Power load accurate prediction system based on artificial intelligence

Abstract

The invention discloses a power load accurate prediction system based on artificial intelligence, and the system comprises a data collection module which is used for collecting multi-source data through a preset path; the data preprocessing module is used for preprocessing the multi-source data according to the data type to obtain processed data; the data fusion module is used for carrying out fusion processing on the processed data by utilizing data fusion to obtain input data in a unified format; the model training module is used for training a constructed deep learning model based on the input data to obtain a prediction model; and the prediction module is used for reasoning current data by adopting the prediction model to obtain a power load prediction result. And the effectiveness and accuracy of power load prediction are improved.

Description

Technical Field

[0001] The present invention belongs to the field of electric power information technology, and more specifically, relates to an artificial intelligence-based accurate prediction system for electric power load. Background Art

[0002] Power load forecasting is an important part of power system planning and operation. Its purpose is to ensure that the power system can achieve economic operation and safety and stability while meeting load demand through reasonable prediction of future loads. However, power loads have significant nonlinear, time-varying and random characteristics, and traditional load forecasting methods are difficult to meet the requirements of high-precision forecasting.

[0003] In the related technology, load forecasting methods mainly include statistical analysis methods: such as regression analysis, time series analysis, etc. These methods are based on historical load data for prediction, but due to the complexity and variability of power load, the prediction accuracy is often not high. The prediction method based on physical models uses physical models to simulate the operation of the power system and obtains load forecast results through calculations, but the establishment and maintenance of physical models requires a lot of prior knowledge and data, and the calculation complexity is high. The method based on empirical rules relies on the experience and knowledge of power system experts and makes predictions through rule bases, but due to the limitations and subjectivity of empirical rules, the accuracy and reliability of the prediction results are difficult to guarantee.

[0004] Therefore, how to improve the effectiveness and accuracy of power load forecasting is a key issue that technical personnel in this field are concerned about. Summary of the invention

[0005] The purpose of this application is to provide an artificial intelligence-based accurate power load prediction system to improve the effectiveness and accuracy of power load prediction.

[0006] In view of the above defects or improvement needs of the prior art, the present invention provides an artificial intelligence-based power load accurate prediction system, comprising:

[0007] A data collection module, used to collect multi-source data through a preset path; wherein the preset path includes an API interface, a sensor network, and a data sharing platform, and the multi-source data includes historical power load data, meteorological data, socio-economic indicators, and user behavior data;

[0008] A data preprocessing module, used to preprocess the multi-source data according to data types to obtain processed data; wherein the preprocessing includes data cleaning, normalization, missing value filling, and data enhancement; the data types include time series data, spatial data, and classified data;

[0009] A data fusion module, used for fusing the processed data by using data fusion to obtain input data in a unified format;

[0010] A model training module, used to train the constructed deep learning model based on the input data to obtain a prediction model;

[0011] The prediction module is used to use the prediction model to infer the current data to obtain the power load prediction result.

[0012] Optionally, the data acquisition module is specifically used to collect the multi-source data from different data sources; wherein the multi-source data include power load historical data, meteorological data, socio-economic indicators, and user behavior data; the power load historical data is data collected from a load monitoring system to record power load conditions over a historical period of time; the meteorological data include temperature data, humidity data, rainfall data, and wind speed data; the socio-economic indicators include gross domestic product data, population growth rate data, and energy price data; the user behavior data include consumers' electricity usage pattern data; the electricity usage pattern data include electricity usage time distribution data and holiday electricity usage distribution data.

[0013] Optionally, the data preprocessing module is specifically used to remove noise data, process outliers, and fill missing data from the multi-source data to obtain cleaned multi-source data; convert data from different data sources in the cleaned multi-source data to the same dimensional range to obtain normalized multi-source data; and expand the data set of the normalized multi-source data through data transformation and data generation technology to obtain the processed data.

[0014] Optionally, the data fusion module is specifically used to perform feature engineering on the processed data to obtain key features; integrate the key features of different modalities into fused data in a unified feature space through a fusion strategy; and perform dimensionality reduction processing on the high-dimensional fused data through principal component analysis or non-negative matrix decomposition to obtain the input data.

[0015] Optionally, the model training module is specifically used to determine the corresponding deep learning model according to the input data characteristics of the prediction task; use the input data to train the deep learning model, and optimize the deep learning model using cross-validation and hyperparameter adjustment to obtain the prediction model.

[0016] Optionally, it also includes: a prediction result analysis module, which is used to calculate an error index between the power load prediction result and the actual power load data, and evaluate the prediction performance of the prediction model based on the error index.

[0017] Optionally, it also includes: a forecast result display module, which is used to use chart tools to display the change trend of the power load forecast results.

[0018] Optionally, it also includes: a scenario simulation module, which is used to perform load forecasting simulation on the prediction model based on different hypothetical scenarios to obtain corresponding simulation results, and adjust decision data for the power load forecasting results under different conditions based on different simulation results.

[0019] Optionally, it also includes: a decision data output module, which is used to perform decision processing on the power load forecast result according to a decision strategy to obtain scheduling data.

[0020] Optionally, it also includes: an abnormal load processing module, which is used to detect the power load prediction result based on an abnormal detection algorithm, and when abnormal data is detected, perform data correction processing based on the abnormal data.

[0021] The present application provides an artificial intelligence-based accurate power load prediction system, comprising: a data acquisition module, which is used to collect multi-source data through a preset path; wherein the preset path includes an API interface, a sensor network, and a data sharing platform, and the multi-source data includes power load historical data, meteorological data, socio-economic indicators, and user behavior data; a data preprocessing module, which is used to preprocess the multi-source data according to the data type to obtain processed data; wherein the preprocessing includes data cleaning processing, normalization processing, missing value filling processing, and data enhancement processing; the data type includes time series data, spatial data, and classified data; a data fusion module, which is used to fuse the processed data using data fusion to obtain input data in a unified format; a model training module, which is used to train a constructed deep learning model based on the input data to obtain a prediction model; a prediction module, which is used to use the prediction model to infer current data to obtain a power load prediction result.

[0022] The invention has the following beneficial effects: the data acquisition module comprehensively collects multi-source data such as power load historical data, meteorological data, socio-economic indicators and user behavior data; the data preprocessing module cleans, normalizes, fills in missing values ​​and performs data enhancement processing on multi-source data, thereby improving the quality and reliability of the data; the data fusion module fuses the preprocessed data to obtain input data in a unified format; the model training module trains the deep learning model based on the input data; the prediction module uses the trained prediction model to infer the current data, thereby realizing an accurate power load prediction system based on artificial intelligence, lowering the threshold for power load prediction and improving the precision and accuracy of power load prediction. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] 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 embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0024] Figure 1 A schematic diagram of the structure of an artificial intelligence-based accurate power load prediction system provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] The purpose of this application is to provide an artificial intelligence-based accurate power load prediction system to improve the effectiveness and accuracy of power load prediction.

[0026] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention. In addition, the technical features involved in the various embodiments of the present invention described below can be combined with each other as long as they do not conflict with each other.

[0027] The following is an example of an artificial intelligence-based accurate power load prediction system provided by the present application.

[0028] Please refer to Figure 1 , Figure 1 A schematic diagram of the structure of an artificial intelligence-based accurate power load prediction system provided in an embodiment of the present application.

[0029] In this embodiment, the system may include:

[0030] The data collection module 100 is used to collect multi-source data through a preset path; wherein the preset path includes an API interface, a sensor network, and a data sharing platform, and the multi-source data includes historical power load data, meteorological data, social and economic indicators, and user behavior data;

[0031] Among them, rich data sources can comprehensively reflect the various factors affecting power load and improve the accuracy of prediction. For example, real-time power system operation data can be obtained through the API interface, combined with field environmental data collected by the sensor network and socio-economic data obtained from the data sharing platform, providing multi-dimensional information support for accurate prediction.

[0032] The data preprocessing module 200 is used to preprocess the multi-source data according to the data type to obtain processed data; wherein the preprocessing includes data cleaning processing, normalization processing, missing value filling processing, and data enhancement processing; the data type includes time series data, spatial data, and classified data;

[0033] Among them, data cleaning processing can remove noise and outliers, improve data quality, and make model training more accurate and stable. For example, remove erroneous records in power load data to avoid misleading the prediction results. Normalization processing scales the data to a uniform range, which helps the model converge faster and improve generalization capabilities. Missing value filling processing ensures the integrity of the data and avoids prediction bias caused by missing data. For example, it reasonably fills in missing meteorological data for certain time periods. Data enhancement processing increases the diversity of data and improves the robustness and generalization capabilities of the model.

[0034] The data fusion module 300 is used to perform fusion processing on the processed data by using data fusion to obtain input data in a unified format;

[0035] Among them, data in a unified format facilitates subsequent model processing and analysis, improving the efficiency and accuracy of the model. In other words, time series data and classification data from different data sources are integrated into unified structured data, making it easier for the model to extract features and calculate.

[0036] A model training module 400 is used to train the constructed deep learning model based on input data to obtain a prediction model;

[0037] The prediction module 500 is used to use the prediction model to infer the current data to obtain the power load prediction result.

[0038] In summary, this embodiment uses the data acquisition module 100 to comprehensively collect multi-source data such as power load historical data, meteorological data, socio-economic indicators, and user behavior data. The data preprocessing module 200 cleans, normalizes, fills in missing values, and performs data enhancement processing on the multi-source data, thereby improving the quality and reliability of the data. The data fusion module 300 fuses the preprocessed data to obtain input data in a unified format. The model training module 400 trains the deep learning model based on the input data. The prediction module 500 uses the trained prediction model to infer the current data, thereby realizing an accurate power load prediction system based on artificial intelligence, lowering the threshold for power load prediction, and improving the precision and accuracy of power load prediction.

[0039] Furthermore, the data collection module 100 is specifically used to collect multi-source data from different data sources; wherein the multi-source data includes power load historical data, meteorological data, socio-economic indicators, and user behavior data; power load historical data is data collected from the load monitoring system to record power load conditions over a historical period of time; meteorological data includes temperature data, humidity data, rainfall data, and wind speed data; socio-economic indicators include gross domestic product data, population growth rate data, and energy price data; user behavior data includes consumers' electricity consumption pattern data; electricity consumption pattern data includes electricity consumption time distribution data and holiday electricity consumption distribution data.

[0040] It can be seen that the specific multi-source data collected by the data acquisition module 100 from different data sources are further clarified, including power load historical data (historical load conditions from the load monitoring system), meteorological data (such as temperature, humidity, rainfall, wind speed, etc.), socio-economic indicators (such as gross domestic product, population growth rate, energy prices, etc.) and user behavior data (such as electricity consumption time distribution, holiday electricity consumption distribution and other electricity consumption pattern data). The specific content of data collection is stipulated, which enables the system to obtain more comprehensive and specific data on factors affecting power load, and provide a richer information basis for accurate prediction. For example, accurate meteorological data and detailed user behavior data can more accurately reflect the changing patterns of power load.

[0041] Furthermore, the data preprocessing module 200 is specifically used to remove noise data, process outliers, and fill missing data from multi-source data to obtain cleaned multi-source data; convert data from different data sources in the cleaned multi-source data to the same dimensional range to obtain normalized multi-source data; and expand the data set of the normalized multi-source data through data transformation and data generation technology to obtain processed data.

[0042] It can be seen that the function of data preprocessing is implemented more specifically, noise and outliers are removed to improve data quality, normalization processing makes data from different data sources comparable, which is helpful for model training and analysis, and data set expansion increases data diversity, further improving the performance and generalization ability of the model.

[0043] Furthermore, the data fusion module 300 is specifically used to perform feature engineering on the processed data to obtain key features; integrate the key features of different modes into fused data in a unified feature space through a fusion strategy; and perform dimensionality reduction processing on high-dimensional fused data through principal component analysis or non-negative matrix decomposition to obtain input data.

[0044] It can be seen that through feature engineering and fusion strategies, the key information in the data is fully mined and integrated, and dimensionality reduction processing improves the data processing efficiency and model training speed, while reducing the impact of redundant information on the model, allowing the model to focus more on key features and improve the accuracy and efficiency of prediction.

[0045] Furthermore, the model training module 400 is specifically used to determine the corresponding deep learning model according to the input data characteristics of the prediction task; use the input data to train the deep learning model, and use cross-validation and hyperparameter adjustment to optimize the deep learning model to obtain a prediction model.

[0046] It can be seen that selecting a suitable model according to data characteristics improves the model's pertinence and adaptability. Cross-validation and hyperparameter adjustment help find the optimal model parameters, improve the model's performance and generalization ability, and thus obtain a more accurate prediction model.

[0047] Furthermore, it also includes: a prediction result analysis module, which is used to calculate the error index between the power load prediction result and the actual power load data, and evaluate the prediction performance of the prediction model based on the error index.

[0048] It can be seen that being able to quantitatively evaluate the accuracy and reliability of the prediction model provides a basis for the improvement and optimization of the model, which helps to continuously improve the prediction performance of the model.

[0049] Furthermore, it also includes: a prediction result display module, which is used to use a chart tool to display the change trend of the power load prediction results.

[0050] It can be seen that the prediction result display module intuitively displays the changing trend of the prediction results, which helps users understand and analyze the changes in power load more clearly and provides a more intuitive basis for decision-making.

[0051] Furthermore, it also includes: a scenario simulation module, which is used to perform load forecasting simulation on the prediction model based on different hypothetical scenarios, obtain corresponding simulation results, and adjust decision data for power load forecasting results under different conditions based on different simulation results.

[0052] Furthermore, it also includes: a decision data output module, which is used to make decisions on the power load forecast results according to the decision strategy to obtain scheduling data.

[0053] It can be seen that it can provide specific decision-making data for the dispatch and management of the power system, which is helpful to achieve the optimal allocation and efficient utilization of power resources.

[0054] Furthermore, it also includes: an abnormal load processing module, which is used to detect the power load prediction results based on the abnormal detection algorithm, and when abnormal data is detected, perform data correction processing based on the abnormal data.

[0055] It can be seen that the abnormal load processing module can detect and process abnormal data in a timely manner, improve the reliability and stability of the prediction results, help avoid decision-making errors caused by abnormal data, and ensure the normal operation of the power system.

[0056] The various embodiments in the specification are described in a progressive manner, and each embodiment focuses on the differences from other embodiments. The same or similar parts between the various embodiments can be referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0057] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the composition and steps of each example have been generally described in the above description according to function. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may 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.

[0058] The steps of the method or algorithm described in conjunction with the embodiments disclosed herein may be implemented directly using hardware, a software module executed by a processor, or a combination of the two. The software module may be placed in a random access memory (RAM), a memory, a read-only memory (ROM), an electrically programmable ROM, an electrically erasable programmable ROM, a register, a hard disk, a removable disk, a CD-ROM, or any other form of storage medium known in the art.

[0059] The above is a detailed introduction to an artificial intelligence-based accurate power load prediction system provided by the present application. This article uses specific examples to illustrate the principles and implementation methods of the present application. The description of the above embodiments is only used to help understand the method of the present application and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principles of the present application, several improvements and modifications can be made to the present application, and these improvements and modifications also fall within the scope of protection of the claims of the present application.

Claims

1. An artificial intelligence-based power load accurate prediction system, characterized in that: include: A data collection module, used to collect multi-source data through a preset path; wherein the preset path includes an API interface, a sensor network, and a data sharing platform, and the multi-source data includes historical power load data, meteorological data, socio-economic indicators, and user behavior data; A data preprocessing module, used to preprocess the multi-source data according to data types to obtain processed data; wherein the preprocessing includes data cleaning, normalization, missing value filling, and data enhancement; the data types include time series data, spatial data, and classified data; A data fusion module, used for fusing the processed data by using data fusion to obtain input data in a unified format; A model training module, used to train the constructed deep learning model based on the input data to obtain a prediction model; The prediction module is used to use the prediction model to infer the current data to obtain the power load prediction result.

2. The power load accurate prediction system according to claim 1, characterized in that: The data acquisition module is specifically used to collect the multi-source data from different data sources; wherein the multi-source data includes power load historical data, meteorological data, socio-economic indicators, and user behavior data; the power load historical data is data collected from the load monitoring system to record the power load conditions in historical time; the meteorological data includes temperature data, humidity data, rainfall data, and wind speed data; the socio-economic indicators include gross domestic product data, population growth rate data, and energy price data; the user behavior data includes consumers' electricity consumption pattern data; the electricity consumption pattern data includes electricity consumption time distribution data and holiday electricity consumption distribution data.

3. The power load accurate prediction system according to claim 2 is characterized in that: The data preprocessing module is specifically used to remove noise data, process outliers, and fill missing data from the multi-source data to obtain cleaned multi-source data; convert data from different data sources in the cleaned multi-source data to the same dimension range to obtain normalized multi-source data; and expand the data set of the normalized multi-source data through data transformation and data generation technology to obtain the processed data.

4. The power load accurate prediction system according to claim 3 is characterized in that: The data fusion module is specifically used to perform feature engineering on the processed data to obtain key features; integrate the key features of different modes into fused data in a unified feature space through a fusion strategy; and perform dimensionality reduction processing on the high-dimensional fused data through principal component analysis or non-negative matrix decomposition to obtain the input data.

5. The power load accurate prediction system according to claim 4, characterized in that: The model training module is specifically used to determine the corresponding deep learning model according to the input data characteristics of the prediction task; use the input data to train the deep learning model, and use cross-validation and hyperparameter adjustment to optimize the deep learning model to obtain the prediction model.

6. The power load accurate prediction system according to claim 5, characterized in that: Also includes: The prediction result analysis module is used to calculate the error index between the power load prediction result and the actual power load data, and evaluate the prediction performance of the prediction model based on the error index.

7. The power load accurate prediction system according to claim 6, characterized in that: Also includes: The forecast result display module is used to use chart tools to display the changing trend of the power load forecast results.

8. The power load accurate prediction system according to claim 7, characterized in that: Also includes: The scenario simulation module is used to perform load forecasting simulation on the prediction model based on different hypothetical scenarios to obtain corresponding simulation results, and to adjust decision data on the power load forecasting results under different conditions based on different simulation results.

9. The power load accurate prediction system according to claim 8, characterized in that: Also includes: The decision data output module is used to perform decision processing on the power load forecast result according to the decision strategy to obtain scheduling data.

10. The power load accurate prediction system according to claim 9, characterized in that: Also includes: The abnormal load processing module is used to detect the power load prediction result based on the abnormal detection algorithm, and when abnormal data is detected, perform data correction processing based on the abnormal data.

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