Resumen
Understanding how, why and when energy consumption changes provides a tool for decision makers throughout the power networks. Thus, energy forecasting provides a great service. This research proposes a probabilistic approach to capture the five inherent dimensions of a forecast: three dimensions in space, time and probability. The forecasts are generated through different models based on artificial neural networks as a post-treatment of point forecasts based on shallow artificial neural networks, creating a dynamic ensemble. The singular value decomposition (SVD) technique is then used herein to generate temperature scenarios and project different futures for the probabilistic forecast. In additional to meteorological conditions, time and recency effects were considered as predictor variables. Buildings that are part of a university campus are used as a case study. Though this methodology was applied to energy demand forecasts in buildings alone, it can easily be extended to energy communities as well.