شماره ركورد كنفرانس :
5517
عنوان مقاله :
Medium-Term Load Forecasting of Iran Khodro Company using Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) Deep Neural Networks (ICCIA2023)
پديدآورندگان :
Pahlawan Mahasta ma.pahlavan@iko.ir Department of Electrical Engineering, Islamic Azad University, Tehran Central Branch , Tehran, Iran , Barzamini Roohollah r.barzamini.eng@iauctb.ac.ir,barzamini@gmail.com Department of Electrical Engineering, Islamic Azad University, Tehran Central Branch , Tehran, Iran , Yasini Seyyed Abolfazl abolfazlyasini@email.kntu.ac.ir Department of Aerospace Engineering, K. N. Toosi University of Technology, Tehran, Iran
كليدواژه :
Medium Term Load Forecasting (MTLF) , convolutional neural network , deep neural network , linear regression , long short , term memory neural network (LSTM)
عنوان كنفرانس :
نهمين كنفرانس بين المللي كنترل، ابزار دقيق و اتوماسيون
چكيده فارسي :
This paper presents a high-accuracy prediction method for Medium Term Load Forecasting (MTLF) of a manufacturing plant, specifically Iran Khodro Company, using a convolutional neural network (CNN) and a long short-term memory neural network (LSTM). The performance of this method is compared with classical regression techniques such as linear regression, ridge regression, and lasso. The results demonstrate a coefficient of determination (r2_score) of 0.95 for the test data using the deep neural network algorithm, while the classical methods achieve an r2_score of 0.81. This significant difference demonstrates the superior capability of our proposed method. The model utilizes historical data based on past electric charge as input to train the deep learning-based neural network and implement the proposed algorithm. The monthly energy consumption data spanning 9 years from 2011 to 2019 for Iran Khodro Company is employed in this research.
