(GMR Institute of Technology, India)(2) R. Dharmaprakash
(Panimalar Engineering College, India)(3) S. Sathya
(S. A. Engineering College, India)*corresponding author
AbstractThis article presents a machine learning model for predicting energy consumption in the steel industry, which aids in energy management, cost reduction, environmental regulation compliance, informed decision-making for future energy investments, and contributes to sustainability. The dataset used for the prediction model comprises 11 attributes and 35,040 instances. The CatBoost prediction algorithm was employed for energy consumption prediction, and hyperparameter optimization was performed using GridSearchCV with 5-fold cross-validation. The developed model has undergone a comparative analysis based on both Root Mean Squared Error (RMSE) and Mean Absolute Percentage Error (MAPE) metrics, demonstrating its promise for accurate energy consumption prediction on both the training and test sets. The proposed model accurately predicts energy consumption for different load types, achieving impressive results on both the training set (RMSE=0.382, R2=0.999, MAPE=1.139) and the test set (RMSE=1.073, R2=0.998, MAPE=1.142). These findings highlight the potential of CatBoost as a valuable tool for energy management and conservation, enabling organizations to make informed decisions, optimize resource allocation, and promote sustainability.
KeywordsEnergy Consumption; Data Analysis; Predictive Modeling; Machine Learning in Steel Industry; Energy Optimization
|
DOIhttps://doi.org/10.31763/ijrcs.v4i1.1234 |
Article metrics10.31763/ijrcs.v4i1.1234 Abstract views : 474 | PDF views : 210 |
Cite |
Full Text Download
|
References
[1] W. Long et al., “Quantitative assessment of energy conservation potential and environmental benefits of an iron and steel plant in China,” Journal of Cleaner Production, vol. 273, p. 123163, 2020, https://doi.org/10.1016/j.jclepro.2020.123163.
[2] B. Gajdzik, R. Wolniak, and W. W. Grebski, “Electricity and Heat Demand in Steel Industry Technological Processes in Industry 4.0 Conditions,” Energies, vol. 16, no. 2, p. 787, 2023, https://doi.org/10.3390/en16020787.
[3] B. Sizirici, Y. Fseha, C. S. Cho, I. Yildiz, and Y. J. Byon, “A Review of Carbon Footprint Reduction in. Construction Industry, from Design to Operation,” Materials vol. 14, no. 20, p. 6094, 2021, https://doi.org/10.3390/ma14206094.
[4] B. A. Salih, P. Wongthongtham, G. Morrison, K. Coutinho, M. Al-Okaily, and A. Huneiti, “Short-term renewable energy consumption and generation forecasting: A case study of Western Australia,” Heliyon, vol. 8, no. 3, p. e09152, 2022, https://doi.org/10.1016/j.heliyon.2022.e09152.
[5] M. Awad and R. Khanna, “Machine Learning,” Efficient Learning Machines, pp. 1-8, 2015, https://doi.org/10.1007/978-1-4302-5990-9_1.
[6] M. K. M. Shapi, N. A. Ramli, and L. J. Awalin, “Energy consumption prediction by using machine learning for smart building: Case study in Malaysia,” Developments in the Built Environment, vol. 5, p. 100037, 2021, https://doi.org/10.1016/j.dibe.2020.100037.
[7] M. R. Braun, H. Altan, and S. B. M. Beck, “Using regression analysis to predict the future energy consumption of a supermarket in the UK,” Applied Energy, vol. 130, pp. 305-313, 2014, https://doi.org/10.1016/j.apenergy.2014.05.062.
[8] J. Fattah, L. Ezzine, Z. Aman, H. E. Moussami, and A. Lachhab, “Forecasting of demand using ARIMA model,” International Journal of Engineering Business Management, vol. 10, 2018, https://doi.org/10.1177/1847979018808673.
[9] R. Tehseen, M. S. Farooq, and A. Abid, “Earthquake Prediction Using Expert Systems: A Systematic Mapping Study,” Sustainability, vol. 12, no. 6, p. 2420, 2020, https://doi.org/10.3390/su12062420.
[10] J. Schmidt, M. R. G. Marques, S. Botti, and M. A. L. Marques, “Recent advances and applications of machine learning in solid-state materials science,” npj Computational Materials, vol. 5, no. 1, p. 83, 2019, https://doi.org/10.1038/s41524-019-0221-0.
[11] Y. S. Kao, K. Nawata, and C. Y. Huang, “Predicting Primary Energy Consumption Using Hybrid ARIMA and GA-SVR Based on EEMD Decomposition,” Mathematics, vol. 8, no. 10, p. 1722, 2020, https://doi.org/10.3390/math8101722.
[12] I. Ridwana, N. Nassif, and W. Choi, “Modeling of Building Energy Consumption by Integrating Regression Analysis and Artificial Neural Network with Data Classification,” Buildings, vol. 10, no. 11, p. 198, 2020, https://doi.org/10.3390/buildings10110198.
[13] A. Baba, “Advanced AI-based techniques to predict daily energy consumption: A case study,” Expert Systems with Applications, vol. 184, p. 115508, 2021, https://doi.org/10.1016/j.eswa.2021.115508.
[14] N. S. Truong, N. T. Ngo, and A. D. Pham, “Forecasting Time-Series Energy Data in Buildings Using an Additive Artificial Intelligence Model for Improving Energy Efficiency,” Computational Intelligence and Neuroscience, vol. 2021, 2021, https://doi.org/10.1155/2021/6028573.
[15] J. S. Chou and D. S. Tran, “Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders,” Energy, vol. 165, pp. 709-726, 2018, https://doi.org/10.1016/j.energy.2018.09.144.
[16] F. D. Rueda, J. D. Suárez, and A. D. R. Torres, “Short-Term Load Forecasting Using Encoder-Decoder WaveNet: Application to the French Grid,” Energies, vol. 14, no. 9, p. 2524, 2021, https://doi.org/10.3390/en14092524.
[17] D. Ramos, P. Faria, Z. Vale, J. Mourinho, and R. Correia, “Industrial Facility Electricity Consumption Forecast Using Artificial Neural Networks and Incremental Learning,” Energies, vol. 13, no. 18, p. 4774, 2020, https://doi.org/10.3390/en13184774.
[18] S. Taheri, B. Talebjedi, and T. Laukkanen, “Electricity Demand Time Series Forecasting Based on Empirical Mode Decomposition and Long Short-Term Memory,” Energy Engineering, vol. 118, no. 6, pp. 1577–1594, 2021, https://doi.org/10.32604/EE.2021.017795.
[19] R. Shwartz-Ziv and A. Armon, “Tabular data: Deep learning is not all you need,” Information Fusion, vol. 81, pp. 84–90, 2022, https://doi.org/10.1016/j.inffus.2021.11.011.
[20] P. P. Shinde and S. Shah, “A Review of Machine Learning and Deep Learning Applications,” 2018 Fourth International Conference on Computing Communication Control and Automation (ICCUBEA), pp. 1-6, 2018, https://doi.org/10.1109/ICCUBEA.2018.8697857.
[21] C. Chatfield, “Exploratory data analysis,” European Journal of Operational Research, vol. 23, no. 1, pp. 5-13, 1986, https://doi.org/10.1016/0377-2217(86)90209-2.
[22] D. Błaszczok, T. Trawiński, M. Szczygieł, and M. Rybarz, “Forecasting of Reactive Power Consumption with the Use of Artificial Neural Networks,” Electronics vol. 11, no. 13, 2022, https://doi.org/10.3390/electronics11132005.
[23] K. Sekar, K. Kanagarathinam, S. Subramanian, E. Venugopal, and C. Udayakumar, “An Improved Power Quality Disturbance Detection Using Deep Learning Approach,” Mathematical Problems in Engineering, vol. 2022, 2022, https://doi.org/10.1155/2022/7020979.
[24] B. C. B. Haarman, R. F. R. D. Lek, W. A. Nolen, R. Mendes, H. A. Drexhage, and H. Burger, “Feature-expression heat maps – A new visual method to explore complex associations between two variable sets,” Journal of Biomedical Informatics, vol. 53, pp. 156-161, 2015, https://doi.org/10.1016/j.jbi.2014.10.003.
[25] C. Fan, M. Chen, X. Wang, J. Wang, and B. Huang B, “A Review on Data Preprocessing Techniques Toward Efficient and Reliable Knowledge Discovery from Building Operational Data,” Frontiers in Energy Research, vol. 9, 2021, https://doi.org/10.3389/fenrg.2021.652801.
[26] D. B. Rubin, “Inference and missing data,” Biometrika, vol. 63, no. 3, pp. 581–592, 1976, https://doi.org/10.1093/biomet/63.3.581.
[27] J. T. Hancock and T. M. Khoshgoftaar, “CatBoost for big data: an interdisciplinary review,” Journal of Big Data, vol. 7, no. 94, 2020, https://doi.org/10.1186/s40537-020-00369-8.
[28] Y. Zhang, Z. Zhao, and J. Zheng, “CatBoost: A new approach for estimating daily reference crop evapotranspiration in arid and semi-arid regions of Northern China,” Journal of Hydrology, vol. 588, p. 125087, 2020, https://doi.org/10.1016/j.jhydrol.2020.125087.
[29] V. R. Joseph and A. Vakayil, “SPlit: An Optimal Method for Data Splitting,” Technometrics, vol. 64, no. 2, pp. 166-176, 2020, https://doi.org/10.1080/00401706.2021.1921037.
[30] K. Kanagarathinam, D. Sankaran, and R. Manikandan, “Machine learning-based risk prediction model for cardiovascular disease using a hybrid dataset,” Data & Knowledge Engineering, vol. 140, p. 102042, 2022, https://doi.org/10.1016/j.datak.2022.102042.
[31] Z. M. Alhakeem, Y. M. Jebur, S. N. Henedy, H. Imran, Bernardo, L. F. A. Bernardo, and H. M. Hussein, “Prediction of Ecofriendly Concrete Compressive Strength Using Gradient Boosting Regression Tree Combined with GridSearchCV Hyperparameter-Optimization Techniques,” Materials, vol. 15, no. 21, p. 7432, 2022, https://doi.org/10.3390/ma15217432.
[32] S. Prusty, S. Patnaik, and S. K. Dash, “SKCV: Stratified K-fold cross-validation on ML classifiers for predicting cervical cancer,” Frontiers in Nanotechnology, vol. 4, 2022, https://doi.org/10.3389/fnano.2022.972421.
[33] S. A. Agnes, A. A. Solomon, and K. Karthick, “Wavelet U-Net++ for accurate lung nodule segmentation in CT scans: Improving early detection and diagnosis of lung cancer,” Biomedical Signal Processing and Control, vol. 87, p. 105509, 2024, https://doi.org/10.1016/j.bspc.2023.105509.
[34] K. Kanagarathinam, S. K. Aruna, S. Ravivarman, M. Safran, S. Alfarhood, and W. Alrajhi, “Enhancing Sustainable Urban Energy Management through Short-Term Wind Power Forecasting Using LSTM Neural Network,” Sustainability, vol. 15, no. 18, p. 13424, 2023, https://doi.org/10.3390/su151813424.
[35] G. Ponkumar, S. Jayaprakash, and K. Kanagarathinam, “Advanced Machine Learning Techniques for Accurate Very-Short-Term Wind Power Forecasting in Wind Energy Systems Using Historical Data Analysis,” Energies, vol. 16, no. 14, p. 5459, 2023, https://doi.org/10.3390/en16145459.
[36] G. Ravindiran, G. Hayder, K. Kanagarathinam, A. Alagumalai, and C. Sonne, “Air quality prediction by machine learning models: A predictive study on the indian coastal city of Vishakhapatnam,” Chemosphere, vol. 338, p. 139518, 2023, https://doi.org/10.1016/j.chemosphere.2023.139518.
[37] G. Li et al., “Performance of Regression Models as a Function of Experiment Noise,” Bioinformatics and Biology Insights, vol. 15, 2021, https://doi.org/10.1177/11779322211020315.
[38] K. Sekar, S. Kumar. S, and K. Karthick, “Power Quality Disturbance Detection using Machine Learning Algorithm,” 2020 IEEE International Conference on Advances and Developments in Electrical and Electronics Engineering (ICADEE), pp. 1-5, 2020, https://doi.org/10.1109/ICADEE51157.2020.9368939.
[39] B. S. Kumar, R. Cristin, K. Karthick, and T. Daniya, “Study of Shadow and Reflection based Image Forgery Detection,” 2019 International Conference on Computer Communication and Informatics (ICCCI), pp. 1-5, 2019, https://doi.org/10.1109/ICCCI.2019.8822057.
[40] V. E Sathishkumar et al., “Industry Energy Consumption Prediction Using Data Mining Techniques,” International Journal of Energy, vol. 11, no. 1, pp. 7-14, 2020, http://dx.doi.org/10.21742/ijeic.2020.11.1.02.
Refbacks
- There are currently no refbacks.
Copyright (c) 2023 K. Karthick, R. Dharmaprakash R, S. Sathya
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
| About the Journal | Journal Policies | Author | Information |
International Journal of Robotics and Control Systems
e-ISSN: 2775-2658
Website: https://pubs2.ascee.org/index.php/IJRCS
Email: ijrcs@ascee.org
Organized by: Association for Scientific Computing Electronics and Engineering (ASCEE), Peneliti Teknologi Teknik Indonesia, Department of Electrical Engineering, Universitas Ahmad Dahlan and Kuliah Teknik Elektro
Published by: Association for Scientific Computing Electronics and Engineering (ASCEE)
Office: Jalan Janti, Karangjambe 130B, Banguntapan, Bantul, Daerah Istimewa Yogyakarta, Indonesia