| dc.description.abstract | The necessity of weather forecasting in general and solar power in specific using deep
learning is completely reasonable for weather prediction, transportation planning, and the
overall social human being. Accurate solar power provides the essential role of
sophisticated forecasting tools in improving agricultural efficiency for farmers, forwarded
information for logistics activities. Driven by the necessity to assess solar energy's
potential and allocation within Vietnam, specifically focusing on Da Nang, this research
offers critical insights into the feasibility and enhancement of solar power resources. The
methodology leverages a Knowledge Distillation strategy to refine the prediction process
by filtering the behavior from an elaborate teacher model to a lightweight student model.
The data utilized in this study includes variables such as wind velocity, humidity, air
temperature, Direct Normal Irradiance (DNI), Global Tilted Irradiance (GTI), and the
direction of wind, enabling a comprehensive examination of their relationship with GHI.
As the current conventional weather forecasting techniques face a numerous of difficulties
such as: diverse of correlated features, huge volume, industrial impact, human impact.
This approach allows for the efficient prediction of GHI, thus streamlining the feature
analysis. The outcomes indicate that the Knowledge Distillation method, from the teacher
to the student model, significantly outperforms a direct training approach on the student
model, showcasing the method's effectiveness in boosting prediction accuracy. The
forecasting metrics showed a significant result in predicting from two scenarios which is
trained from teacher to student and other model is trained directly to student. This result
indicates the importance of training model from distillation teacher. | en_US |
