Maria Kaselimi

TL;DR: A Bayesian-optimized bidirectional Long Short -Term Memory (LSTM) method for energy disaggregation, which is structured in a modular way to address multi-dimensionality issues that arise when the number of appliances increase.

TL;DR: A non-causal adaptive context-aware bidirectional deep learning model for energy disaggregation that harnesses the representational power of deep recurrent Long Short-Term Memory neural networks, while fitting two basic properties of NILM problem which state of the art methods do not appropriately account for.

TL;DR: This paper proposes a convolutional neural network-based architecture with inputs and outputs formed as data sequences taking into consideration an appliance’s previous states for better estimation of its current state, and endows CNN models with a recurrent property in order to better capture energy signal interdependencies.

TL;DR: The proposed deep learning-based approach for ionospheric modeling exploits the advantages of Long Short-Term Memory (LSTM) Recurrent Neural Networks (RNN) for timeseries modeling and predicts the total electron content per satellite from a specific station by making use of a causal, supervised deep learning method.

TL;DR: In this paper, the authors proposed EnerGAN++, a model based on Generative Adversarial Networks (GAN) for robust energy disaggregation, in which the autoencoder achieves a non-linear power signal source separation.