Non-intrusive electric equipment detection method based on big data and supervised learning algorithm

Abstract

The invention discloses a non-intrusive electric equipment detection method based on big data and a supervised learning algorithm, and mainly relates to the technical field of electric power detection. Comprising the following steps: data acquisition and preprocessing: collecting total load data of a power system through non-intrusive load monitoring equipment, and performing abnormal value elimination, data normalization and sliding window segmentation processing; feature extraction and importance analysis: extracting key features from the preprocessed data by using a supervised learning algorithm, and performing feature importance evaluation; and model training and optimization are carried out, a multi-task learning model is constructed, and the accuracy of load decomposition is improved by iteratively optimizing model parameters. The non-intrusive electric equipment detection method has the beneficial effects that the invention provides an innovative non-intrusive electric equipment detection method based on big data and a supervised learning algorithm. The method aims at remarkably improving the accuracy and efficiency of load monitoring by combining the big data processing capacity and the high-performance machine learning model.

Description

Technical Field

[0001] The present invention relates to the technical field of power detection, and specifically to a non-intrusive electrical equipment detection method based on big data and supervised learning algorithms. Background Technique

[0002] The statements in this part only provide background technical information related to the present invention and do not necessarily constitute prior art.

[0003] Under the background of the rapid development of smart grid technology, power load monitoring has become a key link in improving the intelligence level of the power grid. Traditional power load monitoring methods require installing independent monitoring devices on each electrical equipment, which not only increases the initial investment cost but also brings maintenance complexity. Especially in large-scale power systems, the feasibility of this method is severely challenged. Therefore, it is particularly important to find a more cost-effective and adaptable load monitoring technology.

[0004] The non-intrusive load monitoring (NILM) technology has emerged. It infers the operating states of various electrical equipment by only analyzing the total load data of the power system, thus avoiding the complex hardware deployment of installing independent monitoring devices on each electrical equipment. The NILM technology not only significantly reduces the cost but also improves the flexibility of monitoring, becoming an important part of smart grid technology. However, the existing NILM technology still faces several challenges, which limit its effect in practical applications.

[0005] First of all, the insufficient utilization of data features is one of the main problems faced by the current NILM technology. Since traditional methods mainly rely on low-frequency data features, and these features are often interfered by various factors during extraction, resulting in limited feature extraction ability and difficulty in accurately identifying multi-state devices or complex electricity consumption patterns. This not only affects the accuracy of load monitoring but also limits the application of NILM technology in complex power systems.

[0006] Secondly, the poor generalization performance of the model is also a key factor restricting the development of NILM technology. Existing load monitoring models are often trained based on specific datasets. When the models are applied to different residential or industrial electricity consumption environments, their adaptability is weak, resulting in large fluctuations in monitoring accuracy. This limitation makes it difficult for NILM technology to meet the needs of different users during the promotion and application process.

[0007] In addition, the low data processing efficiency is also one of the challenges faced by NILM technology. With the rapid development of smart grid technology, the amount of data generated by power systems has increased explosively. However, the existing algorithms often cannot match the actual demand in terms of computing efficiency when dealing with massive power data, resulting in the difficulty of realizing real-time monitoring. This not only affects the real-time performance of NILM technology but also limits its wide application in smart grids. Summary of the Invention

[0008] The object of the present invention is to provide a non-invasive electrical equipment detection method based on big data and supervised learning algorithms. The present invention proposes an innovative non-invasive electrical equipment detection method based on big data and supervised learning algorithms. This method aims to significantly improve the accuracy and efficiency of load monitoring by combining big data processing capabilities and high-performance machine learning models. The present invention uses big data technology to deeply mine and analyze the total load data of the power system, extracts features with load monitoring value; at the same time, uses supervised learning algorithms to train and optimize the features, and constructs a load monitoring model suitable for different electricity usage environments. Through this method, the present invention not only improves the accuracy of load monitoring, but also enhances the generalization performance of the model and the data processing efficiency.

[0009] To achieve the above object, the present invention is realized through the following technical solutions:

[0010] Non-invasive electrical equipment detection method based on big data and supervised learning algorithms

[0011] It includes the following steps:

[0012] S1. Data collection and preprocessing, collecting the total load data of the power system through non-invasive load monitoring equipment, and performing outlier removal, data normalization, and sliding window segmentation processing;

[0013] It also includes a data cleaning step for removing noise and redundant information in the data.

[0014] S2. Feature extraction and importance analysis, using supervised learning algorithms to extract key features from the preprocessed data and perform feature importance evaluation;

[0015] Use supervised learning algorithms such as random forest or gradient boosting tree for feature extraction and importance evaluation.

[0016] S3. Model training and optimization, constructing a multi-task learning model, including a feature extraction layer, a feature fusion layer, and multiple load decomposition branches, and improving the accuracy of load decomposition by iteratively optimizing model parameters;

[0017] Use loss functions such as mean square error loss and cross-entropy loss for iterative optimization of model parameters.

[0018] S4. Load monitoring and anomaly detection, inputting the real-time collected total load data into the trained model, outputting the power time series and operating status of each electrical equipment, and comparing the real-time power curve with the preset standard power curve to identify abnormal electricity usage behaviors;

[0019] It also includes an alarm trigger mechanism that immediately triggers an alarm and generates an exception report when abnormal power consumption behavior is recognized.

[0020] Data collection and preprocessing: Collect key characteristic parameters such as voltage, current, and power through non-intrusive load monitoring devices deployed on the power system bus. Use techniques such as outlier removal, data normalization, and sliding window segmentation processing to preprocess the original data and generate a high-quality training data set.

[0021] Feature extraction and importance analysis: Use supervised learning algorithms (such as random forest, gradient boosting tree, etc.) to extract features with load monitoring value from the preprocessed data. Through feature importance evaluation, classify the features into different weight categories to provide optimized input for subsequent model training.

[0022] Model training and optimization: Build a load monitoring model based on multi-task learning, which includes a feature extraction layer, a feature fusion layer, and multiple load decomposition branches. Use loss functions such as mean square error loss and cross-entropy loss to iteratively optimize the model parameters to improve the accuracy of load decomposition and device status recognition.

[0023] Load monitoring and anomaly detection: Input the total load data collected in real time into the trained model, and output the power time series and operating state probability distribution of each electrical device. By comparing the real-time power curve with the preset standard power curve, identify abnormal power consumption behavior and immediately trigger the alarm mechanism.

[0024] Compared with the prior art, the beneficial effects of the present invention are as follows:

[0025] First, by combining big data technology and supervised learning algorithms, the present invention realizes the in-depth mining and analysis of the total load data of the power system, effectively extracts features with load monitoring value, and significantly improves the accuracy and efficiency of load monitoring. This innovation not only solves the limitations of traditional power monitoring methods in terms of cost, deployment complexity, and adaptability, but also provides key technical support for the development of smart grid technology.

[0026] Second, the multi-task learning model constructed by the present invention realizes the accurate recognition and monitoring of complex power consumption patterns through the coordinated action of the feature extraction layer, the feature fusion layer, and multiple load decomposition branches. This model design not only improves the accuracy of load decomposition, but also enhances the generalization performance of the model, enabling it to be applicable to different residential or industrial power consumption environments and meeting the needs of different users.

[0027] In addition, the present invention utilizes big data technology to achieve rapid processing and analysis of massive power data, meeting the requirements of real-time monitoring. This innovation not only improves the intelligent level of the power system but also provides strong support for the sustainable development of the power industry. Meanwhile, the low-cost deployment feature of the present invention also gives it broader promotion value in practical applications.

[0028] In summary, the non-intrusive electrical equipment detection method based on big data and supervised learning algorithms proposed by the present invention shows significant advantages in aspects such as the accuracy, efficiency, generalization performance of load monitoring, and data processing efficiency, making important contributions to the development of smart grid technology and the sustainable development of the power industry. Brief Description of the Drawings

[0029] Attached Figure 1 is the flowchart of the non-intrusive electrical equipment detection method based on big data and supervised learning algorithms of the present invention. Detailed Embodiments

[0030] The present invention will be further described below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. In addition, it should be understood that after reading the content taught by the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by this application.

[0031] The non-intrusive electrical equipment detection method of the present invention based on big data and supervised learning algorithms has the following main structural steps:

[0032] Data acquisition is the basic link of the entire system, and its quality directly affects the accuracy and reliability of subsequent analysis. In a household or industrial power consumption environment, non-intrusive load monitoring devices are deployed at the incoming bus. These devices have the characteristics of high precision and high reliability and can real-time monitor key characteristic parameters of the power system, such as voltage, current, power, etc. By real-time collecting these data, the system can comprehensively master the operating state of the power consumption environment. The data transmission link uses wireless or wired communication technology to ensure that the collected data can be efficiently and stably transmitted to the data processing center, providing reliable data support for subsequent analysis.

[0033] Data preprocessing is a crucial step to ensure data quality. Since there may be noise, outliers, or missing values in the original data, directly using this data will lead to biases in the analysis results. Therefore, the system first uses statistical methods or machine learning algorithms to identify and remove outliers from the data. After removing the outliers, the data quality is significantly improved. Next, for data with different dimensions, the system uses methods such as linear normalization or min-max normalization to convert the data into a unified scale. This step not only eliminates the impact of dimensional differences on the analysis results but also provides standardized input for subsequent feature extraction and model training. In addition, to process continuous data streams, the system uses the sliding window technique to divide the data into multiple fixed-length data segments. Each segment of data serves as the input for subsequent feature extraction and model training, ensuring the continuity and real-time nature of data processing.

[0034] Feature extraction is a key step in mining useful information from the original data. The system uses supervised learning algorithms to extract key features from the preprocessed data. These features can reflect key information such as the operating status and energy consumption patterns of electrical devices, providing high-quality input for subsequent model training. For example, by analyzing the characteristics of the current waveform, the system can identify the start and stop states of different devices; by the power characteristics, the system can judge the energy consumption level of the devices. After feature extraction, the system further conducts feature selection, screening out features that have a significant impact on the model's prediction performance through feature importance evaluation. This step not only reduces the data dimension but also improves the generalization ability of the model.

[0035] In the importance analysis stage, the system assigns different weights to each feature according to the feature importance evaluation results. These weights reflect the contribution degree of the features to the model's prediction results, providing optimized input for subsequent model training. At the same time, the system uses visualization tools such as heatmaps and bar charts to display the importance distribution of the features. Through visual analysis, users can intuitively understand the model's decision-making process, thus better trusting and using the output results of the system.

[0036] Model construction is the core link of the entire system. The system designs a multi-task learning model that includes a feature extraction layer, a feature fusion layer, and multiple load decomposition branches. The feature extraction layer is responsible for extracting key features from the input data; the feature fusion layer fuses different features to form a comprehensive feature vector; the load decomposition branches are responsible for decomposing the comprehensive feature vector into the power time series and operating status of each electrical device. This multi-task learning model can handle multiple related tasks simultaneously, improving the efficiency and accuracy of the model.

[0037] During the model training phase, the system sets initial values for model parameters using methods such as random initialization or pre-trained model weights. Subsequently, optimization algorithms such as gradient descent and Adam are used to continuously adjust the model parameters to minimize the error between the model's output and the true labels. To evaluate the generalization performance of the model, the system uses cross-validation techniques to divide the dataset into a training set and a validation set to ensure the adaptability of the model under different electricity usage environments. During the model evaluation phase, the system uses performance metrics such as accuracy, recall, and F1-score to evaluate the prediction performance of the model. At the same time, by comparing the impact of different features on the model's prediction performance, the effectiveness of feature extraction and importance analysis is further verified.

[0038] Load monitoring is one of the main functions of the system. During the real-time monitoring phase, the system inputs the total load data collected in real-time into the trained model for prediction. The model outputs the power time series and the probability distribution of the operating states of each electrical device, providing intuitive electricity usage information for users or managers. For example, the system can display data such as the electricity consumption and operating time of each device in real-time to help users optimize their electricity usage behavior.

[0039] Anomaly detection is another important function of the system. The system sets the threshold for anomaly detection based on historical data or user requirements and compares and analyzes the real-time power curve with the preset standard power curve. When an abnormal electricity usage behavior is identified, the system immediately triggers the alarm mechanism and generates an anomaly report. For example, when the electricity consumption of a certain device suddenly increases or decreases, the system will issue an alarm to remind the user to check the device status. This real-time anomaly detection function not only improves electricity usage safety but also provides strong support for fault troubleshooting.

[0040] To maintain the accuracy and adaptability of the model, the system regularly updates and maintains the model. During the data collection phase, the system regularly collects new load data for updating and optimizing the existing model. After the newly collected data is cleaned and preprocessed, the data quality is ensured to meet the requirements. During the model update phase, the system adjusts the model parameters according to the new data to maintain the accuracy and adaptability of the model. At the same time, the new data is used for model verification to ensure that the performance of the updated model meets expectations.

[0041] During the maintenance and management phase, the system records key information during the model operation, such as input data, prediction results, anomaly reports, etc. These log information provides important basis for subsequent analysis and optimization. In addition, the system also collects user feedback and continuously improves the model according to the requirements. For example, users can put forward optimization suggestions based on the actual usage situation, and the system adjusts the model parameters or function design according to these suggestions to better meet the user's needs.

[0042] The present invention proposes an innovative non-invasive method for detecting electrical appliances based on big data and supervised learning algorithms. This method aims to significantly improve the accuracy and efficiency of load monitoring by combining big data processing capabilities and high-performance machine learning models. The present invention utilizes big data technology to deeply mine and analyze the total load data of the power system, extracting features valuable for load monitoring; at the same time, it uses supervised learning algorithms to train and optimize the features, constructing a load monitoring model applicable to different electricity usage environments. Through this method, the present invention not only improves the accuracy of load monitoring, but also enhances the generalization performance of the model and the data processing efficiency.

Claims

1. A non-intrusive electrical equipment detection method based on big data and supervised learning algorithm, characterized in that: The following steps are involved: S1. Data collection and preprocessing: collect total load data of the power system through non-intrusive load monitoring equipment, and perform outlier removal, data normalization and sliding window segmentation processing; S2. Feature extraction and importance analysis: Use supervised learning algorithms to extract key features from preprocessed data and evaluate feature importance; S3. Model training and optimization: building a multi-task learning model, including feature extraction layer, feature fusion layer and multiple load decomposition branches, and improving the accuracy of load decomposition by iteratively optimizing model parameters; S4. Load monitoring and anomaly detection: input the total load data collected in real time into the trained model, output the power time series and operating status of each electrical equipment, and compare the real-time power curve with the preset standard power curve to identify abnormal power consumption behavior.

2. The non-intrusive electrical equipment detection method based on big data and supervised learning algorithm according to claim 1 is characterized in that: The step S1 also includes a data cleaning step for removing noise and redundant information in the data.

3. The non-intrusive electrical equipment detection method based on big data and supervised learning algorithm according to claim 1 is characterized in that: In the step S2, supervised learning algorithms such as random forest or gradient boosting tree are used to extract features and evaluate importance.

4. The non-intrusive electrical equipment detection method based on big data and supervised learning algorithm according to claim 1 is characterized in that: In the step S3, loss functions such as mean square error loss and cross entropy loss are used to iteratively optimize the model parameters.

5. The non-intrusive electrical equipment detection method based on big data and supervised learning algorithm according to claim 1 is characterized in that: The step S4 also includes an alarm triggering mechanism, which immediately triggers an alarm and generates an abnormality report when abnormal electricity usage behavior is identified.

6. The non-intrusive electrical equipment detection method based on big data and supervised learning algorithm according to claim 1 is characterized in that: It also includes a model updating step to regularly collect new load data to update and optimize the existing model to adapt to changes in the power system.

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