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Retrofitting Neural Networks with Nature Inspired Optimization Algorithms, Improving Energy Efficiency in Steel Production
Sarah A. ALabbas *
Education Directorate of Najaf province, 54001-Najaf, Iraq
Abstract:   (70 Views)
Given the huge growth in urban innovation over the past 10 years, smart cities require rational and workable solutions for transportation, building infrastructure, environmental conditions, and human enjoyment. This paper presents and explores data-mining-based predicted energy consumption models for a small-scale, intelligent steel company in South Korea. Devices built on the Internet of Things (IoT) are used to collect and process energy use data in order to forecast. Among the data used are leading and following currents, carbon dioxide emissions, load kinds, reactive power, and power factor. The Leagues Championship Algorithm (LCA), Evaporation-rate Water Cycle Algorithm (ERWCA), Multiverse Optimization Algorithm (MVO), Cuckoo Optimization Algorithm (COA), and Stochastic Fractal Search (SFS). The following metrics are used to evaluate the models' predictive power: root mean square error, mean absolute error (MAE), and coefficient of variation (R2) (RMSE). With the greatest R2 values (0.99800 during testing and 0.99815 during training), the Multilayer Perceptron (MLP) arrangement augmented by ERWCA performs exceptionally well and demonstrates a strong correlation between the predicted and actual energy consumption. Moreover, ERWCA has the lowest RMSE, a measure of the least number of prediction errors (2.09627 in the testing phase and 2.03778 in the training phase). Comparably low RMSE values (2.59167 during testing and 2.50700 during training) and excellent performance in terms of R2 values (0.99695 during testing and 0.99720 during training) also suggest that SFS could be used to improve MLP models for accurate energy consumption forecasts in smart city industrial buildings. These findings show how reliable and accurate the SFS and ERWCA energy use estimations are.
 
Keywords: Energy Consumption, Steel Production, Artificial Neural Network, Metaheuristic
Full-Text [PDF 3740 kb]   (40 Downloads)    
Type of Study: Research | Subject: General
Received: 2025/01/7 | Accepted: 2025/09/1
* Corresponding Author Address: Najaf-Iraq
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