Comprehensive energy system operation management method based on load prediction

Abstract

The invention discloses an integrated energy system operation management method based on load prediction, and belongs to the technical field of energy system management, and the method specifically comprises the steps: collecting operation parameters of energy use equipment, the parameters comprising current waveform characteristics, surface temperature distribution and medium flow change; analyzing a time sequence change rule of the operation parameters, and extracting characteristic indexes related to equipment aging; establishing an energy consumption prediction correction model according to the characteristic indexes, and dynamically adjusting a theoretical energy consumption calculation value of the energy use equipment; inputting the corrected theoretical energy consumption calculation value into an energy distribution optimization model to generate a load distribution instruction of the energy network; after executing the load distribution instruction, comparing the deviation between the actual energy consumption and the correction theoretical value, and updating the parameter weight of the energy consumption prediction correction model; according to the invention, continuous and stable operation and optimal management of the integrated energy system in the equipment aging process are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of energy system management, and particularly 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 prediction models and load distribution strategies. Traditional methods usually establish an energy consumption prediction model based on the rated parameters of equipment and historical operation data, and optimize energy distribution in combination with real-time load demands. Some advanced solutions introduce equipment status monitoring technology, 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 prediction 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: The traditional energy consumption prediction 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.

[0006] The purpose of the present invention can be achieved through the following technical solutions: An operation management method for an integrated energy system based on load forecasting, comprising the following steps: Collect the operation parameters of energy-using equipment, and the parameters include current waveform characteristics, surface temperature distribution, and medium flow rate changes; Analyze the time-series change law of the operation parameters, and extract characteristic indicators related to equipment aging; Establish an energy consumption prediction correction model based on the described characteristic indicators, and dynamically adjust the calculated value of the theoretical energy consumption of energy-using equipment; Input the corrected calculated value of the theoretical energy consumption 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.

[0007] As a further solution of the present invention: The process of collecting and processing the operating parameters includes: Decompose the harmonic components of the current waveform, extract the proportion of the total harmonic energy above the third harmonic in the fundamental wave energy. When the proportion value exceeds a set multiple of the reference value at the time of new equipment operation for three consecutive sampling periods, it is marked as a characteristic of conductor contact aging; 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 expansion rate of the high-temperature area 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 characteristic of valve adjustment mechanism wear; 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.

[0008] As a further solution of the present invention: The method for extracting 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 index; Establish a heat dissipation efficiency attenuation 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, 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.

[0009] As a further solution of the present invention: The working process of the energy consumption prediction correction model includes: Add an aging correction term to the equipment theoretical energy consumption calculation formula. The correction term is obtained by weighted calculation of the conductor oxidation rate index, the heat dissipation efficiency attenuation 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 index in the recent unit time; For equipment in the critical period of aging, introduce a secondary 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.

[0010] As a further solution of the present invention: the calculation method of the secondary compensation factor includes: Obtain the historical data of all characteristic indicators of the equipment at the current aging stage, and calculate the ratio of the variance to the mean of the indicator values within the most recent hour; when the ratio exceeds the safety threshold corresponding to the equipment type, superimpose a compensation component proportional to the variance value on the basic correction amount; for equipment 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 and input it into the correction model; after the equipment enters the maintenance cycle, reset the compensation factor and recalibrate the basic parameters.

[0011] As a further solution of the present invention: 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, redistribute the task ratio according to the difference in the aging stage to increase the load ratio of newly put-in devices by a set percentage; for devices with sudden changes in energy consumption correction values 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; check the response delay parameters of associated devices when generating the instruction, and adjust the instruction sending 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.

[0012] As a further solution of the present invention: 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 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 equipment aging degree; generate a load impact analysis report after the buffer operation is completed for optimizing the subsequent buffer step division strategy.

[0013] As a further solution of the present invention: the deviation comparison and model update process includes: Sort the actual energy consumption data by equipment type. After deducting the basic energy consumption change caused by environmental temperature fluctuations, calculate the absolute difference between the predicted value and the corrected value. For equipment with a difference exceeding the set threshold, trace back the extraction process of its aging characteristic indicators to verify the time alignment accuracy of the data acquisition nodes. After confirming the data validity, adjust the weight 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. After each weight update, recalculate the goodness of fit of the historical data for the previous three days, and retain the update plan with improved goodness of fit.

[0014] As a further solution of the present invention: Deducting the basic energy consumption change caused by environmental temperature fluctuations specifically includes: Arrange a temperature sensor array around the equipment to collect real-time temperature data at different height levels; establish a conduction model between the equipment 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 equipment type: use the moving average method to eliminate the influence of short-term fluctuations for temperature-sensitive equipment, and use the lag difference method to separate the long-term trend term for thermal inertia equipment; 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.

[0015] Advantages of the present invention: Through the collection and analysis of multi-dimensional operating parameters such as current waveform, surface temperature distribution, and medium flow rate change, 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; through strategies such as preferentially reducing the load weight of equipment in the aging acceleration period, setting a buffer load area to smooth load mutations, and optimizing the operation timing of multi-equipment collaboration, the intelligent allocation of energy and the balanced utilization of the entire life cycle of the equipment are realized; introducing an environmental temperature fluctuation compensation algorithm and a historical data goodness of fit verification mechanism, dynamically updating the model parameter weights, and ensuring the continuous stable operation and optimized management of the system during the equipment aging process. Description of the Drawings

[0016] The present invention will be further described below with reference to the drawings.

[0017] Figure 1 It is a flow diagram of the present invention. Specific Embodiments

[0018] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described 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 belong to the scope of protection of the present invention.

[0019] Please refer to Figure 1 As shown, the present invention is a method for operating and managing an integrated energy system based on load forecasting, including the following steps: First, use high-precision sensors to comprehensively monitor energy-using devices, and collect operating parameters such as current waveform characteristics, surface temperature distribution, and medium flow rate changes. Among them, for current waveform acquisition, a high-sampling-frequency current transformer is used to 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 device; the change in the medium flow rate is monitored in real time by an intelligent flowmeter to ensure the accuracy and real-time of the flow data.

[0020] 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 device; based on the surface temperature distribution data, divide the monitoring area, analyze the changes in the high-temperature area, and judge the heat dissipation performance of the device; 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 device aging.

[0021] Then, based on the extracted characteristic indicators, construct an energy consumption prediction correction model. This model is based on the theoretical energy consumption calculation formula of the device, introduces an aging correction term, and adopts strategies such as linear compensation and exponential compensation according to different aging stages of the device to dynamically adjust the theoretical energy consumption calculation value of the energy-using device, making the energy consumption prediction more in line with the actual operating state of the device.

[0022] Subsequently, input the corrected theoretical energy consumption calculation value 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 devices and improve energy utilization efficiency.

[0023] Finally, after executing the load distribution instruction, collect the actual energy consumption data of the device in real time, compare it with the corrected theoretical value, and calculate the deviation between the two. Based on the deviation analysis result, use machine learning algorithms to update the parameter weights of the energy consumption prediction correction model, continuously optimize the model performance, enable the system to continuously self-learn and adjust during the device operation process, and achieve long-term stable and efficient operation.

[0024] In a preferred embodiment of the present invention, the process of collecting and processing the operating parameters includes: The collection and processing of operating parameters adopt a method combining high-precision sensors and intelligent algorithms to accurately capture the characteristics of equipment aging. 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 proportion 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.

[0025] For the surface temperature distribution of the equipment, the 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 this 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.

[0026] In the processing of the medium flow rate data, the flow rate signal is collected by an intelligent flowmeter. After removing the noise by 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 is a wear characteristic in the valve regulating mechanism. Finally, the characteristic data such as the current harmonic ratio, the expansion rate of the high-temperature area, 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.

[0027] In another preferred embodiment of the present invention, the method for extracting the characteristic indicators includes: The process of extracting the characteristic indicators 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, such as 5 minutes as a window. 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 speed of the conductor, and then generate a conductor oxidation rate indicator.

[0028] In terms of heat dissipation performance evaluation, a heat dissipation efficiency decay coefficient is established based on 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 decay of the heat dissipation efficiency. 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 regulating mechanism.

[0029] The three indicators of the conductor oxidation rate index, the heat dissipation efficiency decay coefficient, and the valve response delay index are respectively compared with the factory parameters of the equipment to calculate the offset of the current aging degree relative to the initial state. Based on the size of the offset, 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.

[0030] In another preferred embodiment of the present invention, the working process of the energy consumption prediction correction model includes: The prediction accuracy is significantly improved by constructing a composite correction system. The model is based on the theoretical energy consumption calculation formula of the equipment, 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 the accurate matching of the correction term with the actual aging state of the equipment.

[0031] For equipment aging in the stable 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 coefficient to adjust the energy consumption base in a superimposed manner. For example, when the equipment operating environment is stable and the aging speed is slow, the model fine-tunes the energy consumption prediction value according to the average offset per hour to make the correction process transition smoothly and avoid large fluctuations in the prediction data.

[0032] For equipment aging in the acceleration period, the model enables an exponential compensation mode to adapt to the rapidly changing aging trend. The system real-time monitors the rising slope of the aging index, and obtains the dynamic change amount of the aging rate through differential calculation. Based on this, the correction amplitude is amplified using an exponential function to ensure that the energy consumption prediction value can quickly respond to the decline of the equipment performance. For example, when the conductor oxidation rate rises sharply, the model automatically increases the correction weight to make the predicted energy consumption closer to the actual growth trend.

[0033] For aging equipment in the critical period, the model introduces a secondary compensation factor to build a dual safeguard mechanism. While correcting the energy consumption base, by calculating the uncertainty of equipment performance fluctuations, an additional safety redundancy is added. In the specific calculation process, the system first collects the historical data of all characteristic indicators of the current aging stage of the equipment, and uses statistical methods to calculate the ratio of the variance to the mean of the indicator values within 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.

[0034] 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 secondary compensation factor to input into the correction model to achieve the collaborative optimization of complex energy systems. In addition, when the equipment enters the maintenance period, 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.

[0035] In another preferred embodiment of the present invention, the generation process of the load distribution instruction includes: The generation process of the load distribution instruction deeply integrates the equipment aging state and the characteristics of the energy network, and realizes the intelligent allocation of the load through multi - strategy collaborative optimization. The system first re - calculates 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. In this process, a dynamic weight adjustment mechanism is adopted. For equipment in the aging acceleration period, according to the severity of its aging index, the allocation weight is gradually reduced in a gradient manner to avoid the risk of equipment failure caused by over - use.

[0036] 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, acceleration period, and critical period in which the equipment is located, combined with the equipment performance parameters, the task allocation ratio of each device is dynamically adjusted. In specific implementation, a strategy for increasing the load ratio of newly - put - in equipment is set, for example, increasing the load ratio of the new equipment by 15% - 20%, giving full play to its high - efficiency operation advantage, and at the same time reducing the operation pressure of the aging equipment to achieve load balance among the devices.

[0037] When it is detected that the energy consumption correction value has a sudden change due to equipment aging, the system quickly activates the buffer load area construction mechanism. In the energy network topology, two key nodes directly connected to the aging equipment are automatically selected as 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. Each step corresponds to a different load adjustment ability.

[0038] During the operation of the buffer load area, once a sudden change in the load of the aging equipment is detected, 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 continuously monitors the stability indicators of adjacent nodes, such as voltage fluctuations and flow stability. If the fluctuation magnitude exceeds the pre-set 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 degree of equipment aging, 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, combines various parameters in this buffer process, and optimizes the division strategy of the subsequent buffer steps through machine learning algorithms to improve the response efficiency and adjustment accuracy of the buffer mechanism.

[0039] When generating the load distribution instruction, 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 associated devices and using a time synchronization algorithm to adjust the instruction issuance time, it ensures precise cooperative operation of multiple devices when executing the load distribution instruction. After the instruction is executed, the system immediately starts a high-precision energy consumption tracking mode, uses the sensor network and data acquisition system to completely record the actual energy consumption data, operating status and other information of each device in the first complete operation cycle, providing detailed data support for subsequent model optimization and strategy adjustment.

[0040] In another preferred embodiment of the present invention, the deviation comparison and model update process includes: The deviation comparison and model update process ensures that the energy consumption prediction correction model continuously adapts to the changes in the equipment operation state through systematic data processing and intelligent optimization strategies. The system first classifies and organizes the collected actual energy consumption data in detail according to the equipment type, and constructs a dynamic compensation mechanism to obtain accurate energy consumption deviation values for the interference caused by environmental temperature fluctuations to the equipment energy consumption.

[0041] 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.

[0042] After obtaining the net energy consumption deviation, the system compares it with the corrected predicted value and calculates the absolute difference. For devices whose difference exceeds the set threshold, the data quality backtracking mechanism is activated to deeply check the extraction process of aging characteristic indicators. Through timestamp verification and synchronization algorithms, the time alignment accuracy of multi-source data acquisition nodes such as current waveform, temperature distribution, and medium flow is verified to ensure the temporal and spatial consistency of the data. After confirming the validity of the data, 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 equipment 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 equipment; if the actual energy consumption continues to be lower than the predicted value, the compensation intensity of the heat dissipation efficiency attenuation coefficient is increased to optimize the model's evaluation of the equipment's heat dissipation performance. After each weight update, the system uses the cross-validation method to recalculate the fitting degree of the historical data of 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 to achieve adaptive optimization of model parameters and ensure the prediction accuracy and reliability of the energy consumption prediction and correction model throughout the life cycle of the equipment.

[0043] The above is a detailed description of an embodiment of the present invention, but the content is only a preferred embodiment of the present invention and cannot be considered to limit the scope of implementation of the present invention. All equivalent changes and improvements made within the scope of the present invention should still fall within the scope of the patent coverage of the present invention.

Claims

1. A method for operation and management of an integrated energy system based on load forecasting, characterized in that: The following steps are involved: Collecting operating parameters of energy-using equipment, including current waveform characteristics, surface temperature distribution, and medium flow changes; Analyze the temporal variation of the operating parameters and extract characteristic indicators related to equipment aging; Establishing an energy consumption prediction and correction model based on the characteristic indicators to dynamically adjust the theoretical energy consumption calculation value of energy-using equipment; The corrected theoretical energy consumption calculation value is input into the energy distribution optimization model to generate the load distribution instruction of the energy network; After executing the load distribution instruction, the deviation between the actual energy consumption and the corrected theoretical value is compared, and the parameter weights of the energy consumption prediction correction model are updated.

2. The method for operation and management of an integrated energy system based on load forecasting according to claim 1, characterized in that: The collection and processing process of the operating parameters includes: Decompose the harmonic components of the current waveform and extract the ratio of the sum of the third and above harmonic energies to the fundamental wave energy. When the ratio value exceeds the set multiple of the benchmark value when the equipment is newly put into operation for three consecutive sampling cycles, it is marked as a conductor contact aging feature; The monitoring area is divided according to the surface temperature distribution data of the equipment, and the area exceeding the dynamic temperature threshold is marked as a high-temperature area. The expansion rate of the high-temperature area is calculated. When the expansion rate is in the opposite direction of the equipment load rate, the heat dissipation performance is determined to be degraded. The number of flow fluctuations per unit time is extracted from the medium flow data. When the fluctuation frequency is positively correlated with the equipment operating time, it is identified as a wear feature of the valve regulating mechanism. The current harmonic ratio, high-temperature area expansion rate, and number of flow fluctuations are aligned by timestamp to generate an equipment aging status matrix.

3. The method for operation and management of an integrated energy system based on load forecasting according to claim 1, characterized in that: The method for extracting the characteristic index includes: The current harmonic proportion data is processed by sliding window, and the difference between the maximum value and the mean value within the window period is calculated to generate the conductor oxidation rate index; the heat dissipation efficiency attenuation coefficient is established according to the degree of deviation between the high-temperature area expansion rate and the load rate; the distribution density of the medium flow fluctuation times at different stages of equipment operation is counted to generate the valve response delay index; the three indicators are compared with the factory parameters of the equipment respectively, and the offset of the current aging degree relative to the initial state is calculated; the aging stage is divided into a stable period, an accelerated period and a critical period according to the size of the offset.

4. The method for operation and management of an integrated energy system based on load forecasting according to claim 3 is characterized in that: The working process of the energy consumption prediction correction model includes: An aging correction term is added to the theoretical energy consumption calculation formula of the equipment. The correction term is obtained by weighted calculation of the conductor oxidation rate index, the heat dissipation efficiency attenuation coefficient and the valve response delay index. For equipment that is in a stable aging 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 in accelerated aging period, exponential compensation mode is adopted to dynamically amplify the correction range according to the rising slope of the aging index in the most recent unit time; For critical aging equipment, a secondary compensation factor is introduced to increase the safety redundancy while correcting the energy consumption base; after each correction, the deviation between the actual energy consumption data and the predicted value is recorded to update the weight coefficient.

5. The method for operation management of an integrated energy system based on load forecasting according to claim 4 is characterized in that: The calculation method of the secondary compensation factor includes: Obtain historical data of all characteristic indicators of the current aging stage of the equipment, and calculate the ratio of the variance to the mean of the indicator value in the last hour; when the ratio exceeds the safety threshold corresponding to the equipment type, superimpose a compensation component proportional to the variance value on the basic correction amount; for equipment involving multi-energy coupling, add cross-system impact assessment items, and calculate the amount of additional energy consumption caused by its aging that is transmitted to other energy networks; input the sum of the compensation component and the transmission amount as a secondary compensation factor into the correction model; after the equipment enters the maintenance cycle, reset the compensation factor and recalibrate the basic parameters.

6. The method for operation and management of an integrated energy system based on load forecasting according to claim 3 is characterized in that: The generation process of the load distribution instruction includes: Recalculate the load distribution of the energy network based on the corrected equipment energy consumption value, and give priority to reducing the allocation weight of equipment in the accelerated aging period; for multiple devices in the same functional unit, reallocate the task ratio according to the difference in aging stages, so that the load share of newly invested equipment increases by a set percentage; for devices with sudden changes in energy consumption correction values ​​due to aging, mark them as aging mutation devices, insert buffer load areas between their upstream and downstream nodes, and smooth the steep gradient of the load distribution curve; verify the response delay parameters of the associated devices when generating instructions, and adjust the instruction issuance time parameters to achieve multi-device collaborative operation; start the energy consumption tracking mode immediately after the instruction is executed, and record the actual data of the first complete operation cycle.

7. The method for operation and management of an integrated energy system based on load forecasting according to claim 6, characterized in that: The method for setting the buffer load area includes: In the energy network topology, two nodes directly connected to the aging mutation equipment are selected as buffer boundaries; the maximum adjustable load capacity of the boundary node is calculated and divided into multiple buffer steps according to a preset ratio; when a load mutation of the aging mutation equipment is detected, the buffer steps are enabled step by step according to the mutation amplitude to absorb the impact; after each buffer step is enabled, the stability index of the adjacent nodes is monitored, and if the fluctuation amplitude exceeds the safety value, the next step is activated; after the mutation ends, the buffer load is gradually released in reverse order, and the release rate is negatively correlated with the degree of equipment aging; after the buffer operation is completed, a load impact analysis report is generated to optimize the subsequent buffer step division strategy.

8. The method for operation and management of an integrated energy system based on load forecasting according to claim 1, characterized in that: The deviation comparison and model updating process includes: The actual energy consumption data is sorted by equipment category, and after deducting the basic energy consumption change caused by ambient temperature fluctuations, the absolute difference with the revised predicted value is calculated; for equipment whose difference exceeds the set threshold, the extraction process of its aging characteristic indicators is traced back to verify the time alignment accuracy of the data acquisition node; after confirming the validity of the data, the correction model weight is adjusted in the direction of the difference: when the actual energy consumption continues to be higher than the predicted value, the weight coefficient of the conductor oxidation index is increased; when the actual energy consumption continues to be lower than the predicted value, the compensation intensity of the heat dissipation efficiency attenuation coefficient is increased; after each weight update, the fitting degree of the historical data of the previous three days is recalculated, and the update plan with improved fitting degree is retained.

9. The method for operation and management of an integrated energy system based on load forecasting according to claim 8, characterized in that: The basic energy consumption changes after deducting the ambient temperature fluctuations include: Arrange a temperature sensor array around the equipment to collect real-time temperature data at different altitudes; establish a conduction model between the equipment casing temperature and the ambient temperature, and calculate the influence coefficient of temperature change on basic energy consumption; select a compensation algorithm based on the equipment type: use the sliding average method to eliminate the impact of short-term fluctuations for temperature-sensitive equipment, and use the lagged difference method to separate long-term trend items for thermal inertia equipment; 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 ambient temperature sensor fails, enable the simulation compensation based on the temperature data of the same period in history.

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