Comparing Machine Learning Models for Solar Energy Prediction
Author Information
Author(s): Nguyen Huu Nam, Tran Quoc Thanh, Ngo Canh Tung, Nguyen Duc Dam, Tran Van Quan
Primary Institution: Institute for Hydropower and Renewable Energy, Vietnam Academy for Water Resources, Hanoi, Vietnam
Hypothesis
Can machine learning models accurately predict solar energy output using weather-related input variables?
Conclusion
The CatBoost model outperformed other machine learning models in predicting solar energy output, but the overall predictive accuracy was limited due to the absence of specific photovoltaic panel data.
Supporting Evidence
- The CatBoost model achieved the highest R2 value of 0.608 during training.
- The study identified ambient temperature and humidity as the most influential factors in solar energy predictions.
- Machine learning models were evaluated using metrics such as R2, MAE, and RMSE.
Takeaway
This study used different computer programs to guess how much solar energy can be made based on weather conditions, and found that one program was the best at it.
Methodology
Five machine learning models (CatBoost, XGBoost, LightGBM, Gradient Boosting, KNN) were trained on a dataset of 21,045 samples using weather-related input variables to predict solar energy output.
Potential Biases
Potential biases may arise from the dataset's limited geographical context and the absence of diverse environmental conditions.
Limitations
The study's predictive accuracy was limited by the lack of photovoltaic panel-specific technical data and the dataset's geographical and technological constraints.
Digital Object Identifier (DOI)
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