An advanced short-term wind power forecasting framework based on the optimized deep neural network models

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.© 2022 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).fi=vertaisarvioitu|en=peerReviewed

The Investigation of E-Business Trends by Using Social Network Analysis Technique during 1980 to 2015

In today’s world, the global nature of business and advances in information and communication technology, forced organizations to use emerging technologies to maintain themselves competitive. In recent years, electronic business (e-learning) has been adopted by many organizations. Thereby companies can improve their operational efficiency, profitability and competitive position. This research tried by using burst detection algorithm in scientometrics, to examine all keywords, titles, premier authors, universities, countries as well as co-authors network in the field of e-business . Hence, all relevant articles in the Web of Science database - as a reference for this study- during 1980 to 2015 have been investigated. In this regard, 4697 articles extracted and burst detection algorithm was used to analyz

Analytical Evaluation of Emerging Scientific Fields in Middle East Tourism Industry Using the Text Mining Algorithms

Tourism is an important industry from different aspects for government, industry, religious groups, anthropologists and tourists. The study of the scientific trend in the tourism sector can increase the knowledge of each group, and ultimately, promotes researchers' awareness of this area and appropriate decision making of business owners. The purpose of the present research is to investigate the scientific trend of tourism in all Middle Eastern countries, using the text-exploration algorithm of sequence exploration, which was selected as the most reliable scientific database of the world for reviewing articles. The purpose of this research is to investigate the scientific trend of tourism in all Middle Eastern countries, using the text-exploration algorithm of sequence exploration, which was selected as the most reliable scientific database of the world for reviewing articles. For this purpose, the Web of Science database was selected as the world's most authoritative database for reviewing articles. A total of 1141 articles were searched for the word "tourism", taking into account the Middle East countries on the database. Among the various methods for extracting and analyzing scientific trends, the valid method of text mining was selected using the Burst Detection algorithm, and this method is used to analyze and evaluate the extracted papers, which also has significant results. The results of the analysis of the articles show that some scientific areas have more repetition in the title, keywords, and abstracts of international articles which indicates the importance of these areas of science to other areas and the need for more attention to them in the field of tourism