May 27, 2024

Sajad Ahmadian

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


An advanced short-term wind power forecasting framework based on the optimized deep neural network models
Type Article
Deep neural networks; Evolutionary computation; Neuroevolution; Optimization; Wind power forecasting
Researchers Seyed Mohammad Jafar Jalali، Sajad Ahmadian، mahdi khodayar، Abbas Khosravi، Miadreza Shafie-khah، Saeid Nahavandi، Joao Catalao


With the continued growth of wind power penetration into conventional power grid systems, wind power forecasting plays an increasingly competitive role in organizing and deploying electrical and energy systems. The wind power time series, though, often present non-linear and non-stationary characteristics, allowing them quite challenging to estimate precisely. The aim of this paper is in proposing a novel hybrid model named Evol-CNN in order to predict the short-term wind power at 10-min interval up to 3-hr based on deep convolutional neural network (CNN) and evolutionary search optimizer. Specifically, we develop an improved version of Grey Wolf Optimization (GWO) algorithm by incorporating two effective modifications in its original structure. The proposed GWO algorithm is more effective than the original version due to performing in a faster way and the ability to escape from local optima. The proposed GWO algorithm is utilized to find the optimal values of hyperparameters for deep CNN model. Moreover, the optimal CNN model is employed to predict wind power time series. The main advantage of the proposed Evol-CNN model is to enhance the capability of time series forecasting models in obtaining more accurate predictions. Several forecasting benchmarks are compared with the Evol-CNN model to address its effectiveness. The simulation results indicate that the Evol-CNN has a significant advantage over the competitive benchmarks and also, has the minimum error regarding of 10-min, 1-hr and 3-hr ahead forecasting.