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In a future Smart Grid context, increasing challenges in managing the stochastic local energy supply and demand are expected. This increased the need of more accurate energy prediction methods in order to support further complex decision-making processes. Although many methods aiming to predict the energy consumption exist, all these require labelled data, such as historical or simulated data. Still, such datasets are not always available under the emerging Smart Grid transition and complex people behaviour. Our approach goes beyond the state-of-the-art energy prediction methods in that it does not require labelled data. Firstly, two reinforcement learning algorithms are investigated in order to model the building energy consumption. Secondly, as a main theoretical contribution, a Deep Belief Network (DBN) is incorporated into each of these algorithms, making them suitable for continuous states. Thirdly …