A Comparative Study of LSTM/GRU Models for Energy Long-Term Forecasting in IoT Networks

Abstract

Internet of Things (IoT) applications suffer from network security, sensor deployment, energy consumption, etc. Recent studies show that most energy is consumed in the case of smart homes applications. Households consume up to 40% of total energy in countries of the European Union. Thus, the improvement of energy management is considered as a huge challenge in IoT application. Machine learning especially deep learning models have been applied to deduce about optimal model which can predict the energy consumption at long term. In this article, we aim to deduce the effective predictive model with less errors in the predictions tests. We propose to compare Gated Recurrent Unit(GRU) and Long Short-Term Memory (LSTM). Therefore, to deduce about the optimal model, several metrics have been evaluated such as R-squared, the minimum mean squared error (MSE), mean absolute error (MAE),root mean squared error (RMSE),etc. These metrics have been applied because they serve different purposes and provide complementary information about the model's accuracy and fit.