A Machine Learning Consumer Power is a M.Tech project topic for Electrical Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
A Machine Learning Consumer Power Project Details
| Abstract |
Rapid urbanization and the rise of household electrical appliances have pushed global energy demand higher, creating a need for better demand‑side management. Traditional digital meters can’t analyze data in real time or predict future bills, which limits how much consumers can help balance the load. This research examines a Machine Learning‑based Consumer Power Management System (MLCPMS) that operates within a smart‑grid environment. The approach develops a consumer power management (CPM) algorithm that studies daily electricity use and automatically creates dynamic bills. By using predictive machine‑learning models, the system identifies load patterns, forecasts peak‑demand periods, and schedules appliances to ease stress on the grid. Simulations show that the MLCPMS outperforms conventional
digital meters, delivering more accurate consumption forecasts and clearer billing. The study offers a practical framework for intelligent demand‑side management, aiding smart‑grid optimization, load forecasting, and consumer‑focused energy‑saving strategies. The proposed model provides a solid base for future smart‑grid integration and real‑time pricing.
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| Reference Paper |
A Machine Learning based Consumer Power Management System using Smart Grid |
| Domain |
Electrical Engineering |
| Sub-Domain |
Power Systems / Smart Grid |
| PDF Download |
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| Get Help |
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