Electric quantity prediction platform management system based on power generation operation analysis

Abstract

The invention discloses an electric quantity prediction platform management system based on power generation operation analysis, and relates to the technical field of power dispatching, a trained power consumption demand prediction model is used to predict the power consumption state in a power supply area, a power supply prediction data set is generated, and if it is predicted that power supply is abnormal, the power supply is abnormal. A multi-level alarm mechanism is started in the power supply area, and an alarm instruction is sent to the outside; dividing the power supply area into a plurality of sub-areas according to the key factors obtained through identification, and outputting a corresponding response strategy by a response strategy library according to the user group portraits in the sub-areas; and constructing a supply anomaly degree and an anomaly threshold value according to the feedback data after the response strategy is executed, and selecting a corresponding feedback measure for the power supply area according to a relationship between the supply anomaly degree and the anomaly threshold value. When the power demand and supply in the power supply area are abnormal, targeted and personalized response is quickly made, and current power supply imbalance is avoided.

Description

Technical Field

[0001] The present invention relates to the technical field of electric power dispatching, and in particular to an electric power forecasting platform management system based on power generation operation analysis. Background Art

[0002] Electricity forecasting based on power generation operation analysis refers to determining the electricity value in a specific time period in the future through comprehensive analysis and prediction of the power generation of generators, the operating characteristics of the power system, historical electricity data, and various factors that may affect electricity (such as regional economy, policies, climate, etc.). This helps to formulate more reasonable power generation plans and scheduling strategies to ensure the balance of electricity supply and demand. It also helps new energy companies optimize their business decisions and reduce market risks.

[0003] In the Chinese invention patent with application publication number CN117318183, a power dispatching method and system are disclosed, which can be applied to the field of power system automation technology. The method includes: based on the electric load forecast data in the power dispatching plan data of the target scenario in the future period and the historical electric load data set of the target scenario, determine the target historical electric load data matching the electric load forecast data from the historical electric load data set; based on the target historical electric load data, determine the target bus load in the future period; determine the target braking output in the future period and the reactive output in the future period; based on the target bus load, target braking output and reactive output, determine the scenario risk evaluation result of the target scenario in the future period; determine the scenario risk evaluation result to characterize the potential risk of the power dispatching plan, optimize the power dispatching plan data, obtain the target power dispatching plan data, and dispatch the power system according to the target power dispatching plan data.

[0004] When power generation facilities, such as power plants, regional microgrids, and other power generation devices generate electricity and supply power to the outside, it is necessary to consider the power supply and demand data in the power supply area, and use these data as feedback to adjust the power generation status and power dispatch status to avoid and reduce the occurrence of over-generation or insufficient power supply, and reduce the frequency of power waste or power shortages.

[0005] In the existing power forecasting management method, the power demand in the power supply area is predicted based on the meteorological conditions data and user power consumption data in the power supply area, and a timely response is made when there is an abnormality in the power supply to avoid further deepening of the current abnormal situation. However, when there is a certain degree of imbalance in the power supply and demand situation in the power supply area, in this scenario, if an overall response to the power supply abnormality in the power supply area is still made based on the power supply forecast, it will be difficult to alleviate the upcoming power supply abnormality problem.

[0006] To this end, the present invention provides an electricity forecasting platform management system based on power generation operation analysis. Summary of the invention

[0007] 1. Technical issues to be resolved

[0008] In view of the deficiencies of the prior art, the present invention provides an electricity forecasting platform management system based on power generation operation analysis. The power demand forecasting model after training is used to predict the power consumption status in the power supply area to generate a power supply forecasting data set. If it is predicted that there is an abnormal power supply, a multi-level alarm mechanism is enabled in the power supply area, and an alarm instruction is issued to the outside; the power supply area is divided into several sub-areas based on the key factors identified and obtained, and the corresponding response strategy is output by the response strategy library based on the user group portrait in the sub-area; the supply abnormality and abnormal threshold are constructed from the feedback data after the response strategy is executed, and the corresponding feedback measures are selected for the power supply area based on the relationship between the supply abnormality and the abnormal threshold. When there is an abnormality in power demand and supply in the power supply area, a targeted and personalized response is made quickly, thereby solving the technical problems raised in the background technology.

[0009] (II) Technical solution

[0010] To achieve the above objectives, the present invention is implemented through the following technical solutions: an electricity forecasting platform management system based on power generation operation analysis includes a demand analysis unit, which generates an imbalance value Spm after collecting power demand scenario data in the power supply area, and sends a data collection instruction to the outside if the imbalance value Spm exceeds expectations; wherein the imbalance value Spm is generated from the power data in the power demand scenario data set in the following manner:

[0011]

[0012] Where: N is the total number of regions, T is the total number of time periods, D i,t is the electricity demand of region i in time period t, S i,t is the power supply of region i in time period t, U i,k =D i,t -S i,t , is the power imbalance value of region i in time period t, α is the cumulative effect coefficient, and its value falls within [0,1], β is the time attenuation coefficient, and its value falls within [0,1]; k represents a past time period, ranging from 1 to t-1;

[0013] A prediction unit, which uses the trained power demand prediction model to predict the power consumption status in the power supply area and generates a power supply prediction data set;

[0014] The alarm unit, if it is predicted that there is an abnormality in the power supply, activates a multi-level alarm mechanism in the power supply area and issues an alarm command to the outside;

[0015] The strategy response unit divides the power supply area into several sub-areas based on the key factors identified and obtained, and outputs the corresponding response strategy from the response strategy library based on the user group portraits in the sub-areas;

[0016] The feedback unit constructs the supply abnormality Soj and the abnormality threshold [Ba, Bb] from the feedback data after executing the response strategy, and selects corresponding feedback measures for the power supply area according to the relationship between the supply abnormality Soj and the abnormality threshold [Ba, Bb].

[0017] Furthermore, after collecting the electricity demand data for each time period in the power supply area, the trained pattern recognition model is used to perform pattern recognition to obtain the peak and non-peak periods of electricity consumption; the electricity demand data and power supply data for peak and non-peak periods are collected separately, and summarized to generate a set of electricity demand scenario data.

[0018] Further, after receiving the data collection instruction, historical electricity consumption data and meteorological data in the power supply area are collected, and a historical electricity consumption scenario data set in the power supply area is generated; part of the data is extracted from the historical electricity consumption scenario data set as sample data, and a power demand forecasting model is obtained through training;

[0019] The trained electricity demand forecasting model is used to forecast the electricity demand during peak hours in the power supply area, and the forecast values ​​of electricity demand, duration and power supply load are obtained. The forecast data at multiple time nodes obtained continuously are aggregated to generate a power supply forecast data set.

[0020] Furthermore, if the most recent power supply load forecast value exceeds the load threshold, or if there are multiple consecutive power supply load forecast values, the power supply load forecast values ​​are arranged along the time axis. If the power supply load is on an upward trend, a first-level alarm instruction is issued to the outside. If the number of first-level alarm instructions received within the preset alarm cycle exceeds the expectation, a warning value D is generated based on the issuance status data of the first-level alarm instruction. M (X t ).

[0021] Furthermore, if the warning value D M (X t ) exceeds the warning threshold beyond expectations, and issues a secondary alarm command to the outside; warning value D M (X t ) is obtained as follows:

[0022]

[0023] Where: X t The standardized multivariate time series vector includes the standardized values ​​of the alarm instruction time series and the power supply load time series; A i is the regression coefficient matrix of the VAR model, which is used to describe the relationship between the lag term and the vector X t The influence of, p is the lag order of the VAR model, The inverse of the residual covariance matrix, μ e is the residual mean.

[0024] Furthermore, after receiving the second-level alarm command, the power supply and demand data, meteorological data, power usage scenario data and power usage data in the power supply area are collected, and the principal component analysis is performed on the power supply demand data to obtain the key factors affecting the power supply and the degree of influence of each key factor.

[0025] Furthermore, after obtaining the time, location information and impact degree of the key factors, a pre-trained clustering algorithm is used to classify several key factors, and the power supply area is divided into several sub-areas; an electronic map covering the power supply area is constructed, and each sub-area and the user information therein are marked on the electronic map.

[0026] Furthermore, after collecting user-related information, user profiling and behavior analysis are performed to obtain user group profiles; personalized response strategies are constructed for different user groups, including dynamic electricity price strategies, electricity consumption reward strategies, and power saving strategies: based on the correspondence between user group profiles and response strategies, the response strategy library outputs corresponding response strategies.

[0027] Furthermore, after the user group in the power supply area executes the response strategy, the power demand scenario data in each sub-area is collected within a preset observation period to generate an imbalance value Spm, and the supply anomaly Soj is constructed by several imbalance values. After continuously obtaining several supply anomaly degrees Soj along the time axis, the anomaly threshold [Ba, Bb] is constructed by the continuous supply anomaly degrees Soj.

[0028] Furthermore, if the supply anomaly Soj exceeds the anomaly threshold [Ba, Bb], several sub-areas with the most serious power supply gap are selected as power compensation areas, and the backup generator sets are activated to supply power to the power compensation areas;

[0029] If the supply anomaly Soj is within the anomaly threshold [Ba, Bb], the dispatching target is to reduce the supply anomaly Soj in the power supply area, and the balance of power supply and demand is used as a constraint. The pre-trained particle swarm optimization algorithm outputs the optimal power dispatching strategy, executes the power dispatching strategy, and dispatches the power in the non-power compensation area to the power compensation area;

[0030] If the supply abnormality Soj is lower than the abnormal threshold [Ba, Bb], no additional processing is performed on the power supply in the sub-area.

[0031] (III) Beneficial effects

[0032] The present invention provides an electricity forecasting platform management system based on power generation operation analysis, which has the following beneficial effects:

[0033] 1. The constructed imbalance value Spm can be used to judge and evaluate the imbalance between power supply and demand in the current power supply area. If the imbalance in power supply is high, it means that there may be a large gap in power supply in some areas, which needs to be adjusted in time.

[0034] 2. By issuing alarm instructions to external parties based on the judgment of the power supply load level, the power supply early warning process is completed, and timely processing can be carried out when the power supply is unbalanced.

[0035] 3. Build warning values ​​based on the release status data of the first-level alarm command and establish a second-level alarm mechanism to prevent a single alarm or static alarm from being ignored, ensure the reliability of the alarm mechanism, and switch and replace the release channels of the alarm command to make the alarm process smoother.

[0036] 4. Formulate corresponding feedback plans or adjustment plans based on key factors, and divide the power supply area into several sub-areas based on the differences in key factors in each area or location. When there are power supply problems, corresponding treatment methods can be adopted to improve the targeted treatment.

[0037] 5. By profiling users and obtaining corresponding user characteristics, targeted and personalized response strategies can be matched for the same user groups. When problems with power supply occur in the power supply area, targeted treatment can be carried out to prevent the existing power supply imbalance from becoming further aggravated.

[0038] 6. Based on the supply anomaly degree Soj, the effectiveness of the given response strategy can be judged as a whole, and through the dynamic changes of the supply anomaly degree Soj and the anomaly threshold [Ba, Bb], the current power supply status can be dynamically evaluated and maintained consistent with the actual status.

[0039] 7. According to the relationship between the supply abnormality Soj and the abnormal threshold [Ba, Bb], the corresponding feedback measures are selected for the power supply area, and the feedback data of the response measures are used as feedback to re-match the feedback measures for the power supply area, deal with the current persistent power supply imbalance, and alleviate the current power imbalance.

[0040] 8. By building a multi-level alarm and early warning mechanism and a multi-level response feedback mechanism, targeted and personalized responses can be made quickly when there are abnormalities in power demand and supply in the power supply area, which can alleviate and avoid the current or ongoing power supply imbalance problem. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 It is a flow chart of the method for managing an electric quantity prediction platform based on power generation operation analysis of the present invention;

[0042] Figure 2 It is a schematic diagram of the structure of the power forecasting platform management system based on power generation operation analysis of the present invention. DETAILED DESCRIPTION

[0043] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0044] See also Figure 1 The present invention provides a method for managing an electric quantity prediction platform based on power generation operation analysis, comprising:

[0045] Step 1: After collecting the power demand scenario data in the power supply area, an imbalance value Spm is generated. If the imbalance value Spm exceeds the expectation, a data collection instruction is issued to the outside;

[0046] The step 1 includes the following contents:

[0047] Step 101: training a convolutional neural network with the labeled data to obtain a trained pattern recognition model; after collecting power demand data in each time period in the power supply area, using the trained pattern recognition model to perform pattern recognition to obtain the peak power consumption period and non-peak power consumption period;

[0048] Collect power demand data, power supply data, and supply-demand imbalance data during peak and off-peak periods, and aggregate them to generate a power demand scenario data set;

[0049] When in use, the power demand data of each time period is collected, and the peak and non-peak periods of power consumption can be distinguished through data recognition. Different power supply feedback strategies can be adopted for different periods;

[0050] Step 102: Generate an imbalance value Spm from the power data in the power demand scenario data set in the following manner:

[0051]

[0052] Where: N is the total number of regions, T is the total number of time periods, D i,t is the electricity demand of region i in time period t, S i,t is the power supply of region i in time period t, U i,k =D i,t -S i,t , is the power imbalance value of region i in time period t, α is the cumulative effect coefficient, and its value falls within [0,1], β is the time attenuation coefficient, and its value falls within [0,1]; k represents a past time period, ranging from 1 to t-1;

[0053] Pre-set imbalance thresholds based on historical data and expectations for managing imbalances in power supply and demand;

[0054] If the imbalance value Spm exceeds the imbalance threshold, it means that the current power supply imbalance is high, which may cause some areas to be unable to obtain effective power supply, while some areas may have a large degree of surplus power supply, so timely adjustment is required. At this time, a data collection instruction is issued to the outside;

[0055] When using, combine the contents in steps 101 and 102:

[0056] After determining the power supply and demand data for each time period, the imbalance value Spm constructed can be used to judge and evaluate the degree of imbalance between power supply and demand in the current power supply area. If the imbalance in power supply is high, it means that there may be a large gap in power supply in some areas, which needs to be adjusted in time.

[0057] In the existing power forecasting management method, the power demand in the power supply area is predicted based on the meteorological conditions data and user power consumption data in the power supply area, and a timely response is made when there is an abnormality in the power supply to avoid further deepening of the current abnormal situation. However, when there is a certain degree of imbalance in the power supply and demand situation in the power supply area, in this scenario, if an overall response to the power supply abnormality in the power supply area is still made based on the power supply forecast, it will be difficult to alleviate the upcoming power supply abnormality problem.

[0058] Step 2: Use the trained electricity demand forecasting model to predict the electricity consumption status in the power supply area and generate a power supply forecast data set;

[0059] The step 2 includes the following contents:

[0060] Step 201: after receiving the data collection instruction, collect historical electricity consumption data and meteorological data in the power supply area, for example, historical electricity consumption data, monthly, quarterly and annual electricity consumption data segmented by industry, scale and region, electricity consumption data of special events (such as high temperature in summer, cold in winter, large-scale activities, etc.); electricity consumption data of various types of users (industrial, commercial, residential, agricultural); meteorological data: temperature (including daily maximum, minimum and average temperature), humidity, wind speed, precipitation, sunshine duration, atmospheric pressure, historical meteorological data and real-time meteorological monitoring data and meteorological forecast data, and summarize and generate a set of historical electricity consumption scene data in the power supply area;

[0061] Step 202: extract some data from the historical electricity consumption scenario data set as sample data, train a machine learning algorithm with the sample data, and obtain a trained electricity demand prediction model;

[0062] Use the trained electricity demand forecasting model to forecast the electricity demand during peak hours in the power supply area, obtain the forecast values ​​of power demand, duration and power supply load, and aggregate the forecast data at multiple time nodes obtained continuously to generate a power supply forecast data set;

[0063] When using, combine the contents in steps 201 and 202:

[0064] The electricity demand forecasting model obtained by training based on the collected data can respond to the electricity demand data in the next several time periods, such as peak hours and non-peak hours, in advance when there is a gap in electricity supply, for example, by turning on backup power generation equipment;

[0065] Step 3: If power supply anomalies are predicted, a multi-level alarm mechanism is activated in the power supply area, and an alarm instruction is issued to the outside;

[0066] The step three includes the following contents:

[0067] Step 301: Set the load threshold for the peak period according to historical data. If the most recent power supply load forecast value exceeds the load threshold, or if there are multiple consecutive power supply load forecast values, arrange the power supply load forecast values ​​along the time axis. If the power supply load is on an upward trend, issue a first-level alarm instruction to the outside.

[0068] When in use, after obtaining the load data in the power supply area, it is judged whether the power supply load is gradually increasing based on the relationship between the obtained load data and the load threshold. By issuing an alarm instruction to the external part based on the judgment of the power supply load degree, the power supply early warning process is completed, and timely processing can be carried out when the power supply is unbalanced;

[0069] Step 302: If the number of first-level alarm instructions received within the preset alarm period exceeds the expected number, after standardization, a warning value D is generated based on the state data of the first-level alarm instructions. M (X t ), as follows:

[0070]

[0071] Where: X t The standardized multivariate time series vector includes the standardized values ​​of the alarm instruction time series and the power supply load time series; A i is the regression coefficient matrix of the VAR model, which is used to describe the lag term when the vector x t The influence of, p is the lag order of the VAR model, The inverse of the residual covariance matrix, μ e is the residual mean;

[0072] Pre-set warning thresholds based on historical data and management expectations of the degree of power supply imbalance;

[0073] If the warning value D M (X t ) If the warning threshold is exceeded beyond expectations, it means that the current power supply imbalance is serious, and a second-level alarm command is issued to the outside;

[0074] When using, combine the contents in steps 301 and 302:

[0075] Based on the release status data of the first-level alarm instruction, the warning value is constructed and the second-level alarm mechanism is established to prevent a single alarm or a static alarm from being ignored and ensure the reliability of the alarm mechanism. The release channel of the alarm instruction can also be switched and replaced to prevent the first-level alarm instruction from being ignored due to a fault in the current notification channel, making the alarm process smoother.

[0076] Step 4: Divide the power supply area into several sub-areas based on the key factors identified and obtained, and output the corresponding response strategy from the response strategy library based on the user group portraits in the sub-areas;

[0077] The step 4 includes the following contents:

[0078] Step 401: After receiving the second-level alarm instruction, collect power supply and demand data, meteorological data, power usage scenario data and power usage data in the power supply area, perform principal component analysis on the power supply demand data, and obtain key factors affecting power supply and the degree of influence of each key factor;

[0079] Step 402: after obtaining the time, location information and impact degree of the key factors, a pre-trained clustering algorithm is used to classify the key factors, and the power supply area is divided into a plurality of sub-areas; an electronic map covering the power supply area is constructed, and each sub-area and the user information therein are marked on the electronic map;

[0080] When in use, principal component analysis is enabled after the multi-level alarm mechanism is enabled. The main factors or key factors that will affect the power supply load in the power supply area can be adjusted to improve the current power load level and the dilemma of power supply imbalance when the conditions are met. Correspondingly, corresponding feedback plans or adjustment plans can be formulated based on the key factors, and the power supply area can be divided into several sub-areas based on the different key factors in each area or location. When there are power supply problems, corresponding treatment methods can be adopted respectively to improve the targeted treatment.

[0081] Step 403: After collecting user-related information, such as power consumption, power consumption time period, power consumption type and purpose, user group portraits are obtained after user portrait and behavior analysis;

[0082] Build personalized response strategies for different user groups, including dynamic electricity price strategy: adjust electricity prices according to peak load conditions, electricity reward strategy, point reward, cash back, etc., to encourage users to reduce electricity consumption during peak hours; power saving strategy: provide energy-saving suggestions and power optimization solutions, and summarize the above response strategies to generate a response strategy library; according to the correspondence between user group portraits and response strategies, the response strategy library outputs the corresponding response strategy;

[0083] When using, combine the contents in steps 401 to 403:

[0084] When in use, the power demand and supply in the power supply area are predicted, and on the basis of obtaining several sub-areas, by profiling the users and obtaining the corresponding user characteristics, targeted and personalized response strategies can be matched for the same user groups. When problems arise in the power supply in the power supply area, they can be dealt with in a targeted manner so that the existing power supply imbalance will not be further aggravated.

[0085] Step 5: construct the supply anomaly degree Soj and the anomaly threshold [Ba, Bb] from the feedback data after executing the response strategy, and select the corresponding feedback measures for the power supply area according to the relationship between the supply anomaly degree Soj and the anomaly threshold [Ba, Bb];

[0086] The step five includes the following contents:

[0087] Step 501: After the user group in the power supply area executes the response strategy, the power demand scenario data in each sub-area is collected within a preset observation period to generate an imbalance value Spm, and the corresponding sub-area is marked with the imbalance value. The supply anomaly degree Soj is constructed by a plurality of imbalance values, wherein the imbalance value Spm is linearly normalized according to the following method:

[0088]

[0089] Weight coefficient: 0≤V1≤1, 0≤V2≤1 and V2+V1=1; i=1, 2, …, k, k is the number of sub-regions, Spm i is the imbalance value of the ith sub-region, Spm a is its mean value, is the qualified reference value for the imbalance value;

[0090] After obtaining several supply anomaly degrees Soj continuously along the time axis, the anomaly threshold [Ba, Bb] is constructed by the continuous supply anomaly degrees Soj as follows:

[0091]

[0092] Among them, Soj max is the maximum value of supply anomaly, Soj min is the minimum value of supply anomaly, Soj a is the mean of supply anomaly, Soj i is the supply anomaly degree in the i-th sub-period;

[0093] When in use, after taking corresponding response strategies in each sub-area respectively, corresponding feedback data is taken, and the supply abnormality Soj is constructed by the corresponding feedback data. According to the supply abnormality Soj, the current degree of power supply imbalance in the power supply area can be judged as a whole, and the effectiveness of the response strategy given before can be judged; and through the dynamic changes of the supply abnormality Soj and the abnormal threshold [Ba, Bb], the dynamic evaluation of the current power supply status is realized, and the consistency with the actual status is maintained.

[0094] Step 502: Select corresponding feedback measures for the power supply area according to the relationship between the supply abnormality Soj and the abnormal threshold [Ba, Bb], where:

[0095] If the supply anomaly Soj exceeds the anomaly threshold [Ba, Bb], several sub-areas with the most serious power supply gap are selected as power compensation areas, and the backup generator sets are activated to supply power to the power compensation areas;

[0096] If the supply anomaly Soj is within the anomaly threshold [Ba, Bb], the dispatching target is to reduce the supply anomaly Soj in the power supply area, and the balance of power supply and demand is used as a constraint. The pre-trained particle swarm optimization algorithm outputs the optimal power dispatching strategy, executes the power dispatching strategy, and dispatches the power in the non-power compensation area to the power compensation area;

[0097] If the supply anomaly degree Soj is lower than the anomaly threshold [Ba, Bb], no additional processing is performed on the power supply in the sub-area;

[0098] When using, combine the contents in steps 501 to 502:

[0099] After taking personalized response plans for each sub-area, by constructing the supply anomaly degree Soj as feedback, and selecting corresponding feedback measures for the power supply area based on the relationship between the supply anomaly degree Soj and the anomaly threshold [Ba, Bb], starting from the entire power supply area again, the feedback data of a response measure is used as feedback, and the feedback measures are re-matched for the power supply area to deal with the current persistent power supply imbalance and alleviate the current power imbalance.

[0100] In combination with the above application, when there is an imbalance between the current power supply and demand in the power supply area, on the basis of predicting the power supply and demand data, by constructing a multi-level alarm and early warning mechanism and a multi-level response and feedback mechanism, when there are abnormalities in power demand and supply in the power supply area, targeted and personalized responses can be made quickly, which can alleviate and avoid the current or ongoing power supply imbalance problem.

[0101] See also Figure 2 The present invention provides an electricity forecasting platform management system based on power generation operation analysis, including:

[0102] The demand analysis unit collects the power demand scenario data in the power supply area and generates an imbalance value Spm. If the imbalance value Spm exceeds the expectation, a data collection instruction is issued to the outside.

[0103] A prediction unit, which uses the trained power demand prediction model to predict the power consumption status in the power supply area and generates a power supply prediction data set;

[0104] The alarm unit, if it is predicted that there is an abnormality in the power supply, activates a multi-level alarm mechanism in the power supply area and issues an alarm command to the outside;

[0105] The strategy response unit divides the power supply area into several sub-areas based on the key factors identified and obtained, and outputs the corresponding response strategy from the response strategy library based on the user group portraits in the sub-areas;

[0106] The feedback unit constructs the supply abnormality Soj and the abnormality threshold [Ba, Bb] from the feedback data after executing the response strategy, and selects corresponding feedback measures for the power supply area according to the relationship between the supply abnormality Soj and the abnormality threshold [Ba, Bb].

[0107] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0108] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0109] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only some logical function divisions. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0110] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0111] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. The power forecasting platform management system based on power generation operation analysis is characterized by: include, The demand analysis unit generates an imbalance value Spm after collecting the power demand scenario data in the power supply area. If the imbalance value Spm exceeds the expectation, a data collection instruction is issued to the outside. The imbalance value Spm is generated by the power data in the power demand scenario data set in the following manner: Where: N is the total number of regions, T is the total number of time periods, D i,t is the electricity demand of region i in time period t, S i,t is the power supply of region i in time period t, U i,k =D i,t -S i,t , is the power imbalance value of region i in time period t, α is the cumulative effect coefficient, and its value falls within [0,1], β is the time attenuation coefficient, and its value falls within [0,1]; k represents a past time period, ranging from 1 to t-1; A prediction unit, which uses the trained power demand prediction model to predict the power consumption status in the power supply area and generates a power supply prediction data set; The alarm unit, if it is predicted that there is an abnormality in the power supply, activates a multi-level alarm mechanism in the power supply area and issues an alarm command to the outside; The strategy response unit divides the power supply area into several sub-areas based on the key factors identified and obtained, and outputs the corresponding response strategy from the response strategy library based on the user group portraits in the sub-areas; The feedback unit constructs the supply abnormality Soj and the abnormality threshold [Ba, Bb] from the feedback data after executing the response strategy, and selects corresponding feedback measures for the power supply area according to the relationship between the supply abnormality Soj and the abnormality threshold [Ba, Bb].

2. The power forecasting platform management system based on power generation operation analysis according to claim 1, characterized in that: After collecting the power demand data of each period in the power supply area, the trained pattern recognition model is used to perform pattern recognition to obtain the peak and non-peak power consumption periods; The power demand data and power supply data during peak and non-peak periods are collected separately, and summarized to generate a power demand scenario data set.

3. The power forecasting platform management system based on power generation operation analysis according to claim 2, characterized in that: After receiving the data collection instruction, the historical electricity consumption data and meteorological data in the power supply area are collected, and the historical electricity consumption scene data set in the power supply area is generated; Extract some data from the historical electricity consumption scenario data set as sample data to train and obtain the electricity demand prediction model; The trained electricity demand forecasting model is used to forecast the electricity demand during peak hours in the power supply area, and the forecast values ​​of electricity demand, duration and power supply load are obtained. The forecast data at multiple time nodes obtained continuously are aggregated to generate a power supply forecast data set.

4. The power forecasting platform management system based on power generation operation analysis according to claim 3 is characterized in that: If the most recent power supply load forecast value exceeds the load threshold, or there are multiple consecutive power supply load forecast values, the power supply load forecast values ​​are arranged along the time axis. If the power supply load is on an upward trend, a first-level alarm instruction is issued to the outside; if the number of first-level alarm instructions received within the preset alarm cycle exceeds the expectation, a warning value D is generated based on the issuance status data of the first-level alarm instruction. M (X t ).

5. The power forecasting platform management system based on power generation operation analysis according to claim 4, characterized in that: If the warning value D M (X t ) exceeds the warning threshold beyond expectations, and issues a secondary alarm command to the outside; among which, the warning value D M (X t ) is obtained as follows: Where: X t The standardized multivariate time series vector includes the standardized values ​​of the alarm instruction time series and the power supply load time series; A i is the regression coefficient matrix of the VAR model, which is used to describe the relationship between the lag term and the vector X t The influence of, p is the lag order of the VAR model, The inverse of the residual covariance matrix, μ e is the residual mean.

6. The power forecasting platform management system based on power generation operation analysis according to claim 5, characterized in that: After receiving the second-level alarm command, the power supply and demand data, meteorological data, power usage scenario data and power usage data in the power supply area are collected, and the principal component analysis is performed on the power supply demand data to obtain the key factors affecting power supply and the degree of influence of each key factor.

7. The power forecasting platform management system based on power generation operation analysis according to claim 6 is characterized in that: After obtaining the time, location information and impact degree of the key factors, a pre-trained clustering algorithm is used to classify several key factors, and then the power supply area is divided into several sub-areas; an electronic map covering the power supply area is constructed, and each sub-area and the user information within it are marked on the electronic map.

8. The power forecasting platform management system based on power generation operation analysis according to claim 7, characterized in that: After collecting user-related information, conduct user profiling and behavior analysis to obtain user group profiles; Build personalized response strategies for different user groups, including dynamic electricity price strategies, electricity consumption reward strategies and electricity saving strategies. According to the correspondence between user group portraits and response strategies, the response strategy library outputs the corresponding response strategies.

9. The power forecasting platform management system based on power generation operation analysis according to claim 8, characterized in that: After the user group in the power supply area executes the response strategy, the power demand scenario data in each sub-area is collected within the preset observation period to generate an imbalance value Spm, and the supply anomaly Soj is constructed by several imbalance values ​​Spm. After continuously obtaining several supply anomaly degrees Soj along the time axis, the anomaly threshold [Ba, Bb] is constructed by the continuous supply anomaly degrees Soj.

10. The power forecasting platform management system based on power generation operation analysis according to claim 9, characterized in that: If the supply anomaly degree Soj is lower than the anomaly threshold [Ba, Bb], no additional processing is performed on the power supply in the sub-area; If the supply anomaly Soj is within the anomaly threshold [Ba, Bb], the dispatching target is to reduce the supply anomaly Soj in the power supply area, and the balance of power supply and demand is used as a constraint. The pre-trained particle swarm optimization algorithm outputs the optimal power dispatching strategy, executes the power dispatching strategy, and dispatches the power in the non-power compensation area to the power compensation area; If the supply anomaly Soj exceeds the anomaly threshold [Ba, Bb], several sub-areas with the most serious power supply gap are selected as power compensation areas, and the backup generator sets are enabled to supply power to the power compensation areas.

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