Long-Term Electricity Demand Forecasting in the Steel Complex Micro-Grid Electricity Supply Chain—A Coupled Approach

Sepehr Moalem, Roya M. Ahari, Ghazanfar Shahgholian (), Majid Moazzami and Seyed Mohammad Kazemi
Additional contact information
Sepehr Moalem: Industrial Engineering Department, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran
Roya M. Ahari: Industrial Engineering Department, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran
Ghazanfar Shahgholian: Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran
Majid Moazzami: Department of Electrical Engineering, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran
Seyed Mohammad Kazemi: Industrial Engineering Department, Najafabad Branch, Islamic Azad University, Najafabad 8514143131, Iran

Energies, 2022, vol. 15, issue 21, 1-17

Abstract: Demand forecasting produces valuable information for optimal supply chain management. The basic metals industry is the most energy-intensive industries in the electricity supply chain. There are some differences between this chain and other supply chains including the impossibility of large-scale energy storage, reservation constraints, high costs, limitations on electricity transmission lines capacity, real-time response to high-priority strategic demand, and a variety of energy rates at different hours and seasons. A coupled demand forecasting approach is presented in this paper to forecast the demand time series of the metal industries microgrid with minimum available input data (only demand time series). The proposed method consists of wavelet decomposition in the first step. The training subsets and the validation subsets are used in the training and fine-tuning of the LSTM model using the ELATLBO method. The ESC dataset used in this study for electrical demand forecasting includes 24-h daily over 40 months from 21 March 2017, to 21 June 2020. The obtained results have been compared with the results of Support Vector Machine (SVM), Decision Tree, Boosted Tree, and Random Forest forecasting models optimized using the Bayesian Optimization (BO) method. The results show that performance of the proposed method is well in demand forecasting of the metal industries.

Keywords: electricity supply chain; long short-term memory neural network (LSTM); hyper-parameter; ELATLBO; wavelet transform; micro-grid; Bayesian optimization (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/15/21/7972/pdf (application/pdf)
https://www.mdpi.com/1996-1073/15/21/7972/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:15:y:2022:i:21:p:7972-:d:954847

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().