Adaptive LSR fusion of multi-scale 3-branch CNN-BiLSTM, LSTM and CNN-BiLSTM models for robust multi-seasonal and multi-horizon weather forecasting in renewable energy management

Published: 2026
Unconventional Resources
ISBN/ISSN: 2666-5190

Abstract
Accurate and robust forecast of meteorological variables such as wind speed, solar irradiance and ambient temperature is challenging because of their nonlinear, non-stationary behavior as well as season-dependent dynamics. This study proposes an adaptive least squares regression fusion-based ensemble method for multi-horizon multi-season weather forecasting by combining three individual deep learning models: a proposed multi-scale three-branch convolutional neural network with bidirectional long short-term memory, a classical long short-term memory, and convolutional neural network with bidirectional long short-term memory. The least squares regression fusion adaptively assigns the appropriate weights to each individual model according to the weather variable, season, and forecast horizon.

Evaluation tests conducted on a one-year dataset for different short-term horizons reveal the superior performance of least squares regression fusion compared to all individual deep learning models in terms of accuracy and performance stability. For instance, at 1-h forecasting horizon, the outcomes show, a root mean square error reduction ranged from 3 to 12% for irradiance, 3–10% for wind speed and 4–10% for temperature. Additionally, a high coefficient of determination was observed, approximately equal to 0.99, implying a strong temporal correlation between the predicted and observed weather variables throughout the four seasons.

Statistical analyses, including paired t-tests with false discovery rate correction, confirm that least squares regression fusion consistently outperforms individual models, achieving the highest win rates for wind speed (65.8%) and irradiance (55.8%), while remaining competitive for temperature (46.4%).
Overall, the adaptive least squares regression fusion framework effectively integrates heterogeneous deep learning models, dynamically adjusting their corresponding contribution, and achieves an effective forecast of weather variables for short-term multi-horizons and across all four seasons.