Long-Term Demand Forecasting in Electrical Energy Supply Chain of Espidan Ironstone Industry using Deep Learning and Extreme Learning Machine

Subject Areas : Renewable energy

Sepehr  Moalem ¹ (Department of Industrial Engineering- Najafabad Branch, Islamic Azad University, Najafabad, Iran)
Roya  M. Ahari ² (Department of Industrial Engineering- Najafabad Branch, Islamic Azad University, Najafabad, Iran)
Ghazanfar  Shahgholian ³ (Smart Microgrid Research Center- Najafabad Branch, Islamic Azad University, Najafabad, Iran)
Majid  Moazzami ⁴ (Department of Electrical Engineering- Najafabad Branch, Islamic Azad University, Najafabad, Iran)
Seyed Mohammad  Kazemi ⁵ (Department of Industrial Engineering- Najafabad Branch, Islamic Azad University, Najafabad, Iran)

Received: 2020-12-22 Accepted : 2021-03-13 Published : 2022-05-22

Keywords: Support vector machine, Wavelet Transform, Decision tree, deep learning, intensive learning machine, electrical supply chain, demand forecasting, mean absolute percentage error,

Abstract :

Espidan ironstone industries is one of the most consumed power industries in the electricity supply chain of Isfahan province as the second industrial hub of the country and one of the main suppliers of raw materials in the supply chain of the country's steel industry. Planning in a large-scale electricity supply chain, in a space full of uncertainty, is begin with electricity demand forecasting.In this paper, a hybrid long-term demand forecasting method in the electricity supply chain of Isfahan's ironstone industries using a combined data mining method including wavelet transform,deep learning and intensive learning machine is proposed. The used data in this study is according to the recorded information from the electrical energy demand signal of Espidan ironstone industries in a period of 40 months in the form of 24-hours. The data in a part of the study period due to the lack of production of this industry in some hours are interrupted. So that only 40% of the data had a value and the remaining, 60% were zero. This subject led to information deficiencies and increases the forecasting error up to 40% in the first step of the proposed algorithm. By completing the first step of the proposed model with intense learning machine (ELM) the forecasting error is reduced and it was possible to create an improved forecasting model for supervised training. Finally, simulation results are compared with other available approaches such as support vector machine and decision tree. The results show the improvement and reduction of error and a significant increase in the accuracy of the proposed method in long-term demand forecasting in the electricity supply chain of Espidan ironstone industries.

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