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

Ensemble Forecasting of Monthly Electricity Demand using Pattern Similarity-based Methods