IEEE Transactions on Pattern Analysis and Machine Intelligence
Iterative RELIEF for Feature Weighting: Algorithms, Theories, and Applications
IEEE Transactions on Pattern Analysis and Machine Intelligence
Design of input vector for day-ahead price forecasting of electricity markets
Expert Systems with Applications: An International Journal
Normalized mutual information feature selection
IEEE Transactions on Neural Networks
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Using mutual information for selecting features in supervised neural net learning
IEEE Transactions on Neural Networks
Day-ahead electricity price forecasting by a new hybrid method
Computers and Industrial Engineering
Computers and Industrial Engineering
Hi-index | 0.00 |
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.