May 27, 2024

Sajad Ahmadian

Academic rank: Assistant professor
Education: Ph.D in Computer Engineering
Phone: 09188339565
Faculty: Faculty of Information Technology


Solar irradiance forecasting using a novel hybrid deep ensemble reinforcement learning algorithm
Type Article
Solar irradiance forecasting; Deep neural networks; Evolutionary computation; Ensemble strategy; Deep reinforcement learning
Researchers Seyed Mohammad Jafar Jalali، Sajad Ahmadian، Bahareh Nakisa، mahdi khodayar، Abbas Khosravi، Saeid Nahavandi، Syed Mohammed Shams Islam، Miadreza Shafie-khah، Joao Catalao


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.