Different states of multi-block based forecast engine for price and load prediction

This work proposes different prediction models based on multi-block forecast engine for load and price forecast in electricity market. Due to high correlation of load and price signals, the density of this reaction can affect the demand curve and shift it in market. Furthermore, to improve the operation and planning improvement in the power system, an accurate prediction model can play an important role. So, in this paper, a complex prediction approach is presented based on feature selection, and multi-stage forecast engine. The forecast engine is comprised of multi-block neural network (NN) and optimized by an intelligent algorithm to increase the training mechanism and forecasting abilities. Moreover, different models of multi-block forecast engine are presented in this paper to choose the effective model. In other words, different combinations of NN are tested in the same prediction condition to show their abilities. The proposed model is tested over real-world engineering test cases through comparison with other prediction methods. Obtained results demonstrate the validity of the proposed model.