Affiliations: Department of Electrical Engineering, Science and Research branch, Islamic Azad University, Tehran, Iran | Department of Electrical Engineering, Yasoj Branch, Islamic Azad University, Yasoj, Iran
Note: [] Corresponding author. Sirus Mohammadi, Department of Electrical Engineering, Yasoj Branch, Islamic Azad University, Yasoj, Iran. Tel./Fax: +98 7115644923; E-mail: mr.rost.2020@gmail.com
Abstract: Wind turbine power forecasting is one of the most challenging and tedious issues in the power engineering field. In this way, we suggest a probabilistic method based on a sufficient optimization tool to construct optimal prediction intervals (PIs). Here we use combined lower upper bound method to capture the uncertainty of forecasting. In order to find the optimal PIs, a suitable multi-objective approach is employed to satisfy both PI coverage probability and PI width equally. According to the high complexity and nonlinearity of the problem, a new optimization algorithm called modified firefly algorithm (MFA) is employed to find the optimal PIs. The proposed modification method makes use of levy flight operator and crossover operations to increase the diversity of the fireflies in the population. In order to see the satisfying performance of the proposed method, the practical dataset of the Wind Farm located near Cape Jervis on the Fleurieu Peninsula in Australia is used as the case study.
