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To improve the design of the electricity infrastructure and the efficient deployment of distributed and renewable energy sources, a new paradigm for the energy supply chain is emerging, leading to the development of smart grids. There is a need to add intelligence at all levels in the grid, acting over various time horizons. Predicting the behavior of the energy system is crucial to mitigate potential uncertainties. An accurate energy prediction at the customer level will reflect directly in efficiency improvements in the whole system. However, prediction of building energy consumption is complex due to many influencing factors, such as climate, performance of thermal systems, and occupancy patterns. Therefore, current state-of-the-art methods are not able to confine the uncertainty at the building level due to the many fluctuations in influencing variables. As an evolution of artificial neural network (ANN)-based prediction …