipeline equipment 43 is equipment that controls the water flowing through the pipeline 41. The pipeline equipment 43 controls the pressure and flow rate of water in the water distribution area 30. The pipeline equipment 43 is installed in the pipeline 41. The pipeline equipment 43 includes a valve 43b and a pump 43p.

[0020] The valve 43b is a device that controls the flow of water. The valve 43b has a function of reducing water pressure. The valve 43b may be capable of stopping the water or changing the direction of the water flow. The valve 43b is installed in the pipeline 41. A plurality of valves 43b may be provided. The valves 43b may be installed at various positions in the pipeline 41. For example, the valve 43b is installed in a water distribution pipe 41b between different water distribution areas 30. Specifically, the valve 43b is installed between a water distribution area 30 on a highland side and a water distribution area 30 on a lowland side. In the example shown in FIG. 1 , the valve 43b is installed between an upstream middle area 33 and a downstream middle area 33 (for example, between adjacent middle areas 33). Water has a tendency to flow from highland to lowland, so when comparing the highland side (upstream side) with the lowland side (downstream side), the water pressure is higher on the lowland side. Therefore, a valve 43b is installed between the water distribution area 30 on the high ground side and the water distribution area 30 on the low ground side. The valve 43b reduces the water pressure in the water distribution area 30 on the low ground side. The valve 43b may be installed within the same (common) water distribution area 30.

[0021] The pump 43p is a machine that pumps water. The pump 43p may be a pressure pump that pressurizes water. The pump 43p may be a lifting pump that pumps water from lowlands to highlands. The pump 43p is installed in the pipeline 41. Multiple pumps 43p may be provided. The pump 43p may be installed in the water purification and distribution facility 20 (for example, pump station 23b). The pump 43p may be installed in the water distribution pipe 41b.

[0022] The pipe sensor 45 (see FIG. 2) detects information about the water flowing through the pipe 41. The pipe sensor 45 may be a flow rate sensor that detects the flow rate of water. The pipe sensor 45 may be a pressure sensor that detects the pressure of water. The pipe sensor 45 may detect both the flow rate and the pressure of water. The pipe sensor 45 is installed at various positions in the pipe 41. The pipe sensor 45 may be installed for each water distribution area 30 (described later). Below, each component of the water supply system 10 (e.g., water distribution area 30) will be described with reference to FIG. 1.

[0023] As shown in FIG. 2, the calculator 70 is a computer that inputs and outputs signals, performs calculations (processing), and stores information. The functions of the calculator 70 are realized by executing programs (e.g., a water demand prediction program, a pipeline control program, etc.) stored in a storage unit 70a of the calculator 70 by a calculation unit 70b. The calculator 70 may be distributed across multiple units (a distributed system may be configured). For example, the storage unit 70a and the calculation unit 70b may be located in different locations. Furthermore, for example, the storage unit 70a may be distributed across multiple units (the same applies to the calculation unit 70b). For example, various information (such as sequential information D1 and pipeline sensor information D45, which will be described later) is input to the calculator 70. For example, the calculator 70 predicts water demand for each of multiple water distribution areas 30 based on the sequential information D1 (details will be described later). For example, the calculator 70 outputs a signal for controlling (e.g., remotely controlling) the pipeline equipment 43. The computer 70 includes a storage unit 70a and a calculation unit 70b. Focusing on the functions of the computer 70, the computer 70 (for example, the calculation unit 70b) includes an information acquisition unit 71, a prediction unit 73, a pipeline control planning unit 75, and a pipeline control unit 77.

[0024] The storage unit 70a stores information. The storage unit 70a may store programs. The storage unit 70a may store statistical information D3 (described later). The calculation unit 70b performs calculations (processing) of information.

[0025] The information acquisition unit 71 acquires information. The information acquisition unit 71 acquires sequential information D1 (described later). The information acquisition unit 71 may acquire information other than the sequential information D1.

[0026] The prediction unit 73 predicts the water demand for each of the multiple water distribution areas 30. Hereinafter, the prediction of water demand made by the prediction unit 73 will also be simply referred to as "prediction." The prediction unit 73 makes the prediction based on the sequential information D1 acquired by the information acquisition unit 71. The prediction unit 73 may make the prediction based on the sequential information D1 and information other than the sequential information D1. The water demand predicted by the prediction unit 73 is the amount of water expected to be used in the water distribution area 30.

[0027] The water distribution area 30 that the prediction unit 73 targets for prediction is preferably a relatively small water distribution area 30, for example, a water distribution area 30 based on a building (e.g., small area 35). The water distribution area 30 that the prediction unit 73 targets for prediction may also be a water distribution area 30 that is larger than the small area 35 (specifically, medium area 33, large area 31, etc.). The water distribution area 30 that the prediction unit 73 targets for prediction may also be both the small area 35 and the water distribution area 30 that is larger than the small area 35.

[0028] The prediction made by the prediction unit 73 is a prediction for a predetermined time ahead (slightly in the future) from real time. This "predetermined time ahead" is, for example, within one hour ahead (approximately real time). This "predetermined time" may be one minute or more, ten minutes or more, or within one hour. This "predetermined time" may be longer than one hour, within two hours, or within three hours.

[0029] Specifically, the prediction unit 73 performs predictions using AI (artificial intelligence). More specifically, the computer 70 (e.g., the storage unit 70a) stores learning data (trained models, teacher data) in advance (before prediction). This learning data is obtained by learning the correlation between the sequential information D1 and water demand from a large amount of past sequential information D1 (big data) and a large amount of water demand information (big data). The prediction unit 73 then outputs (predicts) future water demand based on the sequential information D1 input to the prediction unit 73 (specifically, input to the information acquisition unit 71 and then to the prediction unit 73) and the learning data. Note that the learning data may be generated by reinforcement learning based on the sequential information D1 input to the computer 70 for water demand prediction and a detected value of water demand (actual usage amount, pipeline sensor information D45, described later). Specific examples of the correlation between the sequential information D1 and water demand will be described later.

[0030] The pipeline control planning unit 75 sets (calculates, determines) a pipeline control plan based on the water demand predicted by the prediction unit 73. The pipeline control plan set by the pipeline control planning unit 75 is a plan for controlling water flowing through the pipelines 41, and is a plan for controlling the pipeline equipment 43 (e.g., pumps 43p, valves 43b). Specifically, the pipeline control planning unit 75 sets (manages, plans) an appropriate water pressure for each of the multiple water distribution areas 30 according to the water demand for each of the multiple water distribution areas 30 predicted by the prediction unit 73. This "appropriate pressure" is a pressure that can suppress water leakage (water waste) in the pipelines 41 and that can distribute an appropriate amount of water (no water shortage). For example, the pipeline control planning unit 75 sets a high water pressure in a water distribution area 30 where water demand is increasing, and sets a low water pressure in a water distribution area 30 where water demand is decreasing.

[0031] The pipeline control unit 77 controls the water flowing through the pipeline 41 based on the water demand predicted by the prediction unit 73. For example, the pipeline control unit 77 controls the pipeline devices 43 based on the pipeline control plan set by the pipeline control planning unit 75. The pipeline control unit 77 controls the pump 43p. The pipeline control unit 77 may adjust the number of pumps 43p in operation, may adjust the pressure of water discharged by the pump 43p, or may adjust the flow rate of water discharged by the pump 43p. The pipeline control unit 77 controls the valve 43b, and specifically, adjusts the opening degree of the valve 43b.

[0032] (Details of the sequential information D1 and details of the forecast of water demand) As described above, the prediction unit 73 predicts the water demand based on the sequential information D1. Alternatively, the prediction unit 73 may predict the water demand based on the sequential information D1 and information other than the sequential information D1.

[0033] The sequential information D1 is information that is updated sequentially. The sequential information D1 is information that is updated multiple times a day. The sequential information D1 may include information that is updated periodically, or may include information that is updated non-periodically (for example, approximately periodically). The sequential information D1 may include quasi-static information. The update interval of the sequential information D1 of quasi-static information is longer than one minute but not longer than one hour. The sequential information D1 may include quasi-dynamic information. The update interval of the sequential information D1 of quasi-dynamic information is longer than one second but not longer than one minute. The sequential information D1 may include dynamic information. The update interval of the sequential information D1 of dynamic information is, for example, one second (in seconds). The update interval of the sequential information D1 may be longer than one hour. The sequential information D1 includes information of various contents, and the update interval of the sequential information D1 may differ depending on the content of the information.

[0034] Sequential information D1 is information that correlates with water demand. Sequential information D1 is information that changes before the actual amount of water used changes. Note that pipe sensor information D45, which will be described later, is information that changes when the actual amount of water used changes, and is not included in sequential information D1. As will be described later, sequential information D1 includes information on the movements of water consumers (hereinafter, water consumers may also be simply referred to as "consumers"). Sequential information D1 may include information other than information on the movements of consumers.

[0035] The sequential information D1 may include current information. For example, the sequential information D1 may include detected current information (specific examples of detection will be described later), or may include current information set (updated) by an organization, institution, etc. The sequential information D1 may include future information. For example, the sequential information D1 may include predicted information. This predicted information may include information predicted by the computer 70, or may include information obtained by the computer 70 that has been predicted outside the computer 70 (by a device different from the computer 70). The sequential information D1 may include scheduled information (described later).

[0036] The sequential information D1 may include time information. This time information is information on the time change of the sequential information D1. Specifically, this time information is information indicating how the sequential information D1 changes (or does not change) over time. For example, the sequential information D1 including time information may include information (described later) indicating how the movement state of the consumer changes over time. For example, the sequential information D1 including time information may include information indicating how the water demand changes over time. For example, the sequential information D1 including time information may include schedule information (e.g., personal schedule information, schedule information for events, etc.) described later.

[0037] (Information on the movement of consumers) The sequential information D1 may include information on the movement of the consumer subject (the consumer subject's movements). The information on the movement of the consumer subject may include information indicating that the consumer subject is moving, or may include information indicating that the consumer subject is not moving (staying). The sequential information D1 may include information that directly indicates the movement of the consumer subject, or may include information that indirectly indicates the movement of the consumer subject. The information on the movement of the consumer subject may include information on the water distribution area 30 related to the movement of the consumer subject. Specifically, the information on the movement of the consumer subject may include information (location, type, etc.) of the water distribution area 30 from which the consumer subject moved, information on the water distribution area 30 to which the consumer subject moved, or information on the water distribution area 30 where the consumer subject stays. If the water distribution area 30 is set in stages (e.g., large area 31, medium area 33, small area 35, etc.), the information on the movement of the consumer subject may include information (location, type, etc.) of the water distribution area 30 of each stage.

[0038] The sequential information D1 may include information on the movement of people (human movements). The consuming subject may include a person. The sequential information D1 may include information on the movement of things other than people. The consuming subject may include things other than people. The above-mentioned "things other than people" may be, for example, vehicles that consume water (e.g., fire engines, sprinkler trucks, etc.).

[0039] (Prediction of water demand based on information on consumer movements) A specific example of a prediction of water demand based on the sequential information D1 when the sequential information D1 includes information on the movement of consumers is as follows: As described above, each component of the water supply system 10 (e.g., water distribution area 30) will be described with reference to FIG.

[0040] (Source and destination positions) The sequential information D1 preferably includes information on one or both of the location of the water distribution area 30 from which the consumer entity moves and the location of the water distribution area 30 to which the consumer entity moves. An example of the reason for this will be described below, assuming that the consumer entity is a person. The more people there are in a certain water distribution area 30, the higher the water demand for that water distribution area 30 is likely to be. Therefore, the sequential information D1 shown in FIG. 2 preferably includes information on the location of the water distribution area 30 from which the person moves. In this case, the prediction unit 73 can predict that the water demand for the water distribution area 30 from which the person moves will be low. Furthermore, the fewer people there are in a certain water distribution area 30, the lower the water demand for that water distribution area 30 is likely to be. Therefore, the sequential information D1 preferably includes information on the location of the water distribution area 30 to which the person moves. In this case, the prediction unit 73 can predict that the water demand for the water distribution area 30 to which the person moves will be high. Furthermore, even if the consumer entity is something other than a person, the future water demand for one or both of the water distribution areas 30 from which the consumer entity moves and the water distribution area 30 to which the consumer entity moves will change. Therefore, it is preferable that the sequential information D1 includes information on one or both of the location of the water distribution area 30 from which the consumer entity moved and the location of the water distribution area 30 to which the consumer entity moved.

[0041] (Source and destination types) The sequential information D1 preferably includes information on one or both of the types of water distribution area 30 from which the consumer entity moves and the type of water distribution area 30 to which the consumer entity moves. An example of the reason for this will be described below in which the consumer entity is a person. Even if the number of people in the water distribution area 30 is the same, the water demand may differ depending on the type of water distribution area 30. For example, even if the number of people in the water distribution area 30 is the same, the water demand may be higher in a residential area 30r than in an office district 30o. Therefore, the sequential information D1 preferably includes information on the type of water distribution area 30 from which the consumer entity moves. In this case, the prediction unit 73 can predict the water demand according to the type of water distribution area 30 from which the consumer entity moves. Furthermore, the sequential information D1 preferably includes information on the type of water distribution area 30 to which the consumer entity moves. In this case, the prediction unit 73 can predict the water demand according to the type of water distribution area 30 to which the consumer entity moves. Furthermore, even if the consumer entity is something other than a person, the future water demand may change depending on the type of water distribution area 30 from which the consumer entity moves or the type of water distribution area 30 to which the consumer entity moves or the type of water distribution area 30 to which the consumer entity moves. Therefore, it is preferable that the sequential information D1 includes one or both types of information: the type of water distribution area 30 from which the consumer entity moved, and the type of water distribution area 30 to which the consumer entity moved.

[0042] (Time information regarding the movement of consumers) The sequential information D1 preferably includes time information (travel time, stay time, etc.) related to the movement of the consumer. An example of the reason for this will be described below in which the consumer is a person. For example, the future water demand will change depending on the time information related to the person's movement. Specifically, it is assumed that a person starts moving from the source water distribution area 30 and arrives at the destination water distribution area 30 after a certain travel time has elapsed, and the water demand in the destination water distribution area 30 will be high. Furthermore, for example, if a person is in the water distribution area 30 in a residential area 30r, it is assumed that this person will stay in this water distribution area 30 until it is time to go to work. Then, it is assumed that the water demand in this water distribution area 30 will be approximately constant until it is time to go to work. In this way, the future water demand will change depending on the time information related to the person's movement (travel time, stay time, etc.). Therefore, it is preferable that the sequential information D1 includes time information related to the person's movement. Furthermore, even if the consumer is something other than a person, the future water demand will change depending on the time information related to the consumer's movement. Therefore, it is preferable that the sequential information D1 includes time information regarding the movement of the consumer. Then, it is preferable that the prediction unit 73 predicts the water demand for each of the multiple water distribution areas 30 based on the time information regarding the movement of the consumer.

[0043] The time information regarding the movement of the consumer subject may include information on travel time or information on stay time. For example, the travel time information may be predicted based on information on the movement speed of the consumer subject. The travel time information may be predicted based on information on the means of transportation (walking, public transportation, automobile, etc.). If the consumer subject is a person and the person travels by public transportation, the travel time information may be predicted based on operation information of the public transportation, etc. If the person travels by automobile, the travel time information may be predicted based on traffic congestion information. The time information regarding the movement of the consumer subject may be schedule information (e.g., a schedule of when and where the person will be) described below, or may be information predicted based on schedule information.

[0044] (Cause of Movement) The sequential information D1 may include information on the cause of the consumer's movement. The reason for this is, for example, as follows: There is some cause (reason) for the consumer to move or stay. For example, if the consumer is a person and a crowd-pulling event is being held, many people will move to the event venue, and the water demand in the water distribution area 30 of the event venue is expected to be high. Furthermore, if a disaster occurs before the start of work (or school), many people are likely to stay in the residential area 30r, and the water demand in the residential area 30r will remain high, while the water demand in the business district 30o will remain low. Furthermore, even if the consumer is something other than a person, the water demand in the water distribution area 30 will change depending on the cause of the consumer's movement. For example, if the consumer is a fire engine, the water demand in the water distribution area 30 to which the fire engine moves will change depending on the cause of the fire engine's movement (disaster, fire, etc.). Therefore, the sequential information D1 may include information on the cause of the consumer's movement. In this case, the prediction unit 73 can predict the movement of the consumer subject based on information on the cause of the consumer subject's movement. For example, the prediction unit 73 can predict the location and type of the consumer subject's origin and destination based on information on the cause of the consumer subject's movement. Then, the prediction unit 73 can predict the water demand for each of the multiple water distribution areas 30 according to the information on the cause of the consumer subject's movement.

[0045] (Sequential information D1, which is different from information on the movement of consumers) The sequential information D1 may include sequential information D1 that is different from the information on the movement of the consumer subject. This "sequential information D1 that is different from the information on the movement of the consumer subject" is information that is unrelated (or only weakly related) to the movement of the consumer subject, and is information that is correlated with the amount of water demand. "Sequential information D1 that is different from the information on the movement of the consumer subject" is, for example, information on the operating status of facilities that use water (such as factories).

[0046] (Causes of changes in water demand and information on the time of change) The sequential information D1 may include information on the cause of the change in water demand. The prediction unit 73 may predict time information on the change in water demand depending on the cause of the change in water demand. The information on the cause of the change in water demand may include, for example, one or more of the following information: location information, type information, and situation information (such as attributes described below) of the water distribution area 30. The "time information on the change in water demand" predicted by the prediction unit 73 is information that indicates how the water demand changes over time. The "change in water demand" is an increase or decrease in water demand. Specifically, the time information on the change in water demand is information that indicates whether the water demand changes suddenly, gradually, or periodically. A specific example of when the prediction unit 73 predicts time information on the change in water demand is as follows.

[0047] The time information of the change in water demand predicted by the prediction unit 73 may include information that the water demand will suddenly change (increase or decrease). Furthermore, the time information of the change in water demand may include information that the water demand will return to its original state (the state before the sudden change) immediately after the sudden change. For example, suppose an event that causes a change in water demand occurs in a certain water distribution area 30. At this time, the prediction unit 73 sets (e.g., adds or changes) an attribute (an example of sequential information D1) for this water distribution area 30 indicating a sudden change in water demand. More specifically, suppose a fire breaks out in a certain water distribution area 30 (an example of a cause of a change in water demand). At this time, the prediction unit 73 changes the attribute of this water distribution area 30 from a normal area to a fire area (adds or changes the attribute) based on the information about the cause, that is, the fire. At this time, the prediction unit 73 predicts a sudden increase in water demand in this water distribution area 30. After that, the fire is extinguished in this water distribution area 30 (an example of a cause). At this time, the prediction unit 73 changes the attribute of this water distribution area 30 from a fire area to a normal area (cancels or changes the attribute) based on the information on the cause that the fire has been extinguished. At this time, the prediction unit 73 predicts that the water demand in this water distribution area 30 will soon (suddenly) decrease.

[0048] The time information of the change in water demand predicted by the prediction unit 73 may include information that the water demand suddenly changed and that it will take a long time (e.g., several months or several years) for the water demand to return to its original state. Specifically, suppose that an infectious disease (such as COVID-19) (an example of a cause) occurs in a certain water distribution area 30. At this time, the prediction unit 73 changes the attribute (an example of the sequential information D1) of this water distribution area 30 from a normal area to an emergency declaration area. After a long time has passed since the outbreak of the infectious disease, the prediction unit 73 changes this water distribution area 30 from an emergency declaration area to a normal area. At this time, the prediction unit 73 may predict that it will take a long time to change the area from an emergency declaration area to a normal area based on information about the cause, that is, the outbreak of the infectious disease. Furthermore, the prediction unit 73 may predict that it will take a long time for the water demand to return to its original state after the area is changed from an emergency declaration area to a normal area.

[0049] The time information about the change in water demand predicted by the prediction unit 73 may include information that the water demand will change over a long period of time (e.g., several months or several years). For example, a water distribution area 30 (a rural area) with few people and many farm fields is developed over a long period of time (slowly) and changes into an area with many people and houses (a residential area 30r). In this case, the water demand for this water distribution area 30 changes over a long period of time. At this time, the prediction unit 73 predicts that the water demand for the water distribution area 30 will change (increase) over a long period of time based on information about the cause of development in the water distribution area 30.

[0050] The time information on changes in water demand may include information that water demand changes periodically. Specifically, in a water distribution area 30 where a school is located, when the school is on a long vacation, the number of people in this water distribution area 30 will be smaller than usual (when the school is not on a long vacation), and water demand will be lower than usual. Based on information on the cause of periodic long vacations in this water distribution area 30, the prediction unit 73 predicts that water demand in this water distribution area 30 will change periodically.

[0051] (Further specific example of sequential information D1) Specific examples of the content of the sequential information D1 and the correlation between the sequential information D1 and the water demand are as follows.

[0052] (Smart City) The sequential information D1 may include information used to realize a smart city. The information used to realize a smart city is information detected by sensors (including, for example, cameras) installed throughout the city. The information used to realize a smart city may include information on people's movements, or may include information other than information on people's movements.

[0053] (Information terminal base station usage information) The sequential information D1 may include base station usage information of an information terminal carried by a person. The base station usage information is information indicating which base station the information terminal is accessing (communicating with). The information terminal may be, for example, a smartphone, a mobile phone, a tablet, or a personal computer. The base station usage information is information that can detect the movement (motion) of a large number of people as a whole. Therefore, the base station usage information can be used to predict the water demand in the water distribution area 30, based on the origin, destination, and location of the people. When information on people's movements is obtained based on the base station usage information, it is not necessary to obtain information on each individual person to obtain information on people's movements. In this case, the computer 70 does not need to handle information that includes personal information. Note that, as described below, the sequential information D1 may include personal information.

[0054] (Location information of information terminal) The sequential information D1 may include location information of an information terminal carried by a person. The location information of the information terminal is information that can directly detect the movement (or stay) of an individual person and is information that can detect the movement of a person in detail. Therefore, the location information of the information terminal can be used to predict the water demand in the water distribution area 30 for the person's origin, destination, and stay location.

[0055] (Entrance / exit information) The sequential information D1 may include information that a person has passed through an entrance / exit. The sequential information D1 may also include information that a person has not passed through an entrance / exit. An entrance / exit may be an entrance / exit of a building (such as a house or a building), a ticket gate at a station, or an entrance / exit of a vehicle. Information that a person has passed (or not passed) through an entrance / exit of a building is information that can be used to detect the number of people in the building and can be used to predict the water demand in the water distribution area 30 of the building. Information that a person has passed through a ticket gate at a station or an entrance / exit of a vehicle is information that can be used to detect the movement of people and can be used to predict the water demand in the water distribution area 30 from which a person has moved and the water distribution area 30 to which a person has moved.

[0056] (Electricity Information) The sequential information D1 may include power information. This power information may include the amount of power consumed, or may include information on which appliances are being used. The power information can be used to predict water demand in the water distribution area 30 where power is being used (or not being used). For example, the power information may include information that can detect information about people in the water distribution area 30 where power is being used (whether they are present or not, the number of people, etc.). Furthermore, for example, the power information may include information that can detect the operating status of facilities (facilities that use water) where power is being used.

[0057] (Building information) The sequential information D1 may include building information. The building information may include information that can detect information about people in the building (whether they are present or not, the number of people, etc.). The building information may include the entrance / exit information described above, may include power information, or may include information that combines the entrance / exit information and the power information. For example, if the front door (an example of an entrance / exit) of a house is opened or closed and the power to the house drops, it is possible to detect (or predict) that people in the house have left the house and are no longer at home. The building information can detect information about people in the building and can be used to predict the water demand in the water distribution area 30 of the building. Note that the building information may include information other than information about people in the building, such as information about the operating status of facilities that use water.

[0058] (Scheduled information) The sequential information D1 may include schedule information.

[0059] The sequential information D1 may include personal schedule information. The personal schedule information may include, for example, information entered as a schedule into an information terminal (such as information on a calendar or schedule). The personal schedule information is information that can be used to predict people's movements. For example, the personal schedule information may be information that can predict places where many people will gather, or information that can predict places where few people will gather. Specifically, the personal schedule information may include information such as days when the person will go to the office, days when the person will work from home, and plans to attend an event. More specifically, the personal schedule information can be used to predict, for example, that many people will gather in an office district 30o (company), increasing the water demand in the office district 30o, and that fewer people will gather in a residential district 30r, decreasing the water demand in the residential district 30r. The personal schedule information can also be used to predict, for example, that many people will gather in a water distribution area 30 where an event is being held, increasing the water demand in this water distribution area 30.

[0060] The sequential information D1 may include schedule information of organizations, institutions, etc. The schedule information of organizations, institutions, etc. may be information that can predict information on people's movements, or information that can predict information different from information on people's movements. The schedule information of organizations, institutions, etc. may include event information (described later), traffic information (described later), fire information (described later), facility operation information, etc. This "organization, institution, etc." may be, for example, a country, a local government, a company, a non-profit organization, etc. The schedule information of organizations, institutions, etc. may be acquired from organizations, institutions, etc. (the water demand prediction system 1 may cooperate with organizations, institutions, etc.).

[0061] (Traffic information) The sequential information D1 may include traffic information, which is information that can detect and predict people's movements and can be used to predict water demand.

[0062] The sequential information D1 may include information about the use of public transportation. For example, the information about the use of public transportation may include information indicating that a person has passed through a ticket gate at a station (entering the ticket gate to board a bus, or exiting the ticket gate), or information indicating that a person has boarded or disembarked a bus. The information about the use of public transportation may be used to detect the movement of a person, such as the source or destination water distribution area 30.

[0063] The sequential information D1 may include operation information of public transportation. The operation information may include information indicating whether the public transportation is operating normally, whether it is running later than normal, whether it is stopped, etc. The operation information may also include information on the planned suspension of public transportation.

[0064] The sequential information D1 may include road traffic information, which may include vehicle movement information, congestion information (information on current congestion or predictions of future congestion), and traffic regulation information (road construction, events, etc.).

[0065] (Information indicating a gathering of people) The sequential information D1 may include information indicating that people are gathering. The information indicating that people are gathering can be used to predict the water demand in the water distribution area 30 where people are gathering.

[0066] The sequential information D1 may include event information. The event information may include information that an event is taking place (information that an event is currently taking place or information that an event is scheduled to take place in the future), information about the number of people attending the event, or information about the number of people expected to attend the event. Examples of events include festivals, sports events, concerts, and exhibitions. The event information may be acquired from organizations, institutions, etc. that have event information (the water demand prediction system 1 may cooperate with organizations, institutions, etc.). The event information is information that allows prediction that a large number of people will gather at the event, and information that allows prediction that the water demand in the water distribution area 30 where the event is held will be high.

[0067] The sequential information D1 may include information indicating a gathering of people other than an event. The information indicating a gathering of people other than an event may include, for example, information on whether a commercial facility, a theme park, or the like is open or not, information on its business hours, etc.

[0068] (Weather Information) The state of people's movement changes depending on the weather conditions. For example, when a weather disaster occurs, people's movement is restricted. Furthermore, even when no weather disaster occurs, people's movement patterns change depending on the weather conditions. Therefore, weather conditions are information that can be used to predict people's movement and water demand. Therefore, the sequential information D1 may include weather information. The weather information may include, for example, information on precipitation, temperature, typhoons, heavy rain, etc. The weather information may be acquired from an organization or institution that has weather information (the water demand prediction system 1 may cooperate with an organization or institution). The organization or institution that has weather information may be a weather company (such as a weather association), or a national or local government. The weather information may also be used to predict the operating status of facilities that use water (such as factories).

[0069] (Fire department information) The sequential information D1 may include firefighting information. When a fire breaks out, water is used to extinguish the fire. For example, when a fire breaks out, a fire hydrant directly connected to the pipeline 41 (water pipe) is opened, and the water flowing through the pipeline 41 is used. Also, when a fire breaks out, water from a fire water tank buried underground is used. When a fire breaks out, water is used to extinguish the fire, so it is expected that the water demand in the water distribution area 30 where the fire broke out will increase. Therefore, the firefighting information is information that can be used to predict water demand. The firefighting information may include information that a fire has broken out, information about the location of the fire, information about fire drills, and information about the dispatch status of fire engines. The firefighting information may be acquired from local governments or fire departments (the water demand prediction system 1 may cooperate with fire departments, etc.).

[0070] (Information other than sequential information D1) The computer 70 (pipeline control unit 77) may perform pipeline control based on the sequential information D1 and information other than the sequential information D1. The information other than the sequential information D1 may include statistical information D3 and pipeline sensor information D45.

[0071] (Statistics Information D3) The prediction unit 73 may predict the water demand for each of the multiple water distribution areas 30 based on the statistical information D3 and the sequential information D1. The statistical information D3 is information on past water demand. The statistical information D3 may include information indicating the relationship between the weather (weather conditions over several days to several months) and the water demand. The statistical information D3 may include information indicating the relationship between the day of the week and the water demand. The statistical information D3 may include information on the water demand when people are moving around normally (when no unexpected events are occurring). Note that the learning data used by the prediction unit 73 to predict the water demand based on the sequential information D1 is not included in the "statistical information D3" in this embodiment.

[0072] The prediction unit 73 may predict the water demand based on the statistical information D3, for example, as follows. The prediction unit 73 may correct (modify) the predicted water demand depending on the degree of agreement between the statistical information D3 and the sequential information D1. For example, the prediction unit 73 may calculate the degree of agreement between information on people's movements, which is the sequential information D1, and information on people's movements under normal circumstances, which is the statistical information D3. For example, the prediction unit 73 may detect whether these pieces of information match (detect whether people's movements are normal (as expected)). The prediction unit 73 may correct the water demand depending on the degree of agreement between these pieces of information. For example, the prediction unit 73 may bring the predicted water demand closer to the water demand in the statistical information D3 as the degree of agreement between these pieces of information increases (the prediction unit 73 may reduce the amount of correction). The prediction unit 73 may correct the predicted water demand more significantly from the water demand in the statistical information D3 as the degree of agreement between these pieces of information decreases.

[0073] (Forecast based only on statistical information D3) The prediction unit 73 can appropriately predict water demand based on the sequential information D1 and the statistical information D3. On the other hand, prediction of water demand based only on the statistical information D3 may result in insufficient prediction accuracy (prediction errors). The reason for this is as follows. For example, the current situation may differ significantly from the past statistical information D3. For example, as described above, the movement of people when an unexpected event occurs may differ significantly from the movement of people under normal circumstances (see FIG. 4). In such cases, the actual water demand differs significantly from the water demand in the past statistical information D3. Therefore, it is not possible to predict water demand corresponding to the movement of people when an unexpected event occurs based only on the past statistical information D3. Therefore, the prediction accuracy of water demand may be insufficient (prediction errors may occur). On the other hand, when the prediction unit 73 predicts water demand based on the statistical information D3 and the sequential information D1, the prediction accuracy of water demand can be improved compared to when water demand is predicted based only on the statistical information D3.

[0074] (Examples of people moving when an unexpected event occurs) Below, specific examples of people's movements under normal circumstances and specific examples of people's movements when an unexpected event occurs will be described.

[0075] (Examples of people's movements during normal times) Referring to Figure 3, a specific example of people's movements during normal times (weekdays when no unexpected events occur) commuting to work (or school) and home is described. Figure 3 shows the number of people in a residential area 30r, station Sr in the residential area 30r, business district 30o, and station So in the business district 30o. In Figure 3, the number of people in the water distribution area 30 is represented by the darkness of the color of the rectangle. At night (7 PM to 8 AM), the number of people in the residential area 30r is higher than the number of people in the business district 30o (i.e., water demand is high). In the morning (8 AM to 9 AM), people move from the residential area 30r to station Sr (to go to work), so the number of people in the residential area 30r decreases (i.e., water demand decreases), and the number of people at station Sr increases. During the day (9 AM to 6 PM), people move from station So in the business district 30o to the business district 30o (to go to work), so the number of people in the business district 30o increases and the number of people at station So decreases. During the day, the number of people in residential area 30r is lower than the number of people in business district 30o. In the evening (6:00 PM to 7:00 PM), people move from business district 30o to station So (going home), so the number of people in business district 30o decreases and the number of people at station So increases.

[0076] (In the event of an unexpected event) When an unexpected event occurs, human movement may be restricted. As a result, human movement conditions differ from those under normal circumstances. Unexpected events include, for example, infectious diseases (such as COVID-19), bad weather (such as torrential rain or typhoons), and disasters (such as earthquakes or typhoons). FIG. 4 shows the number of people (an example of statistical information D3, described later) in the residential area 30r and the business district 30o during the night and day. This example shows the number of people in both cases where there is and is not a restriction on human movement due to an unexpected event (specifically, COVID-19). As shown in FIG. 4, the situation regarding human movement differs significantly between cases where there is and is not a restriction on human movement. Specifically, during the day when there are no restrictions on human movement, the number of people in the residential area 30r is low (i.e., water demand is low) and the number of people in the business district 30o is high (i.e., water demand is high). On the other hand, during the day when restrictions on people's movements are in place, the number of people in residential area 30r increases due to the use of telecommuting (i.e., water demand increases), while the number of people in office area 30o decreases (i.e., water demand decreases). At night, whether there are restrictions on people's movements or not, the number of people in residential area 30r increases (i.e., water demand increases) while the number of people in office area 30o decreases (i.e., water demand decreases).

[0077] As such, the situation of people's movement differs significantly between when a sudden event occurs and when no sudden event occurs (normal times). For this reason, it is preferable that the sequential information D1 shown in FIG. 2 includes information on people's movement. In this case, even when a sudden event occurs, the sequential information D1 reflects people's movement that differs from normal times due to the sudden event. It is also preferable that the prediction unit 73 predicts water demand based on the sequential information D1 that includes information on people's movement. In this case, the water demand prediction system 1 can not only predict water demand under normal circumstances, but also appropriately predict water demand when there is a sudden change in people's movement (a change from normal times), such as when a sudden event occurs.

[0078] (Pipeline sensor information D45) The computer 70 (pipe control unit 77) may perform pipeline control (feedback control) based on the water demand predicted based on the sequential information D1 and the pipeline sensor information D45. The pipeline sensor information D45 is information detected by the pipeline sensor 45. More specifically, the pipeline sensor information D45 is information on the state of water flowing through the pipeline 41, detected by the pipeline sensor 45. The pipeline sensor information D45 is information on the current or approximately current state of water. The pipeline sensor information D45 may include pressure information, flow rate (usage amount) information, or both pressure and flow rate information.

[0079] A specific example of pipeline control based on the sequential information D1 and pipeline sensor information D45 is as follows: The pipeline sensor 45 detects values ​​(e.g., pressure and flow rate) indicating the state of water passing through the pipeline 41. The pipeline control unit 77 determines whether the detected pressure and flow rate values ​​(pipe sensor information D45) are within upper and lower limit values. These upper and lower limit values ​​are set to values ​​that can suppress water leakage from the pipeline 41 and ensure that an appropriate amount of water is distributed to the water distribution area 30. These upper and lower limit values ​​are set in advance in the computer 70 (before determining whether the values ​​are within the upper and lower limit values). Then, if the pipeline sensor information D45 is not within the upper or lower limit values, the pipeline control unit 77 corrects the control signal to be output to the pipeline device 43 so that the pipeline sensor information D45 is within the upper and lower limit values. In addition, if the pipeline sensor information D45 is not within the upper or lower limit values, the pipeline control planning unit 75 may modify the pipeline control plan, or the prediction unit 73 may modify the water demand so that the pipeline sensor information D45 is within the upper or lower limit values.

[0080] When the computer 70 (pipeline control unit 77) performs pipeline control based on the water demand predicted based on the sequential information D1 and the pipeline sensor information D45, it is preferable that a pipeline sensor 45 be provided for each water distribution area 30 (for each distribution pipe 41b in each water distribution area 30). When a pipeline sensor 45 is provided for each water distribution area 30, more appropriate pipeline control can be achieved. It is also preferable that a pipeline sensor 45 be provided for each smallest unit (small area 35) of the water distribution area 30. Note that a pipeline sensor 45 may also be provided for each water distribution area 30 that is larger than the smallest unit (small area 35) of the water distribution area 30 (for each medium area 33 or large area 31).

[0081] (Control based only on pipe sensor information D45) The computer 70 (pipe control unit 77) can appropriately perform pipeline control based on the water demand predicted based on the sequential information D1 and the pipeline sensor information D45. On the other hand, pipeline control based only on the pipeline sensor information D45 is a feedback-only response that monitors the amount of water used and takes action based on the monitoring results, which can cause delays in pipeline control (action). Specific examples of problems that can cause delays in pipeline control are as follows:

[0082] One problem caused by delayed pipeline control is the issue of water supply shortages. If water (purified water) is produced after high water usage is detected, there is a risk of a water supply shortage. The reason for this is as follows: Water is not delivered to the water distribution area 30 immediately after it is produced. Specifically, water usually passes through the water purification plant 21, distribution reservoir 23a, pumping station 23b, and distribution pipe 41b (water distribution pipe network) in this order before being distributed to the water distribution area 30. Therefore, it takes time from when water is produced at the water purification plant 21 until it is delivered to the water distribution area 30. Therefore, if water is produced after high water usage is detected, there is a risk of a water supply shortage.

[0083] On the other hand, if too much water is produced relative to the demand, the water will not be used, causing the problem of chlorine leaching from the water. The reason for this is as follows. For example, the amount of chlorine in the water coming out of the faucets in the water distribution area 30 is measured using a residual chlorine meter. Then, based on the chlorine amount, the amount of chlorine deemed necessary is injected at the water purification plant 21. However, if the produced water is not used, the chlorine will self-decompose and lose its chlorine from the water (purified water). Purified water that has lost its chlorine is no longer purified water. This is why it becomes necessary to inject chlorine.

[0084] Another problem that can cause delays in pipeline control is that the water pressure is either too high or too low. The reasons for this are as follows: If the water pressure is increased after a high amount of water usage is detected, the timing of the pressure increase is delayed. If the actual water pressure is low compared to the appropriate pressure for the amount of water usage, there is a risk that water will not be supplied appropriately. On the other hand, it is possible to increase the water pressure in advance so that it can respond to increases and decreases in water usage. However, in this case, the actual water pressure will be higher than the appropriate pressure for the amount of water usage, making it more likely that water will leak from the water distribution pipe 41b.

[0085] As described above, when pipeline control is performed based solely on current (including approximately current) pipeline sensor information D45, delays in pipeline control occur. On the other hand, when pipeline control is performed by combining water demand prediction (feedforward type) based on sequential information D1 with pipeline control (feedback type) according to current pipeline sensor information D45, delays in pipeline control can be reduced. Specifically, since an appropriate amount of water can be produced, problems of supply shortages caused by insufficient water production can be reduced, and problems of chlorine loss caused by producing too much water can be reduced. Furthermore, since water pressure can be appropriately adjusted, problems of water leakage from the water distribution pipe 41b caused by excessive pressure can be reduced, and problems of supply shortages caused by excessive pressure can be reduced.

[0086] (Regarding control based only on statistical information D3 and pipe sensor information D45) If pipeline control is performed based on statistical information D3 and pipeline sensor information D45, rather than on sequential information D1, the accuracy of water demand prediction in the event of an unexpected event will be insufficient, and delays in pipeline control will become a problem.In contrast to this, if water demand is predicted based on sequential information D1, the accuracy of water demand prediction can be increased, and if pipeline control is performed based on the water demand predicted based on sequential information D1, the problem of delays in pipeline control can be reduced.

[0087] (Effects of the first invention) The water demand prediction system 1 shown in Fig. 1 has the following effects: The water demand prediction system 1 includes an information acquisition unit 71, a prediction unit 73, and a pipeline control unit 77 shown in Fig. 2.

[0088] [Configuration 1-1] The information acquisition unit 71 acquires sequential information D1. The sequential information D1 is information that is updated multiple times a day and includes information on the movement of water consumers.

[0089] [Configuration 1-2] The prediction unit 73 predicts the water demand for each of the plurality of water distribution areas 30 (see FIG. 1) based on the sequential information D1 acquired by the information acquisition unit 71.

[0090] [Configuration 1-3] The pipeline control unit 77 controls the water flowing through the pipeline 41 that delivers water to the water distribution area 30 based on the water demand predicted by the prediction unit 73.

[0091] The above [Configuration 1-1] and [Configuration 1-2] enable the accuracy of water demand prediction to be improved compared to when water demand is predicted without relying on the sequential information D1 (compared to conventional methods). Specifically, the accuracy of water demand prediction can be improved compared to when water demand is predicted based on statistical information D3 without relying on the sequential information D1. For example, when an unexpected event occurs, the movement status of the water consumer changes in response to the unexpected event, and the sequential information D1 changes. In this case, the prediction unit 73 can accurately predict water demand based on the sequential information D1 that reflects the unexpected event (can respond to the unexpected event).

[0092] Furthermore, in the above [Configuration 1-1], the sequential information D1 includes information on the movement of the water consumer. When the consumer moves, the water demand is likely to change. Furthermore, when the consumer does not move (stays), the water demand is unlikely to change. Therefore, in the above [Configuration 1-1], the sequential information D1 includes information on the movement of the consumer. Therefore, the prediction unit 73 predicts the water demand based on the sequential information D1 including information on the movement of the consumer. Therefore, the prediction unit 73 can predict the water demand with high accuracy.

[0093] In the above [Configuration 1-3], the pipeline control unit 77 controls the water flowing in the pipeline 41 that delivers water to the water distribution area 30 based on the water demand predicted by the prediction unit 73. Therefore, the above [Configuration 1-1], [Configuration 1-2], and [Configuration 1-3] make it possible to improve the accuracy of water demand prediction compared to the past, and to control the water delivered to the water distribution area 30 (the water flowing in the pipeline 41) based on the accurately predicted water demand.

[0094] (Effects of the second invention) [Configuration 2] The sequential information D1 includes information on one or both of the location of the water distribution area 30 from which the consumer entity moved and the location of the water distribution area 30 to which the consumer entity moved.

[0095] The above [Configuration 2] provides the following effects. When a water consumer moves, the water demand at the consumer's origin is likely to decrease. Furthermore, when a consumer moves, the water demand at the consumer's destination is likely to increase. Therefore, the water demand prediction system 1 is equipped with the above [Configuration 2]. Therefore, the prediction unit 73 can accurately predict the water demand in one or both of the water distribution areas 30 of the consumer's origin and destination.

[0096] (Effect of the third invention) [Configuration 3] The sequential information D1 includes information on one or both of the type of water distribution area 30 from which the consumer entity moves and the type of water distribution area 30 to which the consumer entity moves.

[0097] The above [Configuration 3] provides the following effect. For example, when the consumer is a person, even if the number of people in a certain water distribution area 30 is the same, the water demand will differ depending on the type of water distribution area 30 (e.g., residential area 30r, business area 30o, etc.). Also, when the consumer is an entity other than a person, the water demand may differ depending on the type of water distribution area 30 in which the consumer is located. Therefore, the water demand prediction system 1 is equipped with the above [Configuration 3]. Therefore, the prediction unit 73 can accurately predict the water demand for the water distribution area 30 according to the type of water distribution area 30.

[0098] (Effect of the fourth invention) [Configuration 4] The prediction unit 73 predicts the water demand for each of the multiple water distribution areas 30 based on the information on the time change of the sequential information D1.

[0099] The above [Configuration 4] provides the following effects. The sequential information D1 may change over time. As a result of the sequential information D1 changing over time, the water demand may change over time. Therefore, the water demand prediction system 1 is equipped with the above [Configuration 4]. Therefore, the prediction unit 73 can more accurately predict the water demand in the water distribution area 30 based on the information on the time changes in the sequential information D1.

[0100] (Effect of the fifth invention) [Configuration 5] The pipeline control unit 77 controls the water flowing through the pipeline 41 based on one or both of the statistical information D3 on past water demand and the pipeline sensor information D45, as well as the sequential information D1. The pipeline sensor information D45 is information on the detected state of the water flowing through the pipeline 41.

[0101] The above-mentioned [Configuration 5] provides the following [Effect 5a] or [Effect 5b]. Note that the pipeline control unit 77 controlling the water flowing through the pipeline 41 based on the sequential information D1 includes the pipeline control unit 77 controlling the water flowing through the pipeline 41 based on the water demand predicted by the prediction unit 73 based on the sequential information D1.

[0102] [Effect 5a] When the pipeline control unit 77 controls the water flowing through the pipeline 41 based on the statistical information D3 and the sequential information D1, the following effect is obtained. In the above [Configuration 1-1], [Configuration 1-2], and [Configuration 1-3], the prediction unit 73 predicts the water demand based on the sequential information D1, and the pipeline control unit 77 controls the water flowing through the pipeline 41 based on the water demand predicted by the prediction unit 73. Therefore, the water flowing through the pipeline 41 can be controlled based on the predicted water demand corresponding to an unexpected event. On the other hand, if no unexpected event occurs (under normal circumstances), the water demand can be predicted to some extent based on the past statistical information D3. Therefore, the pipeline control unit 77 can appropriately control the water flowing through the pipeline 41 based on the past statistical information D3. Therefore, the water demand prediction system 1 includes the above [Configuration 6]. Therefore, the pipeline control unit 77 can appropriately control the water supplied to the water distribution area 30 according to various situations, such as when an unexpected event occurs and when it does not occur.

[0103] [Effect 5b] When the pipe control unit 77 controls the water flowing in the pipe 41 based on the pipe sensor information D45 and the sequential information D1, the following effect can be obtained. In this case, the pipe control unit 77 can appropriately control the water flowing in the pipe 41 based on information on the state of the water flowing in the pipe 41, which is the state of the water that is actually detected (i.e., the pipe sensor information D45).

[0104] The pipeline control unit 77 may perform control based on both the statistical information D3 and the pipeline sensor information D45, and the sequential information D1. In this case, the water flowing through the pipeline 41 can be more appropriately controlled than when control is performed based on only one of the statistical information D3 and the pipeline sensor information D45, and the sequential information D1.

[0105] (Variation) The above-described embodiments (including modified examples within the embodiments (the same applies hereinafter)) may be modified in various ways. For example, the number of components in the above-described embodiments may be changed, or some of the components may not be provided. For example, the connections between the components shown in FIGS. 1 and 2 may be changed. For example, the inclusion relationships between the components may be changed in various ways. For example, a component described as a lower-level component included in a higher-level component may not be included in this higher-level component, but may be included in another component. For example, what is described as multiple different elements may be combined into a single element. For example, what is described as a single element may be provided as multiple different elements. For example, the computer 70 (see FIG. 2) may perform substantially the same processing (prediction, control, etc.) as the above-described embodiments. For example, the processing procedure, information used in the processing, etc. may be modified in various ways. Specifically, the computer 70 may perform processing using information that can be converted into various types of information used in the above-described embodiments. The processing performed by the computer 70 may be combined in various ways. For example, each component may have only a portion of its respective characteristics (function, arrangement, shape, operation, etc.).

[0106] The water demand prediction system 1 (or pipeline control system) is configured to perform each of the above operations. A water demand prediction program that causes a calculator 70 (computer) to execute processing to perform each of the above operations may be provided, or a pipeline control program may be provided. A water demand prediction method that performs each of the above operations may be performed, or a pipeline control method may be performed. Each of the above operations may be considered a "step" in the program and method. For example, information acquisition by the information acquisition unit 71 may be considered an "information acquisition step," and prediction by the prediction unit 73 may be considered a "prediction step." [Explanation of symbols]

[0107] 1. Water demand forecasting system 30 Water distribution area 41 Pipeline 71 Information Acquisition Department 73 Prediction Department 77 Pipeline control section D1 Sequential information D3 Statistics D45 Pipeline sensor information

Claims

1. an information acquisition unit that acquires sequential information that is updated multiple times a day and includes information on the movement of water consumers; a prediction unit that predicts water demand for each of a plurality of water distribution areas based on the sequential information acquired by the information acquisition unit; a pipeline control unit that controls water flowing through a pipeline that delivers water to the water distribution area based on the water demand predicted by the prediction unit; Equipped with Water demand forecasting system.

2. The water demand prediction system according to claim 1, The sequential information includes information on one or both of the location of the water distribution area from which the consumer entity moves and the location of the water distribution area to which the consumer entity moves. Water demand forecasting system.

3. The water demand prediction system according to claim 1, The sequential information includes one or both of the types of information of the type of the water distribution area from which the consumer entity moves and the type of the water distribution area to which the consumer entity moves. Water demand forecasting system.

4. The water demand prediction system according to claim 1, The prediction unit predicts the water demand for each of the plurality of water distribution areas based on information of time changes in the sequential information. Water demand forecasting system.

5. The water demand prediction system according to claim 1, The pipeline control unit One or both of statistical information, which is information on past water demand, and pipe sensor information, which is information on the detected state of water flowing through the pipe; The sequential information; and controlling the water flowing through the pipe based on the Water demand forecasting system.

Images