Development of artificial neural networks for an energy storage system generated power prediction

As energy utility firms expand their production outputs from renewable energy resources, interest in investment in an energy storage system (ESS) will increase in the coming years. This article determines the optimal number of hidden nodes and the maximum number of epochs to generate the best prediction results for this generated power estimation. Primary energy efficiency, energy ratio, and ambient air temperature were used to predict the generated power of an ESS. Experimental data sets for compressed air energy storage (CAES) were used to train a single hidden layer artificial neural network (ANN) model. The developed ANN models for CAES and compressed air storage with humidification (CASH) show quick convergence to the targeted error function. Based on eight different artificial neural network architectures, a 3−5−1 ANN architecture generated the best results. This architecture mean squared error was 0.00042. Using experimental data for CASH to validate the single hidden layer model, the ANN model performed satisfactorily.