Huang, Jiaqing and Wang, Linlin and Siddik, Abu Bakkar and Abdul Samad, Zulkiflee and Bhardwaj, Arpit and Singh, Bharat (2023) Forecasting GHG emissions for environmental protection with energy consumption reduction from renewable sources: A sustainable environmental system. Ecological Modelling, 475. ISSN 0304-3800, DOI https://doi.org/10.1016/j.ecolmodel.2022.110181.
Full text not available from this repository.Abstract
The long-term viability of energy resources as a main input is essential to achieve long-term economic growth of a country and the energy efficiency significantly reduces energy consumption and greenhouse gas emissions, supporting environmental sustainability. As a result, a number of governments, led by those in the developed world, are making an effort to enact laws governing energy efficiency. This study suggests cutting-edge methods for forecasting greenhouse gas emissions and reducing energy demand from renewable sources based on a sustainable environment. Utilizing the statistical regression neural network (SRNN), greenhouse gas emissions have been predicted, and the deep neural network's (DNN) energy efficiency has increased. The SRNN_DNN intensity method out predicts evaluated MLR (multiple linear regression) and second- and third-order non-linear MPR (multiple polynomial regression) techniques according to MAPE (mean absolute percentage error) results. Furthermore, presented methods are considered suitable for computing GHG emissions due to the high accuracy of the SRNN DNN model. The anticipated greenhouse gas emissions related to energy were remarkably similar to the actual emissions of EU (European Union) nations.
| Item Type: | Article |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | GHG emissions; Energy efficiency; Statistic regression neural network; Deep neural network; Environmental sustainability |
| Subjects: | G Geography. Anthropology. Recreation > GE Environmental Sciences |
| Divisions: | Faculty of the Built Environment |
| Depositing User: | Ms Zaharah Ramly |
| Date Deposited: | 28 Nov 2023 09:06 |
| Last Modified: | 28 Nov 2023 09:06 |
| URI: | http://eprints.um.edu.my/id/eprint/39340 |
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