We present here a combined aggregative short-term load forecasting method for smart grids, a novel methodology that allows to obtain a global prognosis by summing up the forecasts on the compounding individual loads. More accurately, we detail here three new approaches, namely bottom-up aggregation (with and without bias correction), top-down aggregation (with and without bias correction), and regressive aggregation. Further, we have devised an experiment to compare their results, evaluating them with two datasets of real data and showing the feasibility of aggregative forecast combinations for smart grids.

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Abstract-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.

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Abstract-Short-term load forecasting (STLF) has become an essential tool in the electricity sector. It has been classically object of vast research since energy load prediction is known to be nonlinear. In a previous work, we focused on non-residential building STLF, an special case of STLF where weather has negligible influence on the load. Now we tackle more modern buildings in which the temperature does alter its energy consumption. This is, we address here fully-HVAC (Heating, Ventilating, and Air Conditioning) ones. Still, in this problem domain, the forecasting method selected must be simple, without tedious trial-and-error configuring or parametrising procedures, work with scarce (or any) training data and be able to predict an evolving demand curve. Following our preceding research, we have avoided the inherent non-linearity by using the work day schedule as day-type classifier. We have evaluated the most popular STLF systems in the literature, namely ARIMA (autoregressive integrated moving average) time series and Neural networks (NN), together with an Autoregressive Model (AR) time series and a Bayesian network (BN), concluding that the autoregressive time series outperforms its counterparts and suffices to fulfil the addressed requirements, even in a 6 day-ahead horizon.