As households are equipped with smart meters,supervised Machine Learning(ML)models and especially Non-Intrusive Load Monitoring(NILM)disaggregation algorithms are becoming increasingly important.To be robust,these mod...As households are equipped with smart meters,supervised Machine Learning(ML)models and especially Non-Intrusive Load Monitoring(NILM)disaggregation algorithms are becoming increasingly important.To be robust,these models require a large amount of data,which is difficult to collect.Consequently,the generation of meaningful synthetic data is becoming more relevant.We use a simulation framework to generate multiple datasets using different techniques and evaluate their quality statistically by measuring the performance of NILM models for transferability.We demonstrate that the method of data generation is crucial to train ML models in a meaningful way.The experiments conducted reveal that adding noise to the synthetic smart meter data is essential to train robust NILM models for transferability.The best results are obtained when this noise is derived from unknown appliances for which no ground truth data is available.Since we observed that NILM models can provide unstable results,we develop a reliable evaluation methodology,based on Cochran’s sample size.Finally,we compare the quality of the generated synthetic data with real data and observe that multiple NILM models trained on synthetic data perform significantly better than those trained on real data.展开更多
With the advent of the Smart Home domain and the increasingly widespread application of Machine Learning(ML),obtaining power consumption data is becoming more and more important.Collecting real-world energy data using...With the advent of the Smart Home domain and the increasingly widespread application of Machine Learning(ML),obtaining power consumption data is becoming more and more important.Collecting real-world energy data using sensors is time consuming,expensive,error-prone and in some situations not possible.Therefore,we present the VA-Creator,a framework to create Virtual Appliances(VAs).These VAs synthesize power consumption patterns(PCPs)based on Neural Networks(NNs)which adapt their architecture to the training data structure to simplify the creation of new VAs.To be able to generate all appliance types available in a typical household we use various kinds of NN,including Multilayer Perceptrons(MLPs),Long Short-Term Memorys(LSTMs)and a specific Generative Adversarial Network(GAN)as well as different ML techniques such as XGBoost,selecting the appropriate technique depending on each appliance’s characteristics.We then compare the results of the ML models against real data and evaluate them by using Dynamic time Warping(DTW)as well as the classification performance of an MLP discriminator as metrics.Additionally,to ensure that the VAs allow to meaningfully train ML models,we use them to generate synthetic data and then train Non intrusive Load Monitoring(NILM)models in an extensive evaluation.The presented evaluation provides evidence that the VA models produce realistic and meaningful results.展开更多
基金funded by the German Ministry for Economics Affairs and Climate Action(BMWK)within the project ForeSightNEXT.
文摘As households are equipped with smart meters,supervised Machine Learning(ML)models and especially Non-Intrusive Load Monitoring(NILM)disaggregation algorithms are becoming increasingly important.To be robust,these models require a large amount of data,which is difficult to collect.Consequently,the generation of meaningful synthetic data is becoming more relevant.We use a simulation framework to generate multiple datasets using different techniques and evaluate their quality statistically by measuring the performance of NILM models for transferability.We demonstrate that the method of data generation is crucial to train ML models in a meaningful way.The experiments conducted reveal that adding noise to the synthetic smart meter data is essential to train robust NILM models for transferability.The best results are obtained when this noise is derived from unknown appliances for which no ground truth data is available.Since we observed that NILM models can provide unstable results,we develop a reliable evaluation methodology,based on Cochran’s sample size.Finally,we compare the quality of the generated synthetic data with real data and observe that multiple NILM models trained on synthetic data perform significantly better than those trained on real data.
基金funded by the German Federal Ministry for Economic Affairs and Climate Action(BMWK)as part of the ForeSightNEXT projectby the German Federal Ministry of Education and Research(BMBF)as part of the ENGAGE project.
文摘With the advent of the Smart Home domain and the increasingly widespread application of Machine Learning(ML),obtaining power consumption data is becoming more and more important.Collecting real-world energy data using sensors is time consuming,expensive,error-prone and in some situations not possible.Therefore,we present the VA-Creator,a framework to create Virtual Appliances(VAs).These VAs synthesize power consumption patterns(PCPs)based on Neural Networks(NNs)which adapt their architecture to the training data structure to simplify the creation of new VAs.To be able to generate all appliance types available in a typical household we use various kinds of NN,including Multilayer Perceptrons(MLPs),Long Short-Term Memorys(LSTMs)and a specific Generative Adversarial Network(GAN)as well as different ML techniques such as XGBoost,selecting the appropriate technique depending on each appliance’s characteristics.We then compare the results of the ML models against real data and evaluate them by using Dynamic time Warping(DTW)as well as the classification performance of an MLP discriminator as metrics.Additionally,to ensure that the VAs allow to meaningfully train ML models,we use them to generate synthetic data and then train Non intrusive Load Monitoring(NILM)models in an extensive evaluation.The presented evaluation provides evidence that the VA models produce realistic and meaningful results.
