AN INTELLIGENT HYBRID NEURAL NETWORK MODEL IN RENEWABLE ENERGY SYSTEMS

This paper presents a hybrid neural network approach to predict wind speed automatically in renewable energy systems. Wind energy is one of the renewable energy systems with lowest cost of production of electricity with largest resources available. By the reason of the fluctuation and volatility in wind, the wind speed prediction provides the challenges in the stability of renewable energy system. The aim is to compute predicted wind speed based on hybrid model which integrates a Self Organizing Map (SOM) and Back propagation (BP) neural network. The simulation result shows that the proposed approach provides significant result of wind speed prediction with less error rates. Due to seasonality, single computing models have some disadvantages such as fluctuality, randomness and unstable. These disadvantages are rectified by using hybrid computing neural network models. Wind speed prediction is an important in the field of wind power plants.