Affiliations: [a]
Department of Electrical and Computer Engineering, University of Michigan – Dearborn, Michigan, USA
| [b]
Department of Electrical and Computer Engineering, Safashahr Branch, Islamic Azad University, Safashahr, Iran
Correspondence:
[*]
Corresponding author. Omid Avatefipour, Department of Electrical and Computer Engineering, University of Michigan – Dearborn, Michigan, USA. E-mail: [email protected].
Abstract: In this paper, a new combined method based on Clonal Selection Algorithm (CSA) and Artificial Neural Network (ANN) machine learning algorithm has been presented for the Short Term Load Forecasting (STLF) application. Compared to the other existing evolutionary based algorithm in this area, the proposed technique exploits both the ANN’s learning properties for solving the nonlinear and complex problems and CSA population-based algorithm for global and local search. Moreover, in order to select the most informative and irredundant features from the input feature set, a new feature selection method is introduced by using fuzzy set theory and fuzzy clustering techniques. In regards to overall performance enhancement of CSA algorithm, three sub-modifications are proposed to expand the search capability of CSA and avoid premature convergence. Finally, in order to demonstrate the effectiveness and superiority of proposed method compared to other existing methods, the real dataset of daily peak value of electric load consumption is provided and simulation results reveal the improved forecasting accuracy of the proposed method over the other popular techniques in the STLF application.
Keywords: Short Term Load Forecasting (STLF), optimization techniques, Clonal Selection Algorithm (CSA), Artificial Neural Network (ANN), fuzzy-based feature selection
