Transportation energy demand forecasting in Taiwan based on metaheuristic algorithms

Lashgari, Ali; Hosseinzadeh, Hasan; Khalilzadeh, Mohammad; Milani, Bahar; Ahmadisharaf, Amin; Rashidi, Shima

doi:10.1080/15567036.2022.2062072

Cited by 9 publications

(9 citation statements)

References 39 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, despite the many researches conducted in this field, no extensive research has been done in Iran, considering the Iranian Muslim population. In addition, in the limited research conducted in the field of halal products in Iran, religious issues and the halal nature of these products in terms of Islamic Sharia have been emphasised (Gordon et al 2021;Lashgari et al 2022). Given to this point that huge impact of the advertising text on human behaviour and market, it is vital to note the common values of audiences and should always align itself with different sub-cultures.…”

Section: Islamic Digital Adsmentioning

confidence: 99%

Culture and religion creolisation impact on digital advertisement of Muslim users of Instagram

Mirvaisi¹,

Kaffashpoor

2023

HTS Teologiese Studies / Theological Studies

View full text Add to dashboard Cite

Over the last decade, digitalisation has been a subject of increasing attention among scholars and practitioners. The effect of culture and religion on advertisements, consumerism and marketing, is deniable. The main goal of this research is to present a comprehensive conceptual model based on cultural and religious diversity in digital marketing. This research mainly includes introduction creolisation concept and the elements (religion, music, clothes and custom) as the most important factor in digital advertisement and branding. In this regard, this study examines Instagram consumer products users from five different subcultures in Iran (Lor, Kurdish, Baluch, Arab and Azeri) and considers the impact of creolisation elements on digital advertising, brand identity and brand globalisation. Data were collected by sending 1200 questionnaire via email to subcultures and then analysed using LISREL software.Contribution: The results demonstrated that Islamic religion between participants affects their brand loyalty, therefore the Islamic worth in advertising is necessary. It was suggested that creolisation elements significantly affect digital advertisement, brand identity and brand globalisation. Consequently, all hypotheses were accepted. On the other hand, the impact of brand identity and digital advertisement on the brand globalisation was also confirmed. Discussion and managerial implications concerning creolisation were explained.

show abstract

Section: Islamic Digital Adsmentioning

confidence: 99%

Culture and religion creolisation impact on digital advertisement of Muslim users of Instagram

Mirvaisi¹,

Kaffashpoor

2023

HTS Teologiese Studies / Theological Studies

View full text Add to dashboard Cite

show abstract

“…They showed that in general, it is not possible to prove the superiority of one model over another with certainty, and the accuracy of forecasts depends on the type of demand and its structure [4]. Lashgari, et al presented a demand forecasting model based on a meta-heuristic algorithm that was used to forecast transportation energy demand in Taiwan [32]. Huang et al used the Recurrent Neural Network (RNN) model to forecast oil demand in China during the period of tourism growth in this country [33].…”

Section: Literature Reviewmentioning

confidence: 99%

Oil Demand Forecasting in Importing and Exporting Countries: AI-Based Analysis of Endogenous and Exogenous Factors

Zhu

2023

Sustainability

View full text Add to dashboard Cite

Given the prevalence of the digital world, artificial intelligence (AI) stands out as one of the most prominent technologies for demand prediction. Although numerous studies have explored energy demand forecasting using machine learning models, previous research has been limited to incorporating either a country’s macroeconomic characteristics or exogenous elements as input variables. The simultaneous consideration of both endogenous and exogenous economic elements in demand forecasting has been disregarded. Furthermore, the stability of machine learning models for energy exporters and importers facing varying uncertainties has not been adequately examined. Therefore, this study aims to address these gaps by investigating these issues comprehensively. To accomplish this objective, data from 30 countries spanning the period from 2000 to 2020 was selected. In predicting oil demand, endogenous economic variables, such as carbon emissions, income level, energy price, gross domestic product (GDP), population growth, urbanization, trade liberalization, inflation, foreign direct investment (FDI), and financial development, were considered alongside exogenous factors, including energy sanctions and the COVID-19 pandemic. The findings indicate that among the input variables examined in demand forecasting, oil sanctions and the COVID-19 pandemic have had the most significant impact on reducing oil demand, while trade liberalization has proven to be the most influential factor in increasing oil demand. Furthermore, the support vector regression (SVR) model outperforms other models in terms of lower prediction error, as revealed by the error assessment of statistical models and AI in forecasting oil demand. Additionally, when comparing the stability of models in oil exporting and importing countries facing different levels of demand uncertainty, the SVR model demonstrates higher stability compared to other models.

show abstract

“…With a root mean square error of 30.325 μg/m 3 and a squared correlation coefficient of 0.928, the results show that ELM-SO performs better than other compared techniques in terms of prediction. Lashgari et al [ 23 ] presented a novel strategy to improve Taiwan's transportation energy consumption predictions. The research introduced an Improved variant of the Emperor Penguin Optimizer (IEPO).…”

Section: Introductionmentioning

confidence: 99%

Towards greener futures: SVR-based CO2 prediction model boosted by SCMSSA algorithm

Adegboye,

Feda,

Agyekum

et al. 2024

Heliyon

View full text Add to dashboard Cite

Transportation energy demand forecasting in Taiwan based on metaheuristic algorithms

Cited by 9 publications

References 39 publications

Culture and religion creolisation impact on digital advertisement of Muslim users of Instagram

Culture and religion creolisation impact on digital advertisement of Muslim users of Instagram

Oil Demand Forecasting in Importing and Exporting Countries: AI-Based Analysis of Endogenous and Exogenous Factors

Towards greener futures: SVR-based CO2 prediction model boosted by SCMSSA algorithm

Contact Info

Product

Resources

About