An operation management method for integrated energy systems based on load forecasting

Abstract

The present invention discloses an operation management method for an integrated energy system based on load forecasting, belonging to the technical field of energy system management. Specifically, it includes: collecting the operation parameters of energy-using equipment, where the parameters include current waveform characteristics, surface temperature distribution, and medium flow rate changes; analyzing the time-series change rules of the operation parameters and extracting characteristic indicators related to equipment aging; establishing an energy consumption prediction correction model according to the characteristic indicators to dynamically adjust the theoretical energy consumption calculation value of the energy-using equipment; inputting the corrected theoretical energy consumption calculation value into the energy distribution optimization model to generate a load distribution instruction for the energy network; after executing the load distribution instruction, comparing the deviation between the actual energy consumption and the corrected theoretical value, and updating the parameter weights of the energy consumption prediction correction model; the present invention realizes the continuous and stable operation and optimized management of the integrated energy system during the equipment aging process.

Description

Technical Field

[0001] The present invention relates to the technical field of energy system management, and particularly relates to an operation management method for an integrated energy system based on load forecasting. Background Art

[0002] Under the background of the global advocacy of energy conservation, emission reduction and efficient energy utilization, the integrated energy system has become an important direction for achieving sustainable energy development due to its characteristics of multi-energy collaborative optimization. Such systems integrate various energy forms such as electricity, heat, and gas, and use intelligent technologies for unified scheduling and management, aiming to improve energy utilization efficiency, reduce carbon emissions and ensure the stability of energy supply. With the continuous progress of technologies such as the Internet of Things, big data analysis and artificial intelligence, the integrated energy system is increasingly widely used in industrial, commercial and residential fields, and the requirements for its operation management to be more refined and intelligent are becoming more urgent.

[0003] Currently, the operation management method for an integrated energy system based on load forecasting mainly relies on traditional energy consumption forecasting models and load distribution strategies. Traditional methods usually establish energy consumption forecasting models based on the rated parameters and historical operation data of equipment, and optimize energy distribution in combination with real-time load demands. Some advanced solutions introduce equipment status monitoring technologies, and evaluate equipment performance by collecting operation parameters such as current and temperature, and perform comprehensive processing on multi-dimensional operation parameters.

[0004] However, the traditional energy consumption forecasting model does not fully consider the impact of performance degradation during equipment aging on energy consumption, resulting in a large deviation between the predicted value and the actual energy consumption, affecting the operation efficiency and economy of the system. The extraction of equipment aging characteristics is not comprehensive enough, it is difficult to accurately identify early aging signs, and there is a lack of a dynamic adjustment mechanism and cannot adapt to changes in the degree of equipment aging. Summary of the Invention

[0005] The purpose of the present invention is to provide an operation management method for an integrated energy system based on load forecasting, and solve the following technical problems:

[0006] The traditional energy consumption forecasting model does not fully consider the impact of performance degradation during equipment aging on energy consumption, resulting in a large deviation between the predicted value and the actual energy consumption, and the extraction of equipment aging characteristics is not comprehensive enough, making it difficult to accurately identify early aging signs.

[0007] The purpose of the present invention can be achieved through the following technical solutions:

[0008] An operation management method for an integrated energy system based on load forecasting, comprising the following steps:

[0009] Collect the operation parameters of energy-using equipment, and the parameters include current waveform characteristics, surface temperature distribution and medium flow rate changes;

[0010] Analyze the temporal variation law of the operating parameters and extract characteristic indicators related to equipment aging;

[0011] Establish an energy consumption prediction correction model based on the characteristic indicators and dynamically adjust the theoretical energy consumption calculation value of the energy-using equipment;

[0012] Input the corrected theoretical energy consumption calculation value into the energy distribution optimization model to generate a load distribution instruction for the energy network;

[0013] After executing the load distribution instruction, compare the deviation between the actual energy consumption and the corrected theoretical value, and update the parameter weights of the energy consumption prediction correction model.

[0014] As a further solution of the present invention: The process of collecting and processing the operating parameters includes:

[0015] Decompose the harmonic components of the current waveform, extract the proportion of the total harmonic energy of the third and higher harmonics in the fundamental wave energy. When the proportion value exceeds a set multiple of the reference value when the equipment is newly put into operation for three consecutive sampling periods, it is marked as a conductor contact aging characteristic;

[0016] Divide the monitoring area according to the equipment surface temperature distribution data, mark the area exceeding the dynamic temperature threshold as a high-temperature area, calculate the area expansion rate of the high-temperature area. When the expansion rate is opposite to the change direction of the equipment load rate, it is determined that the heat dissipation performance has deteriorated; Extract the number of flow fluctuations per unit time from the medium flow data. When the fluctuation frequency is positively correlated with the equipment operation duration, it is identified as a valve adjustment mechanism wear characteristic; Align the current harmonic ratio, high-temperature area expansion rate, and flow fluctuation times according to the time stamp to generate an equipment aging state matrix.

[0017] As a further solution of the present invention: The method for extracting the characteristic indicators includes:

[0018] Perform a sliding window process on the current harmonic ratio data, calculate the difference between the maximum value and the mean value within the window period to generate a conductor oxidation rate indicator; Establish a heat dissipation efficiency decay coefficient according to the deviation degree between the high-temperature area expansion rate and the load rate; Statistically analyze the distribution density of the number of medium flow fluctuations at different stages of equipment operation to generate a valve response delay index; Compare the three indicators with the equipment factory parameters respectively, calculate the offset of the current aging degree relative to the initial state; Divide the aging stage into a stable period, an acceleration period, and a critical period according to the size of the offset.

[0019] As a further solution of the present invention: The working process of the energy consumption prediction correction model includes:

[0020] Add an aging correction term to the theoretical energy consumption calculation formula of the device. The correction term is obtained by weighted calculation of the conductor oxidation rate index, the heat dissipation efficiency decay coefficient, and the valve response delay index. For devices in the stable aging period, the correction term adopts a linear compensation mode, and the energy consumption base is adjusted according to the average value of the hourly aging offset.

[0021] For devices in the accelerated aging period, an exponential compensation mode is adopted, and the correction amplitude is dynamically amplified according to the rising slope of the aging index in the most recent unit time.

[0022] For devices in the critical aging period, a quadratic compensation factor is introduced to increase the safety redundancy while correcting the energy consumption base. After each correction, record the deviation between the actual energy consumption data and the predicted value for updating the weight coefficient.

[0023] As a further solution of the present invention: the calculation method of the quadratic compensation factor includes:

[0024] Obtain the historical data of all characteristic indicators of the device in the current aging stage, and calculate the ratio of the variance to the mean value of the indicator values in the most recent hour. When the ratio exceeds the safety threshold corresponding to the device type, superimpose a compensation component proportional to the variance value on the basic correction amount. For devices involving multi-energy coupling, add a cross-system impact assessment item to calculate the conduction amount of the additional energy consumption caused by its aging to other energy networks. Use the sum of the compensation component and the conduction amount as the quadratic compensation factor and input it into the correction model. After the device enters the maintenance cycle, reset the compensation factor and recalibrate the basic parameters.

[0025] As a further solution of the present invention: the generation process of the load distribution instruction includes:

[0026] Recalculate the load distribution of the energy network according to the corrected energy consumption value of the device, and preferentially reduce the allocation weight of the devices in the accelerated aging period. For multiple devices in the same functional unit, reallocate the task ratio according to the aging stage difference to increase the load ratio of the newly put-in devices by a set percentage. For devices with sudden changes in the energy consumption correction value due to aging, mark them as aging mutation devices, and insert a buffer load area between their upstream and downstream nodes to smooth the steep change gradient of the load distribution curve. When generating the instruction, check the response delay parameters of the associated devices, and adjust the instruction issuance time parameter to achieve coordinated operation of multiple devices. Immediately start the energy consumption tracking mode after the instruction is executed, and record the actual data of the first complete operation cycle.

[0027] As a further solution of the present invention: the setting method of the buffer load area includes:

[0028] In the energy network topology, two nodes directly connected to the aging and mutated device are selected as buffer boundaries; calculate the maximum adjustable load capacity of the boundary nodes and divide it into multiple buffer steps according to a preset ratio; when a load mutation of the aging and mutated device is detected, activate the buffer steps step by step according to the mutation amplitude to absorb the impact; after each buffer step is activated, monitor the stability index of the adjacent nodes, and if the fluctuation amplitude exceeds the safety value, activate the next step; after the mutation ends, gradually release the buffer load in the reverse order, and the release rate is negatively correlated with the degree of device aging; after the buffer operation is completed, generate a load impact analysis report for optimizing the subsequent buffer step division strategy.

[0029] As a further solution of the present invention: the deviation comparison and model update process includes:

[0030] Sort the actual energy consumption data by device type, and after deducting the basic energy consumption change caused by environmental temperature fluctuations, calculate and correct the absolute difference between the predicted value; for devices with a difference exceeding the set threshold, trace back the extraction process of their aging characteristic indicators and verify the time alignment accuracy of the data acquisition nodes; after confirming the data validity, adjust the correction model weight according to the difference direction: when the actual energy consumption is continuously higher than the predicted value, increase the weight coefficient of the conductor oxidation index; when the actual energy consumption is continuously lower than the predicted value, enhance the compensation intensity of the heat dissipation efficiency attenuation coefficient; after each weight update, recalculate the fitting degree of the historical data of the previous three days and retain the update plan with improved fitting degree.

[0031] As a further solution of the present invention: specifically deducting the basic energy consumption change caused by environmental temperature fluctuations includes:

[0032] Arrange a temperature sensor array around the device to collect real-time temperature data at different height levels; establish a conduction model between the device shell temperature and the environmental temperature, and calculate the influence coefficient of temperature change on the basic energy consumption; select a compensation algorithm according to the device type: use the moving average method to eliminate the influence of short-term fluctuations for temperature-sensitive devices, and use the lag difference method to separate the long-term trend term for thermal inertia devices; convert the compensated basic energy consumption change into an equivalent load value and deduct this value from the actual energy consumption data to obtain the net energy consumption deviation; when the environmental temperature sensor fails, enable the temperature data simulation compensation amount based on the historical same period.

[0033] The beneficial effects of the present invention:

[0034] Through the collection and analysis of multi-dimensional operating parameters such as current waveform, surface temperature distribution, and medium flow rate changes, the present invention realizes the accurate identification and quantitative evaluation of equipment aging characteristics; constructs an aging correction term based on the conductor oxidation rate index, heat dissipation efficiency decay coefficient, and valve response delay index, and dynamically adjusts the energy consumption prediction model in combination with the equipment aging stage, significantly improving the accuracy and adaptability of energy consumption prediction; realizes the intelligentization of energy distribution and the balanced utilization of the entire life cycle of equipment through strategies such as preferentially reducing the equipment load weight during the aging acceleration period, setting a buffer load area to smooth load mutations, and optimizing the operation timing of multi-device collaboration; introduces an environmental temperature fluctuation compensation algorithm and a historical data fitting degree verification mechanism to dynamically update the model parameter weights, ensuring the continuous stable operation and optimized management of the system during the equipment aging process. BRIEF DESCRIPTION OF THE DRAWINGS

[0035] The present invention will be further described below in conjunction with the accompanying drawings.

[0036] Figure 1 is a flowchart of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0037] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0038] Please refer to Figure 1 As shown, the present invention is a comprehensive energy system operation management method based on load prediction, including the following steps:

[0039] First, use high-precision sensors to comprehensively monitor energy-using equipment, and collect operating parameters such as current waveform characteristics, surface temperature distribution, and medium flow rate changes. Among them, the current waveform is collected by a high-sampling-frequency current transformer, which can accurately capture the details of the current signal; the surface temperature distribution is measured non-contact by an infrared thermal imager to obtain the surface temperature field data of the equipment; the medium flow rate change is monitored in real time by an intelligent flowmeter to ensure the accuracy and real-time of the flow data.

[0040] Next, use signal processing and data analysis algorithms to deeply analyze the time-series change laws of the operating parameters. For the current waveform, perform harmonic component decomposition to identify potential problems such as conductor contact aging of the equipment; based on the surface temperature distribution data, divide the monitoring area, analyze the change of the high-temperature area, and judge the heat dissipation performance of the equipment; extract the flow fluctuation characteristics from the medium flow rate data, evaluate the state of the valve regulating mechanism, and then extract a series of characteristic indicators related to equipment aging.

[0041] Then, based on the extracted characteristic indicators, an energy consumption prediction correction model is constructed. This model is based on the theoretical energy consumption calculation formula of the equipment, introduces an aging correction term, and adopts strategies such as linear compensation and exponential compensation according to different aging stages of the equipment to dynamically adjust the theoretical energy consumption calculation value of the energy-using equipment, making the energy consumption prediction more in line with the actual operating state of the equipment.

[0042] Subsequently, the corrected theoretical energy consumption calculation value is input into the energy distribution optimization model. This model combines the topological structure and operating constraint conditions of the energy network, and through intelligent optimization algorithms such as genetic algorithms and particle swarm optimization algorithms, generates load distribution instructions for the energy network to achieve reasonable distribution of energy among various equipment and improve energy utilization efficiency.

[0043] Finally, after executing the load distribution instructions, the actual energy consumption data of the equipment is collected in real time, compared with the corrected theoretical value, and the deviation between the two is calculated. Based on the deviation analysis results, machine learning algorithms are used to update the parameter weights of the energy consumption prediction correction model, continuously optimizing the model performance, enabling the system to continuously self-learn and adjust during the equipment operation process, and achieving long-term stable and efficient operation.

[0044] In a preferred embodiment of the present invention, the process of collecting and processing the operating parameters includes:

[0045] The collection and processing of operating parameters adopt a combination of high-precision sensors and intelligent algorithms to accurately capture the aging characteristics of the equipment. In terms of current waveform processing, the fast Fourier transform (FFT) algorithm is used to decompose the harmonic components of the collected current signal, and the proportion of the total harmonic energy of the third and higher harmonics in the fundamental wave energy is extracted. The system monitors this ratio value in real time. Once it exceeds a set multiple (such as 1.5 times) of the reference value when the equipment is newly put into operation for three consecutive sampling periods, it is determined that there is contact aging of the conductor, because contact aging will cause current distortion and lead to abnormal increase in harmonic energy.

[0046] For the surface temperature distribution of the equipment, temperature field data is collected by an infrared thermal imager, and multiple monitoring areas are divided based on the structural and functional characteristics of the equipment. The system dynamically calculates the temperature threshold for each area. For example, a dynamic threshold calculation model based on historical data is used, and the areas exceeding the threshold are marked as high-temperature areas. Further analyze the expansion rate of the high-temperature area. When the rate is opposite to the change direction of the equipment load rate, it indicates that the heat dissipation performance of the equipment has deteriorated, because under normal circumstances, the higher the load rate, the higher the temperature. If the load rate decreases but the high-temperature area is still expanding, it means that the heat dissipation ability has declined.

[0047] In the processing of medium flow rate data, a flow signal is collected by an intelligent flowmeter. After removing noise using a digital filtering algorithm, the number of flow rate fluctuations per unit time is extracted. Through a large amount of data statistical analysis, it is found that when the flow rate fluctuation frequency is positively correlated with the equipment operation duration, it can be identified that there are wear characteristics in the valve adjustment mechanism. Finally, characteristic data such as the current harmonic ratio, the high-temperature area expansion rate, and the number of flow rate fluctuations are accurately aligned according to the time stamp to construct an equipment aging state matrix, providing a comprehensive data basis for subsequent equipment aging assessment.

[0048] In another preferred embodiment of the present invention, the extraction method of the characteristic index includes:

[0049] The extraction process of the characteristic index integrates statistical and signal processing technologies to achieve a quantitative assessment of the equipment aging degree. For the current harmonic ratio data, a sliding window processing method is adopted, and the window size is flexibly set according to the equipment operation characteristics. For example, a 5-minute window is used. Calculate the difference between the maximum value and the mean value of the harmonic ratio within each window period. This difference can reflect the change in the oxidation rate of the conductor, and then generate a conductor oxidation rate index.

[0050] In terms of heat dissipation performance assessment, a heat dissipation efficiency decay coefficient is established according to the deviation degree between the high-temperature area expansion rate and the load rate. By establishing a mathematical model between the two, the relationship between the deviation degree and the heat dissipation efficiency is quantified. The larger the coefficient, the more serious the heat dissipation efficiency decay. For the medium flow rate fluctuation data, the distribution density of the number of flow rate fluctuations during different operation stages of the equipment, namely the running-in period, the stable period, and the aging period, is statistically analyzed to generate a valve response delay index, which can reflect the response sensitivity of the valve adjustment mechanism.

[0051] Compare the three indexes of the conductor oxidation rate index, the heat dissipation efficiency decay coefficient, and the valve response delay index with the factory parameters of the equipment respectively, and calculate the offset amount of the current aging degree relative to the initial state. Based on the size of the offset amount, the equipment aging stage is scientifically divided into a stable period, an acceleration period, and a critical period, providing an important basis for subsequent targeted energy consumption prediction correction and load distribution strategy formulation.

[0052] In another preferred embodiment of the present invention, the working process of the energy consumption prediction correction model includes:

[0053] The prediction accuracy is significantly improved by constructing a composite correction system. The model is based on the equipment theoretical energy consumption calculation formula as the basic framework, introduces an aging correction term composed of the conductor oxidation rate index, the heat dissipation efficiency decay coefficient, and the valve response delay index, and uses the analytic hierarchy process (AHP) or machine learning algorithm to dynamically determine the weights of each index to ensure that the correction term is accurately matched with the actual aging state of the equipment.

[0054] For equipment in the stable aging period, the system adopts a linear compensation mode to maintain the stability of energy consumption prediction. Specifically, by calculating the mean value of the hourly aging offset of the equipment, this value is used as a fixed correction factor to adjust the energy consumption base in a superimposed manner. For example, when the operating environment of the equipment is stable and the aging rate is slow, the model fine-tunes the energy consumption prediction value according to the average offset per hour, enabling a smooth transition of the correction process and avoiding large fluctuations in the predicted data.

[0055] For equipment in the accelerated aging period, the model enables an exponential compensation mode to adapt to the rapidly changing aging trend. The system monitors the rising slope of the aging index in real time and obtains the dynamic change amount of the aging rate through differential calculation. Based on this, an exponential function is used to amplify the correction amplitude to ensure that the energy consumption prediction value can quickly respond to the decline in equipment performance. For example, when the oxidation rate of the conductor rises sharply, the model automatically increases the correction weight, making the predicted energy consumption closer to the actual growth trend.

[0056] For equipment in the critical aging period, the model introduces a quadratic compensation factor to construct a dual safeguard mechanism. While correcting the energy consumption base, by calculating the uncertainty of the equipment performance fluctuation, an additional safety redundancy is added. In the specific calculation process, the system first collects the historical data of all characteristic indicators of the equipment at the current aging stage, and uses statistical methods to calculate the ratio of the variance to the mean value of the indicator values in the most recent hour. This ratio can quantify the instability of the equipment performance. When this ratio exceeds the safety threshold corresponding to the equipment type, such as the threshold obtained through training with historical failure data, the model superimposes a compensation component proportional to the variance value on the basic correction amount.

[0057] For equipment involving multi-energy coupling, the model further expands the evaluation dimension and adds an evaluation item for cross-system impact. By establishing an energy network coupling model, the conduction path of the additional energy consumption caused by equipment aging among different energy subsystems is simulated, and the impact degree on other energy networks is quantified. Finally, the sum of the compensation component and the conduction amount is used as the quadratic compensation factor to be input into the correction model to achieve the collaborative optimization of complex energy systems. In addition, when the equipment enters the maintenance cycle, the system automatically resets the compensation factor and recalibrates the basic parameters based on the equipment performance data after maintenance to ensure that the model continuously maintains the best prediction efficiency.

[0058] In another preferred embodiment of the present invention, the generation process of the load distribution instruction includes:

[0059] The generation process of the load distribution instruction deeply integrates the equipment aging status and the characteristics of the energy network, and realizes the intelligent allocation of the load through the collaborative optimization of multiple strategies. First, the system recalculates the load distribution in the network by using algorithms such as power flow calculation based on the corrected equipment energy consumption value, combined with the topological structure and real-time operation parameters of the energy network. During this process, a dynamic weight adjustment mechanism is adopted. For the equipment in the accelerated aging period, according to the severity of its aging index, the allocation weight is gradually reduced in a gradient manner to avoid the risk of failure caused by overuse of the equipment.

[0060] For multiple devices within the same functional unit, the system constructs a differential load distribution model based on the aging stage. According to different aging stages such as the stable period, the accelerated period, and the critical period of the device, combined with the device performance parameters, the task allocation ratio of each device is dynamically adjusted. When implementing specifically, a strategy for increasing the load proportion of newly put-in devices is set. For example, the load proportion of new devices is increased by 15% - 20%, giving full play to their high-efficiency operation advantages, while reducing the operation pressure of aging devices and achieving load balance among devices.

[0061] When it is detected that the energy consumption correction value has a sudden change due to equipment aging, the system quickly activates the mechanism for constructing a buffer load area. In the topological structure of the energy network, two key nodes directly connected to the aging device are automatically selected as the buffer boundaries, and the buffer area is accurately located by analyzing the connection relationship and energy transmission characteristics between the nodes. Subsequently, the system calculates the maximum adjustable load capacity of the boundary nodes and divides them into multiple buffer steps according to a preset ratio, such as a capacity gradient of 1:2:3, with each step corresponding to a different load adjustment ability.

[0062] During the operation of the buffer load area, once it is detected that the load of the aging device has a sudden change, the system immediately enables the buffer steps level by level from small to large according to the magnitude of the sudden change. For example, if the magnitude of the load sudden change is small, the first-level buffer step is preferentially enabled to absorb the impact. After enabling, the system monitors the stability indicators of adjacent nodes in real time, such as voltage fluctuation and flow stability. If the fluctuation magnitude exceeds the preset safety threshold, the next-level buffer step is automatically activated until the load impact is completely absorbed. When the load sudden change ends, the system gradually releases the buffer load in the reverse order, and the release rate is negatively correlated with the equipment aging degree, that is, the higher the aging degree, the slower the release rate, to avoid causing a secondary impact on the energy network. After the buffer operation is completed, the system automatically generates a detailed load impact analysis report, and combines various parameters in this buffer process to optimize the division strategy of subsequent buffer steps through machine learning algorithms, improving the response efficiency and adjustment accuracy of the buffer mechanism.

[0063] When generating load distribution instructions, the system establishes a device response delay database to store parameters such as the startup time and adjustment speed of each device. By verifying the response delay parameters of the associated devices and using the time synchronization algorithm to adjust the instruction issuance time, it ensures that multiple devices can achieve accurate collaborative operation when executing load distribution instructions. After the instruction is executed, the system immediately starts the high-precision energy consumption tracking mode, using the sensor network and data acquisition system to fully record the actual energy consumption data, operating status and other information of each device during the first complete operation cycle, providing detailed data support for subsequent model optimization and strategy adjustment.

[0064] In another preferred embodiment of the present invention, the deviation comparison and model updating process includes:

[0065] The deviation comparison and model update process ensures that the energy consumption prediction and correction model continues to adapt to changes in the equipment's operating status through systematic data processing and intelligent optimization strategies. The system first finely classifies and organizes the collected actual energy consumption data by equipment type, and builds a dynamic compensation mechanism to obtain accurate energy consumption deviation values in response to the interference of ambient temperature fluctuations on equipment energy consumption.

[0066] Specifically, a multi-level temperature sensor array is arranged at key locations around the equipment, and real-time temperature data of different height layers is collected in multiple dimensions to build a comprehensive ambient temperature field model. Based on the principle of heat transfer, a conduction model between the equipment shell temperature and the ambient temperature is established. Combined with the characteristic parameters such as equipment material and heat dissipation structure, the influence coefficient of temperature change on basic energy consumption is accurately calculated. Differentiated compensation algorithms are used for the temperature response characteristics of different types of equipment: for temperature-sensitive equipment, the sliding average method is used to smooth the temperature data in a short period of time, effectively eliminating the interference of instantaneous fluctuations in ambient temperature on energy consumption data; for equipment with thermal inertia characteristics, the hysteresis difference method is used to separate the long-term trend term of temperature change and accurately quantify the cumulative impact of temperature change on energy consumption. After converting the compensated basic energy consumption change into an equivalent load value, this value is deducted from the actual energy consumption data to obtain a net energy consumption deviation that truly reflects the operating status of the equipment. When the ambient temperature sensor fails, the system automatically enables a simulation compensation mechanism based on historical temperature data for the same period. Through time series analysis and machine learning algorithms, the current ambient temperature change trend is simulated to ensure the continuity and accuracy of energy consumption data processing.

[0067] After obtaining the net energy consumption deviation, the system calculates the absolute difference by comparing it with the corrected prediction value. For devices with a difference exceeding the set threshold, a data quality backtracking mechanism is initiated to deeply verify the extraction process of the aging characteristic indicators. Through the timestamp verification and synchronization algorithm, the time alignment accuracy of multi-source data acquisition nodes such as current waveforms, temperature distributions, and medium flow rates is verified to ensure the spatio-temporal consistency of the data. After confirming the data validity, the system implements a targeted model weight adjustment strategy based on the difference direction: if the actual energy consumption continues to be higher than the predicted value, it indicates that the model underestimates the energy consumption growth caused by device aging. At this time, the weight coefficient of the conductor oxidation index is automatically increased to strengthen the consideration of factors such as internal contact aging of the device; if the actual energy consumption continues to be lower than the predicted value, the compensation intensity of the heat dissipation efficiency decay coefficient is increased to optimize the model's evaluation of the device's heat dissipation performance. After each weight update, the system uses the cross-validation method to recalculate the fitting degree of the historical data for the previous three days. By comparing the fitting effects before and after the update, only the update scheme that significantly improves the fitting degree is retained, realizing the adaptive optimization of the model parameters and ensuring the prediction accuracy and reliability of the energy consumption prediction correction model throughout the device's life cycle.

[0068] The above has described in detail an embodiment of the present invention, but the content described is only a preferred embodiment of the present invention and cannot be considered as limiting the scope of implementation of the present invention. All equivalent changes and improvements made according to the scope of the present invention application should still fall within the scope covered by the patent of the present invention.

Claims

1. A method for operating and managing an integrated energy system based on load forecasting, characterized in that, Including the following steps: Collect the operating parameters of the energy-using equipment, where the parameters include current waveform characteristics, surface temperature distribution, and medium flow rate changes; Analyze the temporal variation law of the operating parameters and extract characteristic indicators related to equipment aging; Establish an energy consumption prediction correction model based on the characteristic indicators and dynamically adjust the theoretical energy consumption calculation value of the energy-using equipment; Input the corrected theoretical energy consumption calculation value into the energy distribution optimization model to generate a load distribution instruction for the energy network; After executing the load distribution instruction, compare the deviation between the actual energy consumption and the corrected theoretical value and update the parameter weights of the energy consumption prediction correction model; The extraction method of the characteristic indicators includes: Perform a sliding window process on the current harmonic ratio data, calculate the difference between the maximum value and the mean value within the window period to generate a conductor oxidation rate indicator; establish a heat dissipation efficiency decay coefficient according to the deviation degree between the high-temperature area expansion rate and the load rate; count the distribution density of the medium flow rate fluctuation times at different stages of equipment operation to generate a valve response delay index; compare the three indicators with the equipment factory parameters respectively and calculate the offset of the current aging degree relative to the initial state; divide the aging stage into a stable period, an acceleration period, and a critical period according to the size of the offset; The working process of the energy consumption prediction correction model includes: Add an aging correction term to the equipment theoretical energy consumption calculation formula, where the correction term is obtained by weighted calculation of the conductor oxidation rate indicator, the heat dissipation efficiency decay coefficient, and the valve response delay index; for equipment aging in the stable period, the correction term adopts a linear compensation mode and adjusts the energy consumption base according to the average value of the aging offset per hour; For equipment aging in the acceleration period, adopt an exponential compensation mode and dynamically amplify the correction amplitude according to the rising slope of the aging indicator in the recent unit time; For equipment aging in the critical period, introduce a quadratic compensation factor to increase the safety redundancy while correcting the energy consumption base; record the deviation between the actual energy consumption data and the predicted value after each correction for updating the weight coefficient; The generation process of the load distribution instruction includes: Recalculate the load distribution of the energy network according to the corrected equipment energy consumption value, and preferentially reduce the allocation weight of equipment in the aging acceleration period; for multiple devices in the same functional unit, reallocate the task ratio according to the aging stage difference to increase the load ratio of newly put-in devices by a set percentage; for devices with a sudden change in the energy consumption correction value due to aging, mark them as aging mutation devices and insert a buffer load area between their upstream and downstream nodes to smooth the steep gradient of the load distribution curve; check the response delay parameters of associated devices when generating the instruction and adjust the instruction issuance time parameter to achieve coordinated operation of multiple devices; immediately start the energy consumption tracking mode after the instruction is executed and record the actual data of the first complete operation cycle.

2. The operation management method of an integrated energy system based on load forecasting according to claim 1, characterized in that The acquisition and processing process of the operating parameters includes: Perform harmonic component decomposition on the current waveform, extract the proportion of the total harmonic energy of the third and higher harmonics in the fundamental wave energy, and when the proportion value exceeds a set multiple of the reference value when the equipment is newly put into operation for three consecutive sampling periods, mark it as a conductor contact aging characteristic; Divide the monitoring area according to the device surface temperature distribution data, mark the area exceeding the dynamic temperature threshold as the high-temperature area, calculate the area expansion rate of the high-temperature area, and determine the degradation of the heat dissipation performance when the expansion rate is opposite to the change direction of the device load rate; extract the number of flow fluctuations per unit time from the medium flow data, and identify it as a wear characteristic of the valve adjustment mechanism when the fluctuation frequency is positively correlated with the device operation duration; align the current harmonic ratio, the high-temperature area expansion rate, and the number of flow fluctuations according to the time stamp to generate the device aging state matrix.

3. The operation management method of an integrated energy system based on load forecasting according to claim 1, wherein, The calculation method of the secondary compensation factor includes: Obtain the historical data of all characteristic indicators of the device at the current aging stage, and calculate the ratio of the variance to the mean of the indicator values in the most recent hour; when the ratio exceeds the safety threshold corresponding to the device type, superimpose a compensation component proportional to the variance value on the basic correction amount; for devices involving multi-energy coupling, add a cross-system impact assessment item to calculate the conduction amount of the additional energy consumption caused by its aging to other energy networks; use the sum of the compensation component and the conduction amount as the secondary compensation factor to input the correction model; after the device enters the maintenance cycle, reset the compensation factor and recalibrate the basic parameters.

4. A method for operating and managing an integrated energy system based on load forecasting according to claim 1, characterized in that, The setting method of the buffer load area includes: Select two nodes directly connected to the aging mutation device in the energy network topology as the buffer boundaries; calculate the maximum adjustable load capacity of the boundary nodes and divide it into multiple buffer steps according to a preset ratio; when a load mutation of the aging mutation device is detected, enable the buffer steps step by step according to the mutation amplitude to absorb the impact; after each buffer step is enabled, monitor the stability indicators of adjacent nodes, and activate the next step if the fluctuation amplitude exceeds the safety value; after the mutation ends, gradually release the buffer load in the reverse order, and the release rate is negatively correlated with the device aging degree; generate a load impact analysis report after the buffer operation is completed to optimize the subsequent buffer step division strategy.

5. A method for operating and managing an integrated energy system based on load forecasting according to claim 1, characterized in that, The deviation comparison and model update process includes: Sort the actual energy consumption data by device type, and calculate the absolute difference from the corrected prediction value after deducting the basic energy consumption change caused by the ambient temperature fluctuation; for devices with differences exceeding the set threshold, trace back the extraction process of their aging characteristic indicators and verify the time alignment accuracy of the data acquisition nodes; after confirming the data validity, adjust the weights of the correction model according to the difference direction: when the actual energy consumption is continuously higher than the predicted value, increase the weight coefficient of the conductor oxidation index; when the actual energy consumption is continuously lower than the predicted value, increase the compensation intensity of the heat dissipation efficiency decay coefficient; recalculate the fitting degree of the historical data of the previous three days after each weight update, and retain the update plan with improved fitting degree.

6. A method for operating and managing an integrated energy system based on load forecasting according to claim 5, characterized in that, Deducting the basic energy consumption change caused by the ambient temperature fluctuation specifically includes: Arrange a temperature sensor array around the device to collect real-time temperature data at different height levels; establish a conduction model between the device shell temperature and the ambient temperature to calculate the influence coefficient of temperature change on the basic energy consumption; select a compensation algorithm according to the device type: use the moving average method to eliminate the influence of short-term fluctuations for temperature-sensitive devices, and use the lag difference method to separate the long-term trend term for thermal inertia devices; convert the compensated change in basic energy consumption into an equivalent load value, and deduct this value from the actual energy consumption data to obtain the net energy consumption deviation; when the ambient temperature sensor fails, enable the simulation compensation amount based on the temperature data of the same historical period.

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