VA-Creator-A Virtual Appliance Creator based on adaptive Neural Networks to generate synthetic power consumption patterns  

作　　者：Michael Meiser Benjamin Duppe Ingo Zinnikus Alexander Anisimov 

机构地区：[1]Deutsches Forschungszentrum für künstliche Intelligenz(DFKI),Stuhlsatzenhausweg 3,Saarbrücken,66123,Saarland,Germany [2]Saarland Informatics Campus,Universität des Saarlandes,Campus,Saarbrücken,66123,Saarland,Germany

出　　处：《Energy and AI》2024年第4期160-200,共41页能源与人工智能(英文)

基　　金：funded by the German Federal Ministry for Economic Affairs and Climate Action(BMWK)as part of the ForeSightNEXT project;by the German Federal Ministry of Education and Research(BMBF)as part of the ENGAGE project.

摘　　要：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.

关 键 词：Smart Home Synthetic Sensor Data Energy data Virtual Appliance Machine Learning Neural Networks Multilayer Perceptron Generative Adversarial Network Dynamic Time Warping Transfer Learning Non-Intrusive Load Monitoring NILMTK 

分 类 号：TP3[自动化与计算机技术—计算机科学与技术]