Deep Learning-Based Time Series Prediction of Micro Gas Turbine Power Output

Abstract

Micro gas turbines play an important role in energy production, and it is critical to make accurate predictions in order to ensure that these turbines operate efficiently. The objective of this study is to predict the power output of micro gas turbines using three different deep learning models (artificial neural network (ANN), convolutional neural network (CNN) and long short-term memory (LSTM)) based on the input voltage data. A time series approach is employed to model the impact of the input voltage on power generation. Another objective of the study is to analyze the efficacy of deep learning-based methods for predicting the power output of micro gas turbines. The results indicate that the ANN model has a MAPE value of approximately 16%, the CNN model has a value of about 26%, and the LSTM model has a value of almost 20%. The ANN model demonstrates the lowest error rate, while the LSTM and CNN models exhibit satisfactory performance. These findings indicate that time series data can be effectively used to predict the power output of micro gas turbines. It is anticipated that in the future, the approaches will be further developed and applied more widely in industry as larger data sets and more sophisticated model improvements become available.