The arrival of the smart grid paradigm has brought a number of novel initiatives that aim at increasing the level of energy efficiency of buildings such as smart metering or demand side management. Still, all of them demand an accurate load estimation. Short-term load forecasting in buildings presents additional requirements, among others the need of prediction models with simple or non-existing parametrisation processes. We extend a previous work that evaluated a number of algorithms to this end. Herewith we present several improvements including a variable data learning window and diverse learning data weighting combinations that further up improve our results. Finally, we have tested all the algorithms and modalities with four different datasets to show how the results hold up.