Digital Twinning LSTM-based Forecasting Model is a M.Tech project topic for Electrical Engineering. It gives students a clear starting point for research, implementation planning, and documentation.
Digital Twinning LSTM-based Forecasting Model Project Details
| Abstract |
Integrating high-penetration solar photovoltaic (PV) systems into modern power grids requires highly accurate, real-time forecasting to balance supply and demand and detect system faults. This research addresses these challenges by developing a Digital Twin of a solar PV system. The methodology integrates three core stages: real-time electrical power output data collection from physical PV panels using smart sensors, the development and calibration of a physical PV system model within MATLAB, and the implementation of a Long Short-Term Memory (LSTM) network to forecast power output 15 minutes in advance using historical data. The digital twin acts as a virtual replica, mirroring the physical system's behavior to improve predictability and operational reliability.
According to the study, the digital twin's simulated outputs align closely with the physical sensor data, with model accuracy validated using the coefficient of correlation. While the paper demonstrates the feasibility of short-term forecasting, limitations include a lack of detailed discussion on long-term seasonal variations or extreme weather anomalies. Fried Engineers provides academic guidance, structuring, and simulation support to help M.Tech students explore digital twin architectures and LSTM-based forecasting models for renewable energy applications.
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| Reference Paper |
Digital Twinning and LSTM-based Forecasting Model of Solar PV Power Output |
| Domain |
Electrical Engineering |
| Sub-Domain |
Power Systems / Renewable Energy / Solar PV |
| PDF Download |
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