JP2008082641A - Control device and control method of heat storage tank heat source system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a control device of a heat storage tank heat source system capable of performing optimum operation of a heat source system comprising a heat storage tank. <P>SOLUTION: This control device for controlling the operation of the heat source system comprising the heat storage tank, comprises a data collecting means for collecting operation actual performance data, an actual performance data storing means for storing the operation actual performance data, an estimating means for estimating air-conditioning load, an operation schedule creating means for creating operation schedule data utilizing the heat storage tank in maximum from the air conditioning load, an operation control means for controlling the operation of the heat source system on the basis of the operation schedule, and an estimated load correcting means for correcting an estimated air conditioning load value after an object time on the basis of a correction coefficient determined on the basis of the difference between the estimated air conditioning load value and an actual air conditioning load value when the difference between the estimated air conditioning load value within the object time and the actual air conditioning load value on the basis of the operation actual performance data in the object time is over a prescribed value. <P>COPYRIGHT: (C)2008,JPO&INPIT

Description

  The present invention relates to a control device and a control method for a heat storage tank heat source system capable of operating a heat source system using a heat storage tank in an optimum state.

  Conventionally, a vertical heat storage tank having an excellent performance with no power is known by utilizing the characteristic that water at 4 ° C. has the highest specific gravity. In buildings using this vertical heat storage tank, load characteristics such as air conditioners (required heat quantity per hour, peak required heat quantity, required number of refrigerators, etc.) are set at the time of design and actual operation. Since it is very difficult to match the states, efficient operation is required.

In addition, as a prior art, there is known an operation control method for an ice heat storage tank type air conditioner that can effectively use ice stored in an ice heat storage tank in any load state (for example, patent document) 1).
Japanese Patent Laid-Open No. 10-089730

  However, since the conventional operation plan was set based on the experience and intuition of experienced observers, it is difficult to set the operation plan so as to maximize the efficiency of the heat source system by effectively using the heat storage tank. There is. In particular, the operation plan by the supervisor has variations, and it is difficult to verify whether or not the operation plan is optimal, so that there is a problem that the optimal operation may not always be performed.

  This invention is made | formed in view of such a situation, and it aims at providing the control apparatus and control method of a heat storage tank heat source system which can perform the optimal driving | operation of the heat source system provided with the heat storage tank.

  The present invention is a control device that controls the operation of a heat source system including a heat storage tank, and includes a data collection unit that collects operation result data, a result data storage unit that stores the operation result data, and an air conditioning load prediction Prediction means, operation plan creation means for generating operation plan data that makes maximum use of the heat storage tank from the air conditioning load, operation control means for performing operation control of the heat source system based on the operation plan, and target Finding the difference between the predicted air conditioning load value in time and the actual air conditioning load value based on the operation performance data within the target time, and when this difference exceeds a predetermined value, the predicted air conditioning load value, Predictive load correction means for correcting a predicted air conditioning load value after the target time by a correction coefficient obtained from a difference from the actual air conditioning load value is provided.

  The present invention is a control device for controlling the operation of a heat source system including a heat storage tank, the meteorological measurement data, data collection means for collecting the operation result data in association with each other, the meteorological measurement data, and the operation Pattern data generation for generating pattern data indicating the relationship between the characteristics of the weather and the building based on the actual measurement data stored in the storage unit, the actual measurement data stored in the storage unit, and the actual operation data Means, pattern data storage means for storing the pattern data, means for acquiring weather forecast data, and predicting the air conditioning load with reference to the weather forecast data and the pattern data stored in the pattern data storage means Predicting means for generating, operation plan generating means for generating operation plan data using the heat storage tank to the maximum from the air conditioning load, Based on the operation plan, the difference between the operation control means for controlling the operation of the heat source system, the predicted air conditioning load value within the target time, and the actual air conditioning load value based on the operation performance data within the target time is obtained. When the difference exceeds a predetermined value, the predicted load for correcting the predicted air conditioning load value after the target time by the correction coefficient obtained from the difference between the predicted air conditioning load value and the actual air conditioning load value And correction means.

  The present invention is a control method for controlling the operation of a heat source system including a heat storage tank, and includes a data collection step for collecting operation result data, a result data storage step for storing the operation result data, and an air conditioning load prediction A prediction step to perform, an operation plan creation step for generating operation plan data that makes maximum use of the heat storage tank from the air conditioning load, an operation control step for performing operation control of the heat source system based on the operation plan, and a target Finding the difference between the predicted air conditioning load value in time and the actual air conditioning load value based on the operation performance data within the target time, and when this difference exceeds a predetermined value, the predicted air conditioning load value, A predicted load correcting step of correcting a predicted air conditioning load value after the target time by a correction coefficient obtained from a difference from the actual air conditioning load value. .

  The present invention is a control method for controlling the operation of a heat source system including a heat storage tank, the meteorological measurement data, a data collection step for collecting the operation result data in association with each other, the meteorological measurement data, and the operation Pattern data generation for generating pattern data indicating the relationship between weather and building characteristics based on the actual data storing step for storing actual data, the actual weather data stored in the storage step, and the actual driving data A step of storing the pattern data, a step of storing the pattern data, a step of acquiring the weather forecast data, and a prediction of the air conditioning load with reference to the weather forecast data and the pattern data stored in the pattern data storage step And the operation plan data that makes the best use of the heat storage tank from the air conditioning load Based on the operation plan creation step to be generated, the operation control step for performing operation control of the heat source system based on the operation plan, the predicted air conditioning load value within the target time, and the operation result data within the target time When the difference with the actual air conditioning load value is obtained and this difference exceeds a predetermined value, the correction coefficient obtained from the difference between the predicted air conditioning load value and the actual air conditioning load value is used to calculate the difference between the target time and the subsequent time. And a predicted load correcting step for correcting the predicted air conditioning load value.

  According to the present invention, an operation plan is created based on the predicted air conditioning load predicted, and there is a difference between the actual air conditioning load based on the operation results and the predicted air conditioning load during the operation based on the operation plan. In this case, the subsequent predicted air conditioning load is corrected with a correction coefficient obtained from the difference between the actual air conditioning load and the predicted air conditioning load, so that the heat source system including the heat storage tank can be optimally operated. Is obtained.

  Hereinafter, the control apparatus of the thermal storage tank heat source system by one Embodiment of this invention is demonstrated with reference to drawings. FIG. 1 is a block diagram showing the configuration of the embodiment. In this figure, reference numeral 1 denotes a heat source system to be operated and controlled, which uses a heat storage tank 11, a turbo refrigerator 12, and free cooling (natural energy (a low outside temperature such as in winter)) and a cooling water production heat source in a cooling tower. It manufactures cold water equivalent to equipment) 13 and an air conditioner 14. Reference numeral 2 denotes a control unit that performs normal heat source control, heat source number control, water supply temperature compensation, return water temperature control, rotation control, start / stop failure control, and power recovery control in order to control the operation of the heat source system 1. . Reference numeral 3 denotes a central monitoring system that monitors and controls the operating status of facilities installed in buildings such as buildings. The central monitoring system 3 accumulates the operational status measurement data of the facility and calculates the 30-minute average value of the outside air temperature humidity and the indoor temperature humidity. Reference numeral 4 denotes a predictive control system that predicts the air conditioning load after the next day and performs control so as to optimize the operation of the heat source system 1. Reference numeral 5 denotes a weather forecast data delivery server that delivers weather forecast information for the next day and after.

  Reference numeral 41 denotes a data collection / accumulation unit that filters measured data acquired from the central monitoring system 1 and accumulates it in the database. Reference numeral 42 denotes an actual measurement data storage unit that stores a database for accumulating data collected by the data collection / accumulation unit 41. The code | symbol 43 extracts the data of arbitrary periods out of the actual measurement data memorize | stored in the actual measurement data memory | storage part 42, and produces | generates the data for ANN (Artificial Neural Network) model (data for learning), and pattern data, It is a pattern data generation unit that performs self-learning of the ANN model using the generated learning data. Reference numeral 44 denotes a pattern data storage unit that stores pattern data generated by the pattern data generation unit 43. Reference numeral 45 generates ANN prediction data from the weather forecast data distributed from the weather forecast data distribution server 5 and the pattern data stored in the pattern data storage unit 44, and uses the ANN model for one hour. It is the load prediction part which estimates the air-conditioning load of an interval. Reference numeral 46 denotes a heat source control unit that controls the heat source based on the predicted air conditioning load value output from the load prediction unit 45. Reference numeral 47 is a predicted load correcting unit that corrects the value of the predicted air conditioning load based on the actual load data of the day.

  Here, a vertical heat storage tank will be described with reference to FIG. FIG. 3 is a diagram illustrating a configuration of a vertical heat storage tank. In the heat source system shown in FIG. 3, the load ((3) shown in FIG. 3) such as an air conditioner is defined by the following equation. That is, in the daytime production state, the load of the air conditioner or the like (FIG. 3 (3)) = the amount of heat produced by the refrigerator (FIG. 3 (1)) + the heat release from the heat storage tank (FIG. 3 (2)). The load of the air conditioner or the like (FIG. 3 (3)) = the amount of heat produced by the refrigerator (FIG. 3 (1)) + Δheat storage heat release (FIG. 3 (2); Δ heat storage heat release means heat storage). The predictive control system 4 shown in FIG. 1 obtains the load heat amount up to 36 hours ahead of the air conditioner and production machine shown in FIG. 3 and, when the obtained heat amount is consumed, the chiller 1 uses midnight power. The number of operating units per hour of the refrigerators 1 to 4 is planned from the combination of the operation of ˜4 and the heat storage capacity of the heat storage tank, and the number of heat sources is controlled. At this time, the predictive control system 4 learns the building characteristic value for deriving the amount of heat consumption as pattern data, and becomes a disturbance factor for the building such as weather forecast data (outside air temperature, humidity, precipitation, solar radiation amount) from the next day onward. Meteorological forecast data) and building characteristic values (pattern data) are used as input parameters to derive the thermal load using the ANN model, and the number of operating heat sources (refrigerators 1 to 4) to satisfy the derived thermal load. To control.

  Next, the operation of the predictive control system 1 shown in FIG. 1 will be described with reference to FIG. First, the central monitoring system 3 measures data such as the outside air temperature, the heat storage tank temperature, the outside air humidity, the remaining heat storage amount, the heat consumption amount, the room temperature, the room humidity, and the power used, and transmits the data to the prediction control system 4. In response to this, the data collection / accumulation unit 41 receives the weather data, room temperature humidity data, power consumption, and the like including the outside air temperature, the heat storage tank temperature, the outside air humidity, the remaining heat storage amount, the heat consumption, the room temperature, the room humidity, and the power consumption. Data and air conditioning load data are received from the central monitoring system 3 and stored in the actual measurement data storage unit 42 (step S1). The actual measurement data stored in the actual measurement data storage unit 42 accumulates data for creating learning data based on past actual performance data, and this data accumulation operation is executed at any time.

  Next, the pattern data generation unit 43 creates learning data based on past performance data based on the actual measurement data stored in the actual measurement data storage unit 42. The learning data here refers to outside air temperature, outside air humidity, room temperature, room humidity, power consumption, air conditioner operating ratio, actual air conditioning load, and the like. Then, ANN model learning (analysis by a neural network) is performed from the learning data, and a thermal responsiveness model of the building is performed. Moreover, the pattern data generation part 43 produces | generates pattern data by performing the patterning process of each data using the thermal responsiveness model of a building, and memorize | stores it in the pattern data storage part 44 (step S2). This model learning and pattern data generation operation are executed at any time.

  Next, the load prediction unit 45 accesses the weather forecast data distribution server 5 at a predetermined time (for example, 3 o'clock, 6 o'clock, 15 o'clock, 20 o'clock) every day and receives weather forecast data. At this time, the received weather forecast data is forecast data for every hour of the temperature, humidity and precipitation of the next day. Then, the load prediction unit 45 predicts the air conditioning load of the next day by matching the received weather forecast data with the pattern data stored in the pattern data storage unit 44, and generates prediction data of the air conditioning load ( In step S3), the predicted air conditioning load data is output to the heat source controller 46.

  Next, the heat source control unit 46 generates the optimum operation plan data for the cold heat source from the predicted air conditioning load (step S4). The operation plan generated here is data that determines the amount of heat storage for the next day, the number of heat sources at the time of heat storage (nighttime), the order of the heat source operation for the next day, the number of heat sources at the time of heat dissipation (daytime), the air conditioning start time, and the like. The heat source control unit 46 transmits the operation plan data generated here to the central monitoring system 3. In response to this, the central monitoring system 3 individually controls operation / stop of the heat storage tank 11, the turbo refrigerator 12, the free cooling 13 and the air conditioner 14 via the control unit 2. As a result, the optimum operation of the heat source system 1 is performed.

  On the other hand, the predicted load correction unit 47 plans based on the predicted load predicted by the load prediction unit 45 based on the actual load data on the day stored in the actual measurement data storage unit 42 during the operation control operation for the heat source system 1. The operation plan thus made is compared with the operation result (step S5), and it is determined whether or not the difference between the plan and the result is within a predetermined range (step S6). If the difference is within the predetermined range as a result of this determination, it is considered that the plan review is not necessary, and the plan is not revised.

  On the other hand, as a result of the determination, if the difference between the plan and the actual result is not within the predetermined range (the difference is large), the predicted load correcting unit 47 corrects the plan data based on the actual data (step S7). Due to the operating characteristics of the heat source system 1 (decrease in return temperature and change in the chilled water production capacity of the turbo chiller 12), the heat storage tank 11 does not necessarily store a certain amount of chilled water. Will occur. Also, regarding the predicted load value, a large thermal load error may occur depending on the actual building usage situation, such as when the weather forecast deviates and it becomes extremely hot. The difference between these residual live heat (available effective heat) and heat load (predicted value that should be used) is often a value that requires reviewing the heat source operation plan. There is a need.

  The predicted load correction unit 47 calculates the total of the predicted air conditioning load and the total of the actual measured air conditioning load values immediately before the target time, and divides the total of the actual measured values by the total predicted value to obtain a correction coefficient (correction coefficient = Measured value / predicted value). Then, the predicted load correcting unit 47 outputs the correction coefficient obtained here to the load predicting unit 45. In response to this, the load predicting unit 45 multiplies the correction coefficient obtained by the predicted load correcting unit 47 for each of the predicted values after the target time already obtained to obtain a correction value for the air conditioning load. In addition, for a time zone in which an actual measurement value within the target time has already been acquired, the actual measurement value is used as a correction value for the air conditioning load. The load predicting unit 45 outputs the corrected air conditioning load value to the heat source control unit 46. In response to this, the heat source control unit 46 reviews the already planned operation plan based on the corrected air conditioning load prediction value. As a result, the heat source system 1 is operated by more accurate predictive control.

Note that a threshold value is set in advance for the operation of a plurality of turbo chillers 12 that have a large effect on power demand, and when an operation plan exceeding the threshold value is made, a demand attention warning is notified to the operator. It may be. As a result, it is possible to re-correct the operation plan so that the demand is not exceeded. Further, the turbo chiller 12 may be preferentially activated during a lunch break (one hour when the power demand value of the factory decreases) or the like, so that a time zone in which there is a margin in power may be used effectively. Further, during the winter period, the operation plan data may be generated so as to actively use the free cooling 13 having a large CO ₂ reduction effect.

  In this way, the predictive control system 4 predicts the air conditioning load of the next day based on the weather forecast data and optimally controls the heat source operation. It is possible to create a heat source operation plan that is effectively utilized and to perform peak shift operation with cold water in the heat storage tank, thereby contributing to energy saving. In particular, the past air conditioning load, outside temperature and humidity, equipment usage status, power consumption, etc. are accumulated and learned in the database, and thermal responsiveness to various building parameters is modeled as ANN, and the weather forecast data for the next day. Since the air conditioning load is derived from this and the operation plan data of the cold heat source that makes the most effective use of the heat storage tank is generated from the predicted heat load, optimal heat source control can be performed.

  Moreover, since the predictive control system 4 has the cold water load prediction value, the chilled water is filled in the heat storage tank at, for example, 8:00 using midnight power, and the cold water in the heat storage tank is used up at 22:00. Since the refrigerator 12 can be operated systematically, the late-night power can be used effectively and the heat storage tank 11 can be used effectively. In addition, since the operation plan for the turbo chiller 12 and the free cooling 13 is automatically created based on the weather forecast and the air conditioning load of the next day, it is possible to select an effective priority chiller and to operate a chiller without waste. It becomes possible.

  In addition, the chiller operation plan is based on the load forecast throughout the day without being affected by the instantaneous flow rate increase or heat consumption increase required by the chilled water load side. It is possible to reduce the number of start / stop times of the refrigerator and to form a thermal storage layer temperature stratification by continuous operation. In addition, the next day's refrigerator operation plan is created on the previous day, so it is possible to operate with a view of the entire load of the day, and automatic operation that responds to changes in the building's usage status is made through correction processing performed every hour. Can be performed. For this reason, it becomes possible to reduce the management effort of a driving | operation.

  Furthermore, it is possible to automatically generate optimum operation plan data without requiring an experienced supervisor. In addition, excessively produced cold water naturally flows into the heat storage tank, so that the heat storage tank serves as a cushion tank, so that the refrigerator can be optimally operated, and the heat storage tank can be used to the maximum extent, Peak shift and peak cut operation are possible.

  1 is recorded on a computer-readable recording medium, and the program recorded on the recording medium is read by the computer system and executed, thereby executing a heat storage tank heat source system. The optimum operation control process may be performed. Here, the “computer system” includes an OS and hardware such as peripheral devices. The “computer-readable recording medium” refers to a storage device such as a flexible medium, a magneto-optical disk, a portable medium such as a ROM and a CD-ROM, and a hard disk incorporated in a computer system. Further, the “computer-readable recording medium” refers to a volatile memory (RAM) in a computer system that becomes a server or a client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line. In addition, those holding programs for a certain period of time are also included.

  The program may be transmitted from a computer system storing the program in a storage device or the like to another computer system via a transmission medium or by a transmission wave in the transmission medium. Here, the “transmission medium” for transmitting the program refers to a medium having a function of transmitting information, such as a network (communication network) such as the Internet or a communication line (communication line) such as a telephone line. The program may be for realizing a part of the functions described above. Furthermore, what can implement | achieve the function mentioned above in combination with the program already recorded on the computer system, and what is called a difference file (difference program) may be sufficient.

It is a block diagram which shows the structure of one Embodiment of this invention. It is a flowchart which shows operation | movement of the system shown in FIG. It is explanatory drawing which shows the structure of a vertical heat storage tank.

Explanation of symbols

  DESCRIPTION OF SYMBOLS 1 ... Heat source system, 11 ... Thermal storage tank (vertical type), 12 ... Turbo refrigerator, 13 ... Free cooling, 14 ... Air conditioner, 2 ... Control part, 3 ... Central monitoring system, 4 ... Predictive control system, 41 ... Data collection / accumulation unit, 42 ... Actual measurement data storage unit, 43 ... Pattern data generation unit, 44 ... Pattern data storage unit, 45 ... load prediction unit, 46 ... heat source control unit, 47 ... predicted load correction unit, 5 ... weather forecast data distribution server

Claims (4)

A control device for controlling the operation of a heat source system including a heat storage tank,
Data collection means for collecting operation performance data;
A record data storage means for storing the operation record data;
A prediction means for predicting the air conditioning load;
An operation plan creation means for generating operation plan data using the heat storage tank to the maximum from the air conditioning load;
Based on the operation plan, operation control means for performing operation control of the heat source system;
A difference between the predicted air conditioning load value within the target time and an actual air conditioning load value based on the operation performance data within the target time is obtained, and when the difference exceeds a predetermined value, the predicted air conditioning load value and A control device for a heat storage tank heat source system, comprising: predicted load correction means for correcting a predicted air conditioning load value after the target time by a correction coefficient obtained from a difference from the actual air conditioning load value. A control device for controlling the operation of a heat source system including a heat storage tank,
A data collection means for collecting actual weather data and driving performance data in association with each other;
Actual measurement data of the weather, and actual data storage means for storing the operation actual data,
Pattern data generation means for generating pattern data indicating the relationship between weather and building characteristics based on the weather measurement data stored in the storage means and the operation performance data;
Pattern data storage means for storing the pattern data;
Means for obtaining weather forecast data;
Prediction means for predicting an air conditioning load with reference to the weather forecast data and the pattern data stored in the pattern data storage means;
An operation plan creation means for generating operation plan data using the heat storage tank to the maximum from the air conditioning load;
Based on the operation plan, operation control means for performing operation control of the heat source system;
A difference between the predicted air conditioning load value within the target time and an actual air conditioning load value based on the operation performance data within the target time is obtained, and when the difference exceeds a predetermined value, the predicted air conditioning load value and A control device for a heat storage tank heat source system, comprising: predicted load correction means for correcting a predicted air conditioning load value after the target time by a correction coefficient obtained from a difference from the actual air conditioning load value. A control method for controlling the operation of a heat source system including a heat storage tank,
A data collection step for collecting operational performance data;
A record data storage step for storing the operation record data;
A prediction step for predicting the air conditioning load;
An operation plan creation step for generating operation plan data using the heat storage tank to the maximum from the air conditioning load;
An operation control step for performing operation control of the heat source system based on the operation plan;
A difference between the predicted air conditioning load value within the target time and an actual air conditioning load value based on the operation performance data within the target time is obtained, and when the difference exceeds a predetermined value, the predicted air conditioning load value and And a predicted load correcting step of correcting a predicted air conditioning load value after the target time by a correction coefficient obtained from a difference from the actual air conditioning load value. A control method for a heat storage tank heat source system, comprising: A control method for controlling the operation of a heat source system including a heat storage tank,
A data collection step for collecting actual weather data and driving performance data in association with each other;
The actual measurement data storing step for storing the actual measurement data of the weather and the operation result data;
A pattern data generation step for generating pattern data indicating the relationship between the characteristics of the weather and the building based on the meteorological actual measurement data stored in the storage step and the operation performance data;
A pattern data storage step for storing the pattern data;
Obtaining weather forecast data;
A prediction step of predicting an air conditioning load with reference to the weather forecast data and the pattern data stored in the pattern data storage step;
An operation plan creation step for generating operation plan data using the heat storage tank to the maximum from the air conditioning load;
An operation control step for performing operation control of the heat source system based on the operation plan;
A difference between the predicted air conditioning load value within the target time and an actual air conditioning load value based on the operation performance data within the target time is obtained, and when the difference exceeds a predetermined value, the predicted air conditioning load value and And a predicted load correcting step of correcting a predicted air conditioning load value after the target time by a correction coefficient obtained from a difference from the actual air conditioning load value. A control method for a heat storage tank heat source system, comprising:

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