Articles
| Open Access |
DOI: PREDICTIVE MODELING OF HOUSEHOLD ENERGY CONSUMPTION IN THE USA: THE ROLE OF MACHINE LEARNING AND SOCIOECONOMIC FACTORS
Muhammad Shoyaibur Rahman Chowdhury , Information Technology, Gannon University, Erie, PA Mohammad Saiful Islam , MS, Management - Information Technology Management, St. Francis College Md Abdullah Al Montaser , Ms-Business Analytics, University of North Texas Mohammad Abul Basher Rasel , MSc Hospitality & Tourism Data Analytics, University of North Texas Ayan Barua , MBA in Business Analytics, Trine University: Angola, US Anchala Chouksey , Masters in financial mathematics, University of North Texas, Denton, Texas Bivash Ranjan Chowdhury , MBA in Management Information System, International American University, Los Angeles, California, USAAbstract
Understanding the pattern of energy use at the household level becomes ever more urgent in light of growing concerns about climate change and resource sustainability in the USA. Energy use depends upon various factors, such as climate, household characteristics, and behavior. Of these, income, education, and size of the family are very vital socio-economic factors that depict energy consumption levels and their pattern. The utmost objective of this research project was to develop predictive models using machine learning techniques to analyze household energy consumption trends in the USA, integrating socioeconomic factors such as income, family size, and education. The dataset retrieved from Kaggle integrates detailed weather patterns with energy consumption data, putting into perspective the interaction between climatic variables and household energy use. It included key features such as temperature, humidity, wind speed, and precipitation, along with time-series data on energy consumption metrics like electricity and natural gas usage at the household level. It provided information on several geographic zones across extended periods, so seasonality and regional variations may be studied. It was complemented with metadata that included timestamps, energy pricing, and household attributes and should therefore be a rich resource for predictive modeling and extracting relationships between weather conditions and energy demand. For this research project, three models were selected: Logistic Regression, Random Forest, and Support Vector Machines, each possessing particular strengths for the nature of the problem. This study employed key performance metrics such as precision, recall, F1-Score, and accuracy. The Random Forest model had the highest value for accuracy, similarly, the highest AUC was for the Random Forest with the best AUC. As such, it was concluded that the Random Forest model provided the best trade-off between true positive rate and false positive rate and can be relied upon for this classification task. The machine learning models generate valuable predictions about household energy use. Particularly, Random Forest models, which are trained on socioeconomic and weather data to predict the likelihood of a given household having high energy usage. The predictions by such models can be used to help energy providers determine when to invoke tiered pricing or encourage energy-saving behavior.
Keywords
Household Energy Consumption, Socioeconomic Factors, Machine Learning
References
Alam, M., Islam, M. R., & Shil, S. K. (2023). AI-Based Predictive Maintenance for US Manufacturing: Reducing Downtime and Increasing Productivity. International Journal of Advanced Engineering Technologies and Innovations, 1(01), 541-567.
Al Mukaddim, A., Nasiruddin, M., & Hider, M. A. (2023). Blockchain Technology for Secure and Transparent Supply Chain Management: A Pathway to Enhanced Trust and Efficiency. International Journal of Advanced Engineering Technologies and Innovations, 1(01), 419-446.
Amiri, S. S., Mueller, M., & Hoque, S. (2023). Investigating the application of a commercial and residential energy consumption prediction model for urban Planning scenarios with Machine Learning and Shapley Additive explanation methods. Energy and Buildings, 287, 112965.
Buiya, M. R., Laskar, A. N., Islam, M. R., Sawalmeh, S. K. S., Roy, M. S. R. C., Roy, R. E. R. S., & Sumsuzoha, M. (2024). Detecting IoT Cyberattacks: Advanced Machine Learning Models for Enhanced Security in Network Traffic. Journal of Computer Science and Technology Studies, 6(4), 142-152.
Buiya, M. R., Alam, M., & Islam, M. R. (2023). Leveraging Big Data Analytics for Advanced Cybersecurity: Proactive Strategies and Solutions. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 14(1), 882-916.
Charfeddine, L., Zaidan, E., Alban, A. Q., Bennasr, H., & Abulibdeh, A. (2023). Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series models. Sustainable Cities and Society, 98, 104860.
Chen, G., Hu, Q., Wang, J., Wang, X., & Zhu, Y. (2023). Machine-learning-based electric power forecasting. Sustainability, 15(14), 11299.
Debnath, P., Karmakar, M., Khan, M. T., Khan, M. A., Al Sayeed, A., Rahman, A., & Sumon, M. F. I. (2024). Seismic Activity Analysis in California: Patterns, Trends, and Predictive Modeling. Journal of Computer Science and Technology Studies, 6(5), 50-60.
Gazi, M. S., Nasiruddin, M., Dutta, S., Sikder, R., Huda, C. B., & Islam, M. Z. (2024). Employee Attrition Prediction in the USA: A Machine Learning Approach for HR Analytics and Talent Retention Strategies. Journal of Business and Management Studies, 6(3), 47-59.
Goriparthi, R. G. (2024). AI-Driven Predictive Analytics for Autonomous Systems: A Machine Learning Approach. Revista de Inteligencia Artificial en Medicina, 15(1), 843-879.
Hasan, M. R. (2024). Revitalizing the electric grid: A machine learning paradigm for ensuring stability in the USA. Journal of Computer Science and Technology Studies, 6(1), 141-154.
Hasanuzzaman, M., Hossain, S., & Shil, S. K. (2023). Enhancing Disaster Management through AI-Driven Predictive Analytics: Improving Preparedness and Response. International Journal of Advanced Engineering Technologies and Innovations, 1(01), 533-562.
Islam, M. Z., Islam, M. S., Al Montaser, M. A., Rasel, M. A. B., Bhowmik, P. K., & Dalim, H. M. (2024). EVALUATING THE EFFECTIVENESS OF MACHINE LEARNING ALGORITHMS IN PREDICTING CRYPTOCURRENCY PRICES UNDER MARKET VOLATILITY: A STUDY BASED ON THE USA FINANCIAL MARKET. The American Journal of Management and Economics Innovations, 6(12), 15-38.
Islam, M. R., Nasiruddin, M., Karmakar, M., Akter, R., Khan, M. T., Sayeed, A. A., & Amin, A. (2024). Leveraging Advanced Machine Learning Algorithms for Enhanced Cyberattack Detection on US Business Networks. Journal of Business and Management Studies, 6(5), 213-224.
Kapp, S., Choi, J. K., & Hong, T. (2023). Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters. Renewable and Sustainable Energy Reviews, 172, 113045.
Kesriklioğlu, E., Oktay, E., & Karaaslan, A. (2023). Predicting total household energy expenditures using ensemble learning methods. Energy, 276, 127581.
Khan, M. T., Akter, R., Dalim, H. M., Sayeed, A. A., Anonna, F. R., Mohaimin, M. R., & Karmakar, M. (2024). Predictive Modeling of US Stock Market and Commodities: Impact of Economic Indicators and Geopolitical Events Using Machine. Journal of Economics, Finance and Accounting Studies, 6(6), 17-33.
Karmakar, M., Debnath, P., & Khan, M. A. (2024). AI-Powered Solutions for Traffic Management in US Cities: Reducing Congestion and Emissions. International Journal of Advanced Engineering Technologies and Innovations, 2(1), 194-222.
Kumar, S. (2023). A novel hybrid machine learning model for prediction of CO2 using socio-economic and energy attributes for climate change monitoring and mitigation policies. Ecological Informatics, 77, 102253.
Mukelabai, M. D., Wijayantha, K. G. U., & Blanchard, R. E. (2023). Using machine learning to expound energy poverty in the global south: Understanding and predicting access to cooking with clean energy. Energy and AI, 14, 100290.
Nasiruddin, M., Al Mukaddim, A., & Hider, M. A. (2023). Optimizing Renewable Energy Systems Using Artificial Intelligence: Enhancing Efficiency and Sustainability. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 14(1), 846-881.
Nguyen, V. G., Duong, X. Q., Nguyen, L. H., Nguyen, P. Q. P., Priya, J. C., Truong, T. H., ... & Nguyen, X. P. (2023). An extensive investigation on leveraging machine learning techniques for high-precision predictive modeling of CO2 emission. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 45(3), 9149-9177.
Ohalete, N. C., Aderibigbe, A. O., Ani, E. C., Ohenhen, P. E., & Akinoso, A. E. (2023). Data science in energy consumption analysis: a review of AI techniques in identifying patterns and efficiency opportunities. Engineering Science & Technology Journal, 4(6), 357-380.
Omogoroye, O. O., Olaniyi, O. O., Adebiyi, O. O., Oladoyinbo, T. O., & Olaniyi, F. G. (2023). Electricity consumption (kW) forecast for a building of interest based on a time series nonlinear regression model. Asian Journal of Economics, Business and Accounting, 23(21), 197-207.
Palani, H., Acosta-Sequeda, J., Karatas, A., & Derrible, S. (2023). The role of socio-demographic and economic characteristics on energy-related occupant behavior. Journal of Building Engineering, 75, 106875.
Pro-AI-Rokibul. (2024). Machine-Learning-FOr-Optimizing-USA-Household-Energy-Consumption/Models/main.ipynb at main · proAIrokibul/Machine-Learning-FOr-Optimizing-USA-Household-Energy-Consumption. GitHub. https://github.com/proAIrokibul/Machine-Learning-FOr-Optimizing-USA-Household-Energy-Consumption/blob/main/Models/main.ipynb
Rahman, M. K., Dalim, H. M., & Hossain, M. S. (2023). AI-Powered Solutions for Enhancing National Cybersecurity: Predictive Analytics and Threat Mitigation. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 14(1), 1036-1069.
Rahman, A., Debnath, P., Ahmed, A., Dalim, H. M., Karmakar, M., Sumon, M. F. I., & Khan, M. A. (2024). Machine learning and network analysis for financial crime detection: Mapping and identifying illicit transaction patterns in global black money transactions. Gulf Journal of Advance Business Research, 2(6), 250-272.
Sarwar, S., Aziz, G., & Tiwari, A. K. (2024). Implication of machine learning techniques to forecast the electricity price and carbon emission: Evidence from a hot region. Geoscience Frontiers, 15(3), 101647.
Singh, A., Yadav, J., Shrestha, S., & Varde, A. S. (2023). Linking alternative fuel vehicles adoption with socioeconomic status and air quality index. arXiv preprint arXiv:2303.08286.
Shi, Z., Wu, L., & Zhou, Y. (2023). Predicting household energy consumption in an aging society. Applied Energy, 352, 121899.
Shawon, R. E. R., Chowdhury, M. S. R., & Rahman, T. (2023). Transforming Urban Living in the USA: The Role of IoT in Developing Smart Cities. International Journal of Machine Learning Research in Cybersecurity and Artificial Intelligence, 14(1), 917-953.
Shawon, R. E. R., Miah, M. N. I., & Islam, M. Z. (2023). Enhancing US Education Systems with AI: Personalized Learning and Academic Performance Prediction. International Journal of Advanced Engineering Technologies and Innovations, 1(01), 518-540.
Shil, S. K., Chowdhury, M. S. R., Tannier, N. R., Tarafder, M. T. R., Akter, R., Gurung, N., & Sizan, M. M. H. (2024). Forecasting Electric Vehicle Adoption in the USA Using Machine Learning Models. Journal of Computer Science and Technology Studies, 6(5), 61-74.
Shawon, R. E. R., Rahman, A., Islam, M. R., Debnath, P., Sumon, M. F. I., Khan, M. A., & Miah, M. N. I. (2024). AI-Driven Predictive Modeling of US Economic Trends: Insights and Innovations. Journal of Humanities and Social Sciences Studies, 6(10), 01-15.
Sumon, M. F. I., Osiujjaman, M., Khan, M. A., Rahman, A., Uddin, M. K., Pant, L., & Debnath, P. (2024). Environmental and Socio-Economic Impact Assessment of Renewable Energy Using Machine Learning Models. Journal of Economics, Finance and Accounting Studies, 6(5), 112-122.
Zhussupbekov, M., Memon, S. A., Khawaja, S. A., Nazir, K., & Kim, J. (2023). Forecasting energy demand of PCM integrated residential buildings: A machine learning approach. Journal of Building Engineering, 70, 106335.
Article Statistics
Copyright License
Copyright (c) 2024 Muhammad Shoyaibur Rahman Chowdhury, Mohammad Saiful Islam, Md Abdullah Al Montaser, Mohammad Abul Basher Rasel, Ayan Barua, Anchala Chouksey, Bivash Ranjan Chowdhury
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors retain the copyright of their manuscripts, and all Open Access articles are disseminated under the terms of the Creative Commons Attribution License 4.0 (CC-BY), which licenses unrestricted use, distribution, and reproduction in any medium, provided that the original work is appropriately cited. The use of general descriptive names, trade names, trademarks, and so forth in this publication, even if not specifically identified, does not imply that these names are not protected by the relevant laws and regulations.