Karim Beiranvand - Dept. of Electrical and Computer Engineering Jundi-Shapur University of Technology, Dezful, Iran
Seyyedeh Fatemeh Molaeezadeh - Dept. of Electrical and Computer Engineering Jundi-Shapur University of Technology, Dezful, Iran
Hamzeh Beiranvand - Dept. of Electrical Engineering Lorestan University, Khorramabad, Iran
Mohammad Hassan Moradi - Dept. of Biomedical Medical Engineering Amirkabir University of Technology, Tehran, Iran

The distribution transformer load forecasting is very essential in the control of future smart grids and economical interfacing of Distributed Resources (DRs) to distribution networks. A distribution transformer connects DRs to the main grid. Exact distribution transformer load forecasting makes an economical DRs scheduling possible. Therefore, in this paper, a Self-Organizing fuzzy neural network (SOFNN) is introduced to perform a five-minute load forecasting for a real-life distribution transformer in Lorestan Electric Power Distribution Company (LEPDC). Simulation results for active and reactive powers show that the proposed SOFNN outperforms ANFIS.

self-organizing fuzzy neural network; distribution transformer; load forecasting;