Neural Network-Based Models for Prediction of Smart Grid Stability

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Abstract
This paper focuses on proposing neural network-based prediction models for smart grid stability. The models are trained using the damped least-squares technique with reaction time, consumed/generated power, and elasticity coefficient as input variables to predict the stability as an output variable. The models' accuracy and effectiveness are compared numerically using R 2 and mean square errors. For all the models, the selected activation functions at the hidden layer is tansig, and purelin at the output layer. The results demonstrated the neural network-based prediction models' adequate performance for training and testing phases. For the system under consideration, the feed-forward neural network's best performance is true in terms of least error and the highest R 2 values.

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