An energy-saving optimization method and system for building heating, ventilation and air conditioning

Abstract

The present invention discloses an energy-saving optimization method for building heating, ventilation and air conditioning. By installing temperature controllers connected to the network in each target energy supply area and intelligent energy balance valves on each conveying pipeline, real-time temperature in the target energy supply area and real-time energy dynamic data of the conveying pipeline are obtained. Based on these data, the energy demand value in the future period can be predicted relatively accurately and immediately sent to the energy production unit, so as to achieve the balance between energy supply and energy use. By installing intelligent energy balance valves, according to the prediction of energy consumption in each area in the future period and sending the energy consumption value to the intelligent energy balance valves installed on each conveying pipeline, the balance valve automatically and dynamically adjusts the energy delivery volume of the target energy supply area, which not only meets the energy consumption requirements of each target energy supply area, but also takes energy conservation into account.

Description

Technical Field

[0001] The present application relates to the field of air conditioning energy-saving technology, and in particular to a building HVAC energy-saving optimization method and system. Background Art

[0002] Building HVAC systems are often used to regulate temperature and humidity within buildings to ensure a comfortable indoor environment. This system generally consists of several key components: a cooling / heat source, a distribution system, and terminal temperature / humidity control. Water is typically used as the energy transfer medium. The cooling / heat source generates heat, the distribution system transports and distributes it to the terminal rooms, and the terminal temperature / humidity control system exchanges the generated heat with the terminal room air through heat exchange, ensuring the room's internal temperature and humidity are maintained at set values for a comfortable working and living environment. Existing HVAC systems transfer heat and cold (hereafter referred to as energy) through outlet and return water pipes. By monitoring physical parameters such as outlet and return water, outlet / return water temperature differentials, and terminal pressure, the operating conditions of the cooling / heat source units and the distribution system are automatically adjusted to determine the amount of energy generated and distributed. In the terminal rooms, temperature / humidity control devices control the heat exchange between the room air and the piping's cooling / hot water, maintaining the room temperature and humidity within a set range.

[0003] However, the existing building HVAC systems have the following main problems when in use:

[0004] First, existing technologies primarily address this issue by measuring the supply and return water temperatures or temperature differences in energy transmission pipelines. However, since it takes time for water to flow from the outlet to the terminal room and then back to the unit, there is a certain delay in the data. This results in dynamic changes in energy terminal applications, which cannot be fed back to the energy supply equipment in a timely manner, causing an imbalance between energy supply and energy use.

[0005] Second, the existing building HVAC system transmits the energy generated by the cold / heat source through the main water supply pipeline system to the terminal energy consumption area, and after passing through the water distribution valve, it is distributed to each energy consumption zone. Usually, the energy distribution ratio of each area is determined according to the characteristics of the area during engineering design, and is also adjusted in place at the time of engineering commissioning. The problem with this method in use is that it is difficult to match the regional energy distribution ratio with the actual application situation, and it is even more difficult to adapt to the dynamically changing energy needs of each area, resulting in unreasonable energy distribution, causing insufficient energy in some areas and excess energy in some areas; or, in order to meet the energy consumption of energy-deficient areas, the energy supply has to be increased, causing the areas that originally had excess energy to become even more excessive, resulting in a large amount of energy waste;

[0006] Third, the existing terminal temperature regulation technology is achieved through various thermostats or similar temperature regulation devices installed in the room. These devices are usually used independently, which is likely to cause the temperature setpoint to be too low or too high, resulting in excessive energy consumption and waste.

[0007] Fourth, the existing method for the energy demand at the terminal is to establish a mathematical model based on indicators such as the characteristics of the building, the change of regional climate over time, and the indoor temperature change, and then estimate the energy consumption demand of each area and space in the building accordingly. Due to the uncertainties of factors such as the characteristics of different buildings, climate change, and usage process, and the fact that not all relevant factors may be considered during the establishment of the mathematical model, the error of the mathematical model established by this method will inevitably be quite large, and the prediction made based on it will also be inaccurate. Summary of the Invention

[0008] Based on this, in view of the above technical problems, a building heating, ventilation, and air conditioning energy-saving optimization method and system are provided. It can solve the problem of the imbalance between the energy supply and energy use of the existing building heating, ventilation, and air conditioning, resulting in a large amount of energy waste.

[0009] In the first aspect, a building heating, ventilation, and air conditioning energy-saving optimization method, the method includes:

[0010] For each target energy supply area, obtain the actual temperature and preset temperature of the target energy supply area, as well as the energy consumption data of the balance valve corresponding to the target energy supply area;

[0011] According to the actual temperature, the preset temperature, and the energy consumption data, determine the predicted value of the energy consumption demand for each target energy supply area;

[0012] Obtain the operation data of each of the N air conditioners in the heating, ventilation, and air conditioning unit;

[0013] According to the M predicted values of the energy consumption demand and the operation data of the N air conditioners, determine the optimal combined operation plan of the heating, ventilation, and air conditioning unit;

[0014] According to the optimal combined operation plan of the heating, ventilation, and air conditioning unit, control the heating, ventilation, and air conditioning unit to supply energy to the conveying system;

[0015] Obtain the operation data of each of the P conveying pipelines in the conveying system;

[0016] According to the M predicted values of the energy consumption demand and the operation data of the P conveying pipelines, determine the optimal combined operation plan of the conveying system;

[0017] According to the optimal combined operation plan of the conveying system, control the conveying system to supply energy to the M target energy supply areas;

[0018] Among them, M, N, and P are all positive integers.

[0019] In the above solution, further optionally, each of the target energy supply areas is provided with a temperature controller and a temperature sensor;

[0020] For each of the target energy supply areas, obtaining the actual temperature and the preset temperature of the target energy supply area, as well as the energy consumption data of the balance valve corresponding to the target energy supply area, includes:

[0021] For each of the target energy supply areas, obtain the actual temperature collected by the temperature sensor corresponding to the target energy supply area, the preset temperature of the temperature controller corresponding to the target energy supply area, and the energy consumption data of the balance valve corresponding to the target energy supply area at a preset time interval.

[0022] In the above solution, further optionally, determining the predicted value of the energy consumption demand for each of the target energy supply areas according to the actual temperature, the preset temperature, and the energy consumption data includes:

[0023] According to the actual temperature T and the preset temperature T S , determine the temperature difference ΔT of the target energy supply area;

[0024] According to the temperature difference ΔT and the preset time interval Δt, calculate the temperature change rate T of the target energy supply area r ;

[0025] According to the temperature change rate T r , determine the energy supply demand of the target energy supply area.

[0026] In the above solution, further optionally, when the energy supply demand of the target energy supply area is a cooling demand, determine the normal temperature volatility control range T of the target energy supply area v01 ;

[0027] According to the temperature change rate T r and the normal temperature volatility control range T v01 , determine the temperature drop rate T of the target energy supply area v1 ;

[0028] When the actual temperature T is greater than the preset temperature T S , and the temperature drop rate T v1 is greater than or equal to the temperature drop rate parameter T vs1 , the predicted value of the energy consumption demand of the target energy supply area is the first energy value;

[0029] When the actual temperature T is greater than the preset temperature T S, and the temperature decrease rate T v1 is less than the temperature decrease rate parameter T vs1 , the predicted energy demand value of the target energy supply area is the second energy value;

[0030] When the actual temperature T is equal to the preset temperature T S , the predicted energy demand value of the target energy supply area is the first energy value;

[0031] When the actual temperature T is less than the preset temperature T S , and the temperature decrease rate T v1 is greater than or equal to the temperature decrease rate parameter T vs1 , the predicted energy demand value of the target energy supply area is the first energy value;

[0032] When the actual temperature T is less than the preset temperature T S , and the temperature decrease rate T v1 is less than the temperature decrease rate parameter T vs1 , the predicted energy demand value of the target energy supply area is the third energy value;

[0033] Among them, the first energy value is the energy value ΔEA1 corresponding to the energy consumption data of the target energy supply area in the previous stage;

[0034] The second energy value is the sum of the first energy increment value EAC1 and the energy value ΔEA1 corresponding to the energy consumption data of the target energy supply area in the previous stage. The first energy increment value EAC1 = (1 + ΔT × 5%) × ΔEA1;

[0035] The third energy value is the sum of the second energy increment value EAC2 and the energy value ΔEA1 corresponding to the energy consumption data of the target energy supply area in the previous stage. The second energy increment value EAC2 = (1 + ΔT × 2.5%) × ΔEA.

[0036] In the above solution, further optionally, the method further includes: when the energy supply demand of the target energy supply area is a heating demand, determining the normal temperature volatility control range T v02 ;

[0037] According to the temperature change rate T r and the normal temperature volatility control range T v02 , determining the temperature rise rate T v2 ;

[0038] When the actual temperature T is less than the preset temperature T S , and the temperature rise rate T v2Greater than or equal to the temperature rise rate parameter T vs2 In this case, the predicted energy demand value of the target energy supply area is the fourth energy value;

[0039] When the actual temperature T is less than the preset temperature T S , and the temperature rise rate T v2 is less than the temperature rise rate parameter T vs2 In this case, the predicted energy demand value of the target energy supply area is the fifth energy value;

[0040] When the actual temperature T is equal to the preset temperature T S In this case, the predicted energy demand value of the target energy supply area is the fourth energy value;

[0041] When the actual temperature T is greater than the preset temperature T S , and the temperature rise rate T v2 is greater than or equal to the temperature rise rate parameter T vs2 In this case, the predicted energy demand value of the target energy supply area is the sixth energy value;

[0042] When the actual temperature T is greater than the preset temperature T S , and the temperature rise rate T v2 is less than the temperature rise rate parameter T vs2 In this case, the predicted energy demand value of the target energy supply area is the fourth energy value;

[0043] Among them, the fourth energy value is the energy value ΔEA2 corresponding to the energy consumption data of the target energy supply area in the previous stage;

[0044] The fifth energy value is the sum of the third energy increment value EAC3 and the energy value ΔEA2 corresponding to the energy consumption data of the target energy supply area in the previous stage. The third energy increment value EAC3 = (1 + ΔT × 5%) × ΔEA2;

[0045] The sixth energy value is the sum of the fourth energy increment value EAC4 and the energy value ΔEA2 corresponding to the energy consumption data of the target energy supply area in the previous stage. The fourth energy increment value EAC4 = (1 + ΔT × 2.5%) × ΔEA2.

[0046] In the above solution, further optionally, obtaining the operation data of each of the N air conditioners in the HVAC unit includes;

[0047] For each air conditioner, according to the air conditioner energy efficiency ratio prediction model corresponding to the air conditioner, determine the optimal energy efficiency ratio, optimal load rate, most economical energy efficiency ratio and most economical load rate of each air conditioner.

[0048] In the above solution, further optionally, determining the optimal combined operation plan of the HVAC unit according to the M predicted energy consumption demand values and the operation data of the N air conditioners includes:

[0049] When the actual power difference between any two of the N air conditioners is less than the first preset power difference, determining the optimal combined operation plan of the N air conditioners of the HVAC unit according to the M predicted energy consumption demand values and the optimal output sorting of the N air conditioners;

[0050] When the actual power difference between any two of the N air conditioners is greater than or equal to the first preset power difference, calculating the power ratio values of the N air conditioners;

[0051] According to the power ratio values of the N air conditioners, arranging the power ratio values of the N air conditioners in descending order to obtain the air conditioner power ratio sorting;

[0052] Determining the optimal combined operation plan of the N air conditioners of the HVAC unit according to the air conditioner power ratio sorting.

[0053] In the above solution, further optionally, obtaining the operation data of each of the P conveying pipelines of the conveying system includes:

[0054] For each of the conveying pipelines, determining the energy efficiency ratio of each of the conveying pipelines according to the conveying pipeline energy efficiency ratio prediction model corresponding to the conveying pipeline;

[0055] Determining the power and the maximum output water flow of each of the conveying pipelines according to the energy efficiency ratio of each of the conveying pipelines.

[0056] In the above solution, further optionally, determining the optimal combined operation plan of the conveying system according to the M predicted energy consumption demand values and the operation data of the P conveying pipelines includes:

[0057] Determining the total demand flow of the M target energy supply areas according to the M predicted energy consumption demand values;

[0058] When the power difference between any two of the P conveying pipelines is less than the second preset power difference, determining the variable frequency of each of the conveying pipelines;

[0059] Determining the optimal combined operation plan of the N conveying pipelines of the conveying system according to the variable frequencies of the P conveying pipelines;

[0060] When there is a power difference between any two of the P conveying pipelines greater than or equal to a second preset power difference, determine the conveying ratio value of each of the P conveying pipelines;

[0061] According to the conveying ratio values of the P conveying pipelines, determine the optimal combined operation plan for the P conveying pipelines of the conveying system.

[0062] In a second aspect, an energy-saving optimization system for building heating, ventilation and air conditioning, the system includes:

[0063] A first acquisition module, configured to acquire, for each of the target energy supply areas, the actual temperature and preset temperature of the target energy supply area, and the energy consumption data of the balance valve corresponding to the target energy supply area;

[0064] A first determination module, configured to determine a predicted value of the energy consumption demand for each of the target energy supply areas according to the actual temperature, the preset temperature, and the energy consumption data;

[0065] A second acquisition module, configured to acquire the operation data of each of the N air conditioners of the heating, ventilation and air conditioning unit;

[0066] A second determination module, configured to determine the optimal combined operation plan of the heating, ventilation and air conditioning unit according to the M predicted values of the energy consumption demand and the operation data of the N air conditioners;

[0067] A first control module, configured to control the heating, ventilation and air conditioning unit to supply energy to the conveying system according to the optimal combined operation plan of the heating, ventilation and air conditioning unit;

[0068] A third acquisition module, configured to acquire the operation data of each of the P conveying pipelines of the conveying system;

[0069] A third determination module, configured to determine the optimal combined operation plan of the conveying system according to the M predicted values of the energy consumption demand and the operation data of the P conveying pipelines;

[0070] A second control module, configured to control the conveying system to supply energy to the M target energy supply areas according to the optimal combined operation plan of the conveying system; where M, N, and P are all positive integers.

[0071] The present invention has at least the following beneficial effects:

[0072] (1) Through the temperature controllers installed in each target energy supply area and connected to the network, and the intelligent energy balance valves installed on each conveying pipeline, the real-time temperature in the target energy supply area and the real-time energy dynamic data of the conveying pipeline are obtained. Based on these data, the energy demand value in the future period can be predicted relatively accurately and immediately sent to the energy production unit, thus achieving the balance between energy supply and energy use.

[0073] (2) By installing intelligent energy balance valves, the invention predicts the energy consumption in each area in the future period according to the algorithm, and sends the energy consumption value to the intelligent energy balance valves installed on each conveying pipeline. The balance valves automatically and dynamically adjust the energy transmission volume of the target energy supply area, which not only meets the energy consumption requirements of each target energy supply area, but also takes into account energy conservation.

[0074] (3) Through the temperature controllers with various networking methods installed inside the target energy supply area, the temperature values of each target energy supply area can be set within a reasonable temperature range, which not only ensures a comfortable environment but also reduces energy waste, thus achieving energy conservation.

[0075] (4) The invention predicts the energy demand in the future period according to the actual energy consumption data of the building and the actual operating temperature / humidity data of each terminal area. This prediction method based on actual operating dynamic data is easier to approach the actual situation and improve the prediction accuracy. BRIEF DESCRIPTION OF THE DRAWINGS

[0076] Figure 1 It is a schematic flow chart of the building heating, ventilation and air conditioning energy conservation optimization method provided by an embodiment of the present invention;

[0077] Figure 2 It is a schematic framework flow chart of the building heating, ventilation and air conditioning energy conservation optimization method provided by an embodiment of the present invention;

[0078] Figure 3 It is a schematic specific flow chart of the building heating, ventilation and air conditioning energy conservation optimization method provided by an embodiment of the present invention;

[0079] Figure 4 It is a schematic flow chart of the internal data analysis of the algorithm server of the building heating, ventilation and air conditioning energy conservation optimization method provided by an embodiment of the present invention;

[0080] Figure 5 It is a schematic flow chart of determining the next-stage energy prediction value of the building heating, ventilation and air conditioning energy conservation optimization method provided by an embodiment of the present invention;

[0081] Figure 6 It is a schematic flow chart of the algorithm server of the building heating, ventilation and air conditioning energy conservation optimization method provided by an embodiment of the present invention giving the optimal operation plan for the unit;

[0082] Figure 7 Schematic diagram of the process for establishing an air-conditioning energy efficiency ratio prediction model for the building heating, ventilation and air-conditioning energy-saving optimization method provided by an embodiment of the present invention;

[0083] Figure 8 Schematic diagram of the process for the algorithm server to give the optimal operation plan of the conveying system in the building heating, ventilation and air-conditioning energy-saving optimization method provided by an embodiment of the present invention;

[0084] Figure 9 Internal structure diagram of a computer device in an embodiment. Specific embodiments

[0085] In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0086] In an embodiment, as Figure 1 shown, a building heating, ventilation and air-conditioning energy-saving optimization method is provided, including the following steps:

[0087] For each of the target energy supply areas, obtain the actual temperature and preset temperature of the target energy supply area, and the energy consumption data of the balance valve corresponding to the target energy supply area;

[0088] According to the actual temperature, the preset temperature and the energy consumption data, determine the predicted value of the energy consumption demand for each of the target energy supply areas;

[0089] Obtain the operation data of each of the N air conditioners in the heating, ventilation and air-conditioning unit;

[0090] According to the M predicted values of the energy consumption demands and the operation data of the N air conditioners, determine the optimal combined operation plan of the heating, ventilation and air-conditioning unit;

[0091] Among them, the determining the optimal combined operation plan of the heating, ventilation and air-conditioning unit according to the M predicted values of the energy consumption demands and the operation data of the N air conditioners is specifically: according to the sum value of the M predicted values of the energy consumption demands and the operation data of the N air conditioners, determine the optimal combined operation plan of the heating, ventilation and air-conditioning unit.

[0092] According to the optimal combined operation plan of the heating, ventilation and air-conditioning unit, control the heating, ventilation and air-conditioning unit to supply energy to the conveying system;

[0093] Obtain the operation data of each of the P conveying pipelines in the conveying system;

[0094] Determine the optimal combined operation plan of the conveying system according to the M predicted energy demand values and the operation data of the P conveying pipelines;

[0095] Among them, determining the optimal combined operation plan of the conveying system according to the M predicted energy demand values and the operation data of the P conveying pipelines is specifically: determining the optimal combined operation plan of the conveying system according to each predicted energy demand value among the M predicted energy demand values and the operation data of the P conveying pipelines.

[0096] Control the conveying system to supply energy to the M target energy supply areas according to the optimal combined operation plan of the conveying system;

[0097] Among them, M, N, and P are all positive integers.

[0098] In the present invention, by installing temperature controllers connected to the network in each target energy supply area and intelligent energy balance valves on each conveying pipeline, the real-time temperature in the target energy supply area and the real-time energy dynamic data of the conveying pipeline are obtained. Based on these data, the energy demand value in the future period can be predicted more accurately and immediately sent to the energy production unit, so as to achieve the balance between energy supply and energy use.

[0099] In one embodiment, as Figure 2 shown, the building heating, ventilation and air conditioning system involved in the present invention consists of several parts such as a cold / heat source unit, a conveying system, an intelligent energy balance valve, an energy use area and an algorithm server. Among them, the cold / heat source unit is responsible for the production of cold / heat energy, the conveying system is responsible for the conveying of energy, the intelligent energy balance valve is responsible for the energy distribution of the energy use area, and the energy use area part is responsible for the heat exchange between the air in the area and the cold / hot water in the pipeline, so as to complete the adjustment of the environmental temperature. The algorithm server provides the optimal control plan for each part of the system composition according to the real-time collected dynamic data. In the figure, the connecting lines marked with numbers are the communication lines for data transmission between each part and the algorithm server, and the other connecting lines are all pipelines for conveying water. The specific working principle is as Figure 3 shown.

[0100] In one embodiment, each of the target energy supply areas is provided with a temperature controller and a temperature sensor;

[0101] For each of the target energy supply areas, obtaining the actual temperature and the preset temperature of the target energy supply area, and the energy use data of the balance valve corresponding to the target energy supply area includes:

[0102] For each of the target energy supply regions, obtain the actual temperature collected by the temperature sensor corresponding to the target energy supply region, the preset temperature of the temperature controller corresponding to the target energy supply region, and the energy consumption data of the balance valve corresponding to the target energy supply region at a preset time interval.

[0103] In one embodiment, as Figure 4 shown, the real-time operation data processing of the building heating, ventilation and air conditioning system involved in the present invention includes: collection of regional thermostat operation data: the algorithm server regularly collects the set temperature of the temperature controller of each target energy supply region and the value of the actual temperature collected by the temperature sensor, and the energy consumption data of each intelligent energy balance valve; collection of cold / heat source unit and conveying system data: the algorithm server regularly collects the real-time data of the cold / heat source unit and the conveying system, and the data includes electricity meter data, energy meter, flow data, relevant data accessed through the communication interface, etc.; control of the thermostat: the algorithm server issues a temperature setting instruction to the networked temperature controller of each energy consumption region according to a pre-determined rule to ensure the environmental comfort and energy conservation of the energy consumption region.

[0104] In one embodiment, determining the predicted value of the energy consumption demand for each target energy supply region according to the actual temperature, the preset temperature, and the energy consumption data includes:

[0105] According to the actual temperature T and the preset temperature T S , determine the temperature difference ΔT of the target energy supply region;

[0106] According to the temperature difference ΔT and the preset time interval Δt, calculate the temperature change rate T r of the target energy supply region;

[0107] According to the temperature change rate T r , determine the energy supply demand of the target energy supply region.

[0108] In one embodiment, when the energy supply demand of the target energy supply region is a cooling demand, determine the normal temperature volatility control range T v01 of the target energy supply region;

[0109] According to the temperature change rate T r and the normal temperature volatility control range T v01 , determine the temperature drop rate T v1 of the target energy supply region;

[0110] When the actual temperature T is greater than the preset temperature T S , and the temperature drop rate T v1 is greater than or equal to the temperature drop rate parameter T vs1When the predicted value of the energy demand in the target energy supply area is the first energy value;

[0111] When the actual temperature T is greater than the preset temperature T S , and the temperature drop rate T v1 is less than the temperature drop rate parameter T vs1 , the predicted value of the energy demand in the target energy supply area is the second energy value;

[0112] When the actual temperature T is equal to the preset temperature T S , the predicted value of the energy demand in the target energy supply area is the first energy value;

[0113] When the actual temperature T is less than the preset temperature T S , and the temperature drop rate T v1 is greater than or equal to the temperature drop rate parameter T vs1 , the predicted value of the energy demand in the target energy supply area is the first energy value;

[0114] When the actual temperature T is less than the preset temperature T S , and the temperature drop rate T v1 is less than the temperature drop rate parameter T vs1 , the predicted value of the energy demand in the target energy supply area is the third energy value;

[0115] Among them, the first energy value is the energy value ΔEA1 corresponding to the energy consumption data of the target energy supply area in the previous stage;

[0116] The second energy value is the sum of the first energy increment value EAC1 and the energy value ΔEA1 corresponding to the energy consumption data of the target energy supply area in the previous stage. The first energy increment value EAC1 = (1 + ΔT × 5%) × ΔEA1;

[0117] The third energy value is the sum of the second energy increment value EAC2 and the energy value ΔEA1 corresponding to the energy consumption data of the target energy supply area in the previous stage. The second energy increment value EAC2 = (1 + ΔT × 2.5%) × ΔEA.

[0118] In one embodiment, the method further includes: when the energy supply demand in the target energy supply area is a heating demand, determining the normal temperature volatility control range T v02 ;

[0119] According to the temperature change rate T r and the normal temperature volatility control range T v02 , determining the temperature rise rate T v2 ;

[0120] When the actual temperature T is less than the preset temperature T S , and the temperature rise rate T v2 is greater than or equal to the temperature rise rate parameter T vs2 , the predicted value of the energy demand in the target energy supply area is the fourth energy value;

[0121] When the actual temperature T is less than the preset temperature T S , and the temperature rise rate T v2 is less than the temperature rise rate parameter T vs2 , the predicted value of the energy demand in the target energy supply area is the fifth energy value;

[0122] When the actual temperature T is equal to the preset temperature T S , the predicted value of the energy demand in the target energy supply area is the fourth energy value;

[0123] When the actual temperature T is greater than the preset temperature T S , and the temperature rise rate T v2 is greater than or equal to the temperature rise rate parameter T vs2 , the predicted value of the energy demand in the target energy supply area is the sixth energy value;

[0124] When the actual temperature T is greater than the preset temperature T S , and the temperature rise rate T v2 is less than the temperature rise rate parameter T vs2 , the predicted value of the energy demand in the target energy supply area is the fourth energy value;

[0125] Wherein, the fourth energy value is the energy value ΔEA2 corresponding to the energy consumption data of the target energy supply area in the previous stage;

[0126] The fifth energy value is the sum of the third energy increment value EAC3 and the energy value ΔEA2 corresponding to the energy consumption data of the target energy supply area in the previous stage, and the third energy increment value EAC3 = (1 + ΔT × 5%) × ΔEA2;

[0127] The sixth energy value is the sum of the fourth energy increment value EAC4 and the energy value ΔEA2 corresponding to the energy consumption data of the target energy supply area in the previous stage, and the fourth energy increment value EAC4 = (1 + ΔT × 2.5%) × ΔEA2.

[0128] In one embodiment, obtaining the operation data of each of the N air conditioners in the HVAC unit includes;

[0129] For each of the air conditioners, according to the air conditioner energy efficiency ratio prediction model corresponding to the air conditioner, determine the optimal energy efficiency ratio, optimal load rate, most economical energy efficiency ratio, and most economical load rate of each air conditioner.

[0130] Determining the optimal combined operation plan of the HVAC unit according to the M predicted values of energy consumption demands and the operation data of the N air conditioners includes:

[0131] When the actual power difference between any two of the N air conditioners is less than the first preset power difference, determine the optimal combined operation plan of the N air conditioners of the HVAC unit according to the M predicted values of energy consumption demands and the optimal output sorting of the N air conditioners;

[0132] When the actual power difference between any two of the N air conditioners is greater than or equal to the first preset power difference, calculate the power ratio values of the N air conditioners;

[0133] According to the power ratio values of the N air conditioners, arrange the power ratio values of the N air conditioners in descending order to obtain the air conditioner power ratio sorting;

[0134] According to the air conditioner power ratio sorting, determine the optimal combined operation plan of the N air conditioners of the HVAC unit.

[0135] In one embodiment, as Figure 6 shown, the algorithm server collects the operating conditions data of the cold / heat source units, combines the summarized energy consumption data of each region, gives the optimal operation plan of the units, and sends it to the on-site controller of the cold / heat source units. The algorithm is described as follows;

[0136] According to the models of each cold / heat source unit, determine the optimal and most economical COP values and PLRs of the unit, and calculate the output energy value:

[0137] Optimal energy output (Hr rated power): H o = HrPLR Max

[0138] Most economical energy output (Hr rated power): H e = HrPLR Min

[0139] According to the terminal energy consumption demand, there are two cases to be considered separately when optimizing the selection of cold / heat source units.

[0140] When the rated output powers of n cold / heat source units vary greatly:

[0141] H 1r > H 2r > H 3r ...Hnr

[0142] COP=H / P=(H1+H2...H n ) / P=H1 / P+H2 / P...H n / P

[0143] COP1=H1 / P1=H 1r PLR1 / P

[0144] COP2=H2 / P2=H 2r PLR2 / P ......

[0146] COP n =H n / P n =H nr PLR n / P

[0147] The ratio value η of each unit i (P i is the input power of each machine, P is the sum of the input power of all units, that is, ):

[0148] η1=P1 / P, η2=P2 / P……η n =P n / P

[0149] COP=η1H1 / P+η2H2 / P...η n H n / P

[0150] Therefore, the total energy efficiency ratio of the unit is:

[0151]

[0152] The sum of the optimal energy efficiency ratios of all units is:

[0153]

[0154] Optimization strategy: give priority to the host with large rated power and work at COP max State, compare the above optimal output and terminal energy demand H o and H, select the host in sequence until the terminal energy demand is met, and the optimal solution for the cooling / heating source unit is completed;

[0155] Take three units, large, medium and small, as an example:

[0156] The number of units n=1,2,3, then the optimal output is H o1 ,H o2 ,H o3 ;

[0157] The power efficiency ratio is H o1 >H o2 >H o3 , the selection principle is:

[0158] H o1 ≧H preferably has the maximum power and high energy efficiency ratio;

[0159] H>H o1 , give priority to units with high power and high energy efficiency ratio;

[0160] Small power units as supplements; such as: H>H o1 ,

[0161] △H1=HH o1 (Select the unit with the maximum power or the highest energy efficiency ratio No. 1),

[0162] △H2=△H1-H o2 (Select the second largest power or second highest energy efficiency ratio unit No. 2)

[0163] △H3=△H2-H o3

[0164] Until H on ≧△H n-1 , that is, the selection is completed, n is the number of units selected;

[0165] When the rated output power of n cooling / heating source units is similar:

[0166] The optimal COP and PLR of each cooling / heating source unit obtained from the above calculations, as well as the optimal output energy value calculated at last:

[0167] Take three units, large, medium and small, as an example:

[0168] The number of units n=1,2,3, then the optimal output is H o1 ,H o2 ,H o3 ;

[0169] The power efficiency ratio is H o1 >H o2 >H o3 ,

[0170] Optimization strategy: Calculate the optimal output value H of each unit o The units are selected in order from large to small values, and the cumulative number of units selected reaches the number that matches the terminal energy demand H, which completes the optimization plan for the cold / heat source units.

[0171] Send the above cooling / heating source unit control plan to the field controller (DDC).

[0172] In one embodiment, as Figure 7 shown, establishing an air-conditioning energy efficiency ratio prediction model includes establishing a mathematical model for each part of the equipment according to the operation data uploaded in real time by parts such as the cold / heat source unit and the conveying system, and iterating according to the continuously uploaded data to make the mathematical model more accurate. The mathematical model is established using a BP neural network. The BP neural network algorithm is as follows:

[0173] Calculate the error. The error e is (Yk is the predicted value of the network, and Ok is the actual expected value):

[0174] Initialize the network, initialize the connection weights ωij, ωjk between the input layer, the hidden layer, and the output layer neurons, initialize the hidden layer and output thresholds a, b, and set the learning rate and activation function.

[0175] Calculate the output of the hidden layer. x represents the input variable, ωij, a, b are the connection weights between the input layer and the hidden layer and the hidden layer threshold respectively. The calculation of the hidden layer output H is:

[0176]

[0177] i is the number of hidden layer nodes, and f is the hidden layer activation function

[0178] Calculate the output layer. H is the output of the hidden layer, ωjk, b are the connection weights and threshold respectively. The output Y is:

[0179]

[0180] e k = Y k - O k

[0181] Forward propagation, update the weights:

[0182]

[0183] Update the threshold:

[0184]

[0185] b k = b k + ηe k , k = 1, 2…m

[0186] where η is the set learning rate to prevent overfitting and underfitting (range 0.01 - 0.25);

[0187] Judge whether the iteration and training are completed. If not, continue to loop according to the flowchart until completion;

[0188] Save the mathematical model of the predictable optimal COP of the cooling and heating unit for subsequent optimization.

[0189] In one embodiment, obtaining the operation data of each of the P conveying pipelines of the conveying system includes:

[0190] For each of the conveying pipelines, determine the energy efficiency ratio of each of the conveying pipelines according to the energy efficiency ratio prediction model corresponding to the conveying pipeline;

[0191] Determine the power and the maximum output water flow rate of each of the conveying pipelines according to the energy efficiency ratio of each of the conveying pipelines.

[0192] Determining the optimal combined operation plan of the conveying system according to the M predicted values of energy consumption demands and the operation data of the P conveying pipelines includes:

[0193] Determine the total required flow rate of the M target energy supply regions according to the M predicted values of energy consumption demands;

[0194] When the power difference between any two of the P conveying pipelines is less than the second preset power difference, determine the variable frequency of each of the conveying pipelines;

[0195] Determine the optimal combined operation plan of the N conveying pipelines of the conveying system according to the variable frequencies of the P conveying pipelines;

[0196] When the power difference between any two of the P conveying pipelines is greater than or equal to the second preset power difference, determine the conveying ratio value of each of the conveying pipelines;

[0197] Determine the optimal combined operation plan of the P conveying pipelines of the conveying system according to the conveying ratio values of the P conveying pipelines.

[0198] In one embodiment, as Figure 8 shown, the algorithm server collects the working condition data of the conveying system, gives the optimal operation plan of the system according to the summarized energy consumption data, and sends it to the field controller of the conveying system. The algorithm is described as follows;

[0199] According to the models of each conveying system, determine the PER value of the energy efficiency ratio of the conveying system, and calculate the total required flow rate F according to the terminal energy consumption demand H and the energy consumption cycle;

[0200] For a parallel system, when the power differences of each conveying system are large, calculate the PER value (η n is the ratio of each pump)

[0201]

[0202] Determine the pf of each conveying system from the energy efficiency ratio curve according to the model established for each conveying system Max , and calculate the F of each conveying system Max (maximum output water flow rate), compare the above outputs with the terminal demand flow rates F and F Max , and sequentially select the conveying systems until the required number of conveying systems is reached to complete the optimization scheme of the conveying system;

[0203] For a parallel system, when the powers of each conveying system are similar, calculate the PER value

[0204]

[0205] Calculate the variable frequency (pf m variable frequency, m = 1…n):

[0206] F = mF n = mF nr pf

[0207] pf = F / mF nr

[0208] Obtain: pf1, pf2…pf n , then:

[0209] Δpf = |pf m - pf max |

[0210] Find the m value corresponding to the smallest absolute value difference between pf m and pf max , which is the optimal combination number scheme of the conveying system;

[0211] Send the above conveying system combination scheme to the field controller (DDC);

[0212] Field device control: The algorithm server sends the predicted energy consumption values for the next time period in each area to the intelligent energy balance valves in each area to complete energy distribution. Through the above process, the technical route adopted by the present invention is based on the big data of the operation process of building heating, ventilation and air conditioning, and realizes the goal of on-demand function.

[0213] In the present invention, DDC: field controller; COP: coefficient of performance; PLR: load rate; EA: energy consumption in the terminal area; H: energy consumption; Hr: rated output energy; Hi: output energy; F: flow rate of the conveying system; pf: variable frequency of the conveying system; m: number of variable-frequency pumps; T: current room temperature; Ts: set temperature; t: current time; t0: initial time; Tr: temperature change rate; Tv: indoor temperature drop rate; Tv0: normal temperature volatility control range; Tvs: temperature rise / fall rate parameter.

[0214] In the present invention, by installing intelligent energy balance valves, predicting the energy consumption of each area in the future period according to the algorithm, and sending the energy consumption values to the intelligent energy balance valves installed on each conveying pipeline, the energy delivery volume of the target energy supply area is automatically and dynamically adjusted by the balance valves, which not only meets the energy consumption requirements of each target energy supply area, but also takes energy conservation into account.

[0215] In the present invention, through the temperature controllers with various networking methods installed inside the target energy supply area, the temperature values of each target energy supply area can be set within a reasonable temperature range, which not only ensures a comfortable environment but also reduces energy waste, thus achieving energy conservation.

[0216] In the present invention, based on the actual energy consumption data of the building and the actual operating temperature / humidity data of each terminal area, the energy consumption demand in the future period is predicted. This prediction method based on actual operating dynamic data is more likely to be close to the actual situation and improve the prediction accuracy.

[0217] The present invention provides a building heating, ventilation and air conditioning energy-saving optimization system, which is applied to M target energy supply areas and includes the following program modules:

[0218] The first acquisition module is used to acquire the actual temperature and preset temperature of each target energy supply area, and the energy consumption data of the balance valve corresponding to the target energy supply area.

[0219] The first determination module is used to determine the predicted value of the energy consumption demand of each target energy supply area according to the actual temperature, the preset temperature and the energy consumption data.

[0220] The second acquisition module is used to acquire the operation data of each of the N air conditioners in the heating, ventilation and air conditioning unit.

[0221] The second determination module is used to determine the optimal combined operation plan of the heating, ventilation and air conditioning unit according to the M predicted values of the energy consumption demand and the operation data of the N air conditioners.

[0222] The first control module is used to control the heating, ventilation and air conditioning unit to supply energy to the conveying system according to the optimal combined operation plan of the heating, ventilation and air conditioning unit.

[0223] A third acquisition module, configured to acquire the operation data of each of the P conveying pipelines of the conveying system;

[0224] A third determination module, configured to determine an optimal combined operation plan of the conveying system according to the M energy demand prediction values and the operation data of the P conveying pipelines;

[0225] A second control module, configured to control the conveying system to supply energy to the M target energy supply areas according to the optimal combined operation plan of the conveying system; where M, N, and P are all positive integers.

[0226] In one embodiment, a computer device is provided. The computer device may be a terminal, and its internal structure diagram may be as shown in Figure 9 The figure. The computer device includes a processor, a memory, a communication interface, a display screen, and an input device connected through a system bus. Among them, the processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and the computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner, and the wireless manner can be implemented through WI FI, a carrier network, NFC (Near Field Communication), or other technologies. When the computer program is executed by the processor, it implements a graphic layout editing method. The display screen of the computer device may be a liquid crystal display screen or an electronic ink display screen, and the input device of the computer device may be a touch layer covered on the display screen, or a button, a trackball, or a touchpad provided on the outer shell of the computer device, or an external keyboard, a touchpad, or a mouse, etc.

[0227] Those skilled in the art can understand that Figure 9 the structure shown in is only a block diagram of some parts of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine some components, or have different component arrangements.

[0228] In one embodiment, a computer device is provided, including a memory and a processor. A computer program is stored in the memory, which involves all or part of the processes in the method of the above embodiment.

[0229] In one embodiment, a computer-readable storage medium is provided, on which a computer program is stored, which involves all or part of the processes in the method of the above embodiment.

[0230] Those of ordinary skill in the art can understand that all or part of the processes in the methods of the above embodiments can be completed by instructing relevant hardware through a computer program. The computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above methods. Among them, any reference to a memory, storage, database, or other medium used in the embodiments provided in the present application can include at least one of non-volatile and volatile memories. Non-volatile memory can include read-only memory (ROM), magnetic tape, floppy disk, flash memory, or optical memory, etc. Volatile memory can include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM can be in various forms, such as static random access memory (SRAM) or dynamic random access memory (DRAM), etc.

[0231] The technical features of the above embodiments can be combined arbitrarily. For the sake of brevity of description, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, it should be considered as the scope described in this specification.

[0232] The above-described embodiments merely represent several implementation manners of the present application. Their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of the invention patent. It should be noted that for those of ordinary skill in the art, without departing from the concept of the present application, several modifications and improvements can still be made, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application should be subject to the appended claims.

Claims

1. A building HVAC energy-saving optimization method, applied to M target energy supply areas, characterized in that: The method comprises: For each target energy supply area, obtaining the actual temperature and preset temperature of the target energy supply area, and energy usage data of the balancing valve corresponding to the target energy supply area; Determining a predicted energy demand value for each target energy supply area based on the actual temperature, the preset temperature, and the energy consumption data; Obtaining operating data of each of N air conditioners in a HVAC unit; Determining an optimal combined operation plan for the HVAC units based on the M energy demand forecast values and the N air conditioner operation data; controlling the HVAC units to supply energy to the delivery system according to the optimal combined operation plan of the HVAC units; Acquiring operation data of each of the P delivery pipelines of the delivery system; Determining an optimal combined operation plan for the transportation system based on the M energy demand forecast values and the operating data of the P transportation pipelines; According to the optimal combined operation plan of the transportation system, controlling the transportation system to supply energy to the M target energy supply areas; Wherein, M, N and P are all positive integers; Each of the target energy supply areas is provided with a temperature controller and a temperature sensor; The step of obtaining, for each target energy supply area, the actual temperature and the preset temperature of the target energy supply area, and the energy usage data of the balancing valve corresponding to the target energy supply area includes: For each target energy supply area, obtaining, at preset time intervals, the actual temperature collected by the temperature sensor corresponding to the target energy supply area, the preset temperature of the temperature controller corresponding to the target energy supply area, and the energy usage data of the balancing valve corresponding to the target energy supply area; The step of determining the predicted energy demand value of each target energy supply area according to the actual temperature, the preset temperature, and the energy consumption data includes: According to the actual temperature and the preset temperature , determine the temperature difference of the target energy supply area ; According to the temperature difference and the preset time interval , calculate the temperature change rate of the target energy supply area ; According to the temperature change rate , determining the energy supply demand of the target energy supply area; When the energy supply demand of the target energy supply area is a cooling demand, determine the normal temperature fluctuation rate control range of the target energy supply area ; According to the temperature change rate And the normal temperature fluctuation rate control range , determine the temperature drop rate of the target energy supply area ; At the actual temperature Greater than the preset temperature , and the temperature drop rate Greater than or equal to the temperature drop rate parameter In the case of , the energy demand forecast value of the target energy supply area is the first energy value; At the actual temperature Greater than the preset temperature , and the temperature drop rate Less than the temperature drop rate parameter In the case of , the energy demand forecast value of the target energy supply area is the second energy value; At the actual temperature Equal to the preset temperature , in the case of , the energy demand forecast value of the target energy supply area is the first energy value; At the actual temperature Less than the preset temperature , and the temperature drop rate Greater than or equal to the temperature drop rate parameter In the case of , the energy demand forecast value of the target energy supply area is the first energy value; At the actual temperature Less than the preset temperature , and the temperature drop rate Less than the temperature drop rate parameter In the case of , the energy demand forecast value of the target energy supply area is the third energy value; Among them, the first energy value is the energy value corresponding to the energy consumption data of the target energy supply area in the previous stage ; The second energy value is the first energy increment value The energy value corresponding to the energy consumption data of the target energy supply area in the previous stage The sum of the first energy increment ; The third energy value is the second energy increment value The energy value corresponding to the energy consumption data of the target energy supply area in the previous stage The sum of the second energy increment .

2. The method according to claim 1, characterized in that The method further includes: determining a normal temperature fluctuation rate control range of the target energy supply area when the energy supply demand of the target energy supply area is a heating demand. ; According to the temperature change rate And the normal temperature fluctuation rate control range , determine the temperature rise rate of the target energy supply area ; At the actual temperature Less than the preset temperature , and the temperature rise rate Greater than or equal to the temperature rise rate parameter In the case of , the energy demand forecast value of the target energy supply area is the fourth energy value; At the actual temperature Less than the preset temperature , and the temperature rise rate Less than the temperature rise rate parameter In the case of , the energy demand forecast value of the target energy supply area is the fifth energy value; At the actual temperature Equal to the preset temperature In the case of , the energy demand forecast value of the target energy supply area is the fourth energy value; At the actual temperature Greater than the preset temperature , and the temperature rise rate Greater than or equal to the temperature rise rate parameter In the case of , the energy demand forecast value of the target energy supply area is the sixth energy value; At the actual temperature Greater than the preset temperature , and the temperature rise rate Less than the temperature rise rate parameter In the case of , the energy demand forecast value of the target energy supply area is the fourth energy value; The fourth energy value is the energy value corresponding to the energy consumption data of the target energy supply area in the previous stage. ; The fifth energy value is the third energy increment value The energy value corresponding to the energy consumption data of the target energy supply area in the previous stage The sum of the third energy increment ; The sixth energy value is the fourth energy increment value The energy value corresponding to the energy consumption data of the target energy supply area in the previous stage The sum of the fourth energy increment .

3. The method according to claim 1, characterized in that The obtaining of the operating data of each of the N air conditioners in the HVAC unit includes: For each of the air conditioners, the optimal energy efficiency ratio, optimal load rate, most economical energy efficiency ratio and most economical load rate of each of the air conditioners are determined according to the air conditioner energy efficiency ratio prediction model corresponding to the air conditioner.

4. The method according to claim 1, wherein Determining the optimal combined operation plan of the HVAC unit based on the M energy demand forecast values and the N air conditioner operation data includes: When the actual power difference between any two of the N air conditioners is less than a first preset power difference, determining an optimal combined operation plan for the N air conditioners of the HVAC unit according to the M energy demand forecast values and the optimal output ranking of the N air conditioners; If the actual power difference between any two of the N air conditioners is greater than or equal to a first preset power difference, calculating a power ratio of the N air conditioners; Arranging the power ratio values of the N air conditioners in descending order according to the power ratio values of the N air conditioners to obtain an air conditioner power ratio ranking; An optimal combined operation plan for the N air conditioners of the HVAC unit is determined according to the air conditioner power ratio ranking.

5. The method according to claim 1, wherein The obtaining of the operating data of each of the P transport pipelines of the transport system includes: For each of the transmission pipelines, determining the energy efficiency ratio of each of the transmission pipelines according to the transmission pipeline energy efficiency ratio prediction model corresponding to the transmission pipeline; The power and maximum output water flow of each of the delivery pipelines are determined according to the energy efficiency ratio of each of the delivery pipelines.

6. The method according to claim 1, characterized in that Determining the optimal combined operation plan of the transportation system based on the M energy demand forecast values and the operation data of the P transportation pipelines includes: Determining the total demand flow of the M target energy supply areas based on the M energy demand forecast values; When the power difference between any two of the P delivery pipelines is less than a second preset power difference, determining the frequency change of each delivery pipeline; Determining an optimal combined operation plan for the N conveying pipelines of the conveying system according to the variable frequencies of the P conveying pipelines; When the power difference between any two of the P delivery pipelines is greater than or equal to a second preset power difference, determining a delivery ratio value of each delivery pipeline; An optimal combined operation plan for the P conveying pipelines of the conveying system is determined according to the conveying ratio values of the P conveying pipelines.

7. A building HVAC energy-saving optimization system, applied to M target energy supply areas, characterized in that: The system comprises: a first acquisition module, configured to acquire, for each target energy supply area, the actual temperature and the preset temperature of the target energy supply area, and energy usage data of the balancing valve corresponding to the target energy supply area; A first determining module is configured to determine a predicted energy demand value for each target energy supply area based on the actual temperature, the preset temperature, and the energy consumption data; A second acquisition module is used to obtain the operating data of each of the N air conditioners in the HVAC unit; a second determining module, configured to determine an optimal combined operation plan for the HVAC units based on the M energy demand forecast values and the N operating data of the air conditioners; a first control module, configured to control the HVAC unit to supply energy to the delivery system according to the optimal combined operation plan of the HVAC unit; A third acquisition module is used to acquire the operation data of each of the P delivery pipelines of the delivery system; a third determining module, configured to determine an optimal combined operation plan for the transportation system based on the M energy demand forecast values and the operation data of the P transportation pipelines; A second control module is configured to control the conveying system to supply energy to the M target energy supply areas according to the optimal combined operation plan of the conveying system; wherein M, N and P are all positive integers; Each of the target energy supply areas is provided with a temperature controller and a temperature sensor; The step of obtaining, for each target energy supply area, the actual temperature and the preset temperature of the target energy supply area, and the energy usage data of the balancing valve corresponding to the target energy supply area includes: For each target energy supply area, obtaining, at preset time intervals, the actual temperature collected by the temperature sensor corresponding to the target energy supply area, the preset temperature of the temperature controller corresponding to the target energy supply area, and the energy usage data of the balancing valve corresponding to the target energy supply area; The step of determining the predicted energy demand value of each target energy supply area according to the actual temperature, the preset temperature, and the energy consumption data includes: According to the actual temperature and the preset temperature , determine the temperature difference of the target energy supply area ; According to the temperature difference and the preset time interval , calculate the temperature change rate of the target energy supply area ; According to the temperature change rate , determining the energy supply demand of the target energy supply area; When the energy supply demand of the target energy supply area is a cooling demand, the normal temperature fluctuation rate control range of the target energy supply area is determined. ; According to the temperature change rate And the normal temperature fluctuation rate control range , determine the temperature drop rate of the target energy supply area ; At the actual temperature Greater than the preset temperature , and the temperature drop rate Greater than or equal to the temperature drop rate parameter In the case of , the energy demand forecast value of the target energy supply area is the first energy value; At the actual temperature Greater than the preset temperature , and the temperature drop rate Less than the temperature drop rate parameter In the case of , the energy demand forecast value of the target energy supply area is the second energy value; At the actual temperature Equal to the preset temperature In the case of , the energy demand forecast value of the target energy supply area is the first energy value; At the actual temperature Less than the preset temperature , and the temperature drop rate Greater than or equal to the temperature drop rate parameter In the case of , the energy demand forecast value of the target energy supply area is the first energy value; At the actual temperature Less than the preset temperature , and the temperature drop rate Less than the temperature drop rate parameter In the case of , the energy demand forecast value of the target energy supply area is the third energy value; Among them, the first energy value is the energy value corresponding to the energy consumption data of the target energy supply area in the previous stage ; The second energy value is the first energy increment value The energy value corresponding to the energy consumption data of the target energy supply area in the previous stage The sum of the first energy increment ; The third energy value is the second energy increment value The energy value corresponding to the energy consumption data of the target energy supply area in the previous stage The sum of the second energy increment .

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