Analyzing and Forecasting Laboratory Energy Consumption Patterns Using Autoregressive Integrated Moving Average Models

This study applied ARIMA modeling to analyze the energy consumption patterns of laboratory equipment over one month, focusing on enhancing energy management in the laboratory. By explicitly examining AC and DC equipment, this study obtained detailed daily operating cycles and periods of inactivity. Advanced differencing and diagnostic checks were used to verify model accuracy and white noise characteristics through enhanced Dickey–Fuller testing and residual analysis. The results demonstrate the model’s accuracy in predicting energy consumption, providing valuable insights into the use of the model. This study highlights the adaptability and validity of the ARIMA model in laboratory environments, contributing to more competent laboratory energy management practices.