Electricity Consumption Prediction in Oil and Gas Equipment Service and Maintenance Workshops Using RNN LSTM

¹ Automotive and Robotics Program, Computer Science Department, BINUS ASO School of Engineering, Bina Nusantara University, Jakarta, Indonesia 11480
² IT Onsite Analyst Department, Schlumberger, Cikarang, Indonesia

^* Corresponding author: muhammad.zacky@binus.ac.id

Abstract

This research offers a Recurrent Neural Network (RNN) and Long Short-Term Memory (LSTM) model for forecasting power usage in a facility that provides oil and gas equipment service and maintenance. The model was used using hourly electricity consumption data. The LSTM model was chosen because of its compatibility with time-series data and its capacity to capture temporal dependencies and patterns in sequential data, which may be utilized to predict future consumption. Experiments were undertaken in this study to determine the ideal model parameters and evaluate the model’s accuracy using the root mean squared error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) metrics. The findings demonstrated that the suggested model accurately predicted electricity usage with a MAPE of 3%. The quality and quantity of available data for the training dataset may, however, affect the accuracy of the model. Overall, our results indicate that the suggested RNN LSTM model can properly estimate factory power use.