Short-Term Load Forecasting of a Distribution Transformer using Self-Organizing Fuzzy Neural Networks

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.

کلیدواژه ها:

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