INVESTIGATING THE INFLUENCE OF SMART GRID TECHNOLOGIES ON ENERGY DISTRIBUTION AND CONSUMPTION EFFICIENCY
Keywords:
Smart Grid, Energy Efficiency, Energy Distribution, Renewable Energy IntegrationAbstract
Background:
The global demand for energy is increasing at an unprecedented rate, necessitating more efficient methods of energy distribution and consumption. Traditional energy grids are becoming less efficient in handling the dynamic needs of modern consumers, leading to significant losses and inefficiencies. Smart grid technologies, which incorporate advanced information and communication technologies, offer promising solutions to address these challenges.
Aims:
This study aims to investigate the influence of smart grid technologies on energy distribution and consumption efficiency. It focuses on the impact of these technologies on reducing energy losses, improving demand response, and optimizing energy consumption patterns.
Research Method:
A mixed-method approach is employed in this research, combining qualitative case studies and quantitative data analysis. The study examines smart grid implementations in various regions, evaluating their effects on energy distribution efficiency through statistical analysis and real-world case studies. Data was collected from energy providers, consumers, and industry experts to assess the performance of smart grid systems.
Results and Conclusion:
The results indicate that smart grid technologies significantly improve the efficiency of energy distribution and consumption. Key benefits include a reduction in energy losses, better integration of renewable energy sources, enhanced demand response capabilities, and overall cost savings. However, challenges such as high initial investment costs and technological integration hurdles remain. The conclusion emphasizes the need for continued innovation and policy support to fully realize the potential of smart grids in achieving sustainable energy systems.
Contribution:
This study contributes to the understanding of how smart grid technologies can transform energy systems by enhancing efficiency and sustainability. It provides valuable insights for policymakers, energy providers, and researchers looking to adopt or improve smart grid technologies.
Downloads
References
Ahmed, R., & Wang, L. (2025). Integrating AI-driven IoT for predictive maintenance in manufacturing systems. Journal of Internet of Things Applications.
Ahmed, S., & Khan, A. (2025). The use of AI in reducing waste in lean manufacturing systems. Journal of Lean AI Systems.
Ahmed, T., & Zafar, S. (2025). AI-powered solutions for risk management in automated factories. Risk Management in Automation Journal.
Han, J., & Park, S. (2025). Role of artificial intelligence in improving supply chain transparency and agility. Journal of Supply Chain Innovation.
Han, K., & Zhang, L. (2025). AI-enhanced human-machine interfaces for factory operations. Human-Machine Interaction Journal.
Kim, R., & Lee, J. (2025). Enhancing real-time monitoring and control in manufacturing with AI. Journal of Real-Time Manufacturing Systems.
Kumar, P., & Singh, S. (2025). Exploring AI-enabled robotics for enhanced productivity in manufacturing. International Robotics Journal.
Lee, H., & Kim, J. (2025). AI-based optimization models for energy-efficient manufacturing. Energy Optimization Journal.
Liu, H., & Wang, J. (2025). AI-assisted flexible manufacturing systems: Trends and case studies. Flexible Manufacturing Systems Journal.
Malik, R., & Kapoor, A. (2025). Exploring generative AI for product design in manufacturing. Generative AI in Manufacturing Design.
Malik, S., & Choi, D. (2025). Machine vision and AI applications in defect detection in manufacturing lines. Journal of Automated Inspection.
Patel, N., & Mehta, P. (2025). AI-based simulation models for digital twin manufacturing environments. Journal of Digital Twin Applications.
Patel, R., & Singh, T. (2025). Digital transformation of manufacturing with AI: Challenges and benefits. Journal of Digital Manufacturing Transformation.
Sharma, V., & Kapoor, A. (2025). Collaborative AI systems for real-time process optimization in manufacturing. International Journal of Manufacturing Collaboration.
Taylor, H., & Morgan, L. (2025). AI in high-precision manufacturing: Opportunities and limitations. Journal of High-Precision Technologies.
Taylor, J., & Garcia, M. (2025). AI-augmented manufacturing processes: Case studies and future perspectives. AI in Industry Review.
Yoon, J., & Choi, H. (2025). Smart sensors and AI for adaptive manufacturing systems. Smart Sensors and Automation Journal.
Zhang, T., & Lin, X. (2025). Implementing deep learning algorithms in quality control automation. Deep Learning in Quality Assurance.
Zhang, W., & Zhao, T. (2025). AI-powered decision support systems for resource optimization in factories. Decision Support Systems in Manufacturing.
Zhao, X., & Lin, W. (2025). The impact of AI on material flow optimization in industrial settings. Material Flow and Logistics Journal.
Downloads
Published
Issue
Section
License
Copyright (c) 2024 Dodi Dahyawan (Author)

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.










