Optimizing Home Energy Usage: HEMS-IoT Integration with Big Data and Machine Learning

Ravikiran Mahadasa

doi:10.18034/apjee.v9i1.731

Authors

Ravikiran Mahadasa Senior ETL Developer, Data Inc. (State of Lousiana), 4634 Monument Valley Dr, Indian Land, SC 29707, USA

DOI:

https://doi.org/10.18034/apjee.v9i1.731

Keywords:

Home Energy Management System (HEMS), Internet of Things (IoT), Big Data Analytics, Machine Learning, Energy Optimization, Smart Home Technology, Sustainable Living

Abstract

The goal of this project is to optimize household energy consumption by combining machine learning (ML), big data analytics, and the Internet of Things (IoT) with household Energy Management Systems (HEMS). The primary goals are to assess how well HEMS-IoT integration contributes to cost savings, environmental sustainability, and energy efficiency in residential contexts. The methodology includes a thorough analysis of current literature, real-world case studies, and experimental results to examine the advantages, restrictions, and policy implications of HEMS-IoT integration. Among the key findings are personalized energy management, cost savings, increased energy efficiency, and home behavioral changes. Policy implications emphasize how crucial it is to address issues with fairness, data privacy, accessibility, and interoperability through proactive regulatory frameworks and policy interventions. The study highlights how HEMS-IoT integration can revolutionize residential energy efficiency and move us closer to a more robust and sustainable energy ecosystem.

Downloads

Download data is not yet available.

References

Akter, T., & Surarapu, P. (2021). Next-Generation Electric Machines: Integration of Power Electronics and Machine Design for Optimal Performance. International Journal of Reciprocal Symmetry and Theoretical Physics, 8, 21-32. https://upright.pub/index.php/ijrstp/article/view/120

Alzahrani, A., Petri, I., Rezgui, Y., Ghoroghi, A. (2020). Developing Smart Energy Communities around Fishery Ports: Toward Zero-Carbon Fishery Ports. Energies, 13(11), 2779. https://doi.org/10.3390/en13112779

Ande, J. R. P. K. (2018). Performance-Based Seismic Design of High-Rise Buildings: Incorporating Nonlinear Soil-Structure Interaction Effects. Engineering International, 6(2), 187–200. https://doi.org/10.18034/ei.v6i2.691

Ande, J. R. P. K., & Khair, M. A. (2019). High-Performance VLSI Architectures for Artificial Intelligence and Machine Learning Applications. International Journal of Reciprocal Symmetry and Theoretical Physics, 6, 20-30. https://upright.pub/index.php/ijrstp/article/view/121

Ande, J. R. P. K., Varghese, A., Mallipeddi, S. R., Goda, D. R., & Yerram, S. R. (2017). Modeling and Simulation of Electromagnetic Interference in Power Distribution Networks: Implications for Grid Stability. Asia Pacific Journal of Energy and Environment, 4(2), 71-80. https://doi.org/10.18034/apjee.v4i2.720

Chen, J., Cheng, G., Chi, S. (2019). Establish the Clean Energy Demand Forecasting Model in China Based on Genetic Algorithms. IOP Conference Series. Earth and Environmental Science, 384(1). https://doi.org/10.1088/1755-1315/384/1/012143

Chen, S. Y. (2019). Use of Green Building Information Modeling in the Net Zero Energy Building Design Assessment. Journal of Environmental Engineering and Landscape Management, 27(3), 174-186. https://doi.org/10.3846/jeelm.2019.10797

Egarter, D., Monacchi, A., Khatib, T., Elmenreich, W. (2016). Integration of Legacy Appliances into Home Energy Management Systems. Journal of Ambient Intelligence and Humanized Computing, 7(2), 171-185. https://doi.org/10.1007/s12652-015-0312-9

Goda, D. R. (2016). A Fully Analytical Back-gate Model for N-channel Gallium Nitrate MESFETs with Back Channel Implant. California State University, Northridge. http://hdl.handle.net/10211.3/176151

Goda, D. R., Yerram, S. R., & Mallipeddi, S. R. (2018). Stochastic Optimization Models for Supply Chain Management: Integrating Uncertainty into Decision-Making Processes. Global Disclosure of Economics and Business, 7(2), 123-136. https://doi.org/10.18034/gdeb.v7i2.725

Khair, M. A. (2018). Security-Centric Software Development: Integrating Secure Coding Practices into the Software Development Lifecycle. Technology & Management Review, 3, 12-26. https://upright.pub/index.php/tmr/article/view/124

Khair, M. A., Mahadasa, R., Tuli, F. A., & Ande, J. R. P. K. (2020). Beyond Human Judgment: Exploring the Impact of Artificial Intelligence on HR Decision-Making Efficiency and Fairness. Global Disclosure of Economics and Business, 9(2), 163-176. https://doi.org/10.18034/gdeb.v9i2.730

Kloppenburg, S., Smale, R., Verkade, N. (2019). Technologies of Engagement: How Battery Storage Technologies Shape Householder Participation in Energy Transitions. Energies, 12(22). https://doi.org/10.3390/en12224384

Konrad, C., Strittmatter, J., Grunert, A., Brule, M., Roth, M. (2013). Regional Energy Concepts – based on Alternative Biomass Cultivation for Rural Areas and its Efficient Energy Usage. International Journal of Sustainable Development and Planning, 8(1), 59-74. https://doi.org/10.2495/SDP-V8-N1-59-74

Lee, S., Choi, D-H. (2020). Energy Management of Smart Home with Home Appliances, Energy Storage System and Electric Vehicle: A Hierarchical Deep Reinforcement Learning Approach. Sensors, 20(7), 2157. https://doi.org/10.3390/s20072157

Mahadasa, R. (2016). Blockchain Integration in Cloud Computing: A Promising Approach for Data Integrity and Trust. Technology & Management Review, 1, 14-20. https://upright.pub/index.php/tmr/article/view/113

Mahadasa, R. (2017). Decoding the Future: Artificial Intelligence in Healthcare. Malaysian Journal of Medical and Biological Research, 4(2), 167-174. https://mjmbr.my/index.php/mjmbr/article/view/683

Mahadasa, R. (2021). Hierarchical Structuring of Fibrous Materials: Toward Tailored Properties for High-Performance Textile Products. Engineering International, 9(2), 165–178. https://doi.org/10.18034/ei.v9i2.698

Mahadasa, R., & Surarapu, P. (2016). Toward Green Clouds: Sustainable Practices and Energy-Efficient Solutions in Cloud Computing. Asia Pacific Journal of Energy and Environment, 3(2), 83-88. https://doi.org/10.18034/apjee.v3i2.713

Mahadasa, R., Goda, D. R., & Surarapu, P. (2019). Innovations in Energy Harvesting Technologies for Wireless Sensor Networks: Towards Self-Powered Systems. Asia Pacific Journal of Energy and Environment, 6(2), 101-112. https://doi.org/10.18034/apjee.v6i2.727

Mahmood, Y., Kama, N., Azmi, A., Ya’acob, S. (2020). An IoT Based Home Automation Integrated Approach: Impact on Society in Sustainable Development Perspective. International Journal of Advanced Computer Science and Applications, 11(1). https://doi.org/10.14569/IJACSA.2020.0110131

Mallipeddi, S. R., & Goda, D. R. (2018). Solid-State Electrolytes for High-Energy-Density Lithium-Ion Batteries: Challenges and Opportunities. Asia Pacific Journal of Energy and Environment, 5(2), 103-112. https://doi.org/10.18034/apjee.v5i2.726

Mallipeddi, S. R., Goda, D. R., Yerram, S. R., Varghese, A., & Ande, J. R. P. K. (2017). Telemedicine and Beyond: Navigating the Frontier of Medical Technology. Technology & Management Review, 2, 37-50. https://upright.pub/index.php/tmr/article/view/118

Mallipeddi, S. R., Lushbough, C. M., & Gnimpieba, E. Z. (2014). Reference Integrator: a workflow for merging similarity-driven multi-source publications. The Steering Committee of the World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp). https://www.proquest.com/docview/1648971371

Mandapuram, M., Mahadasa, R., & Surarapu, P. (2019). Evolution of Smart Farming: Integrating IoT and AI in Agricultural Engineering. Global Disclosure of Economics and Business, 8(2), 165-178. https://doi.org/10.18034/gdeb.v8i2.714

Molla, T., Khan, B., Moges, B., Alhelou, H. H., Zamani, R. (2019). Integrated Optimization of Smart Home Appliances With Cost-effective Energy Management System. CSEE Journal of Power and Energy Systems (JPES), 5(2), 249-258. https://doi.org/10.17775/CSEEJPES.2019.00340

Petri, I., Kubicki, S., Rezgui, Y., Guerriero, A., Li, H. (2017). Optimizing Energy Efficiency in Operating Built Environment Assets through Building Information Modeling: A Case Study. Energies, 10(8), 1167. https://doi.org/10.3390/en10081167

Rafique, S., Nizami, M. S. H., Irshad, U. B., Hossain, M. J., Town, G. E. (2019). A Customer-based-strategy to Minimize the Cost of Energy Consumption by Optimal Utilization of Energy Resources in an Apartment Building. IOP Conference Series. Earth and Environmental Science, 322(1). https://doi.org/10.1088/1755-1315/322/1/012018

Simmons, C. R., Arment, J. R., Powell, K. M., Hedengren, J. D. (2019). Proactive Energy Optimization in Residential Buildings with Weather and Market Forecasts. Processes, 7(12), 929. https://doi.org/10.3390/pr7120929

Surarapu, P. (2016). Emerging Trends in Smart Grid Technologies: An Overview of Future Power Systems. International Journal of Reciprocal Symmetry and Theoretical Physics, 3, 17-24. https://upright.pub/index.php/ijrstp/article/view/114

Surarapu, P. (2017). Security Matters: Safeguarding Java Applications in an Era of Increasing Cyber Threats. Asian Journal of Applied Science and Engineering, 6(1), 169–176. https://doi.org/10.18034/ajase.v6i1.82

Surarapu, P., & Mahadasa, R. (2017). Enhancing Web Development through the Utilization of Cutting-Edge HTML5. Technology & Management Review, 2, 25-36. https://upright.pub/index.php/tmr/article/view/115

Surarapu, P., Ande, J. R. P. K., Varghese, A., Mallipeddi, S. R., Goda, D. R., Yerram, S. R., & Kaluvakuri, S. (2020). Quantum Dot Sensitized Solar Cells: A Promising Avenue for Next-Generation Energy Conversion. Asia Pacific Journal of Energy and Environment, 7(2), 111-120. https://doi.org/10.18034/apjee.v7i2.728

Surarapu, P., Mahadasa, R., & Dekkati, S. (2018). Examination of Nascent Technologies in E-Accounting: A Study on the Prospective Trajectory of Accounting. Asian Accounting and Auditing Advancement, 9(1),89–100. https://4ajournal.com/article/view/83

Tuli, F. A., Varghese, A., & Ande, J. R. P. K. (2018). Data-Driven Decision Making: A Framework for Integrating Workforce Analytics and Predictive HR Metrics in Digitalized Environments. Global Disclosure of Economics and Business, 7(2), 109-122. https://doi.org/10.18034/gdeb.v7i2.724

Varghese, A., & Bhuiyan, M. T. I. (2020). Emerging Trends in Compressive Sensing for Efficient Signal Acquisition and Reconstruction. Technology & Management Review, 5, 28-44. https://upright.pub/index.php/tmr/article/view/119

Washizu, A., Nakano, S., Ishii, H., Hayashi, Y. (2019). Willingness to Pay for Home Energy Management Systems: A Survey in New York and Tokyo. Sustainability, 11(17), 4790. https://doi.org/10.3390/su11174790

Withers, C. Jr. (2019). Considerations for Providing Healthy, Comfortable, Energy-efficient Whole-house Mechanical Ventilation During Humid Weather in Near zero Energy Homes. IOP Conference Series. Materials Science and Engineering, 609(3). https://doi.org/10.1088/1757-899X/609/3/032043

Yerram, S. R., & Varghese, A. (2018). Entrepreneurial Innovation and Export Diversification: Strategies for India’s Global Trade Expansion. American Journal of Trade and Policy, 5(3), 151–160. https://doi.org/10.18034/ajtp.v5i3.692

Yerram, S. R., Goda, D. R., Mahadasa, R., Mallipeddi, S. R., Varghese, A., Ande, J. R. P. K., Surarapu, P., & Dekkati, S. (2021). The Role of Blockchain Technology in Enhancing Financial Security amidst Digital Transformation. Asian Business Review, 11(3), 125–134. https://doi.org/10.18034/abr.v11i3.694

Zhao, L., Liu, Z., Mbachu, J. (2019). Energy Management through Cost Forecasting for Residential Buildings in New Zealand. Energies, 12(15), 2888. https://doi.org/10.3390/en12152888