Elements of information theory
Elements of information theory
Wrappers for feature subset selection
Artificial Intelligence - Special issue on relevance
Overcoming the Myopia of Inductive Learning Algorithms with RELIEFF
Applied Intelligence
Input Feature Selection by Mutual Information Based on Parzen Window
IEEE Transactions on Pattern Analysis and Machine Intelligence
Theoretical and Empirical Analysis of ReliefF and RReliefF
Machine Learning
Variable selection using svm based criteria
The Journal of Machine Learning Research
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
Multiclass SVM-RFE for product form feature selection
Expert Systems with Applications: An International Journal
Input feature selection for classification problems
IEEE Transactions on Neural Networks
Estimating optimal feature subsets using efficient estimation of high-dimensional mutual information
IEEE Transactions on Neural Networks
Training feedforward networks with the Marquardt algorithm
IEEE Transactions on Neural Networks
Expert Systems with Applications: An International Journal
A new prediction strategy for price spike forecasting of day-ahead electricity markets
Applied Soft Computing
| Hi-index | 12.05 |
In new deregulated electricity market, price forecasts have become a fundamental input to an energy company's decision making and strategy development process. However, the exclusive characteristics of electricity price such as non-stationarity, non-linearity and time-varying volatile structure present a number of challenges for this task. In spite of all performed research on this area in the recent years, there is still essential need for more accurate and robust price forecast methods. Besides, there is a lack of efficient feature selection technique for designing the input vector of electricity price forecast. In this paper, a new two-stage feature selection algorithm composed of modified relief and mutual information (MI) techniques is proposed for this purpose. Moreover, cascaded neural network (CNN) is presented as forecast engine for electricity price prediction. The CNN is composed of cascaded forecasters where each forecaster is a neural network (NN). The proposed feature selection algorithm selects the best set of candidate inputs which is used by the CNN. The proposed method is examined on PJM, Spanish and Ontario electricity markets and compared with some of the most recent price forecast techniques.