Method and system for predicting and adjusting power load of user side by using big data

Abstract

The invention relates to the technical field of power management, and discloses a user side power load prediction adjustment method and system using big data, and the method comprises the steps: constructing a power utilization scene library, carrying out the real-time data collection, generating power utilization real-time combined data, generating a first prediction result, and generating a second prediction result. The system corresponds to the method. According to the application, through distinguishing the electricity storage type user side and the non-electricity storage type user side, the electricity utilization combination condition of different types of users under different electricity utilization situations and the introduction of the electricity storage real-time data are fully considered, and the real-time electricity storage state change of the electricity storage type user side is reflected in the generation process of the electricity utilization real-time prediction data and the second prediction result; the defect that a traditional method does not consider the power storage factor is effectively overcome, the accuracy of power load prediction in a complex power utilization environment is remarkably improved, and powerful support is provided for stable operation of a power grid and reasonable resource allocation.

Description

Technical Field

[0001] The present application relates to the field of power management technology, and specifically to a method and system for predicting and adjusting power load at a user end using big data. Background Art

[0002] With the rapid development of the power industry, power load forecasting has become a key link in ensuring the stable operation of the power grid. Traditional power load forecasting technology has the following problems when facing complex and changeable user power consumption behaviors: previous methods usually simply analyze based on historical power consumption data, and fail to fully consider the impact of different power consumption scenarios on the load. Specifically, in special scenarios such as sudden weather changes, holidays, large-scale events, and charging of new energy equipment (such as new energy vehicles), the power demand at the user end will change significantly, but existing technologies are difficult to accurately identify and analyze these changes, resulting in large deviations in the prediction results.

[0003] There are some methods of utilizing big data in the prior art. For example, Chinese patent number CN118889402A discloses a power load forecasting method and system driven by big data. Although it integrates regional power site data and population density data to a certain extent, it is unable to deeply analyze the power consumption scenario corresponding to the power load at the user end during the analysis process, making the analysis of the user end not accurate enough. This leads to the inability to provide a reliable basis for the reasonable scheduling of the power grid and the effective allocation of power resources in actual applications, which seriously affects the safe and stable operation of the power grid and the power supply quality.

[0004] In summary, there is an urgent need for a new technical solution for user-side power load forecasting and adjustment using big data to solve the above technical problems. Summary of the invention

[0005] The purpose of this application is to provide a method and system for predicting and adjusting user-side power load using big data to solve the technical problems raised in the above-mentioned background technology.

[0006] To achieve the above objectives, this application discloses the following technical solutions: In a first aspect, the present application discloses a method for predicting and adjusting user-side power load using big data, the method comprising: A power usage scenario library is constructed based on the collected power usage history data of the user terminals; wherein the user terminals include power storage user terminals and non-power storage user terminals, the power storage user terminals have power storage capabilities, and the non-power storage user terminals do not have power storage capabilities, the power usage history data are used to characterize the independent power usage of the historical user terminals, and the power usage scenario library stores power usage scenarios formed when different user terminals work at the same time, and the power usage scenarios are used to characterize the combined power usage of different user terminals at the same time; Collecting the real-time power consumption data of the user terminal, and when the user terminal is the power storage user terminal, collecting the corresponding real-time power storage data; wherein the real-time power consumption data is used to characterize the real-time independent power consumption of the user terminal, and the real-time power storage data is used to characterize the real-time power storage of the power storage user terminal; The power usage scenario library is used to match the power usage scenario corresponding to the real-time power usage data, and real-time power usage combination data is generated based on the power usage scenario; wherein the real-time power usage combination data is used to characterize the real-time combined power usage of different user terminals at the same time; Using a preset power load forecasting algorithm, and based on the real-time combined power consumption data and the corresponding power consumption scenario, a first forecasting result is generated; wherein the power load forecasting algorithm is used to perform power load forecasting, and the first forecasting result is a result of extensive power forecasting; The real-time electricity storage data and the real-time electricity consumption combined data are used to generate real-time electricity consumption forecast data, and the real-time electricity consumption forecast data, the first forecast result and the power load forecasting algorithm are used to generate a second forecast result; wherein the real-time electricity consumption forecast data is used to characterize the real-time, predicted combined electricity consumption of different user ends, taking into account the simultaneous electricity consumption under the changes of the real-time electricity storage data, and the second forecast result is the result of a refined electricity forecast adjusted after taking into account the real-time electricity storage data.

[0007] Preferably, the construction of the electricity usage scenario library includes: The collected historical electricity consumption data is cleaned, and a first clustering is performed on the cleaned data based on a preset electricity consumption pattern. A second clustering is performed after adding a description of electricity consumption to the result of the first clustering to obtain electricity consumption scenarios, and a database is built using the electricity consumption scenarios to obtain an electricity consumption scenario database; wherein the electricity consumption pattern at least includes the type, quantity and power range of user terminals under the mode, and the electricity consumption description at least includes the electricity consumption time distribution characteristics and charge and discharge characteristics of the user terminals under the electricity consumption scenario, and the electricity consumption time distribution characteristics are used to characterize the electricity consumption time distribution of different user terminals, and the charge and discharge characteristics are used to characterize the charge and discharge conditions of the energy storage user terminals.

[0008] Preferably, the power storage user terminal at least includes energy storage equipment in the power grid and new energy equipment consisting of a new energy vehicle owned by the user.

[0009] Preferably, the collection of real-time electricity storage data includes: The real-time power storage data of the energy storage device and the new energy device are correspondingly collected by using the power collection devices preset in the energy storage device and the new energy device.

[0010] Preferably, the matching of the electricity usage scenarios includes: Extracting the real-time pattern features and real-time scenario features of the real-time power consumption data, calculating the similarity between the real-time power consumption data and the power consumption scenario based on the real-time pattern features and the real-time scenario features, and selecting the power consumption scenario with the highest similarity as the matching result; wherein the real-time pattern features at least include the features corresponding to the type, quantity and power consumption range of the real-time user terminals, and the real-time scenario features at least include the power consumption time distribution features and charging and discharging features of the real-time user terminals; The extraction of the electricity consumption time distribution characteristics includes: Based on the distribution of electricity consumption behaviors of different users over time within a preset prediction period, the electricity consumption concentration and electricity consumption patterns at different users at different times are extracted to obtain the electricity consumption time distribution characteristics; The extraction of the charge and discharge characteristics includes: Based on the charging and discharging behavior of the energy storage user terminal, the impact of the charging and discharging behavior on the overall power load and the impact of power load forecasting and adjustment are extracted to obtain the charging and discharging characteristics; wherein the charging and discharging behavior at least includes charging and discharging power, charging and discharging capacity, charging and discharging efficiency and charging and discharging time rules.

[0011] Preferably, the generation of the real-time combined electricity consumption data includes: According to the matched electricity usage scenario, the user terminals involved are determined to obtain the corresponding real-time electricity usage data, and the obtained real-time electricity usage data is used to construct a real-time electricity usage combined data matrix. The matrix elements of the real-time electricity usage combined data matrix are the real-time mode characteristics of each user terminal. The real-time electricity usage combined data matrix is ​​defined as the real-time electricity usage combined data and output.

[0012] Preferably, the generating of the first prediction result comprises: The real-time combined power consumption data is input into the power load forecasting algorithm, an initial forecast result with a time series is output, and the initial forecast result is corrected using the power consumption scenario to obtain the first forecast result; wherein, the initial forecast result is corrected using the power consumption scenario as follows: The first prediction result is obtained by using the initial prediction result correction formula, and the initial prediction result correction formula is: in, for The initial prediction value at time, for A correction factor based on the power usage scenario at the time, the correction factor is used to characterize the influence of the power usage scenario on the real-time power usage data, For the calculated The first prediction value at a certain moment, wherein the first prediction result is composed of the first prediction value based on a time series.

[0013] Preferably, the generation of the real-time electricity consumption forecast data includes: The real-time power consumption forecast data is calculated using the real-time power consumption forecast data calculation formula, and the real-time power consumption forecast data calculation formula is: in, Indicated in Moment Client Real-time data on electricity consumption, express Moment Client The total number of user terminals in the corresponding real-time combined power consumption data and , Indicated in The predicted adjustment value of the electricity storage user terminal based on the real-time data of electricity storage is obtained at all times, and the predicted adjustment value is obtained based on the electricity usage scenario. For the calculated The real-time prediction value of electricity consumption at a certain moment, wherein the real-time prediction data of electricity consumption is composed of the real-time prediction value of electricity consumption based on a time series.

[0014] Preferably, the generating of the second prediction result comprises: The second prediction result is calculated using the second prediction result calculation formula, wherein the second prediction result calculation formula is: in, The first prediction result corresponds to The first predicted value at time, The preset forecast period The confidence of the real-time power consumption prediction data obtained by the generated error rate is obtained based on a preset confidence query table, in which the confidence of the real-time power consumption prediction data corresponding to different error rates is stored, and the preset prediction period ,in, is the starting time corresponding to the prediction period, is the end time corresponding to the prediction period, The real-time electricity consumption forecast data, For the calculated The second prediction value at the time instant, the second prediction result is composed of the second prediction value based on the time series.

[0015] In a second aspect, the present application discloses a user-side power load prediction and adjustment system using big data, the system is applicable to the user-side power load prediction and adjustment method using big data as described above, the system includes a communication-connected power usage scenario library module, a real-time data acquisition module, a real-time combined data generation module, a first prediction module and a second prediction module; The power usage scenario library module is configured to construct a power usage scenario library based on the collected power usage history data of the user terminal; wherein the user terminal includes a power storage user terminal and a non-power storage user terminal, the power storage user terminal has a power storage capability, and the non-power storage user terminal does not have a power storage capability, the power usage history data is used to characterize the independent power usage of the historical user terminal, and the power usage scenario library stores power usage scenarios formed when different user terminals work at the same time, and the power usage scenarios are used to characterize the combined power usage of different user terminals at the same time; The real-time data acquisition module is configured to: collect the real-time power consumption data of the user terminal, and when the user terminal is the power storage user terminal, collect the corresponding real-time power storage data; wherein the real-time power consumption data is used to characterize the real-time independent power consumption of the user terminal, and the real-time power storage data is used to characterize the real-time power storage of the power storage user terminal; The real-time combined data generation module is configured to: use the power usage scenario library to match the power usage scenario corresponding to the real-time power usage data, and generate real-time combined power usage data based on the power usage scenario; wherein the real-time combined power usage data is used to represent the combined power usage of different user terminals at the same time in real time; The first prediction module is configured to: generate a first prediction result by using a preset power load prediction algorithm and based on the real-time combined power consumption data and the corresponding power consumption scenario; wherein the power load prediction algorithm is used to perform power load prediction, and the first prediction result is a result of extensive power prediction; The second prediction module is configured to: generate real-time electricity consumption prediction data using the real-time electricity storage data and the real-time electricity consumption combined data, and generate a second prediction result using the real-time electricity consumption prediction data, the first prediction result and the power load prediction algorithm; wherein the real-time electricity consumption prediction data is used to characterize the real-time, predicted combined electricity consumption of different user terminals, taking into account the simultaneous electricity consumption under the changes of the real-time electricity storage data, and the second prediction result is the result of a refined electricity prediction adjusted after taking into account the real-time electricity storage data.

[0016] Beneficial effects: The user-side power load forecasting and adjustment method and system using big data of the present application use the collected user-side power consumption historical data to build a power consumption scenario library, and combine the real-time power consumption data with the real-time power storage data to achieve a comprehensive analysis and accurate forecasting and adjustment of the user-side power load; by distinguishing between power storage and non-power storage user terminals, the power consumption combinations of different types of users in different power consumption scenarios are fully considered, and the introduction of real-time power storage data reflects the real-time power storage status changes of the power storage user terminal in the process of generating the real-time power consumption forecast data and the second forecast result, which effectively makes up for the deficiency of the traditional method that does not consider the power storage factor, and significantly improves the accuracy of power load forecasting in complex power consumption environments, providing strong support for the stable operation of the power grid and the rational allocation of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the embodiments of the present application or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present application. For those skilled in the art, other drawings can be obtained based on these drawings without paying any creative work.

[0018] Figure 1 A flowchart of a method for predicting and adjusting user-side power load using big data provided in an embodiment of the present application; Figure 2 A structural block diagram of a user-side power load prediction and adjustment system using big data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present application.

[0020] In this article, the term "comprising" is intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of more restrictions, the elements defined by the sentence "comprising..." do not exclude the presence of other identical elements in the process, method, article or device including the elements.

[0021] The first aspect of this embodiment discloses Figure 1 A method for predicting and adjusting user-side power load using big data is shown, the method comprising: A power usage scenario library is constructed based on the collected power usage history data of the user terminals; wherein the user terminals include power storage user terminals and non-power storage user terminals, the power storage user terminals have power storage capabilities, and the non-power storage user terminals do not have power storage capabilities, the power usage history data are used to characterize the independent power usage of the historical user terminals, and the power usage scenario library stores power usage scenarios formed when different user terminals work at the same time, and the power usage scenarios are used to characterize the combined power usage of different user terminals at the same time; Collect the real-time power consumption data of the user end, and when the user end is a power storage user end, collect the corresponding real-time power storage data; wherein the real-time power consumption data is used to characterize the real-time independent power consumption of the user end, and the real-time power storage data is used to characterize the real-time power storage of the power storage user end; The power consumption scenario library is used to match the power consumption scenario corresponding to the real-time power consumption data, and the real-time power consumption combined data is generated based on the power consumption scenario; wherein the real-time power consumption combined data is used to characterize the real-time combined power consumption of different user terminals at the same time; Using a preset power load forecasting algorithm, and based on the real-time combined power consumption data and its corresponding power consumption scenario, a first forecasting result is generated; wherein the power load forecasting algorithm is used to perform power load forecasting, and the first forecasting result is a result of extensive power forecasting; Real-time electricity consumption prediction data are generated using the real-time electricity storage data and the real-time electricity consumption data, and the second prediction result is generated using the real-time electricity consumption prediction data, the first prediction result and the power load prediction algorithm; wherein the real-time electricity consumption prediction data is used to characterize the real-time and predicted combined electricity consumption of different user ends, taking into account the changes in the real-time electricity storage data, and the second prediction result is the result of the refined electricity prediction adjusted after taking into account the real-time electricity storage data.

[0022] Based on the above, this embodiment fully considers the electricity usage combinations of different types of users in different electricity usage scenarios by distinguishing between electricity storage and non-electricity storage user terminals. The introduction of real-time electricity storage data reflects the real-time storage status changes of the electricity storage user terminals in the process of generating real-time electricity consumption prediction data and the second prediction results, which effectively makes up for the deficiency of traditional methods that do not consider the electricity storage factor, significantly improves the accuracy of power load prediction in complex electricity usage environments, and provides strong support for the stable operation of the power grid and the rational allocation of resources.

[0023] Specifically, the construction of the electricity usage scenario library includes: The collected historical electricity consumption data is cleaned, and a first clustering is performed on the cleaned data based on a preset electricity consumption pattern. A second clustering is performed after adding a description of the electricity consumption situation to the result of the first clustering to obtain electricity consumption scenarios, and a database is built using the electricity consumption scenarios to obtain an electricity consumption scenario database; wherein the electricity consumption pattern at least includes the type, quantity and power range of the user terminals under the mode, and the electricity consumption situation description at least includes the electricity consumption time distribution characteristics and charge and discharge characteristics of the user terminals under the electricity consumption situation, the electricity consumption time distribution characteristics are used to characterize the electricity consumption time distribution situation of different user terminals, and the charge and discharge characteristics are used to characterize the charge and discharge situation of the storage-type user terminals.

[0024] Based on the above, this embodiment uses existing data cleaning and clustering technologies to clean and cluster historical electricity consumption data, and uses the added electricity situation description to build an electricity consumption scenario library, which achieves a detailed description of different electricity consumption scenarios. Its clustering operation based on electricity consumption mode and electricity situation description makes the classification of electricity consumption scenarios more accurate and reasonable, and can better capture the diversity and complexity of user-side electricity consumption behaviors. This not only helps to accurately match subsequent electricity consumption scenarios, but also lays the foundation for considering the impact of various factors on the load in the process of power load forecasting. In particular, when combined with real-time storage data, it can more accurately analyze the impact of the charging and discharging behavior of the storage user side on the overall load, thereby further improving the accuracy of power load forecasting and ensuring the reliability of power grid operation.

[0025] Specifically, the electricity storage user end includes at least energy storage equipment in the power grid and new energy equipment consisting of new energy vehicles owned by the user.

[0026] From the above, this embodiment realizes the effective integration and targeted analysis of power storage resources by clarifying that power storage users include power grid energy storage equipment and new energy vehicles and other new energy equipment. By incorporating these common power storage users into the research scope, the impact of power storage factors on power load can be more comprehensively considered in the subsequent data collection and prediction process. Especially in the collection of real-time power consumption data and real-time power storage data, special data collection is carried out for the special properties of these power storage users, which provides an important data basis for improving the accuracy of power load prediction, so that the prediction results can better adapt to the power environment where new energy equipment is widely used, and promote the stable operation of the power grid.

[0027] Specifically, the collection of real-time data of power storage includes: The real-time data of power storage of the energy storage equipment and the new energy equipment are collected by using the power collection devices preset in the energy storage equipment and the new energy equipment.

[0028] Based on the above, this embodiment utilizes the existing power collection device preset in the energy storage equipment and the new energy equipment to realize the accurate collection of the real-time data of power storage, so as to obtain the power storage status of the energy storage equipment and the new energy vehicle in real time, and ensure the timeliness and accuracy of the real-time data of power storage. Among them, the collection of new energy equipment can be but not limited to collecting its SOC data by using the plug-in installed in the new energy vehicle terminal software, so as to obtain the real-time energy storage data of different users. It is understandable that with the promotion of renewable energy power generation, the promotion of energy storage equipment is obvious. Furthermore, with the popularization of user terminals with power storage capabilities such as new energy vehicles, the centralized charging and discharging behavior of the user terminals will have a significant impact on the power load forecasting and adjustment process. The centralized charging and discharging behavior of the power storage user terminals has significant time characteristics (which depends on the new energy power generation method, such as photovoltaic power generation, and also depends on the user's centralized charging of new energy equipment, such as charging in the evening after get off work). Therefore, accurate real-time power storage data is crucial. It can provide key input information for the generation of subsequent real-time power consumption forecast data and the second forecast results, so that the power load forecasting adjustment can fully consider the changes in the charging and discharging capabilities of the power storage user terminals, and effectively correct the forecast results, thereby significantly improving the accuracy of power load forecasting and enhancing the ability of the power grid to cope with changes in power storage equipment.

[0029] Specifically, the matching of electricity usage scenarios includes: Extracting real-time pattern features and real-time scenario features of real-time power consumption data, calculating the similarity between the real-time power consumption data and the power consumption scenario based on the real-time pattern features and the real-time scenario features, and selecting the power consumption scenario with the highest similarity as the matching result; wherein the real-time pattern features at least include the features corresponding to the type, quantity and power consumption range of the real-time user terminals, and the real-time scenario features at least include the power consumption time distribution features and charging and discharging features of the real-time user terminals; The extraction of electricity consumption time distribution features includes: Based on the distribution of electricity consumption behaviors of different users over time within a preset prediction period, the electricity consumption concentration and electricity consumption patterns at different users at different times are extracted to obtain the electricity consumption time distribution characteristics; The extraction of charge and discharge characteristics includes: Based on the charging and discharging behavior of the energy storage user end, the impact of the charging and discharging behavior on the overall power load and the impact of the power load forecast adjustment are extracted to obtain the charging and discharging characteristics; wherein the charging and discharging behavior at least includes the charging and discharging power, charging and discharging capacity, charging and discharging efficiency and charging and discharging time rules.

[0030] Based on the above, this embodiment utilizes the real-time mode features and real-time scenario features extracted from the real-time data of electricity consumption, and calculates the similarity based on these features and the existing similarity analysis technology to match the electricity consumption scenarios, thereby achieving efficient and accurate matching of electricity consumption scenarios. In the process of extracting features, the detailed analysis of the time distribution characteristics of electricity consumption and the charging and discharging characteristics fully considers the time regularity of the user-side electricity consumption behavior and the charging and discharging characteristics of the power storage user side. By accurately matching the electricity consumption scenarios, it can provide a reliable basis for the generation of subsequent real-time combined electricity consumption data, and thus better combine the actual electricity consumption in the power load forecast. In particular, the comprehensive use of real-time storage data and these features further optimizes the prediction process, improves the accuracy of power load forecasting, and enhances the pertinence of power grid regulation.

[0031] Specifically, the generation of real-time combined electricity consumption data includes: According to the matched electricity consumption scenarios, the user terminals involved are determined to obtain the corresponding real-time electricity consumption data, and the obtained real-time electricity consumption data are used to construct a real-time electricity consumption combined data matrix. The matrix elements of the real-time electricity consumption combined data matrix are the real-time mode characteristics of each user terminal. The real-time electricity consumption combined data matrix is ​​defined as the real-time electricity consumption combined data and output.

[0032] Based on the above, this embodiment uses the matching power consumption scenario to determine the user end and construct a real-time combined power consumption data matrix, thereby realizing the effective integration and structured representation of real-time power consumption data of different user ends. The real-time power consumption data of the relevant user ends are accurately screened out according to the power consumption scenario, and organized into a matrix form, so that the data can be more conveniently processed in the subsequent power load forecasting algorithm. In this process, the real-time mode characteristics, as matrix elements, fully reflect the power consumption of the user end. Combined with the real-time data of power storage, the impact of the power storage user end can be more comprehensively considered, thereby improving the input quality of the power load forecasting algorithm, and ultimately improving the accuracy of the power load forecasting and ensuring the stability of the power grid operation.

[0033] Specifically, the generation of the first prediction result includes: The real-time combined power consumption data is input into the power load forecasting algorithm, an initial forecast result with a time series is output, and the initial forecast result is corrected by the power consumption scenario to obtain a first forecast result; wherein, the initial forecast result corrected by the power consumption scenario is: The first prediction result is obtained by using the initial prediction result correction formula, and the initial prediction result correction formula is: in, for The initial prediction value at time, for The correction factor based on the power consumption scenario at the moment is used to characterize the impact of the power consumption scenario on the real-time power consumption data. For the calculated The first prediction value at the time, the first prediction result is composed of the first prediction value based on the time series.

[0034] It should be noted that the power load prediction algorithm of this embodiment can be any existing algorithm that uses real-time combined power consumption data. The algorithm aims to obtain an initial prediction value. The purpose of this design in this embodiment is to enhance the applicability of this embodiment.

[0035] Furthermore, the correction factor of this embodiment may be a function of time based on the impact of the power usage scenario on the real-time power usage data obtained through regression analysis, thereby constructing the correction factor and using it to correct the initial prediction result.

[0036] Based on the above, this embodiment uses the power consumption scenario to correct the initial prediction results, thereby optimizing the prediction results based on the power consumption scenario information. By introducing the correction factor, the impact of the power consumption scenario on the real-time power consumption data is fully considered, so that the first prediction result can more accurately reflect the actual power load situation. In the process of calculating the correction factor, the various characteristics of the power consumption scenario are combined, and the real-time power storage data are supplemented to further improve the consideration of factors affecting the power load. This correction mechanism effectively improves the reliability of the first prediction result, lays the foundation for the subsequent generation of a more accurate second prediction result, thereby enhancing the accuracy of the power load forecast and facilitating the reasonable scheduling of the power grid.

[0037] Specifically, the generation of real-time electricity consumption forecast data includes: The real-time electricity consumption forecast data calculation formula is used to calculate the real-time electricity consumption forecast data. The real-time electricity consumption forecast data calculation formula is: in, Indicated in Moment Client Real-time data on electricity consumption, express Moment Client The total number of user terminals in the corresponding real-time combined power consumption data and , Indicated in The predicted adjustment value of electricity used by the electricity storage user end is obtained based on the real-time data of electricity storage at all times. The predicted adjustment value is obtained based on the electricity consumption scenario. For the calculated The real-time forecast value of electricity consumption at a certain moment, the real-time forecast data of electricity consumption is composed of the real-time forecast value of electricity consumption based on time series.

[0038] It should be noted that in this embodiment The predicted adjustment value of the storage-type user terminal for serving as electricity obtained at each moment based on the real-time storage data can be a function of time based on the impact of the real-time storage data on the real-time electricity consumption data obtained based on regression analysis. It can be understood that, in a specific example, the centralized charging of new energy equipment will lead to an increase in the real-time electricity consumption data; based on this, this embodiment constructs a predicted adjustment value and uses it to generate real-time predicted electricity consumption data.

[0039] Based on the above, this embodiment uses the real-time storage data and the real-time combined power consumption data to calculate the real-time power consumption prediction data, thereby realizing the real-time prediction of the power load considering the power storage factor. In the calculation formula, the prediction adjustment value obtained based on the real-time storage data can accurately reflect the impact of the charging and discharging of the power storage user end at different times on the overall power load. Combined with the user end power consumption information in the real-time combined power consumption data, the changes in power consumption behavior of various user ends are fully considered. This calculation method makes the real-time power consumption prediction data more in line with the actual power consumption situation, provides important intermediate data for the subsequent generation of the second prediction result, significantly improves the accuracy of the power load prediction, and ensures the stable power supply of the power grid.

[0040] Specifically, the generation of the second prediction result includes: The second prediction result is calculated using the second prediction result calculation formula, and the second prediction result calculation formula is: in, The first prediction result corresponds to The first predicted value at time, The preset forecast period The confidence level of the real-time power consumption forecast data obtained by the generated error rate is obtained based on a preset confidence level query table, which stores the confidence levels of the real-time power consumption forecast data corresponding to different error rates and the preset forecast period. ,in, is the starting time corresponding to the prediction period, is the end time corresponding to the prediction period, To provide real-time forecast data for electricity consumption, For the calculated The second prediction value at the time, the second prediction result is composed of the second prediction value based on the time series.

[0041] Based on the above, this embodiment uses the first prediction result, the real-time electricity consumption prediction data, and the confidence calculated based on the error rate to achieve fine-tuning of the power load prediction result. By setting the confidence of the real-time electricity consumption prediction data based on the empirical value known to those skilled in the art, according to the preset confidence query table, combined with the error rate generated by the prediction period, the weights of the first prediction result and the real-time electricity consumption prediction data in the final prediction can be reasonably weighed. The integration of real-time storage data in real-time electricity consumption prediction data further enhances the accuracy of the second prediction result. This calculation method makes full use of a variety of data information, effectively improves the accuracy of power load prediction, and provides a reliable decision-making basis for the efficient operation of the power grid.

[0042] The second aspect of this embodiment discloses Figure 2 A user-side power load prediction and adjustment system using big data is shown, the system is applicable to the user-side power load prediction and adjustment method using big data as described above, the system includes a power usage scenario library module connected in communication, a real-time data acquisition module, a real-time combined data generation module, a first prediction module and a second prediction module; The power usage scenario library module is configured to construct a power usage scenario library based on the collected power usage history data of the user terminal; wherein the user terminal includes a power storage user terminal and a non-power storage user terminal, the power storage user terminal has a power storage capability, and the non-power storage user terminal does not have a power storage capability, the power usage history data is used to characterize the independent power usage of the historical user terminal, and the power usage scenario library stores power usage scenarios formed when different user terminals work at the same time, and the power usage scenarios are used to characterize the combined power usage of different user terminals at the same time; The real-time data collection module is configured to: collect the real-time power consumption data of the user end, and when the user end is a power storage user end, collect its corresponding real-time power storage data; wherein the real-time power consumption data is used to characterize the real-time independent power consumption of the user end, and the real-time power storage data is used to characterize the real-time power storage of the power storage user end; The real-time combined data generation module is configured to: use the power consumption scenario library to match the power consumption scenario corresponding to the real-time power consumption data, and generate the real-time combined power consumption data based on the power consumption scenario; wherein the real-time combined power consumption data is used to represent the combined power consumption of different user terminals at the same time in real time; The first prediction module is configured to: generate a first prediction result by using a preset power load prediction algorithm and based on the real-time combined power consumption data and its corresponding power consumption scenario; wherein the power load prediction algorithm is used to perform power load prediction, and the first prediction result is a result of extensive power prediction; The second prediction module is configured as follows: using the real-time power storage data and the real-time power consumption data to generate real-time power consumption prediction data, and using the real-time power consumption prediction data, the first prediction result and the power load prediction algorithm to generate the second prediction result; wherein the real-time power consumption prediction data is used to characterize the real-time and predicted combined power consumption of different user ends taking into account the changes in the real-time power storage data, and the second prediction result is the result of the refined power prediction adjusted after taking into account the real-time power storage data.

[0043] It should be noted that the user-end power load prediction and adjustment system using big data of the present embodiment corresponds to the aforementioned user-end power load prediction and adjustment method using big data. Therefore, the contents not specifically described in the user-end power load prediction and adjustment system using big data of the present embodiment may include but are not limited to function definitions, working principles, and technical effects, etc., and may all refer to the records of the aforementioned user-end power load prediction and adjustment method using big data, and this text will not elaborate on them here.

[0044] In summary, the user-side power load forecasting and adjustment method and system using big data in this embodiment use the collected user-side power consumption historical data to build a power consumption scenario library, and combine the real-time power consumption data and the real-time power storage data to achieve a comprehensive analysis and accurate forecasting and adjustment of the user-side power load; by distinguishing between power storage and non-power storage user terminals, the power consumption combinations of different types of users in different power consumption scenarios are fully considered, and the introduction of real-time power storage data reflects the real-time power storage status changes of the power storage user terminal in the process of generating the real-time power consumption forecast data and the second forecast result, which effectively makes up for the deficiency of the traditional method that does not consider the power storage factor, and significantly improves the accuracy of power load forecasting in complex power consumption environments, providing strong support for the stable operation of the power grid and the rational allocation of resources.

[0045] In the embodiments provided in the present application, it should be understood that the embodiments described herein can be implemented in hardware, software, firmware, middleware, code, or any appropriate combination thereof. For hardware implementation, the processor can be implemented in one or more of the following units: an application-specific integrated circuit (ASIC), a digital signal processor (DSP), a digital signal processing device (DSPD), a programmable logic device (PLD), a field programmable gate array (FPGA), a processor, a controller, a microcontroller, a microprocessor, other electronic units designed to implement the functions described herein, or a combination thereof. For software implementation, part or all of the processes of the embodiment can be completed by instructing the relevant hardware through a computer program. When implemented, the above program can be stored in a computer-readable storage medium or transmitted as one or more instructions or codes on a computer-readable storage medium. Computer-readable storage media include computer storage media and communication media, wherein the communication medium includes any medium that facilitates the transmission of a computer program from one place to another. The storage medium can be any available medium that can be accessed by a computer. Computer-readable storage media can include, but are not limited to, RAM, ROM, EEPROM, CD-ROM or other optical disk storage, disk storage media or other magnetic storage devices, or any other medium that can be used to carry or store the desired program code in the form of an instruction or data structure and can be accessed by a computer.

[0046] Finally, it should be noted that the above is only a preferred embodiment of the present application and is not intended to limit the present application. Although the present application has been described in detail with reference to the aforementioned embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to make equivalent substitutions for some of the technical features therein. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present application shall be included in the protection scope of the present application.

Claims

1. A method for predicting and adjusting user-side power load using big data, characterized in that: The method includes: A power usage scenario library is constructed based on the collected power usage history data of the user terminals; wherein the user terminals include power storage user terminals and non-power storage user terminals, the power storage user terminals have power storage capabilities, and the non-power storage user terminals do not have power storage capabilities, the power usage history data are used to characterize the independent power usage of the historical user terminals, and the power usage scenario library stores power usage scenarios formed when different user terminals work at the same time, and the power usage scenarios are used to characterize the combined power usage of different user terminals at the same time; Collecting the real-time power consumption data of the user terminal, and when the user terminal is the power storage user terminal, collecting the corresponding real-time power storage data; wherein the real-time power consumption data is used to characterize the real-time independent power consumption of the user terminal, and the real-time power storage data is used to characterize the real-time power storage of the power storage user terminal; The power usage scenario library is used to match the power usage scenario corresponding to the real-time power usage data, and real-time power usage combination data is generated based on the power usage scenario; wherein the real-time power usage combination data is used to characterize the real-time combined power usage of different user terminals at the same time; Using a preset power load forecasting algorithm, and based on the real-time combined power consumption data and the corresponding power consumption scenario, a first forecasting result is generated; wherein the power load forecasting algorithm is used to perform power load forecasting, and the first forecasting result is a result of extensive power forecasting; The real-time electricity storage data and the real-time electricity consumption combined data are used to generate real-time electricity consumption forecast data, and the real-time electricity consumption forecast data, the first forecast result and the power load forecasting algorithm are used to generate a second forecast result; wherein the real-time electricity consumption forecast data is used to characterize the real-time, predicted combined electricity consumption of different user ends, taking into account the simultaneous electricity consumption under the changes of the real-time electricity storage data, and the second forecast result is the result of a refined electricity forecast adjusted after taking into account the real-time electricity storage data.

2. The method for predicting and adjusting user-side power load using big data according to claim 1, characterized in that: The construction of the electricity usage scenario library includes: The collected historical electricity consumption data is cleaned, and a first clustering is performed on the cleaned data based on a preset electricity consumption pattern. A second clustering is performed after adding a description of electricity consumption to the result of the first clustering to obtain electricity consumption scenarios, and a database is built using the electricity consumption scenarios to obtain an electricity consumption scenario database; wherein the electricity consumption pattern at least includes the type, quantity and power range of user terminals under the mode, and the electricity consumption description at least includes the electricity consumption time distribution characteristics and charge and discharge characteristics of the user terminals under the electricity consumption scenario, and the electricity consumption time distribution characteristics are used to characterize the electricity consumption time distribution of different user terminals, and the charge and discharge characteristics are used to characterize the charge and discharge conditions of the energy storage user terminals.

3. The method for predicting and adjusting user-side power load using big data according to claim 1, characterized in that: The power storage user terminal at least includes energy storage equipment in the power grid and new energy equipment consisting of new energy vehicles owned by the user.

4. The method for predicting and adjusting user-side power load using big data according to claim 3 is characterized in that: The collection of real-time data of power storage includes: The real-time power storage data of the energy storage device and the new energy device are correspondingly collected by using the power collection devices preset in the energy storage device and the new energy device.

5. The method for predicting and adjusting user-side power load using big data according to claim 1, characterized in that: The matching of the electricity usage scenario includes: Extracting the real-time pattern features and real-time scenario features of the real-time power consumption data, calculating the similarity between the real-time power consumption data and the power consumption scenario based on the real-time pattern features and the real-time scenario features, and selecting the power consumption scenario with the highest similarity as the matching result; wherein the real-time pattern features at least include the features corresponding to the type, quantity and power consumption range of the real-time user terminals, and the real-time scenario features at least include the power consumption time distribution features and charging and discharging features of the real-time user terminals; The extraction of the electricity consumption time distribution characteristics includes: Based on the distribution of electricity consumption behaviors of different users over time within a preset prediction period, the electricity consumption concentration and electricity consumption patterns at different users at different times are extracted to obtain the electricity consumption time distribution characteristics; The extraction of the charge and discharge characteristics includes: Based on the charging and discharging behavior of the energy storage user terminal, the impact of the charging and discharging behavior on the overall power load and the impact of power load forecasting and adjustment are extracted to obtain the charging and discharging characteristics; wherein the charging and discharging behavior at least includes charging and discharging power, charging and discharging capacity, charging and discharging efficiency and charging and discharging time rules.

6. The method for predicting and adjusting user-side power load using big data according to claim 5, characterized in that: The generation of the real-time combined electricity consumption data includes: According to the matched electricity usage scenario, the user terminals involved are determined to obtain the corresponding real-time electricity usage data, and the obtained real-time electricity usage data is used to construct a real-time electricity usage combined data matrix. The matrix elements of the real-time electricity usage combined data matrix are the real-time mode characteristics of each user terminal. The real-time electricity usage combined data matrix is ​​defined as the real-time electricity usage combined data and output.

7. The method for predicting and adjusting user-side power load using big data according to claim 1, characterized in that: The generating of the first prediction result includes: The real-time combined power consumption data is input into the power load forecasting algorithm, an initial forecast result with a time series is output, and the initial forecast result is corrected using the power consumption scenario to obtain the first forecast result; wherein, the initial forecast result is corrected using the power consumption scenario as follows: The first prediction result is obtained by using the initial prediction result correction formula, and the initial prediction result correction formula is: in, for The initial prediction value at time, for A correction factor based on the power usage scenario at the time, the correction factor is used to characterize the influence of the power usage scenario on the real-time power usage data, For the calculated The first prediction value at a certain moment, wherein the first prediction result is composed of the first prediction value based on a time series.

8. The method for predicting and adjusting user-side power load using big data according to claim 1, characterized in that: The generation of the real-time electricity consumption prediction data includes: The real-time electricity consumption forecast data is calculated using the real-time electricity consumption forecast data calculation formula, and the real-time electricity consumption forecast data calculation formula is: in, Indicated in Moment Client Real-time data on electricity consumption, express Moment Client The total number of user terminals in the corresponding real-time combined power consumption data and , Indicated in The predicted adjustment value of the electricity storage user end used for electricity obtained at any time based on the real-time data of electricity storage is obtained based on the electricity usage scenario. For the calculated The real-time prediction value of electricity consumption at a certain moment, wherein the real-time prediction data of electricity consumption is composed of the real-time prediction value of electricity consumption based on a time series.

9. The method for predicting and adjusting user-side power load using big data according to claim 1, characterized in that: The generating of the second prediction result includes: The second prediction result is calculated using the second prediction result calculation formula, wherein the second prediction result calculation formula is: in, The first prediction result corresponds to The first predicted value at time, The preset forecast period The confidence of the real-time power consumption prediction data obtained by the generated error rate is obtained based on a preset confidence query table, in which the confidence of the real-time power consumption prediction data corresponding to different error rates is stored, and the preset prediction period ,in, is the starting time corresponding to the prediction period, is the end time corresponding to the prediction period, The real-time electricity consumption forecast data, For the calculated The second prediction value at the time instant, the second prediction result is composed of the second prediction value based on the time series.

10. A user-side power load prediction and adjustment system using big data, the system being applicable to the user-side power load prediction and adjustment method using big data as claimed in any one of claims 1 to 9, characterized in that: The system includes a power usage scenario library module, a real-time data acquisition module, a real-time combined data generation module, a first prediction module and a second prediction module connected in communication; The power usage scenario library module is configured to construct a power usage scenario library based on the collected power usage history data of the user terminal; wherein the user terminal includes a power storage user terminal and a non-power storage user terminal, the power storage user terminal has a power storage capability, and the non-power storage user terminal does not have a power storage capability, the power usage history data is used to characterize the independent power usage of the historical user terminal, and the power usage scenario library stores power usage scenarios formed when different user terminals work at the same time, and the power usage scenarios are used to characterize the combined power usage of different user terminals at the same time; The real-time data acquisition module is configured to: collect the real-time power consumption data of the user terminal, and when the user terminal is the power storage user terminal, collect the corresponding real-time power storage data; wherein the real-time power consumption data is used to characterize the real-time independent power consumption of the user terminal, and the real-time power storage data is used to characterize the real-time power storage of the power storage user terminal; The real-time combined data generation module is configured to: use the power usage scenario library to match the power usage scenario corresponding to the real-time power usage data, and generate real-time combined power usage data based on the power usage scenario; wherein the real-time combined power usage data is used to represent the combined power usage of different user terminals at the same time in real time; The first prediction module is configured to: generate a first prediction result by using a preset power load prediction algorithm and based on the real-time combined power consumption data and the corresponding power consumption scenario; wherein the power load prediction algorithm is used to perform power load prediction, and the first prediction result is a result of extensive power prediction; The second prediction module is configured to: generate real-time electricity consumption prediction data using the real-time electricity storage data and the real-time electricity consumption combined data, and generate a second prediction result using the real-time electricity consumption prediction data, the first prediction result and the power load prediction algorithm; wherein the real-time electricity consumption prediction data is used to characterize the real-time, predicted combined electricity consumption of different user terminals, taking into account the simultaneous electricity consumption under the changes of the real-time electricity storage data, and the second prediction result is the result of a refined electricity prediction adjusted after taking into account the real-time electricity storage data.

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