• 专利附录
  • 专利说明
  • 权利要求
  • 类似技术
  • 同族专利
  • 引用文献
  • 法律状态
  • 优先权
    • 专利摘要:
    An information system for a district heating network (100) comprising at least one power plant (102), a heat distribution network including branched heat transfer pipelines (104) and pumping stations (106), and at the end of the branch pipes a plurality of customer properties (110) with separate heat distribution centers (10) 50) and a control system (20) for controlling the district heating flow according to the set consumption, and its own metering equipment including means for collecting metering data from a customer system adapted to collect information from customer properties (110) on heat supply status ) in accordance with a preselected criterion, characterized in that in at least several customer properties (110) the measuring equipment comprises a control unit (70) with relative capacity information. to calculate a key-performance-index (KPI) value from a plurality of components according to a preselected calculation criterion, to register the KPI value and to transmit the KPI value to the heat producer, the system is adapted to provide real-time information on the heat supply status of the customer properties the KPI value of the customer property (110) and the identification information of the customer property (110), and the system comprises display means for displaying said KPI value to the heat generator. In addition, protection requirements 2-5.
    公开号:FI13016Y1
    申请号:FIU20214060U
    申请日:2020-11-13
    公开日:2021-09-08
    发明作者:Antti Hartman
    申请人:Hoegforsgst Oy；
    IPC主号:
    专利说明:

    The invention relates to an information system for a district heating network comprising e at least one power plant, e a heat distribution network including branched heat transfer control systems according to consumption and its own measuring equipment, which includes means for collecting measurement data from the customer system. The information system is adapted to collect information from customer properties on the status of heat supply, enabling the heat producer to adjust the heat production parameters of the district heating network on the basis of user-specific information to meet the requirements of customer properties according to a pre-selected criterion.
    In the current district heating network ('Suomalainen Kaukolämmitys'', Veli-Matti Mäkelä and Jarmo Tuunanen), the customer equipment measurement center includes a heat meter with N connected flow and return temperature sensors and a flow N 25 sensor. The readings of the measuring devices are transferred from the customer property to the heat seller using remote reading S. Real-time information from the measuring devices can also be provided to the customer = for consumption control. By measuring, billing volumes, 3 energy consumption data, production volumes, heat losses, 3 30 information related to energy savings and information needed by various services are determined. Meters of heat distribution centers in customer properties also enable alarms for temperatures, pressure levels and other functions.
    The pipelines of the district heating network include measuring devices for handling flow and energy measurements, temperature measurements, and pressure and differential pressure measurements, which can be used to monitor the state of heat supply.
    However, in current district heating networks, the power plant often does not have any real-time information on the fulfillment of the criteria set for the heat supply of customer properties and the control margin of the heat distribution center in the customer property. The time delay for heat distribution from the power plant can be up to 8 hours to the extremes of the district heating network. Unnecessary amounts of heat and pressure may therefore be pumped into the district heating network for safety reasons, so that the use of heating energy and the amount of network losses are unnecessarily high. On the other hand, in some situations, the heat and pressure supplied to the district heating network may be insufficient in relation to the actual heat demand, in which case the service level of the customer properties is deficient.
    Korean patent publication KR 20160059849 A discloses an information system for a district heating network, which is adapted to provide real-time information on the status of heat supply to customer properties to a heat producer. However, a problem with the prior art information system is the O 25 information flood for the heat generator. When a huge amount of information about customer properties is provided to the heat producer ro, it is difficult for the heat producer to interpret this flood of information = and make decisions based on it about the total> total state of the heat supply. In addition, the content S 30 of the property-specific information may vary if the property-specific measuring devices differ. The object of the invention is to provide a real-time demand (RTD) information system for a district heating network.
    real-time information on the status of heat supply in customer properties and the control capacity of the heat distribution center in an easy-to-interpret format, which can significantly reduce energy consumption and network losses in heat production while ensuring a good level of service in customer properties. The characteristic features of the present invention appear from the appended claim 1.
    The information system for the district heating network according to the invention comprises at least one power plant, a heat distribution network including branched heat transfer pipelines and pumping stations, and at the end of the branch pipes numerous customer properties with including means for collecting measurement data from the customer system. The information system is adapted to collect information from customer properties on the status of heat supply, enabling the heat producer to adjust the heat production parameters of the district heating network on the basis of user-specific information to meet the requirements of customer properties according to a pre-selected criterion. In at least several customer properties, the metering equipment includes a control unit to calculate a key-performance-index (KPI) value based on relative capacity data 2 of several components according to a preselected calculation criterion, = to register a KPI value and to transmit a KPI value> heat The system is adapted to provide real-time information on the heat supply status of the customer properties to the heat producer in the form of reduced information bundles 5 comprising at least the KPI value of the customer property and the identification information of the customer property. The system comprises display means for displaying the KPI value to the heat generator.
    In this way, the heat producer receives information on the heat supply status of the customer properties and the control margin of the heat distribution centers in an easy-to-interpret form when a single customer property provides the heat producer with only one numerical value (KPI) that describes the customer's satisfaction with heat supply quality. Different properties may even calculate their value differently, but give a uniform value to the heat producer, who can control his own heat supply. In this case, the heat producer does not have to interpret the information provided by each customer property separately, when the customer properties may have different heat supply devices and measuring devices or a different number of heating circuits. At the same time, the amount of communication is also reduced when it is not necessary to send measurement data from dozens of sensors from an individual customer property. All in all, this enables the optimization of flow temperatures in the network, the optimization of pressure, the reduction of heat and transmission losses in the district heating network and a good level of service in customer properties.
    In addition to the KPI value, the information bundle contains at least the customer property identification information. The identification information shall include at least location information. In this case, the heat producer knows where the KPI value has come from and can combine the information bundles regionally, O 25 whereby the heat producer receives information about the regional heat supply status of the branching pipes.
    N = The heat producer can form regional information entities from the information trees of the customer properties, which S 30 comprises the relative N capacity information of the regional customer properties. In this case, the pressure and temperature of the heat transfer tubes can be adjusted regionally.
    The KPI value can be displayed to the heat generator on display means as a numerical value or with various symbols.
    In a particular embodiment of the invention, the system 5 comprises at least a heating network return water temperature sensor and a district heating return water temperature sensor, the reading of which the system is adapted to give a score according to a preselected criterion, which rating is one component for generating a KPI value.
    This tells you how well the target transfers energy.
    If the inlet temperature decreases, then this indicator will decrease.
    In a particular embodiment of the invention, the system comprises at least a heating valve opening sensor and a hot water valve opening sensor, the readings of which the system is adapted to give ratings according to a preselected criterion, which ratings are components for generating a KPI value.
    In this way, information is obtained on how much control space there is still in the heat distribution center.
    If there is plenty of room for maneuver left in several properties, heat production can be reduced.
    On the other hand, if the valves are almost completely open at several sites, it may be necessary to increase the heat output.
    If the valves are a little open, then - heat transfer works well.
    If, on the other hand, almost completely open, then the temperature and pressure of O 25 can no longer be lowered. 3e In one embodiment of the invention, the system = comprises at least a heating network flow temperature sensor> and a district heating supply water temperature sensor, the difference between the readings S30 of which the system is adapted to give a score according to a preselected criterion for.
    This provides information on the relative capacity of the customer property.
    If the temperature
    the difference falls small, can no longer afford to reduce the efficiency of heat production. In one embodiment of the invention, the system comprises at least a heating network flow temperature sensor and a hot water flow temperature sensor, for the difference of which the system is adapted to give a score according to a preselected criterion, which is one component for generating a KPI value. In this way, a sufficient amount of heat is ensured to heat the object.
    In one embodiment of the invention, the system comprises at least a district heating inlet pipe pressure sensor and a district heat return pipe pressure sensor, the reading of which the system is adapted to give a score according to a preselected criterion, which rating is one component for generating a KPI value. This provides information on the relative capacity of the customer property. Preferably, the system comprises the necessary software and storage means with which the system is adapted to form regional information entities and to store the optimal level of heat supply temperature at different outdoor temperature points and adapted to pre-adjust the district heating network heat parameters in advance. 2 In this way, the learning information system enabled by RTD information searches for the optimal level at different outdoor temperature points - heat supply temperature and pressure, so that network losses can be significantly reduced while ensuring a good N level of service in customer properties. Optimal level 5 means a temperature that is high enough to meet the heat demand of customer properties, but on the other hand low enough so that unnecessary energy is not supplied to the district heating network. The information system according to the invention can be used in a method in connection with a district heating network comprising at least one power plant, a heat distribution network including branched heat transfer pipelines and pumping stations, and branch buildings according to consumption and its own measuring equipment, which includes means for collecting measurement data from the customer system. The method collects information from customer properties on the status of heat supply, enabling the heat producer to adjust the heat production parameters of the district heating network on the basis of user-specific information to meet the requirements of customer properties according to a pre-selected criterion. In at least several customer properties, the relative capacity is measured, from which the key-performance-index (KPI) value is calculated, which is a numerical value formed of several components according to a preselected calculation criterion, the KPI value is registered and the KPI value is transmitted to the heat producer. Real-time information on the status of customer properties' heat supply is provided to heat producer O 25 in reduced information bundles, comprising at least the customer's property's KPI value and customer property's identification information. 2 The KPI value is presented to the heat producer. This enables = optimization of flow temperatures in the network, optimization of pressure, - reduction of heat and transmission losses in the district heating network and S 30 in high-quality customer properties.
    S> Preferably, the information trees are formed into regional information entities and the parameters of the branching heat transfer pipelines of the district heating network are adjusted in the district heating network.
    to change the regional pressure or temperature of the revenge according to a preselected criterion. In this case, it is possible to reduce heat production and / or reduce the pressure in the pipeline, which reduces network losses if the RTD information shows that a service level that meets the criterion is achieved in customer properties with significantly less heat and / or pressure. Correspondingly, heat production can be increased and / or the pressure in the pipeline can be increased if the RTD information shows that the level of service in customer properties is insufficient. In this context, a set of information refers to regional entities formed from KPI values, which can be used to form an overall picture of the state of heat supply of customer properties connected to different heat transmission pipelines in the district heating network.
    Preferably, the selected components are graded according to a preselected criterion, and the scores are averaged to form a KPI value, except for the situation where at least one component is given the worst possible rating, in which case the KPI value is formed as the worst possible rating. In this way, information on the status of the heat supply to the customer property can be packaged in an easy-to-read format at the end of the customer property.
    - The components used to calculate the KPI value may be 2 to 20, preferably 4 to 10, and the components ro are formed by reading one of the 2 temperature sensors or the valve opening degree sensor or the pressure sensor of the customer's heat distribution center or = pressure sensor or combinations thereof. In this way - a KPI value describing the heat supply state S 30 of the customer property can be formed sufficiently from the quantities describing the heat supply state N. Preferably, the data of the customer property is compared with the data of a nearby branch of the heat transfer pipeline and the deviation is calculated according to a preselected criterion. In this way, information is obtained on the status of the heat supply and possible equipment faults. Preferably, the time distribution of the heat distribution is taken into account so that, if necessary, the heat generator slows down the reading of the information bundle according to the time delay. In large cities, the time distribution of heat distribution can be up to 8 hours from the main power plant to the ends of the network. RTD information enables an intelligent and learning system that can take into account the time delay to search for the optimal level of heat supply temperature at different outdoor temperature points, whereby network losses can be significantly reduced while ensuring a good level of service in customer properties.
    Preferably, the optimal level for the heat supply temperature at the different outdoor temperature points is stored and the heat production parameters of the district heating network are adjusted in advance at the different outdoor temperature points according to the weather forecast, taking into account the heat distribution time delay. In this way, the learning information system made possible by RTD information retrieves the optimal level of heat supply temperature and pressure at different outdoor temperature points, whereby network losses can be significantly reduced while ensuring a good level of service in customer properties. At the optimal level, this means a temperature that is high enough to meet the heat demand of the customer properties, but on the other hand low enough so that no unnecessary energy is supplied to the district heating network 2.
    = a o Ffullly, the heat supply demand of the district heating network increases. In this case, the ever-decreasing flow of district heating water to the customer property will transfer energy. This adds more capacity to the network for other customers during consumption spikes, allowing district heat production to be maintained at a more even fuel efficiency.
    The invention will now be described in detail with reference to the accompanying drawings illustrating some embodiments of the invention, in which Figure 1 shows a process diagram of the operation of a heat distribution center according to the invention, and Figure 2 shows a schematic diagram of the operation of a district heating network according to the invention. Figure 1 shows a heat distribution center 10 according to the invention as a process diagram. The flow line 36 of the district heating distribution pipeline 34 conveys hot district heating water from the power plant 102 for use by the customer property 110. The hot district heating water passes through the heating valve 41 and the hot water valve 42 of the control system 20 and circulates in the heat exchangers 50, heating each selected application. As shown in Figure 1, the application may be water from the heating pipe 52 of the property or water from the hot water pipe 54. The district heating water cooled from the heat exchangers 50 is returned to the power plant 102 via the distribution line 34 - return line 38. The measuring apparatus 3 of the heat distribution center 10 according to the embodiment of Figure 10 includes -sensor 13, 3 30 o heating network return water temperature sensor 14, N o hot water flow temperature sensor 15, o heating valve opening degree sensor 21, o hot water valve opening degree sensor 22,
    o district heating inlet pipe pressure sensor 31, o district heating return pipe pressure sensor 32. The measuring equipment is connected to a control unit 70 which calculates one KPI value for the object based on the measured values and transfers it to the cloud service for power plant 102 to optimize energy production. The following example discusses generating a single KPI value in a customer property 110.
    The KPI value in this case is defined on a three-point scale so that o level 1 is poor, o level 2 is satisfactory, and o level 3 is good. In this embodiment, the components of the RTD information in the heat distribution center are:
    1. Difference between the readings of the district heating supply water temperature sensor 11 and the heating network flow temperature sensor 12 o level 1: 0-5 ° C o level 2: 5-10 ° CS o level 3: more than 10 ° C ro 25 2 Difference between the district heating return temperature sensor 13 and the heating network return water temperature sensor 14 1luzz o level 1: more than 5 ° CS o level 2: 3 - 5 ° C 3 30 o level 3: 0-3 ° C D
    3. Difference between the readings of the district heating supply water temperature sensor 11 and the domestic hot water temperature sensor 15 o level 1: 0-5 ° C o level 2: 5 -— 10 ° C o level 3: more than 10 ° C
    4. Heating valve opening sensor 21 reading o level 1: 90 - 100 3 o level 2: 50 - 90 3 o level 3: 0 - 50 3
    5. Reading of the hot water valve opening degree sensor 22 o level 1: 75 - 100% o level 2: 30 - 795% o level 3: 0 - 30 3 differences in readings or other property-specific measurements. Thus, different customer properties 110 may generate RTD information in different ways, but a single KPI value is always calculated in a single customer property 110, whereby - the heat producer receives information about the relative capacity of the customer properties 110 in a consistent format. 3 25 D If any of the above RTD information components is 1, then z is the determining factor. The total level is then 1. Otherwise> The KPI value is taken as the average of the components. This is computed S at the local level and transferred to the cloud service, from where it is read to the network user and connected to a map-based network information model that can be used to control the city's heat production and pumping. At the main level,
    district heating delay by slowing down the data if necessary. Levels can be changed by network. The goal is that no object in the city is at level 1 but all are at level 2. Individual objects can be at level 3. In this case, the heat supply temperature is at the optimal level. There can also be more than 3 levels of KPI values, for example 10, in order to obtain a more precise control effect. Level 1 items are reviewed individually and eliminated by adjustments or component replacements. By doing so, the temperature level and pumping of the entire city can be reduced. Example 1:
    1. The reading of the district heating supply water temperature sensor 11 is 75 ° C and the reading of the heating network flow temperature sensor 12 is 67 ° C, the difference between these being 8 ° C. This is level 2.
    2. The reading of the district heating return water temperature sensor 13 is 60 ° C and the reading of the heating network return water temperature sensor 14 is 56 ° C, whereby the difference between them is 4 ° C. This is level 2.
    O 25 3. The reading of the district heating inlet water temperature sensor 11 is ro 70 ° C and the reading of the domestic hot water temperature sensor 2 15 is 58 ° C, whereby the difference between these is 12 ° C. = Then we are at level 3.
    - 4. The reading of the heating valve opening sensor 21 is S 30 70%. This is level 2.
    N 5. The reading 5 of the hot water valve opening sensor 22 is 50%. This is level 2.
    The mean of the levels determined by Fde is 2.2. This can be rounded to a KPI of 2, which is transferred to the cloud service and delivered to power plant 102. The goal is for all sites in the district heating network 100 to be at level 2.
    Example 2:
    1. The reading of the district heating supply water temperature sensor 11 is 80 ° C and the reading of the heating network flow temperature sensor 12 is 72 ° C, the difference between these being 8 ° C. This is level 2.
    2. The reading of the district heating return water temperature sensor 13 is 60 ° C and the reading of the heating network return water temperature sensor 14 is 56 ° C, whereby the difference between them is 4 ° C. This is level 2.
    3. The reading of the district heating inlet water temperature sensor 11 is 70 ° C and the reading of the domestic hot water temperature sensor 15 is 58 ° C, whereby the difference between these is 12 ° C. This is level 3.
    4. The reading of the heating valve opening sensor 21 is 70%. This is level 2.
    5. The reading of the hot water valve opening sensor 22 is 80%. This is level 1.
    O 25 Now component 5 of the RTD information is at level 1, which is the determining factor. The KPI value of an object is recorded in the cloud service as 1.
    2 Such items are individually reviewed and eliminated by E adjustments or component replacements.
    3 S 30 Example 3: s> 1. The reading of the district heating supply water temperature sensor 11 is 80 ° C and the flow temperature of the heating network
    the reading of the sensor 12 is 68 ° C, whereby the difference between them is 12 ° C. This is level 3.
    2. The reading of the district heating return water temperature sensor 13 is 45 ° C and the reading of the heating network return water temperature sensor 14 is 41 ° C, whereby the difference between them is 4 ° C. This is level 2.
    3. The reading of the district heating inlet water temperature sensor 11 is 70 ° C and the reading of the domestic hot water temperature sensor 15 is 58 ° C, whereby the difference between these is 12 ° C. This is level 3.
    4. The reading of the heating valve opening sensor 21 is 40%. This is level 3.
    5. The reading of the hot water valve opening sensor 22 is 20%. This is level 3.
    The mean of the levels determined by Fde is 2.8. This is rounded to a KPI of 3, which is transferred to the cloud service and delivered to power plant 102. Individual sites can be at level 3, but if there are many sites at level 3 - the heat / pressure in the district heating network 100 can be reduced, saving energy. Figure 2 shows in principle a district heating network 100 comprising at least one power plant 102, a plurality of branch heat transfer pipes 104, pumping stations 106 and a number of ro customer properties 110 with their own heat distribution centers 2 10 which determine their own KPI values. The heat transfer tubes = 104 also have several temperature sensors 121 and pressure sensors 122. The power plant 102 reads the values of the heat distribution centers 10 KPI-S 30 from the cloud service into its own analytics tool and thus obtains good data from its network on how the objects are doing in 5 conditions.
    The customer property 110 provides relative capacity information to the power plant 102 as an information bundle that includes, in addition to the KPI value, at least identification information that includes information about the location of the customer property 110. The power plant 102 may form regional information entities 200 from the customer property information trees, which comprise the relative capacity information of the regional customer properties 110. In this case, the pressure and temperature of the heat transfer tubes 104 can be adjusted regionally.
    A map-based network data model similar to Figure 2 can be used to control the city's heat production and pumping. The goal is for all customer properties 110 to be at level 2. Individual customer properties 110 may be at level 3, but if there are many objects at level 3, this is an indication that the temperature and / or pressure of the heat transfer pipe 104 of the district heating network 100 can be lowered and saved. energy. If a large number of customer properties 110 are at level 1, it is an indication that the heat supply is insufficient, necessitating an increase in the temperature and / or pressure of the heat transfer piping 104. Individual Level 1 customer properties 110 are individually reviewed and repaired with site-specific adjustments and / or component replacements. In this way, the temperature level and pumping of the entire city O 25 can be reduced. 3 The RTD information system described above can be utilized = to implement the demand elasticity system described below, - but this demand elasticity system can also be implemented without the RTD information system. s> The goal of the demand flexibility system is uniform fuel efficiency in district heating production. The aim is to be able to produce heat with the most economical fuels and methods.
    as long and evenly as possible. The outdoor temperature can fluctuate significantly even during the day, as can the energy needs of customer properties. The aim is that the district heating network can be used to offset this consumption. Roughly, the flow temperature rises slightly hotter than would be necessary at that moment and sometimes it then falls slightly below the setpoint. The heat distribution centers communicate backwards with RTD information, which is enough for them. If the stock in the district heating network shows signs of dissatisfaction, demand elasticity can typically be introduced for a few hours. This gives time to increase production capacity and drive it to the destinations. Or if there is a prospect of air condensation or a decline in demand, then no other measures are needed, saving money.
    Demand flexibility or power limitation can be done from the customer's point of view, keeping the power taken from the network as constant as possible. In practice, therefore, the heating output is reduced during the hot water peak. That is, the heating flow rate or temperature is allowed to decrease and the heating consumption is transferred to a different point in time. In terms of power, the transition does take place, but it almost does not always bring real flexibility to the system, because in a site with a 2-drive hot water converter, a large part of the hot water O 25 comes from the district heating return water returning from the ro heating converter. That is, in the sense of power, the transition occurs, 2 but in practice at the expense of cooling. = a o Another starting point for demand elasticity is the need for a power plant. That is, the S 30 power plant gives a signal when they would like flexibility. N Now that the elasticity occurs by limiting the flow temperature, 5 the power decreases, but it does not add capacity to the network in the same proportion. This is due to the fact that when the flow is limited, the radiator valves start to open, the pressure
    continuous pumps increase their speed. The flow in the system rises and the cooling begins to weaken. On the primary side of district heating, the flow is reduced proportionally less than the power. In other words, the capacity of the district heating inlet pipe is lost more than the power is limited from the property's point of view. As the problem approaches, the following steps are taken.
    1. The need for demand elasticity can first be verified by means of RTD information.
    2. Once the potential of the network has been used, demand elasticity measures shall be initiated.
    3. Demand elasticity shall be implemented in such a way that the capacity limitation measures of the buildings are at least fully exploited in the network. Limiting the flow temperature is not the right method - especially for sites with pressure-controlled pumps whose flow increases in connection with the power limit. In these locations, this type of power constraint provides only an apparent benefit.
    4. The solution is a function to be added to existing intelligent heat distribution centers, such as HögforsGsT's FiksucsT &, which increases the supply capacity of the district heating network during flexibility up to 50% more than what the properties cut their consumption. The properties involved in the flexibility are considerably less needed than other O 25 methods and their impact is much greater for the whole. In the method, during the power limitation, the water flow in the radiator network is reduced, which causes an improvement in the cooling of the network and the transfer of energy to the customer's building with an ever-decreasing flow of district heating water. The use of property 3 of the property is limited, but the flow of district heating water tip-> wood considerably more. This adds more capacity to the network for other customers. The method also does not burden the property system with thermal expansion, and there is no risk of pipelines breaking. Together with RTD information, significant flexibility potential is obtained for the entire network.
    The overall solution presented by EF can be further enhanced by installing a DHAC module-based system at a few critical network sites, which will further increase capacity and cooling in the district heating network.
    These systems work best if the property has a lot of heat exchangers that work efficiently. Preferably, the heat exchangers are long exchangers with the ability to operate at lower flows with good cooling. The right basic adjustments of the radiator networks also help the whole unit to function better.
    Before the next power limit, the local heating center can charge heat as follows. The pump speed is reduced and at the same time the flow temperature is raised. When the power limit comes, there is warmer than normal water in the system.
    - This can be applied in particular in the context of the
    N S 25 law with primary heat sources rather than district heating.
    O <Q 00 OF
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    权利要求:
    Claims (5)
    [1]
    An information system for a district heating network (100) comprising e at least one power plant (102), e a heat distribution network including branched heat transfer pipelines (104) and pumping stations (106), and e a plurality of customer properties (110) at the end of the branch pipes heat distribution centers (10) comprising heat exchangers (50) and a control system (20) for controlling the district heating flow according to the set consumption and own metering equipment comprising means for collecting metering data from a customer system, the information system being adapted to collect information adjusting the heat production parameters of the network (100) on the basis of user-specific information to meet the requirements of the customer properties (110) according to a preselected criterion, characterized in that in at least several customer properties (110) to calculate a key factor-N value, i.e. a KPI (key-performance-index) value formed from proportional 3 capacity data, from several components according to a preselected calculation criterion, to register a KPI value 3 30 and to transmit a KPI value to a heat producer, N e system is adapted to provide real-time information on the status of heat supply to customer properties (110) to the heat producer in a reduced
    as bundles comprising at least the KPI value of said customer property (110) and the identification information of the customer property (110), and the system e comprises display means for displaying said KPI value to the heat generator.
    [2]
    Information system according to claim 1, characterized in that the system comprises at least a heating network return water temperature sensor (14) and a district heating return water temperature sensor (13), the difference between the readings of which the system is adapted to give a rating according to a preselected criterion is one component for constructing a KPI.
    [3]
    Information system according to protection claim 1 or 2, characterized in that the system comprises at least a heating valve opening sensor (21) and a hot water valve opening sensor (22), the readings of which the system is adapted to give grades according to a preselected criterion, which are components of the KPI value for the formation of.
    [4]
    Information system according to one of Claims 1 to 3, characterized in that the system comprises at least a flow temperature sensor 3 (12) for the heating network and a temperature sensor (11) for the district heating supply water, for the separation of which the system is adapted. E- to give a grade according to a preselected criterion, which o grade is one component for generating a KPI value-> 30.
    O S
    [5]
    Information system according to one of Claims 1 to 4, characterized in that the system comprises the necessary software and storage means by which the system is adapted to form regional information units (200) and to store the optimum level of heat for the supply temperature at different outdoor temperatures. and adapted to pre-adjust the heat production parameters of the district heating network at different outdoor temperature points according to the weather forecast, taking into account the heat distribution time delay.
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