Autoregressive Integrated Moving Average (ARIMA) and Long Short-Term Memory (LSTM) Network Models for Forecasting Energy Consumptions
Year: 2022
Type: article
Abstract: Unlike other sources of energy, electricity can't be stored. Therefore, an estimation of Energy Consumption (EC) with good accuracy is required to manage demand and supply in the smart grid. Not only good accuracy, but reliability is also on-demand in the prediction model to optimize resource allocation. Therefore, in this study we have implemented and examine two different models: a machine learning model, Autoregressive Integrated Moving Average (ARIMA), and a deep learning-based model Long Short-Term Memory (LSTM). Although ARIMA showed powerful statistical analysis and less robustness, LSTM demonstrated highly accurate results which may stop us to lead false alarming of over-demand and low consumption of energy. In last, we have concluded our result by presenting significant improvement in forecasting energy by LSTM using various evaluation criteria e.g., Mean Square Error (MSE), Root Mean Square Error (RMSE), and other normalized matrices.
Author: Samir M. Shariff
Institution: Taibah University
Language: English
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4281715318