Full metadata record
| DC Field | Value | Language |
|---|---|---|
| dc.contributor | Department of Electrical and Electronic Engineering | en_US |
| dc.contributor.advisor | Bu, Siqi (EEE) | en_US |
| dc.creator | Pan, Shuning | - |
| dc.identifier.uri | https://theses.lib.polyu.edu.hk/handle/200/14129 | - |
| dc.language | English | en_US |
| dc.publisher | Hong Kong Polytechnic University | en_US |
| dc.rights | All rights reserved | en_US |
| dc.title | Data-driven evaluation and optimization on future impact of renewable energy policies | en_US |
| dcterms.abstract | The widespread application of renewable energy is a core strategy for addressing climate change, enhancing energy security, and promoting sustainable development. The demand for renewable energy continues to rise globally, with governments and businesses emphasizing the importance of using renewable energy to combat climate change and achieve sustainable development. However, there is a gap in governments' ability to timely formulate and evaluate renewable energy policies, as the capacity and generation of sustainable energy are influenced by dynamic factors such as local economic prosperity, environmental and technological conditions. Therefore, creating a platform for predicting, evaluating, and optimizing renewable energy policies can meet governments' needs for such policies. | en_US |
| dcterms.abstract | This dissertation comprehensively analyzes renewable energy policies in different regions, summarizes 14 influencing factors, and proposes a novel prediction, evaluation, and optimization platform using a data-driven approach combined with the ESN-GWO model, policy diffusion model, and NSGA-II Model. In the dissertation, I reconstructed a prediction model based on ESN-GWO to improve prediction accuracy; used the policy diffusion model to score policies and optimized policies using the NSGA-II Model to obtain optimal policy guidance. By researching three cities at different stages of development (Singapore, London, and California) and three different scenarios, it was demonstrated that this platform can effectively assess the impact of existing policies and predict future trends under different policy interventions, thus providing specific recommendations for policy optimization. | en_US |
| dcterms.extent | x, 76 pages : color illustrations | en_US |
| dcterms.isPartOf | PolyU Electronic Theses | en_US |
| dcterms.issued | 2024 | en_US |
| dcterms.educationalLevel | M.Sc. | en_US |
| dcterms.educationalLevel | All Master | en_US |
| dcterms.accessRights | restricted access | en_US |
Files in This Item:
| File | Description | Size | Format | |
|---|---|---|---|---|
| 8593.pdf | For All Users (off-campus access for PolyU Staff & Students only) | 3.38 MB | Adobe PDF | View/Open |
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