Multi-step short-term solar energy forecasting using Fourier-enhanced BiLSTM and neural additive models

Seman, Laio Oriel; Stefenon, Stefano Frizzo; Yow, Kin-Choong; Coelho, Leandro dos Santos; Mariani, Viviana Cocco

http://hdl.handle.net/10400.21/22285

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Authors

Seman, Laio Oriel

Stefenon, Stefano Frizzo

Yow, Kin-Choong

Coelho, Leandro dos Santos

Mariani, Viviana Cocco

Abstract(s)

Accurate short and medium-term forecasting is important for mitigating uncertainty and enabling efficient energy grid management. While traditional machine learning and deep learning models offer improved accuracy, they often lack interpretability. To address these limitations, this study proposes a hybrid forecasting framework, called FNO-BiLSTM-NAM, that combines a Fourier Neural Operator (FNO) to extract spectral–temporal features, a Bidirectional Long Short-Term Memory (BiLSTM) network to model sequential dependencies, and a Neural Additive Model (NAM) to quantify feature-wise contributions. The model incorporates multi-scenario forecasting to support energy operators under different uncertainty levels. Experiments conducted on a dataset from a 5 MW PhotoVoltaic (PV) plant demonstrate the superiority of the model. For a 6-hour forecast horizon, the proposed FNO-BiLSTM-NAM model achieved a mean absolute error of 0.0712 and mean squared error of 0.0092, outperforming benchmark models across short- to medium-term horizons. Furthermore, the spectral analysis of the FNO revealed low-pass filtering behavior, highlighting the ability of the model to suppress high-frequency noise. Comparative experiments with five machine and deep learning baseline models confirm the robustness and generalization capacity of the framework. These results underscore the potential of the proposed model for enhancing PV energy forecasting accuracy while maintaining transparency across dynamic operating conditions.

Keywords

Photovoltaic energy forecasting Solar energy Meteorological data Multi-step forecasting Neural additive model Machine learning

URI

http://hdl.handle.net/10400.21/22285

Citation

Seman, L. O., Stefenon, S. F., Yow, K. C., Coelho, L. S., & Mariani, V. C. (2026). Multi-step short-term solar energy forecasting using Fourier-enhanced BiLSTM and neural additive models. Renewable Energy, 257 (1-20). https://doi.org/10.1016/j.renene.2025.124738