  In order to accurately describe real systems with seasonal disturbances,
which normally appear monthly or quarterly cycles, a novel discrete grey
seasonal model, abbreviated as , is put forward by incorporating the seasonal
dummy variables into the conventional model. Moreover, the mechanism and
properties of this proposed model are discussed in depth, revealing the
inherent differences from the existing seasonal grey models. For validation and
explanation purposes, the proposed model is implemented to describe three
actual cases with monthly and quarterly seasonal fluctuations (quarterly wind
power production, quarterly PM10, and monthly natural gas consumption), in
comparison with five competing models involving grey prediction models ,
conventional econometric technology , and artificial intelligences .
Experimental results from the cases consistently demonstrated that the proposed
model significantly outperforms the other benchmark models in terms of several
error criteria. Moreover, further discussions about the influences of different
sequence lengths on the forecasting performance reveal that the proposed model
still performs the best with strong robustness and high reliability in
addressing seasonal sequences. In general, the new model is validated to be a
powerful and promising methodology for handling sequences with seasonal
fluctuations.
