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Implication of machine learning techniques to forecast the electricity price and carbon emission:Evidence from a hot region

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Geoscience Frontiers 2024年第3期15卷 259-271页

作者： Suleman Sarwar ghazala aziz Aviral Kumar Tiwari Department of Finance and Economics College of BusinessUniversity of JeddahJeddahSaudi Arabia Department of Business Administration College of Administrative and Financial SciencesSaudi Electronic UniversityJeddahSaudi Arabia Indian Institute of Management BodhgayaIndia

The current study examines the significant determinants of electricity consumption and identifies an appropriate model to forecast the electricity price *** main contribution is focused on eastern region of Saudi Arabia,a relatively hottest geographical area full of energy resources but with different electricity consumption *** relative irrelevance of temperature as predicting factor of electricity consumption is quite surprising and contradicts the previous *** the eastern region,electricity price has negative association with electricity *** comparing traditional and machine learning,it is found that machine learning techniques offer better *** the machine learning techniques,the support vector machine has the lowest errors in forecasting the electricity ***,the support vector machine approach is used to forecast the trend of carbon emissions caused by electricity *** findings have policy implications and offer valuable suggestions to policymakers while addressing the determinants of electricity consumption and forecasting electricity prices.

关键词： Electricity consumption Carbon emission Artificial neural network Support vector machine Saudi Arabia

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Relevance of hybrid artificial intelligence for improving the forecasting accuracy of natural resource prices

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Geoscience Frontiers 2024年第3期15卷 431-446页

作者： Mei Li Rida Waheed Dervis Kirikkaleli ghazala aziz School of Management and Economics and Shenzhen Finance Institute The Chinese University of Hong KongShenzhen(CUHK-Shenzhen)China Department of Finance and Economics College of BusinessUniversity of JeddahJeddahSaudi Arabia European University of Lefke Faculty of Economic and Administrative ScienceDepartment of Banking and FinanceLefkeTurkey Department of Business Administration College of Administrative and Financial SciencesSaudi Electronic UniversityJeddahSaudi Arabia

The prediction performance of traditional forecasting methods is low due to the high level of complexity in a series of energy *** present study attempts to compare the traditional regression,machine learning tools and hybrid models to conclude the outperforming *** first step is to propose the effective denoising technique for Tadawul energy index,which has confirmed the superiority of CSD based ***,we use the CSD-ARIMA,CSD-ANN,and CSD-RNN as hybrid *** a result,CSD-RNN outperforms both other models in terms of MSE,MAPE,RMSE and *** findings are useful for policy makers,investors and portfolio managers to forecast the energy trends,and hedge the portfolio risk accordingly.

关键词： Hybrid artificial intelligence CSD denoising technique Forecasting Energy prices Saudi Arabia

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Applicability of denoising-based artificial intelligence to forecast the environmental externalities

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Geoscience Frontiers 2024年第3期15卷 469-483页

作者： Dongsheng Cai ghazala aziz Suleman Sarwar Majid Ibrahim Alsaggaf Avik Sinha The College of Nuclear Technology and Automation Engineering Chengdu University of TechnologySichuan Province 610059China Department of Business Administration College of Administrative and Financial SciencesSaudi Electronic UniversityJeddahSaudi Arabia Department of Finance and Economics College of BusinessUniversity of JeddahJeddahSaudi Arabia Management Development Institute Gurgaon India

The current study attempts to compare the hybrid artificial intelligence models to forecast the environmental externalities in Saudi *** have used the denoising based artificial intelligence models to construct hybrid *** comparing the denoising techniques,the CSD-based denoising has ***,we have used the CSD-based hybrid ***-ANN and CSD-RNN are used for denoising-based artificial intelligence models,whereas CSD-ARIMA is used for denoising-based traditional *** these models are used to check and compare their performance in terms of level and direction of prediction for PM_(10).The results show that the CSD-based ANN model has a higher predictability for PM_(10) levels in Saudi Arabia due to low error values and higher Dstat *** comparing original and forecasted data,the superiority of CSD-ANN is evident in predicting the PM_(10) in Saudi ***,this hybrid model can predict the environmental externalities for non-linear and highly noised ***,the findings can be useful in achieving the sustainable development goal.

关键词： Hybrid artificial intelligence Forecasting Saudi Arabia Environment PM_(10)

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