A Medium-Term Load Forecasting of Iran Khodro Company Using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Deep Neural Networks

Abstract

This paper introduces a high-accuracy prediction framework for Medium Term Load Forecasting (MTLF) in a manufacturing plant at Iran Khodro Company, leveraging the power of deep learning. Specifically, the proposed method integrates a Convolutional Neural Network (CNN) with a Long Short-Term Memory (LSTM) neural network, effectively capturing both spatial and temporal dependencies in the data. The performance of this hybrid deep learning model is rigorously evaluated against traditional regression techniques, including Linear Regression, Ridge Regression, and Lasso Regression. experimental results demonstrate a remarkable coefficient of determination (R² score) of 0.95 on the test dataset when employing the deep neural network model, significantly outperforming classical methods, which achieve an R² score of only 0.81. This substantial improvement underscores the superior predictive accuracy and generalization capability of the proposed approach. the model is trained using historical energy consumption data, where past electricity load values serve as inputs for the deep learning architecture. The dataset consists of nine years of monthly energy consumption records (2011–2019) collected from Iran Khodro Company, providing a robust foundation for medium-term load forecasting. The findings of this study highlight the effectiveness of deep learning in industrial energy demand prediction, offering a reliable and scalable solution for optimizing energy management in manufacturing sectors.