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

• Published in: Energies (Basel) 2022-11, Vol.15 (21), p.7972
• Main Authors: Moalem, Sepehr, Ahari, Roya M., Shahgholian, Ghazanfar, Moazzami, Majid, Kazemi, Seyed Mohammad
• Format: Article
• Language: English
• Subjects: Accuracy; Algorithms; Bayesian analysis; Decision making; Distributed generation; ELATLBO; Electric power demand; Electricity; Electricity generation; electricity supply chain; Energy; Energy consumption; Energy storage; Energy use; Forecasting; hyper-parameter; Lines of credit; Logistics; long short-term memory neural network (LSTM); Machine learning; Mathematical models; Mathematical optimization; Metal industry; Methods; micro-grid; Neural networks; Optimization; Steel industry; Supply chains; Support vector machines; Time response; Time series; Transmission lines; wavelet transform
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en15217972

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.