@INPROCEEDINGS{pereiraICMLA, author={Pereira, João and Silveira, Margarida}, booktitle={2018 17th IEEE International Conference on Machine Learning and Applications (ICMLA)}, title="{Unsupervised Anomaly Detection in Energy Time Series Data Using Variational Recurrent Autoencoders with Attention}", year={2018}, volume={}, number={}, pages={1275-1282}, keywords={Big Data;data acquisition;electric power generation;photovoltaic power systems;power engineering computing;power system measurement;power system security;probability;security of data;smart power grids;solar power;time series;variational recurrent autoencoder;energy field;smart grids;smart devices;renewable energies;solar photovoltaic energy generation;smart sensors;energy production;smart monitoring systems;anomalous behaviour;variational self-attention mechanism;solar energy generation time series;energy time series data;unsupervised anomaly detection;Big Data;data acquisition;encoding-decoding process;probabilistic reconstruction scores;Time series analysis;Anomaly detection;Decoding;Training;Data models;Logic gates;Computational modeling;Anomaly Detection, Variational Recurrent Autoencoder, Attention, Solar Photovoltaic Energy}, doi={10.1109/ICMLA.2018.00207}, ISSN={}, month={Dec} }