User water supply flow prediction method and system for smart heating

Abstract

The present invention relates to a method and system for predicting water flow rate of users of smart heating, which is equipped with a water flow rate prediction unit. Each heating user can predict heat load and water flow rate. The water flow rate prediction unit includes a user classification unit, a climate compensation unit, a load calculation unit, and a two-network balancing unit. The method simultaneously considers factors such as the building location of the heating user and the heating conditions of the neighboring households, the impact of climate factors on heating, and the impact of the building type on the heat load prediction, so as to accurately predict the heat load and water flow rate of the heating user to the greatest extent, so that the heating amount of each heating user is more reasonable, easy to promote and accepted by users.

Description

Technical Field

[0001] The present invention belongs to the technical field of centralized heating, and relates to a method and system for predicting user water supply flow of intelligent heating. Background Art

[0002] With the development of society and the improvement of people's living standards, users have higher and higher requirements for heating quality. However, the central heating system generally provides the same heating conditions for each household. The disadvantage of this method is that no matter how much heat each household actually needs, the final heating heat is fixed. This will cause the temperature of some users who are not conducive to heating to be substandard, while the homes of some users who are conducive to heating will be overheated and need to open windows frequently to dissipate heat. This is not conducive to the economic operation and control of the heating system, and at the same time causes a lot of energy waste. In view of this situation, the heat load of each heating user is predicted to provide the water supply flow of each household, realize accurate heating regulation of each household, and improve heating efficiency. Summary of the invention

[0003] The technical problem to be solved by the present invention is generally to provide a method and system for predicting user water supply flow for smart heating.

[0004] To solve the above problems, the technical solution adopted by the present invention is:

[0005] A user water supply flow prediction system for smart heating, the system includes the following modules: user classification unit, load calculation unit, two-network balance unit, climate compensation unit;

[0006] A user classification unit is used to classify heating users, calculate heat load calculation correction coefficients for different heating user types based on historical heating data, and send them to the load calculation unit;

[0007] The classification is based on building location, heating information and heating status of neighboring households;

[0008] The climate compensation unit is used to obtain outdoor calculation parameter information, including outdoor corrected calculation temperature; the outdoor corrected calculation temperature is obtained by analyzing the outdoor temperature, outdoor wind speed, solar radiation, and weather type, and the outdoor corrected calculation temperature is sent to the load calculation unit; the thermal inertia index of the heating user is then calculated based on the outdoor calculation parameter information, the insulation information of the heating building, and the indoor heating target temperature, and the thermal inertia index of the heating user is sent to the second network balancing unit;

[0009] The load calculation unit is used to determine the apartment type of the heating building, adopt different heat load calculation methods for different apartment types, and receive information from the user classification unit and the climate compensation unit, and finally process to obtain the predicted heat load of each heating user, and send the predicted heat load to the secondary network balancing unit;

[0010] The secondary network balancing unit is used to receive the information sent by the load calculation unit and the climate compensation unit, and calculate the water supply flow value and flow control time required by each heating user according to the actual water supply temperature and return water temperature data.

[0011] In the user classification unit, heating users are divided into nine categories and sixty-four subcategories according to their building location information and the heating information of their surrounding neighbors.

[0012] Among them, the nine categories are recorded according to the building location information as 1 top left household, 2 top right household, 3 bottom left household, 4 bottom right household, 5 top household, 6 left household, 7 right household, 8 bottom household, 9 middle household;

[0013] According to the heating status of neighboring households, heat users are divided into A isolated island users, B single-sided heating, C double-sided heating, D three-sided heating, and E four-sided heating;

[0014] Among them, A isolated island users, B unilateral heating, C bilateral heating, D trilateral heating and E quadrilateral heating include 1, 4, 6, 4 and 1 subcategories respectively (please specify). A isolated user refers to a neighboring household with 0 heating households, and there is 1 sub-category, that is, none of the four neighboring households above, below, left, and right are heated; B unilateral heating refers to a neighboring household with 1 heating household, and there are 4 sub-categories, namely, heating for the upper neighboring household, heating for the lower neighboring household, heating for the left neighboring household, and heating for the right neighboring household; C unilateral heating refers to a neighboring household with 2 heating households, and there are 6 sub-categories, namely, heating for the upper and lower neighboring households, heating for the upper left neighboring household, heating for the upper right neighboring household, heating for the left and right neighboring households, heating for the lower left neighboring households, and heating for the lower right neighboring households; D unilateral heating refers to a neighboring household with 3 heating households, and there are 4 sub-categories, namely, heating for the upper and lower left neighboring households, heating for the upper and lower right neighboring households, heating for the upper and left neighboring households, and heating for the lower and left neighboring households; E full-side heating refers to a neighboring household with 4 heating households, and there is 1 sub-category, that is, all of the four neighboring households above, below, left, and right are heated;

[0015] Climate compensation unit, which uses meteorological weather correction to calculate outdoor temperature for load forecasting;

[0016] Referring to historical literature data, the weather types of meteorological weather compensation include sunny, cloudy and rainy days, and 0 is defined as sunny, 1 as cloudy, and 2 as rainy and snowy days;

[0017] According to the weather forecast for the next period, select 0 to 2;

[0018] Compensated outdoor temperature (℃) = outdoor meteorological temperature (℃) + (outdoor temperature on sunny day (℃) + 1.5; outdoor temperature on cloudy day (℃) + 0; outdoor temperature on rainy and snowy day (℃) - 1.5).

[0019] Climate compensation unit, which uses meteorological wind speed correction to calculate outdoor temperature for load forecasting;

[0020] Based on different outdoor meteorological wind speeds, meteorological wind speed compensation is determined by referring to historical literature data and divided into ten wind speed levels;

[0021] Compensated outdoor temperature (℃) = outdoor meteorological temperature (℃) + (wi nd (m / s) less than 0.2 - 0.02; wi nd less than 1.5 - 0.18; wi nd less than 3.3 - 0.49; wi nd less than 5.4 - 0.88; wi nd less than 7.9 - 1.34; wi nd less than 10.7 - 1.87; wi nd less than 13.82 - 2.46; wi nd less than 17.1 - 3.1; wi nd less than 24.4 - 4.52; wi nd greater than 24.4 - 5.292);

[0022] wi nd (m / s) is the wind speed.

[0023] Load prediction algorithms include building and user terminal positioning algorithms and enclosure structure heat consumption calculation algorithms;

[0024] In the building positioning algorithm, according to the building heating standard, based on the geographical orientation of the building, if the geographical orientation of the building is east, west, south or north, then T (T considers the east, west, south and north orientation of the building) is set to 1; if the geographical orientation of the building is northeast, northwest, southeast or southwest, T1 (T1 considers the northeast, northwest, southeast and southwest orientation of the building) is 0;

[0025] The algorithm is as follows:

[0026]

[0027] In the user terminal positioning algorithm, first, according to the location and number of user terminals in the target community, an n-layer m-household matrix, i.e., an n-row m-column matrix, is established to locate the house number of each user terminal. The calculation logic is set up at the unit level to represent the entire building terminal of the building, starting from the first unit of the building, and so on to the next level of user terminals; then, an array is constructed by establishing the number of building floors and the number of users on each floor of the building. The user terminals of the corresponding units are set in the form of a one-dimensional array, from the first household on the first floor to the last household on the top floor; then, all the user numbers contained in the building terminal are input into the front end of the load calculation unit for complete positioning;

[0028] Among them, the building units are divided into the leftmost unit, the rightmost unit, the middle unit, the top floor unit, the bottom floor unit and the four corner units for heat load calculation. The unit end positioning algorithm is as follows:

[0029] #Heat load calculation for the leftmost household

[0030]

[0031]

[0032]

[0033] Among them, q1~q4--heat consumption of the exterior wall in the east, west, south and north directions, q5--heat consumption of the roof (W); q6~q9--heat consumption of the exterior wall in the northeast, northwest, southeast and southwest directions (W); W j1 --Heat consumption of floor slab (W); e, w, aa—heat consumption of east inner wall, west inner wall, upper / lower inner wall of the building (W);

[0034] The heat load of the exterior walls and floor slabs is divided into the basic heat consumption of the maintenance structure and the heat consumption of cold air penetration;

[0035] That is q 1～9 =Q_y 1～9 +Q_z 1～9

[0036] Among them, Q_y 1～9 —Additional heat consumption of maintenance structure (w), Q_z 1～9 —Heat consumption of cold air penetration (W);

[0037] Basic heat consumption of maintenance structure (W) = {area of ​​outdoor walls of selected buildings (m 2 )*Indoor and outdoor temperature difference (℃)*Exterior wall heat transfer coefficient (W / (m 2 ℃)) + area of ​​outdoor windows of each building (m 2 )*Indoor and outdoor temperature difference (℃)*External window heat transfer coefficient (W / (m 2 ·℃)};

[0038] The heat transfer coefficient of exterior walls and windows depends on the material and thickness, etc., and is taken from the specifications or actual measurements.

[0039] The basic heat consumption design algorithm for the exterior wall and roof enclosure structure is as follows:

[0040] Q_x 1～9 =self.Fx[a]*(1-self.fx[a])*K*(self.tn-self.t w )+self.Fx[a]*self.fx[a]*k*(self.tn-self.t w );

[0041] Among them, Q_x 1～9 --Basic heat consumption of the east, west, south, north exterior walls and roof enclosure structure (W); Fx[a]--Area of ​​the east, west, south, north exterior walls and roof (m 2 ); fx[a]--East, west, south, north exterior window area (m2 );t n —Indoor temperature (℃); t w —Outdoor temperature (℃);

[0042] Heat consumption of maintenance structure = basic heat consumption (w) * (direction correction rate + wind force addition rate);

[0043] That is: Q_y 1～9 =Q_x 1～9 *(1+direction correction rate+wind force addition rate);

[0044] Among them, Q_y 1～9 --Heat consumption of the maintenance structure after orientation correction and wind force correction; orientation correction rate is taken from the technical specifications; wind force addition rate is taken from the technical specifications;

[0045] Under the influence of the indoor and outdoor pressure difference caused by wind and heat pressure, the outdoor cold air penetrates into the room through the gaps, escapes after being heated, and the heat consumed by this part of the heated cold air from the outdoor temperature to the indoor temperature is calculated as the heat consumption of cold air penetration;

[0046] Among them, the length of the door and window gaps in the dominant wind direction in winter is taken as the main windward side to calculate the heat loss of infiltration into doors and windows; the heat loss of cold wind infiltration of the east, west, south and north exterior walls and the heat loss of the roof in the northeast, northwest, southeast and southwest directions are calculated by selecting each household of the building, and the heat loss of cold wind infiltration of the east, west, south and north exterior walls and the roof heat loss are corrected; the heat loss of the exterior wall of the enclosure structure is calculated by the following algorithm:

[0047] First, according to the local average outdoor wind speed in winter, the amount of air that penetrates into the room per meter of door and window gaps is calculated:

[0048] if self.v<=1;

[0049] L = 0.4;

[0050] elif self.v<=2;

[0051] L = 1.1;

[0052] elif self.v<=2;

[0053] L = 1.8;

[0054] Where v is the average outdoor wind speed; L is the amount of air that penetrates into the room through the gap between doors and windows per meter (m 3 / (m·h));

[0055] After that, the correction coefficient of the infiltration air volume direction is introduced to calculate the total amount of cold air infiltrating into the room through the gaps of doors and windows in different directions:

[0056] V1～9 =L*self.L 1～9 * Orientation correction factor;

[0057] Among them, V 1～9 —Total amount of air that penetrates into the room through the gaps of doors and windows from the east, west, south, north, northeast, northwest, southeast, and southwest (m 3 / h); L 1～9 --Calculated length of the gaps between doors and windows on the east, west, south and north exterior walls (m); Direction correction coefficients for air infiltration in the east, west, south, north, northeast, northwest, southeast and southwest - 0.1, 0.4, 0.2, 1, 0.7, 1, 0.1, 0.2;

[0058] Heat consumption of cold air penetration (W) = 0.278 * total air volume penetrating into the room through the gaps of doors and windows (m 3 / h)*air density at outdoor calculated temperature (kg / m 3 )* Indoor and outdoor temperature difference (℃);

[0059] After determining the amount of air infiltrating through the gaps between doors and windows, the heat consumption of cold air infiltration is obtained:

[0060] Q_z 1～9 =0.278*V m *e w *(self.t n -self.t w );

[0061] Among them, Q_z 1～9 —Heat consumption of cold wind penetration of exterior walls facing east, west, south, north, northeast, northwest, southeast, and southwest (W); w --Air density at outdoor heating temperature (kg / m 3 );0.278—unit conversion factor;

[0062] According to the heat transfer between each household in the selected building, the heat consumption of the inner wall and floor of the neighboring room and the heat consumption of the roof floor are calculated to correct the household heat load prediction;

[0063] Select the heat consumption of the internal wall of the building (W) = the area of ​​the internal wall of the building (m 2 )*Inner wall heat transfer coefficient (W / (m 2 ℃))* Allowable temperature difference of inner wall (℃);

[0064] Select the heat consumption of the floor at the end of the building (W) = the floor area at the end of the building (m 2 )* Floor heat transfer coefficient (W / (m 2 ℃))*Allowable temperature difference of floor slab (℃);

[0065] Heat consumption of the roof slab of the target building (W) = Floor area of ​​the top floor (m 2 )*Heat transfer coefficient of roof material (W / (m 2 ℃))* Allowable temperature difference of roof (℃);

[0066] The above coefficients are taken from the specifications as described below.

[0067] In-building algorithms such as:

[0068] Heat consumption of the room wall: e = self.F[a]*1.37*4;

[0069] w=self.F[a]*1.37*4

[0070] Heat consumption of room floor: aa = self.F[a]*1.87*4;

[0071] Heat consumption of roof floor: W j1 =self.F[a]*K2*(self.tn-self.tw);

[0072] Where, e, w, aa—heat consumption of east, south, west, north interior walls and floor slabs (W); W j1 —Heat consumption of roof slab (W); K2—Heat transfer coefficient of building roof material (W / (m 2 ·℃)); 1.37, 1.87—Inner wall heat transfer coefficient (W / (m 2 ·℃)); 4--allowable temperature difference.

[0073] For the second network balancing unit,

[0074] Target building circulating water flow (t / h) = 0.86*(target building predicted heat load (w) / (target building supply water temperature (℃) - target building return water temperature (℃))

[0075] That is: G = 0.86*(heat[i,j] / (T g -T h ));

[0076] G--Building household circulating water flow (t / h); heat[i,j]-building household predicted heat load data (w); T g --Water supply temperature at the building's household end (℃); T h --Return water temperature at the building household end (℃); 0.86—Specific heat coefficient of water.

[0077] A method for predicting user water supply flow of smart heating, using the above system; the method comprises the following steps:

[0078] S1: First, the user classification unit determines the information of each user in the heating building, distinguishes heating users from non-heating users, and generates corresponding signal codes, where the signal code of the heating user is H1 and the signal code of the non-heating user is H0; then, the heating users are classified according to their location information and the heating conditions of their neighbors, where

[0079] Solution A divides users into the following categories based on their location information in the heating building:

[0080] If the heating signal code is H0, the user classification signal code C1 is generated;

[0081] If the heating signal code is H1 and the user is located on the top floor, a user classification signal code C2 is generated;

[0082] If the heating signal code is H1 and the user is located on the ground floor, a user classification signal code C3 is generated;

[0083] If the heating signal code is H1 and the users are located on both sides, the user classification signal code C4 is generated;

[0084] If the heating signal code is H1 and the user is in the middle, the user classification signal code C5 is generated;

[0085] Plan B divides heating users into the following categories according to the heating conditions of their neighbors:

[0086] If the heating signal code is H1, and there is no user heating in the neighboring households, the user classification signal code D0 is generated;

[0087] If the heating signal code is H1, and there is only one user in the neighboring household who is heating, then the user classification signal code D1 is generated;

[0088] If the heating signal code is H1, and there are two users in the neighboring household who are heating, then the user classification signal code D2 is generated;

[0089] If the heating signal code is H1, and there are three users in the user's neighboring households who are heating, then the user classification signal code D3 is generated;

[0090] If the heating signal code is H1, and there are four users in the user's neighboring households who are heating, then the user classification signal code D4 is generated;

[0091] Thirdly, the building location information of the users in Scheme A is combined with the heating situation of the users in Scheme B and their neighbors to achieve the classification of heating users. The heat load calculation correction coefficient is calculated based on the historical heating data according to different user classification signal codes, and self-learning can be performed according to the real-time heating data to continuously iterate the heat load calculation correction coefficient, thereby improving the calculation accuracy.

[0092] Then, the heat load calculation correction coefficient calculated by each type of user is sent to the load calculation unit. S2: First, the climate compensation unit determines the outdoor calculated temperature value of the heat load calculation according to the outdoor meteorological parameters; then, the outdoor parameters are obtained to correct the outdoor calculated temperature and sent to the load calculation unit; then, the heating user thermal inertia index is calculated according to the outdoor meteorological parameters, the thermal insulation performance of the heating building and the parameters of the indoor heating target temperature, and the heating user thermal inertia index is sent to the second network balancing unit;

[0093] Outdoor parameters include outdoor temperature, outdoor wind speed, solar radiation and outdoor weather.

[0094] S3: First, the load calculation unit obtains the insulation data of the heating building, determines the apartment type of the heating building, and generates a corresponding signal code; wherein,

[0095] The signal code for a general apartment with north-south ventilation is K1; the signal code for an apartment with a corridor in the middle with only the south exterior wall is K2. The corresponding load calculation method is determined by the corresponding signal code.

[0096] Then, the load calculation unit receives the signals sent by the user classification unit and the climate compensation unit, processes and calculates the predicted heat load value of each heating user, and sends it to the second network balancing unit;

[0097] S4: First, the secondary network balancing unit receives the signals generated by the load calculation unit and the climate compensation unit, and obtains the data of the water supply temperature and return water temperature of the central heating system, and finally calculates the hot water flow and control time of each heating user.

[0098] In step S3, the load calculation method includes the following steps:

[0099] S3.1: The load calculation method is determined by the type of heating building. If it is a general type K1, the load calculation of the middle household takes into account the heat load of the north and south exterior walls; if it is a corridor type K2, the load calculation of the middle household takes into account the heat load of the south exterior wall, and the north wall is used as an interior wall for load calculation;

[0100] S3.2: Obtain the insulation data of the heating building; the insulation data includes the heat transfer coefficient of the exterior wall, the heat transfer coefficient of the exterior window and the window-to-wall ratio, and at the same time obtain the heating area of ​​each heating user for subsequent load calculation;

[0101] S3.3: The climate compensation unit obtains the outdoor calculated temperature, inputs the indoor heating target temperature and calculates the preliminary predicted heat load;

[0102] S3.4: The user classification unit obtains the user classification information of each heating user, corrects the preliminary predicted heat load of each user, and obtains the final predicted heat load value of each household.

[0103] The technical effects provided by the present invention are:

[0104] (1) Heating users are divided into different types according to the different building locations and heating conditions of their neighbors. Different types of heating users have different correction parameters in heat load calculation and water supply flow regulation, so that different types of heating users can achieve a higher heating comfort level, thereby saving energy and reducing emissions;

[0105] (2) The heat load calculation correction factor is obtained by processing and calculating historical heating data, and can be self-learned and iterated through real-time heating data. The heat load calculation correction factor that is continuously iterated will be more accurate and closer to the actual situation, thereby improving the accuracy of subsequent heat load calculations and water supply flow calculations.

[0106] (3) Considering that changes in outdoor meteorological parameters will affect the heating comfort of heating users, in the heat load calculation process, the outdoor correction calculation temperature is introduced to take into account outdoor parameters such as outdoor temperature, outdoor wind speed, solar radiation and weather conditions, and the final calculated heat load of each household is corrected to ensure the comfort of each heating user.

[0107] (4) Considering that the fluctuation of outdoor climate will affect the indoor temperature of heating users, the thermal inertia index is introduced in the balancing process of the second network. It is calculated based on parameters such as the change of outdoor climate, the thermal insulation performance of the heating building and the indoor heating target temperature. It is used to adjust the water supply flow in advance to offset the impact of outdoor climate change on indoor temperature and ensure the comfort of heating users.

[0108] (5) Different calculation methods are used for different apartment types in different buildings. Different heat load calculation methods are used for common apartment types and corridor apartment types to ensure the accuracy of heat load calculation. BRIEF DESCRIPTION OF THE DRAWINGS

[0109] Figure 1 It is a schematic diagram of the water supply flow prediction system of the present invention.

[0110] Figure 2 It is a schematic diagram of the classification of heating users of the present invention. DETAILED DESCRIPTION

[0111] like Figure 1-2 The present invention provides a method for predicting user water supply flow of smart heating, including the following modules: user classification unit, load calculation unit, two-network balance unit, climate compensation unit, such as Figure 1 ;

[0112] The user classification unit is used to classify heating users according to different building locations, different heating information, and different heating status of neighboring households, and calculate the heat load calculation correction coefficient for different heating user types based on historical heating data and send it to the load calculation unit;

[0113] Due to the complex building information and heating information in the heating building, the heating users in the same building location will have different heating amounts due to the different heating conditions of the surrounding neighbors; the heating users in the same heating conditions will also have different heating amounts due to different building locations. Therefore, it is necessary to combine the building location information and the heating information of the neighbors to classify users for load calculation. Users of the same type can be considered to have the same heat load due to similar heating conditions.

[0114] According to the building location information of the heating users and the heating information of the surrounding neighbors, the users can be divided into nine categories and sixty-four subcategories. Among them, the nine categories are based on the building location information, namely 1 top left household, 2 top right household, 3 bottom left household, 4 bottom right household, 5 top household, 6 left household, 7 right household, 8 bottom household, 9 middle household. User examples are as follows Figure 2 As shown in the figure, the heat users can be divided into five categories according to the heating status of the neighboring households: A isolated users, B unilateral heating, C bilateral heating, D trilateral heating, and E quadrilateral heating. A isolated users, B unilateral heating, C bilateral heating, D trilateral heating, and E quadrilateral heating respectively contain 1, 4, 6, 4, and 1 subcategories. Therefore, combining the building location information with the heating information of the surrounding neighbors for classification can divide users into nine categories and 64 subcategories.

[0115] The climate compensation unit is used to obtain outdoor calculation parameter information, including outdoor corrected calculation temperature; the outdoor corrected calculation temperature is obtained by analyzing parameters such as outdoor temperature, outdoor wind speed, solar radiation, weather type, etc., and the outdoor corrected calculation temperature is sent to the load calculation unit; the thermal inertia index of the heating user is then calculated based on parameters such as outdoor calculation parameter information, heating building insulation information and indoor heating target temperature, and the thermal inertia index of the heating user is sent to the secondary network balancing unit;

[0116] Due to different outdoor weather conditions, there will be deviations in the outdoor calculated temperature during the load forecasting process. Therefore, it is necessary to carry out weather compensation and use different types of weather corrections to correct the outdoor calculated temperature for load forecasting to ensure that the load forecast can take into account the changing factors of outdoor weather.

[0117] Meteorological compensation refers to historical data and is divided into three types: sunny, cloudy and rainy. 0 represents sunny, 1 represents cloudy, and 2 represents rainy and snowy. Select 0 to 2 according to the weather forecast for the next period; different types of meteorological weather will make different degrees of correction to the outdoor calculated temperature.

[0118] Compensated outdoor temperature (℃) = outdoor meteorological temperature (℃) + (sunny outdoor temperature (℃) + 1.5; cloudy outdoor temperature (℃) + 0; rainy and snowy outdoor temperature (℃) - 1.5)

[0119] Different outdoor meteorological wind speeds will lead to deviations in the outdoor calculated temperature during the load forecasting process, so meteorological wind speed compensation is required to correct the outdoor calculated temperature of the load forecast with different meteorological wind speeds to ensure that the load forecast can take into account the change factor of the outdoor meteorological wind speed. Meteorological wind speed compensation refers to historical literature data and divides ten wind speed levels. Different levels of meteorological wind speed will correct the outdoor calculated temperature to different degrees.

[0120] Compensated outdoor temperature (℃) = outdoor meteorological temperature (℃) + (wi nd (m / s) less than 0.2 - 0.02; wi nd less than 1.5 - 0.18; wi nd less than 3.3 - 0.49; wi nd less than 5.4 - 0.88; wi nd less than 7.9 - 1.34; wi nd less than 10.7 - 1.87; wi nd less than 13.82 - 2.46; wi nd less than 17.1 - 3.1; wi nd less than 24.4 - 4.52; wi nd greater than 24.4 - 5.292);

[0121] wi nd (m / s) is the wind speed;

[0122] The load calculation unit is used to determine the apartment type of the heating building, adopt different heat load calculation methods for different apartment types, and receive information from the user classification unit and the climate compensation unit, and finally process to obtain the predicted heat load of each heating user, and send the predicted heat load to the secondary network balancing unit;

[0123] The load prediction algorithm involves two parts, namely the building and household positioning algorithm, and the enclosure structure heat consumption calculation algorithm; among them, different parts are divided into different modules for development and calculation according to actual needs to ensure that the load prediction algorithm meets the requirements.

[0124] In the building positioning algorithm, according to the building heating standard and based on the building's geographical orientation, the 0 / 1 judgment method is established by considering the building's own east, west, south, and north orientations and northeast, northwest, southeast, and southwest orientations.

[0125] Building positioning design algorithm: If the building's geographical orientation is east, west, south or north, set T to 1; if the building's geographical orientation is northeast, northwest, southeast or southwest, T1 is 0. The algorithm is as follows:

[0126]

[0127] In the household terminal positioning algorithm, first, according to the location and number of household terminals in the target community, an n-layer m-household matrix, that is, n-row and m-column matrix, is established to locate the house number of each household terminal. The calculation logic is set up according to the unit level to represent the entire building terminal of the building, starting from the first unit of the building, and so on to the next level of household terminals; then, the number of floors of the building and the number of users in each floor of the building are established to construct an array, and the household terminals of the unit are set in the form of a one-dimensional array to set all the household numbers from the first household on the first floor to the last household on the top floor; for example, in the case of one unit of the building and three households on each floor: 101: belongs to the starting household and is the first household on the first floor; 203: belongs to the third household on the second floor; 1002: belongs to the second household on the tenth floor, etc., and the household terminal is positioned in a horizontal and vertical order; finally, all the user household numbers contained in the building terminal are input at the front end for complete positioning;

[0128] Among them, the building is divided into six types of households: the leftmost household, the rightmost household, the middle household, the top floor side household, the bottom floor side household and the four corner side households for heat load calculation. The household positioning algorithm is as follows:

[0129]

[0130]

[0131] Among them, q1~q4--heat consumption of the east, west, south and north exterior walls, q5--heat consumption of the roof (W); q6~q9--heat consumption of the east, west, south and north exterior walls in the northeast, northwest, southeast and southwest directions (W); W j1 --Heat consumption of floor slab (W); e, w, aa—heat consumption of east inner wall, west inner wall, upper / lower inner wall of the building (W)

[0132] The heat load of the exterior walls and floor slabs is divided into two parts: the basic heat consumption of the maintenance structure and the heat consumption of cold air penetration;

[0133] Up / Down is up or down.

[0134] That is q 1～9 =Q_y 1～9 +Q_z 1～9

[0135] Q_y 1～9 —Additional heat consumption of maintenance structure (w), Q_z 1～9 —Heat consumption of cold air penetration (W);

[0136] Basic heat consumption of maintenance structure (W) = {area of ​​outdoor walls of selected buildings (m 2 )*Indoor and outdoor temperature difference (℃)*Exterior wall heat transfer coefficient (W / (m 2 ℃)) + area of ​​outdoor windows of each building (m 2 )*Indoor and outdoor temperature difference (℃)*External window heat transfer coefficient (W / (m2 ·℃)};

[0137] The heat transfer coefficient of exterior walls and windows depends on the material and thickness, etc., and is taken from the specifications or actual measurements.

[0138] The basic heat consumption design algorithm for the exterior wall and roof enclosure structure is as follows:

[0139] Q_x 1～

[0140] 9=self.Fx[a]*(1-self.fx[a])*K*(self.tn-self.t w )+self.Fx[a]*self.fx[a]*k*(self.tn-self.t w )

[0141] Among them, Q_x 1～9 --Basic heat consumption of the east, west, south, north exterior walls and roof enclosure structure (W); Fx[a]--Area of ​​the east, west, south, north exterior walls and roof (m 2 ); fx[a]--East, west, south, north exterior window area (m 2 );t n —Indoor temperature (℃); t w —Outdoor temperature (℃);

[0142] The actual heat consumption will increase or decrease due to various factors such as meteorological conditions and building conditions in different regions. Since these factors require corrections to the room envelope structure, the basic heat consumption of the envelope structure is corrected by an additional orientation correction rate considering the influence of solar radiation on the building. At the same time, the wind force correction rate is made based on the fact that changes in outdoor wind speed will cause the heat transfer coefficient of the outer surface of the envelope structure to increase.

[0143] Heat consumption of maintenance structure = basic heat consumption (w) * (direction correction rate + wind force addition rate);

[0144] That is: Q_y 1～9 =Q_x 1～9 *(1+direction correction rate+wind force addition rate);

[0145] Among them, Q_y 1～9 --After considering various additional rates, the heat consumption of the maintenance structure after orientation correction and wind force correction. Orientation correction rate (for example, obtained from the standard "Civil Regulations" - Qingdao) - East: -0.05, West: -0.05, South: 0.08, North: 0.05, Southeast: -0.12, Southwest: -0.12, Northeast: -0.05, Northwest: -0.05; Wind force additional rate (obtained from the standard "Civil Regulations" - Qingdao) - 0.07.

[0146] Under the influence of the indoor and outdoor pressure difference caused by wind and heat pressure, the outdoor cold air penetrates into the room through the gaps of doors and windows, escapes after being heated, and the heat consumed by heating this part of cold air from the outdoor temperature to the indoor temperature is calculated as the heat consumption of cold wind penetration. The heat consumption of infiltration into doors and windows is calculated based on the length of the gaps of doors and windows in the dominant wind direction in winter as the windward side. The heat consumption of cold wind penetration of the east, west, south and north exterior walls is calculated by selecting each household of the building, and the heat consumption of cold wind penetration of the east, west, south and north exterior walls and the heat consumption of the roof in the northeast, northwest, southeast and southwest directions. The heat consumption of the exterior wall of the enclosure structure is further corrected, and the design algorithm is as follows:

[0147] First, according to the local (Qingdao) average outdoor wind speed in winter, the amount of indoor air infiltrating into the gaps between doors and windows per meter is obtained (obtained from the Civil Regulations):

[0148] if self.v<=1;

[0149] L = 0.4;

[0150] elif self.v<=2;

[0151] L = 1.1;

[0152] elif self.v<=2;

[0153] L = 1.8;

[0154] Where v is the average outdoor wind speed; L is the amount of air that penetrates into the room through the gap between doors and windows per meter (m 3 / (m·h)

[0155] After that, an air infiltration direction correction coefficient is introduced to calculate the total amount of cold air infiltrating into the room through the gaps between doors and windows in different directions:

[0156] V 1～9 =L*self.L 1～9 * Orientation correction factor;

[0157] Among them, V 1～9 —Total amount of air that penetrates into the room through the gaps of doors and windows from the east, west, south, north, northeast, northwest, southeast, and southwest (m 3 / h); L 1～9 --Calculated lengths of door and window gaps on the east, west, south and north exterior walls (m); Direction correction coefficients for air infiltration in the east, west, south, north, northeast, northwest, southeast and southwest - 0.1, 0.4, 0.2, 1, 0.7, 1, 0.1, 0.2 (obtained from the Civil Code);

[0158] Heat consumption of cold air penetration (W) = 0.278 * total air volume penetrating into the room through the gaps of doors and windows (m 3 / h)*air density at outdoor calculated temperature (kg / m 3 )* Indoor and outdoor temperature difference (℃);

[0159] After determining the amount of air infiltrating through the gaps between doors and windows, the heat consumption of cold air infiltration is:

[0160] Q_z 1～9 =0.278*V m *e w *(self.t n -self.t w );

[0161] Among them, Q_z 1～9 —Heat consumption of cold wind penetration of exterior walls facing east, west, south, north, northeast, northwest, southeast, and southwest (W); w --Air density at outdoor heating temperature (kg / m 3 );0.278—unit conversion factor (1KJ / h=0.278W);

[0162] The outer sides of the enclosure structures of different households in a building are not in direct contact with the outdoor air, but the rooms are separated by inner walls and floors, resulting in differences in indoor temperature and heat consumption. The temperature difference between neighboring households causes heat transfer between households. Therefore, for the heat transfer between households in the selected building, the heat consumption of the inner walls and floors of the neighboring rooms and the heat consumption of the roof floor are calculated to further correct the household heat load prediction.

[0163] Select the heat consumption of the internal wall of the building (W) = the area of ​​the internal wall of the building (m 2 )*Inner wall heat transfer coefficient (W / (m 2 ℃))* Allowable temperature difference of inner wall (℃);

[0164] Select the heat consumption of the floor at the end of the building (W) = the floor area at the end of the building (m 2 )* Floor heat transfer coefficient (W / (m 2 ℃))*Allowable temperature difference of floor slab (℃);

[0165] Heat consumption of the roof slab of the target building (W) = Floor area of ​​the top floor (m 2 )*Heat transfer coefficient of roof material (W / (m 2 ℃))* Allowable temperature difference of roof (℃);

[0166] The above coefficients are taken from the specifications as described below.

[0167] In-building algorithms such as:

[0168] Heat consumption of the room wall: e = self.F[a]*1.37*4;

[0169] w=self.F[a]*1.37*4

[0170] Heat consumption of room floor: aa = self.F[a]*1.87*4;

[0171] Heat consumption of roof floor: W j1 =self.F[a]*K2*(self.tn-self.tw);

[0172] Note: e, w, aa—heat consumption of east, south, west, north interior walls and floor slabs (W); W j1 —Heat consumption of roof slab (W); K2—Heat transfer coefficient of building roof material (obtained from the Civil Code) (W / (m 2 ·℃)); 1.37, 1.87—Inner wall heat transfer coefficient (obtained from the Civil Code) (W / (m 2 ℃)); 4--allowable temperature difference (obtained from the Civil Regulations) (℃);

[0173] The secondary network balancing unit is used to receive information from the load calculation unit and the climate compensation unit, and calculate the water supply flow value and flow control time required by each heating user based on data such as the actual water supply temperature and return water temperature.

[0174] In order to make the indoor temperature in the heating system reach the required requirements, dynamic control is achieved by relying on the size and temperature of the heating water flow. By calculating the household water flow of the target building, the predicted heat load obtained by the load prediction module is combined with the real-time supply and return water temperature to calculate the building and household circulating water flow.

[0175] Target building circulating water flow (t / h) = 0.86*(target building predicted heat load (w) / (target building supply water temperature (℃) - target building return water temperature (℃))

[0176] That is: G = 0.86*(heat[i,j] / (T g -T h ));

[0177] Note: G--household circulating water flow rate of the building (t / h); heat[i,j]-household predicted heat load data of the building (w); T g --Water supply temperature at the building's household end (℃); T h --Return water temperature at the building end (℃); 0.86—Specific heat coefficient of water;

[0178] The calculation of water supply flow of each heating user includes the following steps:

[0179] S1: First, the user classification unit determines the information of each user in the heating building, distinguishes heating users from non-heating users, and generates corresponding signal codes, where the signal code of the heating user is H1 and the signal code of the non-heating user is H0; then, the heating users are classified according to their location information and the heating conditions of their neighbors, where

[0180] Solution A divides users into the following categories based on their location information in the heating building:

[0181] If the heating signal code is H0, the user classification signal code C1 is generated;

[0182] If the heating signal code is H1 and the user is located on the top floor, a user classification signal code C2 is generated;

[0183] If the heating signal code is H1 and the user is located on the ground floor, a user classification signal code C3 is generated;

[0184] If the heating signal code is H1 and the users are located on both sides, the user classification signal code C4 is generated;

[0185] If the heating signal code is H1 and the user is in the middle, the user classification signal code C5 is generated;

[0186] Plan B divides heating users into the following categories according to the heating conditions of their neighbors:

[0187] If the heating signal code is H1, and there is no user heating in the neighboring households, the user classification signal code D0 is generated;

[0188] If the heating signal code is H1, and there is only one user in the neighboring household who is heating, then the user classification signal code D1 is generated;

[0189] If the heating signal code is H1, and there are two users in the neighboring household who are heating, then the user classification signal code D2 is generated;

[0190] If the heating signal code is H1, and there are three users in the user's neighboring households who are heating, then the user classification signal code D3 is generated;

[0191] If the heating signal code is H1, and there are four users in the user's neighboring households who are heating, then the user classification signal code D4 is generated;

[0192] Thirdly, the building location information of the users in Scheme A is combined with the heating situation of the users in Scheme B and their neighbors to achieve the classification of heating users. The heat load calculation correction coefficient is calculated based on the historical heating data according to different user classification signal codes, and self-learning can be performed according to the real-time heating data to continuously iterate the heat load calculation correction coefficient, thereby improving the calculation accuracy.

[0193] Then, the heat load calculation correction coefficients calculated by each type of user are sent to the load calculation unit.

[0194] S2: First, the climate compensation unit determines the outdoor calculated temperature value for heat load calculation according to the outdoor meteorological parameters; then, the outdoor parameters are obtained to correct the outdoor calculated temperature and sent to the load calculation unit; then, the thermal inertia index of the heating user is calculated according to the outdoor meteorological parameters, the thermal insulation performance of the heating building and the indoor heating target temperature and other parameters, and the thermal inertia index of the heating user is sent to the secondary network balancing unit;

[0195] Outdoor parameters include outdoor temperature, outdoor wind speed, solar radiation, outdoor weather, etc.;

[0196] S3: First, the load calculation unit obtains the insulation data of the heating building, determines the apartment type of the heating building, and generates a corresponding signal code; wherein,

[0197] The signal code for a general apartment with north-south ventilation is K1; the signal code for an apartment with a corridor in the middle with only the south exterior wall is K2. The corresponding load calculation method is determined by the corresponding signal code.

[0198] Then, the load calculation unit receives the signals sent by the user classification unit and the climate compensation unit, processes and calculates the predicted heat load value of each heating user, and sends it to the second network balancing unit;

[0199] S4: First, the secondary network balancing unit receives the signals generated by the load calculation unit and the climate compensation unit, and obtains data such as the water supply temperature and return water temperature of the central heating system, and finally calculates the hot water flow and control time of each heating user.

[0200] In step S3, the load calculation method includes the following steps:

[0201] S3.1: The load calculation method is determined by the type of heating building. If it is a general type K1, the load calculation of the middle household takes into account the heat load of the north and south exterior walls; if it is a corridor type K2, the load calculation of the middle household only takes into account the heat load of the south exterior wall, and the north wall is used as an interior wall for load calculation;

[0202] S3.2: Obtain the insulation data of the heating building; the insulation data includes the heat transfer coefficient of the exterior wall, the heat transfer coefficient of the exterior window and the window-to-wall ratio, and at the same time obtain the heating area of ​​each heating user for subsequent load calculation;

[0203] S3.3: The climate compensation unit obtains the outdoor calculated temperature, inputs the indoor heating target temperature and calculates the preliminary predicted heat load;

[0204] S3.4: The user classification unit obtains the user classification information of each heating user, corrects the preliminary predicted heat load of each user, and obtains the final predicted heat load value of each household.

[0205] The present invention is fully described for a clearer disclosure, and the prior art is not listed one by one.

[0206] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit them; although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some of the technical features therein with equivalents; it is obvious for those skilled in the art to combine multiple technical solutions of the present invention. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention. The technical contents not described in detail in the present invention are all known technologies.

Claims

1. A user water supply flow prediction system for smart heating, characterized by: The system includes the following modules: user classification unit, load calculation unit, two-network balancing unit, and weather compensation unit; A user classification unit is used to classify heating users, calculate heat load calculation correction coefficients for different heating user types based on historical heating data, and send them to the load calculation unit; The classification is based on building location, heating information and heating status of neighboring households; The climate compensation unit is used to obtain outdoor calculation parameter information, including outdoor corrected calculation temperature; the outdoor corrected calculation temperature is obtained by analyzing the outdoor temperature, outdoor wind speed, solar radiation, and weather type, and the outdoor corrected calculation temperature is sent to the load calculation unit; the thermal inertia index of the heating user is then calculated based on the outdoor calculation parameter information, the insulation information of the heating building, and the indoor heating target temperature, and the thermal inertia index of the heating user is sent to the second network balancing unit; The load calculation unit is used to determine the apartment type of the heating building, adopt different heat load calculation methods for different apartment types, and receive information from the user classification unit and the climate compensation unit, and finally process to obtain the predicted heat load of each heating user, and send the predicted heat load to the secondary network balancing unit; The secondary network balancing unit is used to receive the information sent by the load calculation unit and the climate compensation unit, and calculate the water supply flow value and flow control time required by each heating user according to the actual water supply temperature and return water temperature data.

2. The user water supply flow prediction system for smart heating according to claim 1 is characterized by: In the user classification unit, heating users are divided into nine categories and sixty-four subcategories according to their building location information and the heating information of their surrounding neighbors. Among them, the nine categories are recorded according to the building location information as 1 top left household, 2 top right household, 3 bottom left household, 4 bottom right household, 5 top household, 6 left household, 7 right household, 8 bottom household, 9 middle household; According to the heating status of neighboring households, heat users are divided into A isolated island users, B single-sided heating, C double-sided heating, D three-sided heating, and E four-sided heating; Among them, A isolated island users, B unilateral heating, C bilateral heating, D trilateral heating, and E quadrilateral heating contain 1, 4, 6, 4, and 1 subcategories respectively; A isolated user refers to a neighboring household with no heating, and there is one subcategory, which is that the four neighboring households above, below, left and right are not heated; B unilateral heating refers to a neighboring household with one heating, and there are four subcategories, namely heating for the upper neighboring household, heating for the lower neighboring household, heating for the left neighboring household, and heating for the right neighboring household; C unilateral heating refers to a neighboring household with two heating, and there are six subcategories, namely heating for the upper and lower neighboring households, heating for the upper left neighboring household, heating for the upper right neighboring household, heating for the left and right neighboring households, heating for the lower left neighboring household, and heating for the lower right neighboring households; D unilateral heating refers to a neighboring household with three heating, and there are four subcategories, namely heating for the upper and lower left neighboring households, heating for the upper and lower right neighboring households, heating for the upper and left neighboring households, and heating for the lower and left neighboring households; E full-side heating refers to a neighboring household with four heating, and there is one subcategory, which is that all four neighboring households above, below, left and right are heated.

3. The user water supply flow prediction system for smart heating according to claim 1 is characterized by: Climate compensation unit, which uses meteorological weather correction to calculate outdoor temperature for load forecasting; Referring to historical literature data, the weather types of meteorological weather compensation include sunny, cloudy and rainy days, and 0 is defined as sunny, 1 as cloudy, and 2 as rainy and snowy days; According to the weather forecast for the next period, select 0 to 2; Compensated outdoor temperature (℃) = outdoor meteorological temperature (℃) + (outdoor temperature on sunny day (℃) + 1.5; outdoor temperature on cloudy day (℃) + 0; outdoor temperature on rainy and snowy day (℃) - 1.5).

4. The user water supply flow prediction system for smart heating according to claim 1 is characterized by: Climate compensation unit, which uses meteorological wind speed correction to calculate outdoor temperature for load forecasting; Based on different outdoor meteorological wind speeds, meteorological wind speed compensation is determined by referring to historical literature data and divided into ten wind speed levels; Compensated outdoor temperature (℃) = outdoor meteorological temperature (℃) + (wind (m / s) less than 0.2 - 0.02; wind less than 1.5 - 0.18; wind less than 3.3 - 0.49; wind less than 5.4 - 0.88; wind less than 7.9 - 1.34; wind less than 10.7 - 1.87; wind less than 13.82 - 2.46; wind less than 17.1 - 3.1; wind less than 24.4 - 4.52; wind greater than 24.4 - 5.292); wind(m / s) is the wind speed.

5. The user water supply flow prediction system for smart heating according to claim 1 is characterized by: Load prediction algorithms include building and user terminal positioning algorithms and enclosure structure heat consumption calculation algorithms; In the building positioning algorithm, according to the building heating standard, based on the geographical orientation of the building, if the geographical orientation of the building is east, west, south or north, T is set to 1; if the geographical orientation of the building is northeast, northwest, southeast or southwest, T1 is 0; T means considering the building's east, west, south, and north orientations; T1 means considering the building's northeast, northwest, southeast, and southwest orientations; The algorithm is as follows: In the user terminal positioning algorithm, first, according to the location and number of user terminals in the target community, an n-layer m-household matrix, i.e., an n-row m-column matrix, is established to locate the house number of each user terminal. The calculation logic is set up at the unit level to represent the entire building terminal of the building, starting from the first unit of the building, and so on to the next level of user terminals; then, an array is constructed by establishing the number of building floors and the number of users on each floor of the building. The user terminals of the corresponding units are set in the form of a one-dimensional array, from the first household on the first floor to the last household on the top floor; then, all the user numbers contained in the building terminal are input into the front end of the load calculation unit for complete positioning; Among them, the building units are divided into the leftmost unit, the rightmost unit, the middle unit, the top floor unit, the bottom floor unit and the four corner units for heat load calculation. The unit end positioning algorithm is as follows: Among them, q1~q4--heat consumption of the exterior wall in the east, west, south and north directions, q5--heat consumption of the roof (W); q6~q9--heat consumption of the exterior wall in the northeast, northwest, southeast and southwest directions (W); W j1 --Heat consumption of floor slab (W); e, w, aa—heat consumption of east inner wall, west inner wall, upper / lower inner wall of the building (W); The heat load of the exterior wall and floor is divided into the heat consumption of the maintenance structure and the heat consumption of cold air penetration; that is, q 1～9 =Q_y 1～9 +Q_z 1～9 Among them, Q_y 1～9 —Heat consumption of maintenance structure (w), Q_z 1～9 —Heat consumption of cold air penetration (W); Basic heat consumption of maintenance structure (W) = {area of ​​outdoor walls of selected buildings (m 2 )*Indoor and outdoor temperature difference (℃)*Exterior wall heat transfer coefficient (W / (m 2 ℃)) + area of ​​outdoor windows of each building (m 2 )*Indoor and outdoor temperature difference (℃)*External window heat transfer coefficient (W / (m 2 ·℃)}; The heat transfer coefficient of exterior walls and windows depends on the material and thickness, etc., and is taken from the specifications or measured; The basic heat consumption design algorithm for the exterior wall and roof enclosure structure is as follows: Q_x 1～ 9=self.Fx[a]*(1-self.fx[a])*K*(self.tn-self.t w )+self.Fx[a]*s elf.fx[a]*k*(self.tn-self.t w ); Among them, Q_x 1～9 --Basic heat consumption of the east, west, south, north exterior walls and roof enclosure structure (W); Fx[a]--Area of ​​the east, west, south, north exterior walls and roof (m 2 ); fx[a]--East, west, south, north exterior window area (m 2 );t n —Indoor temperature (℃); t w —Outdoor temperature (℃); Heat consumption of maintenance structure = basic heat consumption (w) * (direction correction rate + wind force addition rate); That is: Q_y 1～9 =Q_x 1～9 *(1+direction correction rate+wind force addition rate); Among them, Q_y 1～9 --Heat consumption of the maintenance structure after orientation correction and wind force correction; orientation correction rate is taken from the technical specifications; wind force addition rate is taken from the technical specifications; Under the influence of the indoor and outdoor pressure difference caused by wind and heat pressure, the outdoor cold air penetrates into the room through the gaps, escapes after being heated, and the heat consumed by this part of the heated cold air from the outdoor temperature to the indoor temperature is calculated as the heat consumption of cold air penetration; Among them, the length of the door and window gaps in the dominant wind direction in winter is taken as the main windward side to calculate the heat loss of infiltration into doors and windows; the heat loss of cold wind infiltration of the east, west, south and north exterior walls and the heat loss of the roof in the northeast, northwest, southeast and southwest directions are calculated by selecting each household of the building, and the heat loss of cold wind infiltration of the east, west, south and north exterior walls and the roof heat loss are corrected; the heat loss of the exterior wall of the enclosure structure is calculated by the following algorithm: First, according to the local average outdoor wind speed in winter, the amount of air that penetrates into the room per meter of door and window gaps is calculated: if self.v<=1; L＝0.4； elif self.v<=2; L＝1.1； elif self.v<=2; L＝1.8； Where v is the average outdoor wind speed; L is the amount of air that penetrates into the room through the gap between doors and windows per meter (m 3 / (m·h)); After that, the correction coefficient of the infiltration air volume direction is introduced to calculate the total amount of cold air infiltrating into the room through the gaps of doors and windows in different directions: V 1～9 =L*self.L 1～9 * Orientation correction factor; Among them, V 1～9 —Total amount of air that penetrates into the room through the gaps of doors and windows from the east, west, south, north, northeast, northwest, southeast, and southwest (m 3 / h); L 1～9 --Calculated length of the gaps between doors and windows on the east, west, south and north exterior walls (m); Direction correction coefficients for air infiltration in the east, west, south, north, northeast, northwest, southeast and southwest - 0.1, 0.4, 0.2, 1, 0.7, 1, 0.1, 0.2; Heat consumption of cold air penetration (W) = 0.278 * total air volume penetrating into the room through the gaps of doors and windows (m 3 / h)*air density at outdoor calculated temperature (kg / m 3 )* Indoor and outdoor temperature difference (℃); After determining the amount of air infiltrating through the gaps between doors and windows, the heat consumption of cold air infiltration is obtained: Q_z 1～9 =0.278*V m *e w *(self.t n -self.t w ); Among them, Q_z 1～9 —Heat consumption of cold wind penetration of exterior walls facing east, west, south, north, northeast, northwest, southeast, and southwest (W); w --Air density at outdoor heating temperature (kg / m 3 );0.278—unit conversion factor; According to the heat transfer between each household in the selected building, the heat consumption of the inner wall and floor of the neighboring room and the heat consumption of the roof floor are calculated to correct the household heat load prediction; Select the heat consumption of the internal wall of the building (W) = the area of ​​the internal wall of the building (m 2 )*Inner wall heat transfer coefficient (W / (m 2 ℃))* Allowable temperature difference of inner wall (℃); Select the heat consumption of the floor at the end of the building (W) = the floor area at the end of the building (m 2 )* Floor heat transfer coefficient (W / (m 2 ℃))*Allowable temperature difference of floor slab (℃); Heat consumption of the roof slab of the target building (W) = Floor area of ​​the top floor (m 2 )*Heat transfer coefficient of roof material (W / (m 2 ℃))* Allowable temperature difference of roof (℃); In-building algorithms such as: Heat consumption of the room wall: e = self.F[a]*1.37*4; w=self.F[a]*1.37*4 Heat consumption of room floor: aa = self.F[a]*1.87*4; Roof floor heat consumption: W j1 =self.F[a]*K2*(self.tn-self.tw); Where, e, w, aa—heat consumption of east, south, west, north interior walls and floor slabs (W); W j1 —Heat consumption of roof slab (W); K2—Heat transfer coefficient of building roof material (W / (m 2 ·℃)); 1.37, 1.87—Inner wall heat transfer coefficient (W / (m 2 ·℃)); 4--allowable temperature difference.

6. The user water supply flow prediction system for smart heating according to claim 1 is characterized by: For the second network balancing unit, Target building circulating water flow (t / h) = 0.86*(target building predicted heat load (w) / (target building supply water temperature (℃) - target building return water temperature (℃)) That is: G = 0.86*(heat[i,j] / (T g -T h )); G--Building household circulating water flow (t / h); heat[i,j]-building household predicted heat load data (w); T g --Water supply temperature at the building's household end (℃); T h --Return water temperature at the building household end (℃); 0.86—Specific heat coefficient of water.

7. A method for predicting user water supply flow of smart heating, characterized by: With the aid of the system according to any one of claims 1 to 6; the method comprises the following steps; S1: First, the user classification unit determines the information of each user in the heating building, distinguishes between heating users and non-heating users, and generates corresponding signal codes, wherein the signal code of the heating user is H1, and the signal code of the non-heating user is H0; then, the heating users are classified according to the location information of the heating users and the heating conditions of the neighboring households, wherein, scheme A divides the users into the following according to the location information of the users in the heating building, if the heating signal code is H0, then the user classification signal code C1 is generated; If the heating signal code is H1 and the user is located on the top floor, a user classification signal code C2 is generated; If the heating signal code is H1 and the user is located on the ground floor, a user classification signal code C3 is generated; If the heating signal code is H1 and the users are located on both sides, the user classification signal code C4 is generated; If the heating signal code is H1 and the user is in the middle, the user classification signal code C5 is generated; Plan B divides heating users into the following categories according to the heating conditions of their neighbors: If the heating signal code is H1, and there is no user heating in the neighboring households, the user classification signal code D0 is generated; If the heating signal code is H1, and there is only one user in the neighboring household who is heating, then the user classification signal code D1 is generated; If the heating signal code is H1, and there are two users in the neighboring household who are heating, then the user classification signal code D2 is generated; If the heating signal code is H1, and there are three users in the user's neighboring households who are heating, then the user classification signal code D3 is generated; If the heating signal code is H1, and there are four users in the user's neighboring households who are heating, then the user classification signal code D4 is generated; Thirdly, the building location information of the users in Scheme A is combined with the heating situation of the users in Scheme B and their neighbors to achieve the classification of heating users. The heat load calculation correction coefficient is calculated based on the historical heating data according to different user classification signal codes, and self-learning can be performed according to the real-time heating data to continuously iterate the heat load calculation correction coefficient, thereby improving the calculation accuracy. Then, the heat load calculation correction coefficients calculated by each type of user are sent to the load calculation unit.

8. The user water supply flow prediction method of smart heating according to claim 7, It is characterized in that: S2: first, the climate compensation unit determines the outdoor calculated temperature value for heat load calculation according to the outdoor meteorological parameters; Then, the outdoor parameters are obtained to correct the outdoor calculated temperature and sent to the load calculation unit; then, the thermal inertia index of the heating user is calculated according to the outdoor meteorological parameters, the thermal insulation performance of the heating building and the parameters of the indoor heating target temperature, and the thermal inertia index of the heating user is sent to the second network balancing unit; Outdoor parameters include outdoor temperature, outdoor wind speed, solar radiation and outdoor weather.

9. The user water supply flow prediction method of smart heating according to claim 8 is characterized by: S3: First, the load calculation unit obtains the insulation data of the heating building, determines the apartment type of the heating building, and generates a corresponding signal code; wherein, The signal code for a general apartment with north-south ventilation is K1; the signal code for an apartment with a corridor in the middle with only the south exterior wall is K2. The corresponding load calculation method is determined by the corresponding signal code. Then, the load calculation unit receives the signals sent by the user classification unit and the climate compensation unit, processes and calculates the predicted heat load value of each heating user, and sends it to the second network balancing unit; S4: First, the secondary network balancing unit receives the signals generated by the load calculation unit and the climate compensation unit, and obtains the data of the water supply temperature and return water temperature of the central heating system, and finally calculates the hot water flow and control time of each heating user.

10. The user water supply flow prediction method of smart heating according to claim 9 is characterized by: In step S3, the load calculation method includes the following steps: S3.1: The load calculation method is determined by the type of heating building. If it is a general type K1, the load calculation of the middle household takes into account the heat load of the north and south exterior walls; if it is a corridor type K2, the load calculation of the middle household takes into account the heat load of the south exterior wall, and the north wall is used as an interior wall for load calculation; S3.2: Obtain the insulation data of the heating building; the insulation data includes the heat transfer coefficient of the exterior wall, the heat transfer coefficient of the exterior window and the window-to-wall ratio, and at the same time obtain the heating area of ​​each heating user for subsequent load calculation; S3.3: The climate compensation unit obtains the outdoor calculated temperature, inputs the indoor heating target temperature and calculates the preliminary predicted heat load; S3.4: The user classification unit obtains the user classification information of each heating user, corrects the preliminary predicted heat load of each user, and obtains the final predicted heat load value of each household.

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