What question did this study set out to answer?

The aim is to enhance the predictability of solar photovoltaic power generation using a new hybrid machine learning model.

Forecasting Solar Photovoltaic Power Generation: A Machine Learning Time Series Model Approach

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Novel hybrid ensemble predicts solar energy variability in power generation, suggesting enhanced forecasting accuracy.

The aim is to enhance the predictability of solar photovoltaic power generation using a new hybrid machine learning model.
Developed a hybrid machine learning time series model integrating physics-based features and data-driven regressors.
Utilized a 34-day dataset from two solar power plants in India to engineer features like irradiation and temperature.
Evaluated multiple machine learning models including linear regression, random forest, and decision trees for performance.
The hybrid model achieved an R 2 value of 98% for Plant 1 and 91% for Plant 2.
Root mean squared errors ranged from 36–66 for Plant 1 and 42–127 for Plant 2.
Linear regression and ridge regression showed superior individual performance among assessed models.

Nahar et al. (2025) studied this question.

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View Full Paper

Ask AI

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Authors

Novel hybrid ensemble predicts solar energy variability in power generation, suggesting enhanced forecasting accuracy.

The aim is to enhance the predictability of solar photovoltaic power generation using a new hybrid machine learning model.
Developed a hybrid machine learning time series model integrating physics-based features and data-driven regressors.
Utilized a 34-day dataset from two solar power plants in India to engineer features like irradiation and temperature.
Evaluated multiple machine learning models including linear regression, random forest, and decision trees for performance.
The hybrid model achieved an R 2 value of 98% for Plant 1 and 91% for Plant 2.
Root mean squared errors ranged from 36–66 for Plant 1 and 42–127 for Plant 2.
Linear regression and ridge regression showed superior individual performance among assessed models.

Nahar et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark