Optimal Placement of Hybrid Wind-Solar System Using Deep Learning Model

Abstract

In this paper, we develop an optimal placement of solar-wind energy systems using restricted Boltzmann machine (RBM). The RBM considers various factors to scale the process of optimal placement and enables proper sizing and placement for attaining increased electricity production from both wind and solar systems. The multiobjective criterion from both solar and wind energy farms simulated on MATLAB simulator shows an increased number of accuracies with reduced mean average error and computation time during training and testing. The results show that the RBM achieves improved rate of finding the optimal placement with a lesser cost and computation time of lesser than 2 ms than other methods.