A new prediction strategy for price spike forecasting of day-ahead electricity markets

Price spikes are distinctive aspects of electricity price impacting its forecast accuracy. Electricity price spikes can also have serious economical effects on the market participants. However, prediction of electricity price spikes is a complex task and most of current electricity price forecast methods focus on prediction of normal prices. In this paper, a new forecast strategy for prediction of both occurrence and value of electricity price spikes is presented. The proposed strategy has a novel feature selection technique based on information theoretic criteria to select a minimum subset of the most informative features for the forecast process. Also, the strategy includes a new closed loop prediction mechanism composed of probabilistic neural network (PNN) and hybrid neuro-evolutionary system (HNES) forecast engines. The effectiveness of the proposed forecast strategy for the prediction of both price spike occurrence and value is extensively evaluated by the real-life data of PJM (Pennsylvania-New Jersey-Maryland) electricity market. The obtained results confirm the validity of the developed approach.