Power consumer electricity demand prediction system based on big data

Abstract

The invention relates to the technical field of power consumption demand prediction, and discloses a power consumer power consumption demand prediction system based on big data, which comprises a data acquisition module, a data integration module, a data processing module, a prediction model module, an application interface module and a system monitoring, maintenance and log module. According to the method, more related features are mined and an advanced prediction algorithm is adopted, so that the accuracy of the prediction model is improved; the method can cope with data of different types and different scales, and has relatively high adaptability; an accurate prediction result is provided for an electric power company, the electric power company is helped to arrange generating capacity in advance, supply and demand are balanced, and data support is provided for power grid dispatching and electric power market transaction; according to the method, the accuracy of power dispatching is remarkably improved, power waste and cost are reduced, the user is allowed to input or adjust parameters to obtain customized prediction, and the system stability, flexibility and user satisfaction are improved.

Description

Technical Field

[0001] The present invention relates to the technical field of electricity demand forecasting, and in particular to an electricity demand forecasting system for power users based on big data. Background Art

[0002] The power system needs to balance power generation and power consumption in real time to maintain the stable operation of the power grid. By predicting the power demand of users, the power dispatching department can arrange the power generation plan in advance to ensure that the power supply matches the demand and avoid power shortage or waste caused by the imbalance between supply and demand. Accurate power demand forecasting can help market participants (such as power generators, retailers, and power traders) formulate more reasonable trading strategies, optimize power procurement and sales plans, and improve market competitiveness. The forecast results can also be used in power futures trading to help market participants avoid price fluctuation risks. With the continuous development of my country's power market, the level of power demand forecasting has become one of the significant indicators of whether the power management of power companies is modern.

[0003] Although the existing electricity demand forecasting system plays an important role in power planning and operation, it still has some shortcomings and limitations: the prediction results of the existing forecasting system may be affected by a variety of uncertain factors, such as extreme weather, economic fluctuations, policy changes, etc., which are often difficult to accurately predict, resulting in the accuracy of electricity demand forecasting being limited; for some special scenarios, such as holidays, emergencies, etc., electricity demand may fluctuate significantly, and the existing forecasting system often finds it difficult to accurately capture these changes; the existing electricity demand forecasting system often makes predictions based on historical data and lacks sufficient flexibility for real-time changing market demand and user behavior.

[0004] What's covered

[0005] The technical problem to be solved by the present invention is to overcome the above technical defects and provide a power demand prediction system for power users based on big data.

[0006] In order to solve the above problems, the technical solution of the present invention is: a power demand forecasting system for power users based on big data, comprising:

[0007] Data collection module: collects users’ historical electricity consumption data, external data and users’ real-time electricity consumption data;

[0008] Data integration module: integrates the collected different data into a unified database;

[0009] Data processing module: cleans and converts the data in the database to improve data quality, reduce the impact of noise and outliers on the prediction model, and extract features related to power demand as input to the prediction model;

[0010] Prediction model module: deployed on the model server, contains a variety of prediction algorithms and models, performs demand prediction based on processed data, and outputs prediction results;

[0011] Application interface module: provides prediction results to users and systems for decision support, provides data visualization functions to facilitate decision makers to understand and use data, and allows users to input or adjust parameters to obtain customized predictions;

[0012] System monitoring, maintenance and logging module: real-time monitoring of system status and performance, and detailed log recording for easy problem tracking and analysis.

[0013] Furthermore, the user's historical electricity consumption data includes daily, monthly and annual electricity consumption; external data includes meteorological data and economic data; and real-time data is electricity consumption data obtained from smart meter real-time monitoring equipment.

[0014] Furthermore, the meteorological data includes temperature, humidity, and season; the economic data includes GDP and industrial production index; and the user types include industrial, commercial, residential, agricultural, and public utility users.

[0015] Further, data cleaning includes:

[0016] Missing value handling: use interpolation, mean value to fill or delete missing data;

[0017] Outlier handling: Identify and handle outlier data points, such as using box plots or Z-score methods;

[0018] Data deduplication: remove duplicate data;

[0019] Time series processing: For historical data, process time series characteristics such as seasonality and trends;

[0020] Data conversion includes:

[0021] Data standardization: standardize data from different sources to the same scale;

[0022] Data discretization: Convert continuous data into discrete data.

[0023] Furthermore, the data processing module calculates the correlation coefficient between each feature and the electricity demand, selects features with high correlation, and creates features;

[0024] Time characteristics: month, season, day of the week, whether it is a holiday;

[0025] Derived characteristics: such as temperature change rate, humidity change rate, load change rate;

[0026] Interaction features: The interaction term between temperature and humidity.

[0027] Furthermore, the prediction model module includes time series analysis, machine learning model, and deep learning model.

[0028] Furthermore, the prediction model module also includes a model training optimization submodule, which uses a cross-validation method to divide the data set into a training set and a validation set, uses the training set to train the model, and evaluates the performance of the model on the validation set; and improves the model performance by adjusting model parameters and using an optimization algorithm to minimize the prediction error.

[0029] Further visualization capabilities include:

[0030] Chart display: Display the comparison between the forecast results and actual power consumption through line chart, bar chart or scatter chart;

[0031] Dashboard: Build dashboards (such as Tableau, Power BI) for real-time data display and monitoring:

[0032] Decision support includes:

[0033] Report generation: Generate electricity demand forecast reports to support power dispatch and decision-making;

[0034] Early warning system: Set the early warning threshold and issue an early warning when the prediction result exceeds the threshold.

[0035] Further, system monitoring includes:

[0036] Performance monitoring: monitor system operation performance, such as response time, throughput, etc.;

[0037] Data quality monitoring: monitor data quality, such as data integrity, accuracy, etc.;

[0038] System maintenance includes:

[0039] Regular updates: Regularly update models and data to ensure the accuracy and timeliness of the system;

[0040] Fault handling: Handle system faults in a timely manner to ensure stable system operation.

[0041] Compared with the existing technology, the advantages of the present invention are: the present invention improves the accuracy of the prediction model by mining more relevant features and adopting advanced prediction algorithms; it can cope with data of different types and sizes and has strong adaptability; it provides accurate prediction results for power companies to help them arrange power generation in advance, balance supply and demand, and provide data support for power grid scheduling and power market transactions; it significantly improves the accuracy of power scheduling, reduces power waste and costs, and allows users to input or adjust parameters to obtain customized predictions, thereby improving system stability, flexibility and user satisfaction. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present invention are clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments described herein, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0043] A power demand prediction system for power users based on big data, comprising:

[0044] Data collection module: collects users' historical electricity consumption data, external data and users' real-time electricity consumption data.

[0045] The user's historical electricity consumption data includes daily, monthly and annual electricity consumption; external data includes meteorological data and economic data; real-time data is the electricity consumption data obtained from the smart meter real-time monitoring device.

[0046] The meteorological data include temperature, humidity, and season; the economic data include GDP and industrial production index; and the user types include industrial, commercial, residential, agricultural, and public utility users.

[0047] Data integration module: Integrate the collected different data into a unified database.

[0048] Data processing module: cleans and converts the data in the database to improve data quality, reduce the impact of noise and outliers on the prediction model, and extracts features related to power demand as input to the prediction model, such as temperature, humidity, holidays, industrial production, etc.

[0049] Data cleaning includes:

[0050] Missing value handling: use interpolation, mean value to fill or delete missing data;

[0051] Outlier handling: Identify and handle outlier data points, such as using box plots or Z-score methods;

[0052] Data deduplication: remove duplicate data;

[0053] Time series processing: For historical data, process time series characteristics such as seasonality and trends.

[0054] Data conversion includes:

[0055] Data standardization: standardize data from different sources to the same scale;

[0056] Data discretization: Convert continuous data into discrete data.

[0057] The data processing module calculates the correlation coefficient between each feature and the electricity demand, selects features with high correlation, and creates features;

[0058] Time characteristics: month, season, day of the week, whether it is a holiday;

[0059] Derived characteristics: such as temperature change rate, humidity change rate, load change rate;

[0060] Interaction features: The interaction term between temperature and humidity.

[0061] Prediction model module: deployed on the model server, contains a variety of prediction algorithms and models, performs demand prediction based on processed data, and outputs prediction results.

[0062] The prediction model module includes time series analysis, machine learning model, and deep learning model. The prediction model module also includes a model training optimization submodule, which uses a cross-validation method to divide the data set into a training set and a validation set, trains the model with the training set, and evaluates the performance of the model on the validation set; and improves the model performance by adjusting the model parameters and using an optimization algorithm to minimize the prediction error.

[0063] Application interface module: provides prediction results to users and systems for decision support, provides data visualization functions to facilitate decision makers to understand and use data, and allows users to input or adjust parameters to obtain customized predictions.

[0064] Visualization capabilities include:

[0065] Chart display: Display the comparison between the forecast results and actual power consumption through line chart, bar chart or scatter chart;

[0066] Dashboard: Build dashboards (such as using Tableau, Power BI) for real-time data display and monitoring.

[0067] Decision support includes:

[0068] Report generation: Generate electricity demand forecast reports to support power dispatch and decision-making.

[0069] System monitoring, maintenance and logging module: real-time monitoring of system status and performance, and detailed log recording for easy problem tracking and analysis.

[0070] System monitoring includes:

[0071] Performance monitoring: monitor system operation performance, such as response time, throughput, etc.;

[0072] Data quality monitoring: Monitor data quality, such as data completeness, accuracy, etc.

[0073] System maintenance includes:

[0074] Regular updates: Regularly update models and data to ensure the accuracy and timeliness of the system;

[0075] Fault handling: Handle system faults in a timely manner to ensure stable system operation.

[0076] This system improves the accuracy of the prediction model by mining more relevant features and adopting advanced prediction algorithms; it can handle data of different types and sizes and has strong adaptability; it provides accurate prediction results for power companies to help them arrange power generation in advance, balance supply and demand, and provide data support for power grid dispatching and power market transactions; it significantly improves the accuracy of power dispatching, reduces power waste and costs, and allows users to input or adjust parameters to obtain customized predictions, thereby improving system stability, flexibility and user satisfaction.

[0077] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are 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 other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0078] Although the embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0079] The present invention and its implementation methods are described above, and such description is not restrictive, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design structures and embodiments similar to the technical solution without creativity, which should all fall within the protection scope of the present invention.

Claims

1. A power demand forecasting system for power users based on big data, characterized in that: include: Data collection module: collects users’ historical electricity consumption data, external data and users’ real-time electricity consumption data; Data integration module: integrates the collected different data into a unified database; Data processing module: cleans and converts the data in the database and extracts features related to power demand as input for the prediction model; Prediction model module: deployed on the model server, contains a variety of prediction algorithms and models, performs demand prediction based on processed data, and outputs prediction results; Application interface module: provides prediction results to users and systems for decision support, provides data visualization functions to facilitate decision makers to understand and use data, and allows users to input or adjust parameters to obtain customized predictions; System monitoring, maintenance and logging module: real-time monitoring of system status and performance, and detailed log recording for easy problem tracking and analysis.

2. The power demand forecasting system for power users based on big data according to claim 1 is characterized by: The user's historical electricity consumption data includes daily, monthly and annual electricity consumption; External data include meteorological data and economic data; Real-time data refers to electricity consumption data obtained from smart meter real-time monitoring devices.

3. The power demand forecasting system for power users based on big data according to claim 2 is characterized by: The meteorological data include temperature, humidity, and season; the economic data include GDP and industrial production index; and the user types include industrial, commercial, residential, agricultural, and public utility users.

4. The power demand forecasting system for power users based on big data according to claim 1 is characterized by: Data cleaning includes: Missing value handling: use interpolation, mean value to fill or delete missing data; Outlier handling: Identify and handle outlier data points, such as using box plots or Z-score methods; Data deduplication: remove duplicate data; Time series processing: For historical data, process time series characteristics such as seasonality and trends; Data conversion includes: Data standardization: standardize data from different sources to the same scale; Data discretization: Convert continuous data into discrete data.

5. The power demand forecasting system for power users based on big data according to claim 4 is characterized by: The data processing module calculates the correlation coefficient between each feature and the electricity demand, selects features with high correlation, and creates features; Time characteristics: month, season, day of the week, holiday, industrial production; Derived characteristics: such as temperature change rate, humidity change rate, load change rate; Interaction features: The interaction term between temperature and humidity.

6. The power demand forecasting system for power users based on big data according to claim 1 is characterized by: The prediction model module includes time series analysis, machine learning model, and deep learning model.

7. The power demand forecasting system for power users based on big data according to claim 6 is characterized by: The prediction model module also includes a model training optimization submodule, which uses a cross-validation method to divide the data set into a training set and a validation set, uses the training set to train the model, and evaluates the performance of the model on the validation set; and improves the model performance by adjusting model parameters and using an optimization algorithm to minimize the prediction error.

8. The power demand forecasting system for power users based on big data according to claim 1 is characterized by: Visualization features: Chart display: Display the comparison between the forecast results and actual power consumption through line chart, bar chart or scatter chart; Dashboard: Build a dashboard for real-time data display and monitoring: Decision support: Report generation: Generate electricity demand forecast reports to support power dispatch and decision-making; Early warning system: Set the early warning threshold and issue an early warning when the prediction result exceeds the threshold.

9. The power demand forecasting system for power users based on big data according to claim 1 is characterized by: System monitoring includes: Performance monitoring: monitor system operation performance, such as response time and throughput; Data quality monitoring: monitor data quality, such as data completeness and accuracy; System maintenance includes: Regular updates: Regularly update models and data to ensure the accuracy and timeliness of the system; Fault handling: Handle system faults in a timely manner to ensure stable system operation.