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Solar Power Forecasting Using Hybrid Deep Learning: Performance Enhancement with Random Forest-BiLSTM and Ensemble

Vivek Sharma¹, Mohit Ranjan Panda², Biswajit Kar³

¹School of Computer Engineering, KiiT-Deemed to be University; vivekshar@gmail.com.

Journal of Visualized Experiments : Jove

|February 23, 2026

Summary

This summary is machine-generated.

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This study introduces a hybrid deep learning ensemble for accurate solar power forecasting, improving prediction accuracy by 6.2% over individual models. The approach enhances renewable energy integration and grid stability.

Area of Science:

Renewable Energy Systems
Artificial Intelligence
Time Series Analysis

Background:

Accurate solar power forecasting is crucial for the stability and grid integration of renewable energy systems.
Deep learning models offer potential for capturing complex temporal dependencies in solar irradiance data.

Purpose of the Study:

To develop and evaluate a hybrid deep learning ensemble approach for solar generation forecasting.
To assess the performance of various hybrid architectures, including RF-BiLSTM, CNN-LSTM, CNN-BiLSTM, CNN-GRU, and CNN-Transformer.

Main Methods:

Five hybrid deep learning architectures were implemented, combining convolutional and recurrent neural networks.
Models were trained and evaluated on historical solar irradiance time series data.

Related Experiment Videos

An ensemble model was created using inverse Mean Absolute Error (MAE) weighted averaging of the top three performing individual models.

Main Results:

The RF-BiLSTM model showed the best individual performance (R² = 0.6568, MAE = 30,728 W).
The ensemble model achieved superior results (R² = 0.6933, MAE = 28,809.89 W), reducing prediction error by 6.2%.
The ensemble approach demonstrated enhanced forecast robustness and accuracy.

Conclusions:

The proposed hybrid deep learning ensemble framework effectively improves solar power forecasting accuracy.
This data-driven solution offers a scalable and robust method for renewable energy forecasting in smart grids.
The findings contribute to better grid integration and operational stability for solar energy systems.