Quality-Aware Photovoltaic Forecasting under Missing Weather Data
Keywords:
sensor outages, extra trees, mixture of experts, interval calibration, multi-horizon predictionAbstract
Operational photovoltaic forecasts often depend on irradiance and temperature measurements that may be interrupted by sensor or communication faults. This study develops Quality-Aware Dual-Path Conformal Photovoltaic Forecasting (QDPCC-PV), which combines a weather-aware Extra Trees expert with a power-only fallback expert. An availability gate changes their weights according to observed weather inputs, while physical clipping enforces feasible output and condition-specific conformal calibration provides prediction intervals. The method was evaluated chronologically on two measured photovoltaic plants sampled every 15 min at 15-, 60-, and 240-min horizons. Under complete inputs, mean absolute errors were 0.0376, 0.0463, and 0.0684 per unit, corresponding to improvements of 32.8%, 45.7%, and 65.3% over persistence. With 60% random weather loss at the 60-min horizon, QDPCC-PV obtained 0.0492 error, compared with 0.0664 for median imputation. The nominal 90% interval reached 91.8% coverage at 240 min but undercovered at shorter horizons because calibration and test residuals shifted over time. The results show that explicit input-quality routing protects point forecasts from weather-data loss, while adaptive calibration remains necessary for reliable short-horizon intervals.
References
Huang, C.-J., & Kuo, P.-H. (2019). Multiple-input deep convolutional neural network model for short-term photovoltaic power forecasting. IEEE Access, 7, 74822-74834. https://doi.org/10.1109/ACCESS.2019.2921238
Yao, T., Wang, J., Wu, H., Zhang, P., Li, S., Wang, Y., Chi, X., & Shi, M. (2021). A photovoltaic power output dataset: Multi-source photovoltaic power output dataset with Python toolkit. Solar Energy, 230, 122-130. https://doi.org/10.1016/j.solener.2021.09.050
Holmgren, W. F., Hansen, C. W., & Mikofski, M. A. (2018). pvlib python: A Python package for modeling solar energy systems. Journal of Open Source Software, 3(29), 884.
Das, U. K., Tey, K. S., Seyedmahmoudian, M., Mekhilef, S., Idris, M. Y. I., Van Deventer, W., Horan, B., & Stojcevski, A. (2018). Forecasting of photovoltaic power generation and model optimization: A review. Renewable and Sustainable Energy Reviews, 81, 912-928. https://doi.org/10.1016/j.rser.2017.08.017
Nie, Y., Nguyen, N. H., Sinthong, P., & Kalagnanam, J. (2023). A time series is worth 64 words: Long-term forecasting with Transformers. In Proceedings of the International Conference on Learning Representations.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, Ł., & Polosukhin, I. (2017). Attention is all you need. In Advances in Neural Information Processing Systems (Vol. 30).
Lim, B., Arik, S. O., Loeff, N., & Pfister, T. (2021). Temporal Fusion Transformers for interpretable multi-horizon time series forecasting. International Journal of Forecasting, 37(4), 1748-1764.
Bai, S., Kolter, J. Z., & Koltun, V. (2018). An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv:1803.01271.
[9] Abdel-Nasser, M., & Mahmoud, K. (2019). Accurate photovoltaic power forecasting models using deep LSTM-RNN. Neural Computing and Applications, 31, 2727-2740.
[Gensler, A., Henze, J., Sick, B., & Raabe, N. (2016). Deep learning for solar power forecasting using AutoEncoder and LSTM neural networks. In Proceedings of the IEEE International Conference on Systems, Man, and Cybernetics (pp. 2858-2865).
El-Kenawy, E.-S. M., Alhussan, A. A., Mattar, E. A., & Radwan, M. (2026). Feature selection and hyperparameter tuning in transformer-based deep learning models for photovoltaic power forecasting using the Swordfish Movement Optimization Algorithm. International Journal of Electrical Power & Energy Systems, 174, 111509. https://doi.org/10.1016/j.ijepes.2025.111509
Tao, K., Zhao, J., Tao, Y., Qi, Q., & Tian, Y. (2024). Operational day-ahead photovoltaic power forecasting based on transformer variant. Applied Energy, 373, 123825. https://doi.org/10.1016/j.apenergy.2024.123825
Sun, Q., Yan, F., Sun, W., & Zhou, Y. (2025). DWT-Former: Fusing wavelet-based multi-scale features and transformer-based temporal representations for photovoltaic power forecasting. Energy, 341, 139283. https://doi.org/10.1016/j.energy.2025.139283
Liu, Y., & Wang, Z. (2025). Robust and efficient multihorizon photovoltaic power forecasting with a dilated multi-scale Transformer. Solar Energy, 302, 114077. https://doi.org/10.1016/j.solener.2025.114077
Yan, K., Liu, J., Zhang, J., Yang, F., Gao, Y., & Du, Y. (2025). Robust photovoltaic forecasting under severe data missingness via multi-domain collaboration and covariate interaction. Applied Energy, 401, 126771. https://doi.org/10.1016/j.apenergy.2025.126771
Vovk, V., Gammerman, A., & Shafer, G. (2005). Algorithmic learning in a random world. Springer.
Romano, Y., Patterson, E., & Candès, E. J. (2019). Conformalized quantile regression. In Advances in Neural Information Processing Systems (Vol. 32).
Xu, C., & Xie, Y. (2021). Conformal prediction interval for dynamic time-series. In Proceedings of the 38th International Conference on Machine Learning (pp. 11559-11569).
Gibbs, I., & Candès, E. (2021). Adaptive conformal inference under distribution shift. In Advances in Neural Information Processing Systems (Vol. 34).
Breiman, L. (2001). Random forests. Machine Learning, 45, 5-32.
Geurts, P., Ernst, D., & Wehenkel, L. (2006). Extremely randomized trees. Machine Learning, 63, 3-42.
Liu, M., Lai, Z., Fang, Y., & Ling, Q. (2025). Day-ahead photovoltaic power forecasting based on corrected numeric weather prediction and domain generalization. Energy and Buildings, 329, 115212. https://doi.org/10.1016/j.enbuild.2024.115212
Hong, T., Pinson, P., Fan, S., Zareipour, H., Troccoli, A., & Hyndman, R. J. (2016). Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond. International Journal of Forecasting, 32(3), 896-913.
Renkema, Y., Visser, L., & AlSkaif, T. (2024). Enhancing the reliability of probabilistic PV power forecasts using conformal prediction. Solar Energy Advances, 4, 100059. https://doi.org/10.1016/j.seja.2024.100059
Solar Power Generation Data. (2020). Kaggle. https://www.kaggle.com/datasets/anikannal/solar-power-generation-data
Diebold, F. X., & Mariano, R. S. (1995). Comparing predictive accuracy. Journal of Business & Economic Statistics, 13(3), 253-263.
Wilcoxon, F. (1945). Individual comparisons by ranking methods. Biometrics Bulletin, 1(6), 80-83.
Holm, S. (1979). A simple sequentially rejective multiple test procedure. Scandinavian Journal of Statistics, 6(2), 65-70.
