Predicting Solar Power Potential via Refurbished ANN in Associated with Artificial Fish Swarm Optimization (AFSO)
The purpose of this paper is to predict the solar power potential for proficient renewable energy management. To accomplish this, an Artificial Neural Network (ANN) having a single hidden layer associated with ten neurons is not an effective network model to predict solar power potential. To configure ANN is an indispensable, but it is complex and consumes more time to compute through manually or trial-and-error. This urges incorporating optimization techniques to configure the ANN model, which undoubtedly reduces time and resolves process complexity. Here, Artificial Fish Swarm Optimization (AFSO) utilizes to configure the ANN model, which exhibits 95.4% predicting performance, which is superior over contest techniques. This performance ensures proficient renewable energy management possible by configuring ANN with the aid of AFSO.
Keywords: Artificial Neural Network (ANN), Solar Photo Voltaic (PV) cells, Artificial Fish Swarm Optimization (AFSO), solar power potential.