Utilizing Artificial Neural Networks to Correlate Energy Consumption and Intensity in Metal Industries for the Midwest States

Mohamed I. Youssef; Kada Kada; Yazeed Abushanab; Qais Alnawafah; Ryoichi S. Amano; Asma Khasawneh

doi:10.31181/smeor21202541

Authors

Mohamed I. Youssef University of Wisconsin Milwaukee, Milwaukee, WI, 53211, USA Author https://orcid.org/0000-0002-0680-3918
Kada Kada University of Wisconsin Milwaukee, Milwaukee, WI, 53211, USA Author https://orcid.org/0009-0001-0404-3062
Yazeed Abushanab University of Wisconsin Milwaukee, Milwaukee, WI, 53211, USA Author https://orcid.org/0000-0001-9848-8785
Qais Alnawafah University of Wisconsin Milwaukee, Milwaukee, WI, 53211, USA Author https://orcid.org/0009-0007-1704-8974
Ryoichi S. Amano University of Wisconsin Milwaukee, ASME Life Fellow Richard & Joanne Grigg Fellow Professor, Milwaukee, WI, 53211, USA Author https://orcid.org/0000-0002-9017-1158
Asma Khasawneh University of Wisconsin Milwaukee, Milwaukee, WI, 53211, USA Author https://orcid.org/0000-0002-9906-3620

DOI:

https://doi.org/10.31181/smeor21202541

Keywords:

Energy Audits, Energy Optimization, Energy Efficiency, Machine Learning, Artificial Intelligence, ANN

Abstract

Energy optimization and analysis are significant in mapping energy consumption and savings for various industries. This study sheds light on utilizing two main energy evaluation parameters: Energy Intensity Utilization (EIU) and Specific energy consumption (SEC). This research paper is founded on an extensive 10-year study, from 2013 to 2023, on more than 196 on-site energy assessments performed on various manufacturing companies in Wisconsin and Northern Illinois. This research is considered a comparative study, which aims to explore the correlation between those two parameters using linear regression analysis using SPSS (Statistical Package for Social Sciences) and validate the outcomes using Artificial Neural Networks (ANN). Besides, it is also employed to assess the most updated methods, procedures, and technological applications that are meant to increase energy efficiency. This study demonstrates the relationship between energy consumption, energy savings, and the effects of energy assessment across several industrial sectors. The results have shown a linear correlation between the EIU and SEC as two of the most significant parameters in measuring energy footprint in the energy-auditing sector. Also, the results have shown that the Annual Energy Savings Percentage (AES%) could have interdependence with both the SEC and EIU energy evaluation parameters, which could provide an energy footprint baseline for any stakeholder interested in penetrating the fabricated metal products industry. The ANN results have shown that underfitting occurs when the data model cannot accurately capture the relationship between the input and output variables. The reasons for the underfitting are that the data points are small (95 data points) for a neural network, and the complexity of the model is simplistic for a machine learning application.

Downloads

Download data is not yet available.

References

Kluczek, A., & Olszewski, P. (2017). Energy audits in industrial processes. Journal of Cleaner Production, 142(4), 3437–3453. https://doi.org/10.1016/j.jclepro.2016.10.123

Maache, M., Kada, C., Amano, R., Kumano, H., Selim, O., & Kada, K. (2024). Experimental & mathematical investigation of thermochemical conversion for horse manure. ASME Journal of Energy Resources Technology. https://doi.org/10.1115/1.4065956

Kada, C., Maache, M., Kada, K., Selim, O. M., Youssef, M. A., & Amano, R. S. (2025). Pyrolysis treatment for sludge and animal manures: Impact of heating rate. Journal of Energy Resources Technology, 1(3). https://doi.org/10.1115/1.4067509

U.S. Energy Information Administration. (2023). Monthly energy review. https://www.eia.gov/totalenergy/data/monthly/

U.S. Energy Information Administration. (2023). Independent statistics & analysis. https://www.eia.gov/energyexplained/us-energy-facts/

Statista Research Department. (2024, October 25). Global energy transition statistics. https://www.statista.com/statistics/267081/electricity-consumption-in-selected-countries-worldwide/

Barbetta, G. P., Canino, P., & Cima, S. (2015). The impact of energy audits on energy efficiency investment of public owners: Evidence from Italy. Energy, 93(1), 1199–1209. https://doi.org/10.1016/j.energy.2015.09.117

Patterson, M., Singh, P., & Cho, H. (2022). The current state of the industrial energy assessment & its impacts on the manufacturing industry. Energy Reports, 93, 7297–7311. https://doi.org/10.1016/j.egyr.2022.05.242

Abdelhadi, A., Salem, A., Abbas, A., Qandil, M., & Amano, R. (2021). Study of energy saving analysis for different industries. Journal of Energy Resources & Technology, 143(5), 9. https://doi.org/10.1115/1.4048249

Giacone, E., & Mancò, S. (2012). Energy efficiency measurement in industrial processes. Energy, 38(1), 331–345. https://doi.org/10.1016/j.energy.2011.11.054

Krarti, M. (2020). Energy audit of building systems: An engineering approach. Taylor & Francis. ISBN 9780367619725

Kalantzis, F., & Revoltella, D. (2019). Do energy audits help SMEs to realize energy-efficiency opportunities? Energy Economics, 83, 229–239. https://doi.org/10.1016/j.eneco.2019.07.005

Bosu, I., Mahmoud, H., & Hassan, H. (2023). Energy audit, techno-economic, and environmental assessment of integrating solar technologies for energy management in a university residential building: A case study. Applied Energy, 341. https://doi.org/10.1016/j.apenergy.2023.121141

Worrell, E., Price, L., & Martin, N. (2001). Energy efficiency and carbon dioxide emissions reduction opportunities in the US iron and steel sector. Energy, 26, 513–536. https://doi.org/10.1016/S0360-5442(01)00017-2

Al Nawafah, Q., Al Nawafah, H., Amano, R., & Abousabae, M. (2024). Enhanced thermal performance in evacuated tube solar collectors using titanium oxide nanoparticle: A computational fluid dynamics (CFD) investigation. In ASME 2024 Power Conference. Washington, District of Columbia, USA. https://doi.org/10.1115/POWER2024-137203

Khokar, I., Farghaly, O., Kothari, A. N., & Deshpande, P. (2024). Towards precision diagnosis: Integrating lexical analysis and deep learning for uncertainty detection and quantification in clinical reports. In IEEE Symposium on Computer-Based Medical Systems. Guadalajara. 10.1109/CBMS61543.2024.00051

Matsunaga, F., Zytkowski, V., Valle, P., & Deschamps, F. (2022). Optimization of energy efficiency in smart manufacturing through the use of cyber-physical systems. International Journal of Electrical and Computer Engineering, 15, 1–14. https://doi.org/10.1115/1.4053868

Kristi, A. A., Susanto, E., Risdiyanto, A., Junaedi, A., Darussalam, R., Rachman, N. A., & Fudholi, A. (2025). Energy analysis of active photovoltaic cooling system using water flow. International Journal of Electrical and Computer Engineering, 15(1), 1–14. http://doi.org/10.11591/ijece.v15i1.pp1-14

Angappan, S., Nataraj, A., Krishnan, L., & Palanisamy, A. (2025). Development of an Internet of Things based smart cold storage with inventory monitoring system. International Journal of Electrical and Computer Engineering, 15(1), 89–98. http://doi.org/10.11591/ijece.v15i1.pp89-98

Amano, R., Khalil, A., Abousabae, M., & Kada, K. (2025). Numerical & experimental analyses of the serpentine channel cooling for a turbine blade in stationary and rotational modes. International Journal of Energy for a Clean Environment, 26(1). 10.1615/InterJEnerCleanEnv.2025056629

Alvianingsih, G., Hashim, H., Jamani, J., & Senen, A. (2025). Determination of biomass energy potential based on regional characteristics using adaptive clustering method. International Journal of Electrical and Computer Engineering, 15(1), 46–55. http://doi.org/10.11591/ijece.v15i1.pp46-55

Saade, M., El-Eissawi, H., & Nada, A. S. (2025). Enhancing the reliance of emergency power supply systems for nuclear facilities using hybrid system. International Journal of Electrical and Computer Engineering, 15(1), 36–45. http://doi.org/10.11591/ijece.v15i1.pp36-45

Kumar, R., Krishnapillai, S., & Venkatarath, G. (2023). Optimization of flow paths of air-cooled heat exchanger. International Journal of Electrical and Computer Engineering, 24(4), 53–65. 10.1615/InterJEnerCleanEnv.2022043633

Boyd, G., & Lee, J. (2019). Measuring plant level energy efficiency and technical change in the U.S. metal-based durable manufacturing sector using stochastic frontier analysis. Energy Economics, 81, 159–174. https://doi.org/10.1016/j.eneco.2019.03.021

Takayabu, H., Kagawa, S., Fujii, H., Managi, S., & Eguchi, S. (2019). Impacts of productive efficiency improvement in the global metal industry on CO2 emissions. Journal of Environmental Management, 248. https://doi.org/10.1016/j.jenvman.2019.109261

Aslanova, N. R., Abdullayeva, E. J., & Beloglazov, A. (2023). Development of methods for managing energy consumption and energy efficiency in a common system. International Journal of Electrical and Computer Engineering, 13(2), 1306–1313. http://doi.org/10.11591/ijece.v13i2.pp1306-1313

Uchaipichat, N., Wibunsin, C., Chokjulanon, K., & Tanthanuch, N. (2025). Computer vision-based sun tracking control for optimizing photovoltaic power generation. International Journal of Electrical and Computer Engineering, 15(2), 1251–1261. http://doi.org/10.11591/ijece.v15i2.pp1251-1261

Qutaina, B., Shehada, A., Yasin, A., & Alsayed, M. (2023). Geographical information systems based site selection methodology for renewable energy systems in Palestinian territories. International Journal of Electrical and Computer Engineering, 13(4), 3622–3630. http://doi.org/10.11591/ijece.v13i4.pp3622-3630

Noriega, A. E., Sousa, S. V., Donolo, P. D., & Quispe, E. C. (2024). Assessing power quality in individual circuits of industrial electrical system. International Journal of Electrical and Computer Engineering, 14(5), 4888–4896. http://doi.org/10.11591/ijece.v14i5.pp4888-4896

Lee, J., & Park, G. L. (2022). Renewable energy allocation based on maximum flow modelling within a microgrid. International Journal of Electrical and Computer Engineering, 12(2), 1180–1188. http://doi.org/10.11591/ijece.v12i2.pp1180-1188

Haidi, T., & Cheddadi, B. (2022). Wind energy integration in Africa: Development, impacts and barriers. International Journal of Electrical and Computer Engineering, 12(5), 4614–4622. http://doi.org/10.11591/ijece.v12i5.pp4614-4622

Kavanathottahil, D. M., Jones, T. A., & Pushpa, M. (2024). A novel and optimized computational framework for energy efficient data dissemination in wireless sensor network. International Journal of Electrical and Computer Engineering, 14(3), 3045–3054. http://doi.org/10.11591/ijece.v14i3.pp3045-3054

Carabalí, D. M., Forero, C. R., & Cadavid, Y. (2018). Energy diagnosis and structuring an energy saving proposal for the metal casting industry. Applied Thermal Engineering, 137, 767–773. https://doi.org/10.1016/j.applthermaleng.2018.04.012

Abbas, I., Saravani, M., Al-Haddad, M., Qandil, M., & Amano, R. (2018). A comparative study of industrial energy assessments for small and medium-sized industrial facilities. 12th International Conference on Energy Sustainability, Lake Buena Vista. https://doi.org/10.1115/ES2018-7550

Ke, J., Price, L., McNeil, M., Khanna, N. Z., & Zhou, N. (2013). Analysis & practices of energy benchmarking for industry from the perspective of systems engineering. Energy, 54, 32–44. https://doi.org/10.1016/j.energy.2013.03.018

U.S. Department of Energy. (2015). Archtoolbox. https://www.energy.gov/sites/prod/files/2015/02/f20/Energy%20Intensity%20Baselining%20&%20Tracking%20Guidance.pdf

Maddox, D., Zhang, J., Xie, Y., Xu, W., Liu, X., Wang, S., & Rosenberg, M. (2022). Energy and energy cost savings analysis of the 2021 IECC for commercial buildings. U.S. Department of Energy. https://www.energycodes.gov/sites/default/files/2022-09/2021_IECC_Commercial_Analysis_Final_2022_09_02.pdf

Bahr, M. T., Immonen, J., Billings, B. W., & Powell, K. M. (2023). Intelligent control of thermal energy storage in the manufacturing sector for plant-level grid response. Processes, 11(7), 1–22. https://doi.org/10.3390/pr11072202

Kim, J. B., Wang, F., Khanna, S., Balakrishnan, B., Uddin, M., & Aman, J. (2023). Digital twin framework for smart campus to reduce greenhouse gas emission. IEEE Smart World Congress (SWC), 1–8. 10.1109/SWC57546.2023.10448799

Farghaly, O., & Deshpande, P. (2024). Leveraging machine learning to predict National Basketball Association player injuries. 2024 IEEE International Workshop on Sport, Technology and Research (STAR), 216–221. 10.1109/STAR62027.2024.10636005

Youssef, M. I., & Webster, B. (2022). A multi-criteria decision making approach to the new product development process in industry. Reports in Mechanical Engineering. 10.31181/rme2001260122y

Więckowski, J., & Sałabun, W. (2025). Comparative sensitivity analysis in composite material selection: Evaluating OAT and COMSAM methods in multi-criteria decision-making. Spectrum of Mechanical Engineering and Operational Research, 2(1), 1–12. https://doi.org/10.31181/smeor21202524

Mishra, A. R., & Rani, P. (2025). Evaluating & prioritizing blockchain networks using intuitionistic fuzzy multi-criteria decision-making method. Spectrum of Mechanical Engineering and Operational Research, 2(1), 78–92. 10.31181/smeor21202527

Sandra, M., Nishanthini, C., Narayanamoorthy, A., & Almakayeel, N. (2025). A smart decision framework for sustainable management of C&D waste using picture fuzzy decision model. Spectrum of Mechanical Engineering & Operational Research, 130–146. https://doi.org/10.31181/smeor21202533

Amano, R., Youssef, M. A., & Youssef, M. I. (2025). Employing linear regression analysis: Investigating the relationship between energy intensity and specific consumption in U.S. Midwest plastic production facilities. International Journal of Energy for a Clean Environment, 26(4), 60–72. 10.1615/InterJEnerCleanEnv.2025054764

U.S. Department of Energy. (2024, February 1). Industrial Assessment Center SIC Index. https://iac.university/indexSic

Bartlett, J. E., Kotrlik, J. W., & Higgins, C. C. (2001). Determining appropriate sample size in survey research. Information Technology, Learning, and Performance Journal, 19(1), 43–50.

Youssef, M. I., & Hausawi, Y. M. (2024). Utilizing the enterprise architecture model to develop the structure of public sector entities in Saudi Arabia. Journal of Engineering Management and Systems Engineering, 3(3), 164–174. https://doi.org/10.56578/jemse030304

Gliem, J. A., & Gliem, R. R. (2003). Calculating, interpreting, and reporting Cronbach’s alpha reliability coefficient for Likert-type scales. https://api.semanticscholar.org/CorpusID:146359317

Bougie, R., & Sekaran, U. (2006). Research methods in business: Skill building approach. John Wiley & Sons. ISBN: 978-1-119-56124-8

Amano, R., Youssef, M. I., & Youssef, M. A. (2025). Exploring the correlation between energy intensity and specific energy consumption in food and kindred industry for the Midwest states. International Journal of Energy for a Clean Environment, 26(4). 10.1615/InterJEnerCleanEnv.2025054884

AbuShanab, Y., Al-Ammari, W. A., Gowid, S., & Sleiti, A. K. (2023). Accurate prediction of dynamic viscosity of polyalpha-olefin boron nitride nanofluids using machine learning. Heliyon, 9(6), e16716. https://doi.org/10.1016/j.heliyon.2023.e16716