  This work presents ensemble forecasting of monthly electricity demand using
pattern similarity-based forecasting methods (PSFMs). PSFMs applied in this
study include $k$-nearest neighbor model, fuzzy neighborhood model, kernel
regression model, and general regression neural network. An integral part of
PSFMs is a time series representation using patterns of time series sequences.
Pattern representation ensures the input and output data unification through
filtering a trend and equalizing variance. Two types of ensembles are created:
heterogeneous and homogeneous. The former consists of different type base
models, while the latter consists of a single-type base model. Five strategies
are used for controlling a diversity of members in a homogeneous approach. The
diversity is generated using different subsets of training data, different
subsets of features, randomly disrupted input and output variables, and
randomly disrupted model parameters. An empirical illustration applies the
ensemble models as well as individual PSFMs for comparison to the monthly
electricity demand forecasting for 35 European countries.
