Publications

Abstract : Accurate demand forecasting is necessary for choosing the right combination of compressors in a compressed air system for better energy saving. Also, prediction within the tolerance is a challenging task due to the stochastic nature of the demand. Therefore, this work proposes an efficient data driven approach for compressed air demand forecasting. We initially use the standard deep learning model Long Short Term Memory (LSTM) for prediction and forecasting. Further we use hybrid model in which we use LSTM to predict the required modes obtained from variational mode decomposition (VMD). We select the best model based on the prediction accuracy. The performance evaluation on the acquired datasets shows that the LSTM plus VMD model forecasts the demand within the specification.