Document Type

Journal Article

Publication Title

Sustainable Energy, Grids and Networks






School of Science




FEDER funds through COMPETE 2020 / Portuguese funds through FCT, POCI-01-0145-FEDER-029803 (02/SAICT/2017)


This is an author accepted manuscript version of an article published by Elsevier in Sustainable Energy, Grids and Networks.

© 2022. This manuscript version is made available under the CC-BY-NC-ND 4.0 license

Jalali, S. M. J., Ahmadian, S., Nakisa, B., Khodayar, M., Khosravi, A., Nahavandi, S., ... Catalão, J. P. S. (2022). Solar irradiance forecasting using a novel hybrid deep ensemble reinforcement learning algorithm. Sustainable Energy, Grids and Networks, 32, article 100903.


Solar irradiance forecasting is a major priority for the power transmission systems in order to generate and incorporate the performance of massive photovoltaic plants efficiently. As such, prior forecasting techniques that use classical modelling and single deep learning models that undertake feature extraction procedures manually were unable to meet the output demands in specific situations with dynamic variability. Therefore, in this study, we propose an efficient novel hybrid solar irradiance forecasting model based on three steps. In the first step, we employ a powerful variable input selection strategy named as partial mutual information (PMI) to calculate the linear and non-linear correlations of the original solar irradiance data. In the second step, unlike the traditional deep learning models designing their architectures manually, we utilize several deep long short term memory-convolutional neural network (LSTM-CNN) models optimized by a novel modified whale optimization algorithm in order to compute the forecasting results of the solar irradiance datasets. Finally, in the third step, we deploy a deep reinforcement learning strategy for selecting the best subset of the combined deep optimized LSTM-CNN models. Through analysing the forecasting results over two real-world datasets gathered from the USA solar irradiance stations, it can be inferred that our proposed algorithm outperforms other powerful benchmarked algorithms in 1-step, 2-step, 12-step, and 24-step ahead forecasting.



Creative Commons License

Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License
This work is licensed under a Creative Commons Attribution-Noncommercial-No Derivative Works 4.0 License.

Available for download on Tuesday, December 31, 2024