  Power load forecast with Machine Learning is a fairly mature application of
artificial intelligence and it is indispensable in operation, control and
planning. Data selection techniqies have been hardly used in this application.
However, the use of such techniques could be beneficial provided the assumption
that the data is identically distributed is clearly not true in load
forecasting, but it is cyclostationary. In this work we present a fully
automatic methodology to determine what are the most adequate data to train a
predictor which is based on a full Bayesian probabilistic model. We assess the
performance of the method with experiments based on real publicly available
data recorded from several years in the United States of America.
