Smart Meters Final Year Projects with Source Code

Byadmin Final Year Projects

Smart Meters Final Year Projects for BE, BTech, ME, MSc, MCA and MTech final year engineering students. These Smart Meters projects give practical experience and help complete final-year submissions. All projects follow IEEE standards and each project includes source code, project thesis report, presentation, project execution and explanation.

Smart Meters Final Year Projects

  1. A QoS-Aware Data Aggregation Strategy for Resource Constrained IoT-Enabled AMI Network in Smart Grid
    This project focuses on making smart electricity networks more efficient and cost-effective. It uses smart meters to collect and send data to the control center without relying on expensive devices. The system combines data from multiple meters in a smart way to reduce workload and make better use of limited resources. Simulations show that this approach improves performance while keeping the network reliable.
  2. Artificial Intelligence Enabled Demand Response Prospects and Challenges in Smart Grid Environment
    This project studies how artificial intelligence and machine learning can help manage energy use more efficiently. It focuses on predicting and controlling electricity demand by understanding user behavior, optimizing prices, and engaging more consumers. The work also explores how blockchain can be used in smart grids to improve fairness and security. Overall, it reviews different methods and programs from various countries and suggests ways to improve demand response systems.
  3. Power Quality Disturbances Detection and Classification Based on Deep Convolution Auto-Encoder Networks
    This project focuses on detecting and identifying power quality problems in smart grids and renewable energy systems. It uses a deep learning method called a Deep Auto-encoder to automatically learn important features from power signals. The system can accurately classify the type of disturbance and find when it starts and ends. The approach is faster and more accurate than traditional methods like SVM, even with noisy data.
  4. ML-RPL Machine Learning-Based Routing Protocol for Wireless Smart Grid Networks
    This project uses machine learning to make wireless smart grid networks work better. It improves an existing routing protocol by predicting the best path for data to travel. The system helps data reach its destination more reliably and quickly. Tests show it delivers more packets with less delay, making the network more efficient.
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Smart Meters Project Synopsis & Presentation

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