May 27, 2024

Sajad Ahmadian

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

Research

Title
A New Ensemble Reinforcement Learning Strategy for Solar Irradiance Forecasting using Deep Optimized Convolutional Neural Network Models
Type Presentation
Keywords
Solar irradiance forecasting, Deep neural networks, Evolutionary computation, Ensemble strategy, Deep reinforcement learning
Researchers Seyed Mohammad Jafar Jalali، mahdi khodayar، Sajad Ahmadian، Miadreza Shafie-khah، Abbas Khosravi، Syed Mohammed Shams Islam، Saeid Nahavandi، Joao Catalao

Abstract

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 based on three steps. In step I, 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 step II, unlike the traditional deep learning models designing their architectures manually, we utilize several deep convolutional neural network (CNN) models optimized by a novel modified whale optimization algorithm in order to compute the forecasting results of the solar irradiance datasets. Finally in step III, we deploy a deep Q-learning reinforcement learning strategy for selecting the best subsets of the combined deep optimized CNN models. Through analysing the forecasting results over two USA solar irradiance stations, it can be inferred that the proposed optimized deep RL-ensemble framework (ODERLEN) outperforms other powerful benchmarked algorithms in different time-step horizons.