Abstract
The nonlinear and seasonal fluctuations have brought great challenges to electricity demand prediction. To this end, a novel seasonal intelligent-order grey forecasting model is proposed, which improves the accuracy of traditional grey models with fixed structure Particle swarm optimization is introduced to search the optimal fraction order. Root mean square error, mean absolute percentage error, goodness of fit and Theil’s U are used as performance criteria to test the model superiority. The novel model and other four benchmark models are employed to predict the electricity demand in Zhejiang province. The results show that the proposed model can better capture seasonal variations of electricity demand, the comprehensive percentage mean error is only 2.347%. This grey prediction model with intelligent parameters can be used to provide the basis of seasonal power supply planning to ensure sustainable development of electricity markets.
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