Time series anomaly detection is the process of identifying deviations from expected patterns within sequential data points over time. It is a fundamental task in all those scenarios where analysing and understanding temporal trends is essential. This field leverages advanced statistical and machine learning techniques to detect irregularities, spikes or unusual patterns in time series data. Nowadays, it has become of great importance to perform anomaly detection on energy consumptions, in order to identify and prevent a wasteful use of energy for a better management of LECs in EU. This thesis investigates state-of-the-art Deep Learning approaches applied on energy consumption data in a completely Unsupervised manner.