Research Of Non-Intrusive Load Monitoring Based On Deep Learning

Non-intrusive load monitoring can monitor the operating status and power consumption of the load in the lower circuit through a single intelligent observation meter.Compared with intrusive load monitoring,it has the advantages of low cost,non-intrusive and easy maintenance.In recent years,with the rapid development of artificial intelligence technology,it provides an effective solution to the complex problems in non-intrusive load monitoring.At the same time,the research process of load monitoring is accelerated by artificial intelligence,which promotes the application and promotion of this technology.In the research of non-intrusive load monitoring based on artificial intelligence technology,there are the following difficult problems:①Construct load fingerprints with discriminative and distinctive features.②For non-intrusive load identification based on deep learning,the model has strong feature modeling ability and superior identification effect while maintaining light weight and minimum computational complexity.③There is a significant deviation between the fluctuating power of loads in the total power sequence and the decomposed power amplitude of a single load.④The actual application environment of the model is complex and changeable,and there are unknown distribution differences between the actual data and the training data.Aiming at the above difficult problems,the non-intrusive load monitoring problem is studied based on the deep learning method.The main research contents include the following four aspects.(1)Aiming at the insufficient ability of the load fingerprint representation and mining the latent features of load constructed manually at present,an image-based learnable load fingerprint construction method is proposed.Firstly,the load current features are extracted based on the time series modeling method,and the extracted feature sequence is mapped to the two-dimensional space,and then the two-dimensional image features are classified by the image recognition method to obtain the load category.In order to prove the effectiveness of the learnable load fingerprint method,three feature mapping methods,namely,Learnable Recursive Graph,Learnable Gramma Matrix,and Generative Graph,are used to conduct experiments on the public non-intrusive load identification dataset.The experimental results show that the feature expression ability of the load fingerprint is obviously improved,and more accurate load identification results can be obtained.The robustness of learnable load fingerprints to inputs is demonstrated through comparative experiments with multiple valid inputs.Based on the establishment of the image classification network,different time series modeling network experiments are carried out,and the results show that the temporal convolutional neural network based on residual learning is more suitable for the construction of the load fingerprint.(2)In order to improve the feature extraction ability of the load identification model,a method for modeling the dependencies between long-range features is proposed.Firstly,a temporal convolutional neural network based on residual learning is established to extract the local features of the load current,then an improved non-local attention module is introduced to model the correlation between local features,and the potential feature information of the load is mined.The proposed method is verified on the set.The experimental results show that the improved non-local attention module effectively improves the load identification ability of the model.Compared with the current load identification model based on deep neural network,the temporal convolutional neural network based on residual learning is lightweight,efficient and accurate.(3)In order to solve the problem of deviation between the predicted amplitude and the actual aggregated power fluctuation amplitude when the load is on,a load decomposition method based on switch state classification is proposed according to the reliability of the sequence-to-point model for load state boundary identification.This method first converts the regression model of load power prediction into a classification model of load state,and uses the classification model to obtain the load switch state sequence.In order to reduce the demand for computing resources in the inference stage of the model,a segmented prediction method is used to reduce inference times.Then based on the on-time and off-grace time thresholds,disturbing states are eliminated and discontinuous intervals are filled.Finally,the power consumption of individual appliances is extracted from the total power sequence through the predicted load switching state sequence.(4)Aiming at the difference between the distribution of target domain data and source domain data used for training,a load decomposition method based on data distribution parameter correction is proposed.In this method,a distribution parameter prediction module is added to the existing load disaggregation network,and the correlation coefficient between the target domain and source domain data distribution predicted by the module is used to correct the prediction results of the model.This method does not need to increase large-scale model parameters,and uses less computing resources to improve the load disaggregation accuracy of the original model.This paper studies the deep learning method of load identification and load decomposition in non-intrusive load monitoring,specifically explores the construction method of load fingerprints,a lightweight and accurate load identification model,an efficient and accurate load disaggregation framework.Compared with the load fingerprint based on manual construction,the proposed learnable load fingerprint effectively improves the accuracy of load identification;By introducing an improved non-local attention module,the feature extraction ability of the deep neural network is enhanced;Compared with the load decomposition framework based on regression model,the proposed disaggregation framework based on switch state effectively improves the accuracy of load disaggregation.Compared with the sequence-to-point based load disaggregation model,the proposed segmental forecasting method greatly reduces the number of inferences of the model while maintaining the accuracy of the disaggregation,which is of great significance for the practical application of non-intrusive load decomposition.