A heating control method and system based on industrial heating forecasting

Abstract

This invention relates to the field of industrial heating control technology, specifically a heating control method and system based on industrial heating prediction. The invention compares current industrial production plan data with recent historical industrial production plan data to determine similarity. Based on the similarity results, it predicts the industrial heat load sequence for future periods. This is then corrected using weather forecast data to obtain a revised industrial heat load sequence. Simultaneously, based on historical industrial heating data, it analyzes the corresponding thermal inertia delay time of industrial heating facilities. Combined with the revised industrial heat load sequence, it generates a heat source output control command for a water source heat pump, enabling advance control of the water source heat pump and improving the response speed of the heat source output. Furthermore, it collects real-time industrial heat load data after heat source output control, compares and determines the industrial heat load deviation value, and adjusts the heat source output control command accordingly. This achieves closed-loop feedback control, improving the operational stability and production adaptability of industrial heating.

Description

Technical Field

[0001] This invention relates to the field of industrial heating control technology, and specifically to a heating control method and system based on industrial heating prediction. Background Technology

[0002] Industrial heating is a crucial component ensuring the continuous and stable operation of industrial production. Changes in industrial production plans, switching of operating status of heat-using equipment, and fluctuations in outdoor weather conditions all directly alter industrial heat load demands. Achieving precise heating control that aligns with actual production needs, ensuring that production process temperatures meet standards while reducing heating energy consumption, is the future direction of industrial heating research and development.

[0003] Chinese Patent Publication No. CN116772283B discloses a heating network regulation method and device. The method first determines the proportion of residential users in the heating area and predicts the heating load according to the proportion threshold in different scenarios. When the proportion of residential users is lower than the threshold, the heating load is directly predicted. When it is higher than or equal to the threshold, the heating satisfaction is determined in combination with the area type. The residential and industrial loads are predicted separately and the residential load is corrected by the satisfaction level. The total predicted heating load is obtained by merging the predicted load and the actual load. The predicted load is compared with the actual load, and the distributed energy storage system is controlled to regulate the heating network, thereby shortening the regulation time lag and improving the response capability of the heating network.

[0004] The existing industrial heating control technology has the following technical defects: 1. The existing technology uses the ratio of residential users to industrial users as the basis for switching heating load forecasting strategies, focusing on the comfort evaluation index of residential heating. When there are industrial users in the heating area, only the industrial load is superimposed as the overall forecast value. The technology does not conduct differentiated heat load forecasting for industrial users' production plans, which leads to a deviation between the industrial heat load forecasting results and the actual production demand. This results in a mismatch between the heat source output and the actual industrial heat demand, affecting the stability of industrial production processes.

[0005] 2. Existing technologies correct the predicted residential heat load based on heating satisfaction, but do not consider the impact of outdoor temperature changes on heating load loss. The lack of meteorological data for accurate correction of industrial heat load results in a discrepancy between the corrected predicted heat load supply and actual demand, leading to waste or insufficient industrial heating energy.

[0006] 3. Existing technologies do not analyze the thermal inertia delay time of industrial heating facilities, making it impossible to pre-control the output of heat sources. This results in a delay in the industrial heat load reaching the expected value after the control command is issued, which in turn causes fluctuations in industrial heating temperature and affects the stability of industrial production processes. Summary of the Invention

[0007] The present invention aims to overcome the defects in the prior art and provide a heating control method and system based on industrial heating forecasting. Through production plan matching forecasting, meteorological load correction, thermal inertia pre-control and deviation adjustment, it realizes accurate forecasting of industrial heating load and intelligent control of water source heat pump.

[0008] The technical solution adopted by the present invention to solve its technical problem is as follows: The present invention provides a heating control method based on industrial heating forecast, including: S1, acquiring current industrial production plan data, comparing it with recent historical industrial production plan data, and determining whether the production plan data are similar.

[0009] S2. When production plan data are similar, the industrial heat load time series for future periods is predicted based on the industrial heat load time series in similar historical records. Otherwise, the industrial heat load time series for future periods is predicted based on the historical industrial heat load data of various heat-consuming equipment.

[0010] S3. Based on the outdoor temperature sequence for future periods in meteorological forecast data, analyze the rate of change of temperature difference, and combine the correlation between the rate of change of temperature difference and the heating load loss ratio to obtain the industrial corrected heat load time series.

[0011] S4. Retrieve historical industrial heating data from each historical heating record, analyze the corresponding thermal inertia delay time of industrial heating facilities, and generate heat source output control commands for water source heat pumps by combining the industrial corrected heat load time sequence.

[0012] S5. Real-time acquisition of industrial heat load after heat source output control, comparison with industrial corrected heat load for the corresponding time period to determine industrial heat load deviation value. If the industrial heat load deviation value is within the preset allowable deviation range, the heat source output control command remains unchanged. Otherwise, based on the deviation direction and deviation magnitude of the industrial heat load deviation value, a compensated industrial corrected heat load is generated, and a new heat source output control command is generated.

[0013] On the other hand, the present invention provides a heating control system based on industrial heating prediction, including a data similarity determination module, a heat load prediction module, a heat load correction module, an output command adjustment module, and a heat load deviation adjustment module.

[0014] The connection between the modules is as follows: the data similarity judgment module is connected to the heat load prediction module, the heat load correction module is connected to both the heat load prediction module and the output command adjustment module, and the heat load deviation adjustment module is connected to the output command adjustment module.

[0015] The data similarity determination module acquires the current industrial production plan data and compares it with recent historical industrial production plan data to determine whether the production plan data are similar.

[0016] The heat load prediction module predicts the industrial heat load sequence for future periods based on similar historical industrial heat load data when production plan data are similar. Conversely, it predicts the industrial heat load sequence for future periods based on historical industrial heat load data of various heat-consuming equipment.

[0017] The heat load correction module analyzes the rate of change of temperature difference based on the outdoor temperature sequence for future periods in meteorological forecast data, and obtains the industrial corrected heat load time series by combining the correlation between the rate of change of temperature difference and the heating load loss ratio.

[0018] The output command adjustment module retrieves historical industrial heating data from each historical heating record, analyzes the corresponding thermal inertia delay time of industrial heating facilities, and generates heat source output control commands for the water source heat pump in conjunction with the industrial corrected heat load timing.

[0019] The heat load deviation adjustment module collects the real-time industrial heat load after the heat source output is adjusted, compares it with the industrial corrected heat load for the corresponding time period to determine the industrial heat load deviation value. If the industrial heat load deviation value is within the preset deviation allowable range, the heat source output adjustment command remains unchanged. Otherwise, based on the deviation direction and deviation magnitude of the industrial heat load deviation value, a compensated industrial corrected heat load is generated, and a new heat source output adjustment command is generated.

[0020] Compared with the prior art, the present invention has the following beneficial effects: (1) The present invention compares the current industrial production plan data with the recent historical industrial production plan data to determine whether the production plan data are similar. Based on the similarity results of the production plan data, the industrial heat load sequence of the future period is predicted, so as to realize the differentiated and accurate prediction of industrial heat load, so that the industrial heat load prediction results are accurately matched with the actual production demand, improve the accuracy of industrial heat load prediction, and ensure the stability of industrial production process.

[0021] (2) Based on the outdoor temperature sequence of future time periods in meteorological forecast data, this invention analyzes the rate of change of temperature difference and combines the correlation between the rate of change of temperature difference and the heating load loss ratio to obtain the industrial corrected heat load sequence, effectively eliminating the impact of ambient temperature fluctuations on the heating load, avoiding the problem of industrial heating energy waste or insufficient energy, ensuring that the corrected heat load matches the actual heating demand, and effectively reducing industrial heating energy consumption.

[0022] (3) This invention retrieves historical industrial heating data from each historical heating record, analyzes the corresponding thermal inertia delay time of industrial heating facilities, and generates a heat source output control command for water source heat pumps by combining the industrial corrected heat load timing. This enables the water source heat pumps to be controlled in advance, overcomes the control delay problem caused by the thermal inertia of heating facilities, and improves the response speed and temperature stability of heat source output.

[0023] (4) The present invention collects the real-time industrial heat load after the heat source output is adjusted, compares and determines the industrial heat load deviation value, and adjusts and controls the heat source output adjustment command to realize closed-loop feedback control, so that the heating system can be dynamically corrected according to the actual operating status, ensuring that the industrial heating temperature continuously meets the standard, and improving the operational stability and production adaptability of industrial heating. Attached Figure Description

[0024] To more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0025] Figure 1 This is a schematic diagram of the method steps of the present invention;

[0026] Figure 2 This is a schematic diagram illustrating the steps in this invention for determining whether production plan data are similar;

[0027] Figure 3 This is a schematic diagram of the system module connections in this invention. Detailed Implementation

[0028] Various exemplary embodiments of the present invention will now be described in detail with reference to the accompanying drawings. It should be noted that, unless otherwise specifically stated, the relative arrangement, numerical expressions, and values ​​of the components and steps set forth in these embodiments do not limit the scope of the invention. Furthermore, it should be understood that, for ease of description, the dimensions of the various parts shown in the drawings are not drawn to actual scale.

[0029] The following description of at least one exemplary embodiment is merely illustrative and is in no way intended to limit the invention or its application or use. Techniques, methods, and apparatus known to those skilled in the art may not be discussed in detail, but where appropriate, such techniques, methods, and apparatus should be considered part of the specification.

[0030] In all examples shown and discussed herein, any specific values ​​should be interpreted as merely exemplary and not as limitations. Therefore, other examples of exemplary embodiments may have different values.

[0031] Please see Figure 1 As shown, the present invention provides a heating control method based on industrial heating forecasting, including: S1, acquiring current industrial production plan data, comparing it with recent historical industrial production plan data, and determining whether the production plan data are similar.

[0032] S2. When production plan data are similar, the industrial heat load time series for future periods is predicted based on the industrial heat load time series in similar historical records. Otherwise, the industrial heat load time series for future periods is predicted based on the historical industrial heat load data of various heat-consuming equipment.

[0033] S3. Based on the outdoor temperature sequence for future periods in meteorological forecast data, analyze the rate of change of temperature difference, and combine the correlation between the rate of change of temperature difference and the heating load loss ratio to obtain the industrial corrected heat load time series.

[0034] S4. Retrieve historical industrial heating data from each historical heating record, analyze the corresponding thermal inertia delay time of industrial heating facilities, and generate heat source output control commands for water source heat pumps by combining the industrial corrected heat load time sequence.

[0035] S5. Real-time acquisition of industrial heat load after heat source output control, comparison with industrial corrected heat load for the corresponding time period to determine industrial heat load deviation value. If the industrial heat load deviation value is within the preset allowable deviation range, the heat source output control command remains unchanged. Otherwise, based on the deviation direction and deviation magnitude of the industrial heat load deviation value, a compensated industrial corrected heat load is generated, and a new heat source output control command is generated.

[0036] Given that the variability in industrial production plans directly determines the accuracy of heat load forecasting, current technologies simply overlay industrial loads as overall forecast values ​​without conducting differentiated analysis of industrial users' production plans. This leads to a mismatch between heat source output and actual heat demand. Therefore, it is necessary to construct feature vectors for production plans and compare their similarity to achieve accurate filtering of historical production plan data.

[0037] Based on this, such as Figure 2 As shown, the specific steps for determining whether production plan data are similar in this invention are as follows: S11, extract the production shift time period, equipment status switching sequence and process temperature requirement value from the current industrial production plan data, and construct the current production plan feature vector.

[0038] The production shift time period represents the distribution of start and end times of each production shift within a future time period (such as the next 24 hours), for example, the day shift is 08:00-16:00 and the night shift is 20:00-04:00; the equipment status switching sequence records the start and stop sequence and switching interval of each heat-using equipment, for example, an industrial production line includes a heating furnace A, a heat preservation furnace B and a dryer C, wherein the heating furnace A is started at 08:30, stopped at 12:00, started again at 20:30, and stopped again at 01:00; the process temperature requirement value represents the process temperature requirement value of each heat-using equipment, for example, the process temperature requirement value of the heating furnace A is 1200℃, the process temperature requirement value of the heat preservation furnace B is 800℃, and the process temperature requirement value of the dryer C is 150℃.

[0039] Preferably, in a specific embodiment of the present invention, the current production plan feature vector is constructed as follows: the production shift time period is converted into a time period coding vector, the future time period is divided into 24 unit time periods, binary codes are generated according to the production shift coverage, production time periods are marked as 1 and non-production time periods are marked as 0, thus forming the production shift time period coding vector.

[0040] The equipment state switching sequence is converted into an equipment start-stop state vector. There are N heating devices. The start-stop state of each heating device in each unit time period is represented by 1 (running) or 0 (stopping), forming an equipment state timing encoding vector.

[0041] The production shift time period encoding vector and the equipment status time sequence encoding vector are concatenated, and the process temperature requirements of each heat-using equipment are included as continuous values ​​in the concatenated feature vector to construct the current production plan feature vector.

[0042] S12. Extract the historical production plan feature vectors corresponding to each historical record from the recent industrial historical production plan data, and calculate the similarity between the current production plan feature vector and the historical production plan feature vectors corresponding to each historical record. The similarity calculation uses cosine similarity, which is calculated by taking the cosine of the angle between the current production plan feature vector and the historical production plan feature vector. The value ranges from 0 to 1, with the closer to 1 indicating a higher similarity between the production plan data.

[0043] In this invention, "recent" refers to the data window within 30 calendar days prior to the current time.

[0044] S13. Record the historical production plan feature vector with the highest similarity as the target historical plan feature vector. If the similarity between the current production plan feature vector and the target historical plan feature vector is higher than the preset plan similarity threshold, the production plan data is determined to be similar; otherwise, the production plan data is determined to be dissimilar.

[0045] It should be noted that the preset plan similarity threshold is set based on engineering experience, for example, a plan similarity threshold of 0.85. When the similarity is higher than 0.85, it indicates that the current production plan is similar to the historical record, and the historical heat load time series can be directly used for prediction; when the similarity is lower than or equal to 0.85, it indicates that there are differences in the current production plan, and it is necessary to re-predict based on the historical data of equipment heat load.

[0046] Considering that the production plan similarity assessment results determine the choice of forecasting strategy, historical heat load time series are valuable for reference when production plans are highly similar; however, when production plans differ significantly, it is necessary to perform equipment-by-equipment forecasting based on historical equipment heat load data and then overlay the results. Existing technologies employ a holistic forecasting approach, resulting in insufficient forecasting accuracy. A scenario-based forecasting strategy is needed to achieve accurate construction of heat load time series.

[0047] Based on this, the specific steps for predicting the industrial heat load time series in the present invention include: S211, recording the historical records corresponding to the target historical plan feature vector as similar historical records, extracting the industrial heat load of each unit time period from the similar historical records in chronological order, and constructing the historical industrial heat load time series.

[0048] S212. Based on the starting time point of the current industrial production plan data, perform time alignment on the historical industrial heat load time series, and extract the industrial heat load dataset corresponding to the future time period from the aligned historical industrial heat load time series.

[0049] The time alignment is based on the starting time of the current production plan data, shifting the starting time of the historical industrial heat load time series to match the current starting time, ensuring the accurate time correspondence between historical data and future forecast periods. For example, if the starting time of the current production plan data is 08:00 tomorrow, then the heat load data corresponding to 08:00 in the historical industrial heat load time series is used as the starting point for extraction.

[0050] S213. Analyze the degree of fluctuation of industrial heat load in each unit time period of the industrial heat load data corresponding to the future time period.

[0051] It should be noted that the fluctuation analysis uses the coefficient of variation for quantification, and the calculation formula is as follows: .

[0052] In the formula, CV is the coefficient of variation. This represents the standard deviation of industrial heat load for each unit of time in the future period. This represents the average industrial heat load for each unit period in the future. The coefficient of variation reflects the degree of fluctuation in industrial heat load; a larger coefficient of variation indicates greater fluctuation in historical data.

[0053] S214. When the fluctuation level is less than the preset fluctuation level threshold, the industrial heat load time sequence for the future time period is constructed based on the average industrial heat load of each unit time period. Otherwise, the maximum and minimum industrial heat loads of all unit time periods are selected, and their average is used to construct the industrial heat load time sequence for the future time period.

[0054] The preset fluctuation threshold is set according to the temperature stability requirements of industrial production processes, for example, 0.2. When the coefficient of variation is less than 0.2, it indicates that the historical heat load data fluctuates smoothly, and the prediction using the mean is reliable; when the coefficient of variation is greater than or equal to 0.2, it indicates that the historical data fluctuates greatly, and the average of the maximum and minimum values ​​is used for prediction to take into account the heat load demand under extreme conditions and avoid insufficient heating due to underestimation of the predicted value.

[0055] Based on this, the specific steps for predicting the industrial heat load time series in the present invention further include: S221, extracting the historical heat load time series set of various heat-using equipment under different production plan data from the historical industrial heat load data of various heat-using equipment, and constructing a historical database of equipment heat load.

[0056] S222. Based on the equipment status switching sequence in the current industrial production plan data, identify all target heat-consuming equipment that is planned to be started in the future period.

[0057] S223. Based on the process temperature demand values ​​in the current production plan data corresponding to each target heat-consuming equipment, filter the historical heat load time series with the same process temperature demand value from the equipment heat load historical database to determine the historical heat load per unit time period.

[0058] In a specific embodiment of the present invention, for example, if the target heat-using equipment is a heating furnace A and the process temperature requirement is 1200℃, then all historical heat load time series records of heating furnace A at a process temperature of 1200℃ are extracted from the database, and the average historical heat load of each unit time period is calculated as the historical heat load of the unit time period.

[0059] S224. Based on the historical heat load of each target heat-consuming equipment per unit time period, and combined with the equipment status switching sequence in the current production plan data, the industrial heat load sequence for future time periods is generated by time sequence overlay and fusion.

[0060] In this invention, the time-series superposition and fusion is accumulated in stages according to the equipment start-up time points. For example, if heating furnace A starts at 08:00 and heat preservation furnace B starts at 09:30, then only the heat load of heating furnace A is accumulated before 09:30, and the heat loads of heating furnace A and heat preservation furnace B are accumulated after 09:30, finally generating a complete industrial heat load time sequence for future periods.

[0061] This invention compares current industrial production plan data with recent historical industrial production plan data to determine whether the production plan data are similar. Based on the similarity results of the production plan data, it predicts the industrial heat load sequence for future periods, achieving differentiated and accurate prediction of industrial heat load. This makes the industrial heat load prediction results accurately match the actual production demand, improves the accuracy of industrial heat load prediction, and ensures the stability of industrial production processes.

[0062] Considering that fluctuations in outdoor meteorological conditions directly affect the heat load loss of heating systems, current technologies do not take into account the impact of outdoor temperature changes on heating load losses. The lack of accurate meteorological data for correcting industrial heat load leads to discrepancies between the corrected predicted heat load supply and actual demand. Therefore, it is necessary to achieve accurate correction of meteorological factors in industrial heat load forecasts by analyzing the rate of change of temperature difference and fitting it with historical loss ratios.

[0063] Based on this, the method for obtaining the industrial corrected heat load time series in this invention is as follows: S31, extract the outdoor temperature sequence for future periods from meteorological forecast data, combine it with the corresponding set environmental heating temperature of the industrial production workshop, compare it to obtain the indoor-outdoor temperature difference sequence for future periods, and calculate the rate of change of temperature difference between adjacent unit time periods. The corresponding set environmental heating temperature of the industrial production workshop is determined according to the production process requirements, for example, set to 20℃.

[0064] It should be noted that the rate of change of temperature difference is the ratio of the difference between indoor and outdoor temperatures in adjacent time periods to the difference between indoor and outdoor temperatures in the previous adjacent time period. The rate of change of temperature difference represents the degree of change of temperature difference between adjacent time periods. A positive value indicates that the temperature difference is increasing, which means that the outdoor temperature is decreasing and the heat load loss is increasing. A negative value indicates that the temperature difference is decreasing, which means that the outdoor temperature is increasing and the heat load loss is decreasing.

[0065] S32. Retrieve the meteorological data and historical industrial heating load loss values ​​corresponding to each historical heating record, and calculate the ratio between the historical industrial heating load loss value and the historical expected heating load of the corresponding record to obtain the heating load loss ratio. The historical industrial heating load loss value is the difference between the actual heating load and the historical expected heating load in the historical heating record.

[0066] S33. Based on the rate of change of temperature difference and the ratio of heating load loss in meteorological data from each historical heating record, fit the correlation between the rate of change of temperature difference and the ratio of heating load loss.

[0067] Preferably, in a specific embodiment of the present invention, the correlation fitting adopts the least squares method for linear regression or polynomial regression fitting to establish a mapping function between the rate of change of temperature difference and the ratio of heating load loss.

[0068] S34. Based on the rate of change of temperature difference between adjacent time periods, match the corresponding heating load loss ratio in the correlation relationship, and calculate the industrial corrected heat load time series for future time periods in combination with the industrial heat load time series for future time periods.

[0069] It should be noted that the industrial corrected heat load time series is obtained by coupling the industrial heat load time series with the heating heat load loss ratio of the corresponding time period. The calculation formula is as follows: .

[0070] In the formula, To correct the heat load for industry, For the predicted heat load in the industrial heat load time series, This represents the heating load loss ratio.

[0071] This invention analyzes the rate of change of temperature difference based on the outdoor temperature sequence of future time periods in meteorological forecast data, and combines the correlation between the rate of change of temperature difference and the heating load loss ratio to obtain the industrial corrected heat load time series. This effectively eliminates the impact of ambient temperature fluctuations on the heating load, avoids the problem of industrial heating energy waste or insufficient energy, ensures that the corrected heat load matches the actual heating demand, and effectively reduces industrial heating energy consumption.

[0072] Considering the thermal inertia of industrial heating facilities, there is a time delay between the issuance of heat source output control commands and the arrival of the expected heat load. This makes it impossible to pre-control and adjust the heat source output, resulting in a mismatch between the control commands and actual demand, and causing temperature fluctuations. It is necessary to determine the thermal inertia delay time through statistical analysis of the delay time in historical heating records, in order to achieve advance control of heat source output.

[0073] Based on this, the method for generating the heat source output control command of the water source heat pump in this invention is as follows: S41, extract the time point when the heat source output control command is issued and the time point when the industrial real-time heat load reaches the expected heating load from the historical industrial heating data of each historical heating record. The expected heating load is the heat load corresponding to the time period of the industrial corrected heat load sequence.

[0074] S42. Calculate the time difference between the time when the industrial real-time heat load reaches the expected heating load and the time when the heat source output control command is issued in each historical heating record, and use it as the delay time.

[0075] S43. Perform statistical analysis on the delay time of all historical heating records, remove abnormal delay times, calculate the average time of the remaining delay time, and use it as the corresponding thermal inertia delay time of industrial heating facilities.

[0076] The abnormal delay time elimination employs either the 3σ criterion or the interquartile range (IQR) method. In a specific embodiment of the invention, when using the IQR method, the 25th percentile (Q1) and 75th percentile (Q3) of the delay time series are calculated, and the IQR is... It will exceed The delay time within the range is identified as an outlier and removed.

[0077] S44. Based on the corresponding thermal inertia delay time of industrial heating facilities, the time axis of the industrial corrected heat load sequence for future periods is shifted forward to generate the heat source pre-controlled heat load sequence of the water source heat pump.

[0078] For example, setting the thermal inertia delay time to This will shift the overall industrial corrected heat load time series for future periods in the negative direction of the time axis. That is, at the current time The output of the generated heat source should be matched Time-based forecasting adjusts heat load to enable advance control.

[0079] S45. Based on the rated heat load-heat source output characteristics of the water source heat pump, the pre-controlled heat load sequence of the heat source is matched with the rated heat load-heat source output characteristics time by time to determine the target heat source output for the corresponding time period. The rated heat load-heat source output characteristics of the water source heat pump are the characteristic curves calibrated at the factory.

[0080] S46. Integrate the heat source pre-control heat load sequence of the water source heat pump and the target heat source output for the corresponding time period to generate a heat source output control command for the water source heat pump.

[0081] This invention retrieves historical industrial heating data from various historical heating records, analyzes the corresponding thermal inertia delay time of industrial heating facilities, and generates heat source output control commands for water source heat pumps by combining industrial corrected heat load timing. This enables advance control of water source heat pumps, overcomes the control lag problem caused by the thermal inertia of heating facilities, and improves the response speed and temperature stability of heat source output.

[0082] Considering that after the heat source output control command is executed, the actual heat load may deviate from the expected value due to factors such as fluctuations in equipment operating status and changes in pipeline heat loss, existing technologies lack real-time feedback correction, leading to accumulated deviations and temperature drift. Therefore, it is necessary to achieve adaptive adjustment of the heat source output control command through real-time heat load acquisition and comparative analysis with the expected value.

[0083] Based on this, the specific method for determining the industrial heat load deviation value in this invention is as follows: S51, the real-time industrial heat load after the real-time heat source output is adjusted is aligned with the industrial corrected heat load of the corresponding time period, the difference between the real-time industrial heat load and the corresponding industrial corrected heat load is calculated, and the difference is used as the industrial heat load deviation value.

[0084] S52. Determine the deviation direction of the industrial heat load deviation value based on its positive or negative attribute, and use the absolute value of the industrial heat load deviation value as the deviation amplitude of the industrial heat load deviation value.

[0085] The deviation direction includes positive deviation (real-time heat load is higher than the corrected heat load, indicating overheating) and negative deviation (real-time heat load is lower than the corrected heat load, indicating underheating). The deviation magnitude is the absolute value of the deviation value. For example, if the deviation value is -50kW, then the deviation direction is negative and the deviation magnitude is 50kW.

[0086] S53. If the industrial heat load deviation is within the preset allowable deviation range, the current heat source output control command of the water source heat pump remains unchanged. The preset allowable deviation range is set to ±5% of the expected heat load, and the implementer can also adjust the allowable deviation range as needed.

[0087] S54. If the industrial heat load deviation exceeds the preset allowable deviation range, the direction and amount of heat load compensation shall be determined according to the deviation direction and deviation magnitude of the industrial heat load deviation.

[0088] The direction of heat load compensation is opposite to the direction of deviation: when the deviation is positive, the direction of heat load compensation is downward to reduce the output of the heat source; when the deviation is negative, the direction of heat load compensation is upward to increase the output of the heat source.

[0089] S55. Based on the direction and amount of heat load compensation, generate the compensated industrial corrected heat load, match the compensated industrial corrected heat load with the rated heat load-heat source output characteristics to obtain the readjusted heat source output, and regenerate the heat source output control command of the water source heat pump.

[0090] This invention collects real-time industrial heat load after heat source output regulation, compares and determines the industrial heat load deviation value, and adjusts and controls the heat source output regulation command to achieve closed-loop feedback regulation. This enables the heating system to dynamically correct itself according to the actual operating status, ensuring that the industrial heating temperature continuously meets the standard and improving the operational stability and production adaptability of industrial heating.

[0091] On the other hand, such as Figure 3 As shown, the present invention provides a heating control system based on industrial heating prediction, including a data similarity determination module, a heat load prediction module, a heat load correction module, an output command adjustment module, and a heat load deviation adjustment module.

[0092] The connection between the modules is as follows: the data similarity judgment module is connected to the heat load prediction module, the heat load correction module is connected to both the heat load prediction module and the output command adjustment module, and the heat load deviation adjustment module is connected to the output command adjustment module.

[0093] The data similarity determination module acquires the current industrial production plan data and compares it with recent historical industrial production plan data to determine whether the production plan data are similar.

[0094] The heat load prediction module predicts the industrial heat load sequence for future periods based on similar historical industrial heat load data when production plan data are similar. Conversely, it predicts the industrial heat load sequence for future periods based on historical industrial heat load data of various heat-consuming equipment.

[0095] The heat load correction module analyzes the rate of change of temperature difference based on the outdoor temperature sequence for future periods in meteorological forecast data, and obtains the industrial corrected heat load time series by combining the correlation between the rate of change of temperature difference and the heating load loss ratio.

[0096] The output command adjustment module retrieves historical industrial heating data from each historical heating record, analyzes the corresponding thermal inertia delay time of industrial heating facilities, and generates heat source output control commands for the water source heat pump in conjunction with the industrial corrected heat load timing.

[0097] The heat load deviation adjustment module collects the real-time industrial heat load after the heat source output is adjusted, compares it with the industrial corrected heat load for the corresponding time period to determine the industrial heat load deviation value. If the industrial heat load deviation value is within the preset deviation allowable range, the heat source output adjustment command remains unchanged. Otherwise, based on the deviation direction and deviation magnitude of the industrial heat load deviation value, a compensated industrial corrected heat load is generated, and a new heat source output adjustment command is generated.

[0098] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, in the form of a computer program product.

[0099] Those skilled in the art will recognize that the modules and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of this application.

[0100] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0101] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0102] Finally, the above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A heating control method based on industrial heating forecasting, characterized in that, include: Obtain current industrial production plan data and compare it with recent historical industrial production plan data to determine whether the production plan data are similar; The method for determining whether production plan data are similar is as follows: Extract production shift time periods, equipment status switching sequence, and process temperature requirement values ​​from the current industrial production plan data to construct a feature vector for the current production plan. Extract the historical production plan feature vectors corresponding to each historical record from recent industrial historical production plan data, and calculate the similarity between the current production plan feature vector and the historical production plan feature vectors corresponding to each historical record. The feature vector of the historical production plan with the highest similarity is recorded as the feature vector of the target historical plan. If the similarity between the current production plan feature vector and the target historical plan feature vector is higher than the preset plan similarity threshold, the production plan data is determined to be similar; otherwise, the production plan data is determined to be dissimilar. When production plan data are similar, the industrial heat load time series for future periods is predicted based on the industrial heat load time series in similar historical records; otherwise, the industrial heat load time series for future periods is predicted based on the historical industrial heat load data of various heat-consuming equipment. The steps for predicting the industrial heat load time series for future periods include: Record the historical records corresponding to the target historical plan feature vector as similar historical records, extract the industrial heat load of each unit period from the similar historical records in chronological order, and construct the historical industrial heat load time series; Based on the start time point of the current industrial production plan data, the historical industrial heat load time series is time-aligned, and the industrial heat load dataset corresponding to the future time period is extracted from the aligned historical industrial heat load time series. A fluctuation analysis is performed on the industrial heat load for each unit time period in the industrial heat load dataset corresponding to future time periods; the fluctuation analysis uses the coefficient of variation for quantification, and the calculation formula is as follows: In the formula, CV is the coefficient of variation. This represents the standard deviation of industrial heat load for each unit of time in the future period. This represents the average industrial heat load for each unit of time in the future period. When the fluctuation level is less than the preset fluctuation level threshold, the industrial heat load time sequence for future periods is constructed based on the average industrial heat load of each unit time period; otherwise, the maximum and minimum industrial heat loads of all unit time periods are selected, and their average is used to construct the industrial heat load time sequence for future periods. Based on the outdoor temperature series for future periods in meteorological forecast data, the rate of change of temperature difference is analyzed, and the correlation between the rate of change of temperature difference and the heating load loss ratio is combined to obtain the industrial corrected heat load time series. The method for obtaining the industrial corrected heat load time series is as follows: The outdoor temperature sequence for future periods is extracted from meteorological forecast data. Combined with the corresponding set ambient heating temperature of industrial production workshops, the indoor and outdoor temperature difference sequence for future periods is obtained by comparison, and the rate of change of temperature difference between adjacent unit periods is calculated. Retrieve the meteorological data corresponding to each historical heating record and the historical industrial heating heat load loss value, and calculate the ratio between the historical industrial heating heat load loss value and the corresponding historical expected heating heat load to obtain the heating heat load loss ratio. Based on the rate of change of temperature difference and the ratio of heating load loss in meteorological data from each historical heating record, the correlation between the rate of change of temperature difference and the ratio of heating load loss is fitted. Based on the rate of change of temperature difference between adjacent time periods, the corresponding heating load loss ratio is matched in the correlation, and the industrial corrected heat load time series for future time periods is calculated in combination with the industrial heat load time series for future time periods. Retrieve historical industrial heating data from each historical heating record, analyze the corresponding thermal inertia delay time of industrial heating facilities, and generate heat source output control commands for water source heat pumps by combining industrial corrected heat load timing. The system collects the real-time industrial heat load after the heat source output is adjusted, compares it with the industrial corrected heat load for the corresponding time period to determine the industrial heat load deviation value. If the industrial heat load deviation value is within the preset allowable deviation range, the heat source output adjustment command remains unchanged. Otherwise, based on the deviation direction and deviation magnitude of the industrial heat load deviation value, a compensated industrial corrected heat load is generated, and a new heat source output adjustment command is generated.

2. The heating control method based on industrial heating forecasting according to claim 1, characterized in that, Conversely, based on historical industrial heat load data of various heat-consuming equipment, the steps for predicting the industrial heat load sequence for future periods also include: Extract the historical heat load time series of various heat-consuming equipment under different production plan data from the historical industrial heat load data of various heat-consuming equipment, and construct a historical database of equipment heat load; Based on the equipment status switching sequence in the current industrial production plan data, identify all target heat-consuming equipment that is planned to be started in the future period; Based on the process temperature demand values ​​in the current production plan data corresponding to each target heat-consuming equipment, the historical heat load time series with the same process temperature demand value is selected from the equipment heat load history database to determine the historical heat load per unit time period. Based on the historical heat load of each target heat-consuming equipment per unit time period, and combined with the equipment status switching sequence in the current production plan data, the industrial heat load sequence for future time periods is generated by time sequence overlay and fusion.

3. The heating control method based on industrial heating forecasting according to claim 1, characterized in that, The method for analyzing the thermal inertia delay time corresponding to industrial heating facilities is as follows: Extract the time points when heat source output control commands are issued and the time points when the real-time industrial heat load reaches the expected heating load from the historical industrial heating data of each heating season. Calculate the time difference between the time point when the real-time industrial heat load reaches the expected heating load and the time point when the heat source output control command is issued in each historical heating record, and use it as the delay time. Statistical analysis was performed on the delay time of all historical heating records. Abnormal delay times were removed, and the average time of the remaining delay time was calculated. This average time was used as the corresponding thermal inertia delay time of the industrial heating facility.

4. The heating control method based on industrial heating forecasting according to claim 3, characterized in that, The method for generating the heat source output control command of the water source heat pump is as follows: Based on the corresponding thermal inertia delay time of industrial heating facilities, the time axis of the industrial corrected heat load sequence for future periods is shifted forward to generate the heat source pre-controlled heat load sequence of water source heat pumps. Based on the rated heat load-heat source output characteristics of the water source heat pump, the heat source pre-controlled heat load sequence is matched with the rated heat load-heat source output characteristics time by time to determine the target heat source output for the corresponding time period. The heat source pre-control heat load sequence of the water source heat pump and the target heat source output for the corresponding time period are integrated to generate the heat source output control command of the water source heat pump.

5. A heating control method based on industrial heating forecasting according to claim 1, characterized in that, The method for determining the industrial heat load deviation value is as follows: The real-time industrial heat load after adjusting the output of the heat source is collected in real time and aligned with the industrial corrected heat load for the corresponding time period. The difference between the real-time industrial heat load and the corresponding industrial corrected heat load is calculated and used as the industrial heat load deviation value. The deviation direction of the industrial heat load deviation value is determined based on its positive or negative attribute, and the absolute value of the industrial heat load deviation value is taken as the deviation amplitude.

6. A heating control method based on industrial heating forecasting according to claim 5, characterized in that, The method for generating the compensated industrial corrected heat load and regenerating the heat source output control command is as follows: If the industrial heat load deviation exceeds the preset allowable deviation range, the direction and amount of heat load compensation shall be determined according to the deviation direction and magnitude of the industrial heat load deviation. Based on the direction and amount of heat load compensation, a compensated industrial corrected heat load is generated. The compensated industrial corrected heat load is then matched with the rated heat load-heat source output characteristics to obtain a readjusted heat source output. Finally, a new heat source output control command for the water source heat pump is generated.

7. A heating control system based on industrial heating forecasting, characterized in that, include: The data similarity determination module acquires the current industrial production plan data and compares it with recent historical industrial production plan data to determine whether the production plan data are similar. The method for determining whether production plan data are similar is as follows: Extract production shift time periods, equipment status switching sequence, and process temperature requirement values ​​from the current industrial production plan data to construct a feature vector for the current production plan. Extract the historical production plan feature vectors corresponding to each historical record from recent industrial historical production plan data, and calculate the similarity between the current production plan feature vector and the historical production plan feature vectors corresponding to each historical record. The feature vector of the historical production plan with the highest similarity is recorded as the feature vector of the target historical plan. If the similarity between the current production plan feature vector and the target historical plan feature vector is higher than the preset plan similarity threshold, the production plan data is determined to be similar; otherwise, the production plan data is determined to be dissimilar. The heat load prediction module predicts the industrial heat load sequence for future periods based on similar historical industrial heat load data when production plan data are similar; otherwise, it predicts the industrial heat load sequence for future periods based on historical industrial heat load data of various heat-consuming equipment. The steps for predicting the industrial heat load time series for future periods include: Record the historical records corresponding to the target historical plan feature vector as similar historical records, extract the industrial heat load of each unit period from the similar historical records in chronological order, and construct the historical industrial heat load time series; Based on the start time point of the current industrial production plan data, the historical industrial heat load time series is time-aligned, and the industrial heat load dataset corresponding to the future time period is extracted from the aligned historical industrial heat load time series. A fluctuation analysis is performed on the industrial heat load for each unit time period in the industrial heat load dataset corresponding to future time periods; the fluctuation analysis uses the coefficient of variation for quantification, and the calculation formula is as follows: In the formula, CV is the coefficient of variation. This represents the standard deviation of industrial heat load for each unit of time in the future period. This represents the average industrial heat load for each unit of time in the future period. When the fluctuation level is less than the preset fluctuation level threshold, the industrial heat load time sequence for future periods is constructed based on the average industrial heat load of each unit time period; otherwise, the maximum and minimum industrial heat loads of all unit time periods are selected, and their average is used to construct the industrial heat load time sequence for future periods. The heat load correction module analyzes the rate of change of temperature difference based on the outdoor temperature sequence for future periods in meteorological forecast data, and obtains the industrial corrected heat load time series by combining the correlation between the rate of change of temperature difference and the heating heat load loss ratio. The method for obtaining the industrial corrected heat load time series is as follows: The outdoor temperature sequence for future periods is extracted from meteorological forecast data. Combined with the corresponding set ambient heating temperature of industrial production workshops, the indoor and outdoor temperature difference sequence for future periods is obtained by comparison, and the rate of change of temperature difference between adjacent unit periods is calculated. Retrieve the meteorological data corresponding to each historical heating record and the historical industrial heating heat load loss value, and calculate the ratio between the historical industrial heating heat load loss value and the corresponding historical expected heating heat load to obtain the heating heat load loss ratio. Based on the rate of change of temperature difference and the ratio of heating load loss in meteorological data from each historical heating record, the correlation between the rate of change of temperature difference and the ratio of heating load loss is fitted. Based on the rate of change of temperature difference between adjacent time periods, the corresponding heating load loss ratio is matched in the correlation, and the industrial corrected heat load time series for future time periods is calculated in combination with the industrial heat load time series for future time periods. The output command adjustment module retrieves historical industrial heating data from each historical heating record, analyzes the corresponding thermal inertia delay time of industrial heating facilities, and generates heat source output control commands for water source heat pumps by combining the industrial corrected heat load time sequence. The heat load deviation adjustment module collects the real-time industrial heat load after the heat source output is adjusted, compares it with the industrial corrected heat load for the corresponding time period to determine the industrial heat load deviation value. If the industrial heat load deviation value is within the preset deviation allowable range, the heat source output adjustment command remains unchanged. Otherwise, based on the deviation direction and deviation magnitude of the industrial heat load deviation value, a compensated industrial corrected heat load is generated, and a new heat source output adjustment command is generated.

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