Modeling hydro, nuclear, and renewable electricity generation in India: An atom search optimization-based EEMD-DBSCAN framework and explainable AI
Background and objective: Tracking clean electricity generation in developing economies is highly challenging owing to the influence of turbulent external factors. Clean electricity is a significant enabler of striving toward environmental sustainability. In this research, we aim to model hydro, nuc...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-01-01
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| Series: | Heliyon |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844023106426 |
| _version_ | 1797337014359556096 |
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| author | Indranil Ghosh Esteban Alfaro-Cortés Matías Gámez Noelia García-Rubio |
| author_facet | Indranil Ghosh Esteban Alfaro-Cortés Matías Gámez Noelia García-Rubio |
| author_sort | Indranil Ghosh |
| collection | DOAJ |
| description | Background and objective: Tracking clean electricity generation in developing economies is highly challenging owing to the influence of turbulent external factors. Clean electricity is a significant enabler of striving toward environmental sustainability. In this research, we aim to model hydro, nuclear, and renewable electricity generation in India through applied predictive modeling. We also strive to uncover the influence of the critical determinants responsible for clean electricity growth. Methodology: We propose a granular predictive framework comprising ensemble empirical mode decomposition, clustering applications in spatial data based on density, including noise, and atom search optimization-based novel optimization methodology to predict absolute figures of clean energy generation. The framework uses a series of socio-economic factors reflecting household demand and industrial growth in India as explanatory variables. Results: The rigorous scrutiny of the predictive framework specifies hydro electricity generation is relatively more predictable during the time horizon influenced by the COVID-19 pandemic. The deployment of dedicated explainable artificial intelligence (AI) tools suggests an increased adoption of clean electricity in selected industrial sectors in India, which broadly governs the evolutionary pattern. Conclusion: The underlying research is the first of its kind to fathom the daily temporal dynamics of clean electricity generation in the Indian context. Consideration of three distinct clean electricity sources during highly volatile time regimes underscores the contribution of the work. The predictive framework survives a stringent performance check, which justifies the robustness of the same. Demand in different industrial sectors in India profoundly influences the growth toward clean electricity. |
| first_indexed | 2024-03-08T09:03:15Z |
| format | Article |
| id | doaj.art-383db33de3f542ad9e4f929214c20886 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-03-08T09:03:15Z |
| publishDate | 2024-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-383db33de3f542ad9e4f929214c208862024-02-01T06:31:52ZengElsevierHeliyon2405-84402024-01-01101e23434Modeling hydro, nuclear, and renewable electricity generation in India: An atom search optimization-based EEMD-DBSCAN framework and explainable AIIndranil Ghosh0Esteban Alfaro-Cortés1Matías Gámez2Noelia García-Rubio3IT & Analytics Area, Institute of Management Technology, Hyderabad, Telangana, IndiaQuantitative Methods and Socio-economic Development Group, Institute for Regional Development (IDR), University of Castilla-La Mancha (UCLM), Albacete, Spain; Faculty of Economics and Business Administration, University of Castilla-La Mancha (UCLM), Albacete, SpainQuantitative Methods and Socio-economic Development Group, Institute for Regional Development (IDR), University of Castilla-La Mancha (UCLM), Albacete, Spain; Faculty of Economics and Business Administration, University of Castilla-La Mancha (UCLM), Albacete, SpainQuantitative Methods and Socio-economic Development Group, Institute for Regional Development (IDR), University of Castilla-La Mancha (UCLM), Albacete, Spain; Faculty of Economics and Business Administration, University of Castilla-La Mancha (UCLM), Albacete, Spain; Corresponding author. Faculty of Economics and Business Administration, University of Castilla-La Mancha (UCLM), Albacete, Spain.Background and objective: Tracking clean electricity generation in developing economies is highly challenging owing to the influence of turbulent external factors. Clean electricity is a significant enabler of striving toward environmental sustainability. In this research, we aim to model hydro, nuclear, and renewable electricity generation in India through applied predictive modeling. We also strive to uncover the influence of the critical determinants responsible for clean electricity growth. Methodology: We propose a granular predictive framework comprising ensemble empirical mode decomposition, clustering applications in spatial data based on density, including noise, and atom search optimization-based novel optimization methodology to predict absolute figures of clean energy generation. The framework uses a series of socio-economic factors reflecting household demand and industrial growth in India as explanatory variables. Results: The rigorous scrutiny of the predictive framework specifies hydro electricity generation is relatively more predictable during the time horizon influenced by the COVID-19 pandemic. The deployment of dedicated explainable artificial intelligence (AI) tools suggests an increased adoption of clean electricity in selected industrial sectors in India, which broadly governs the evolutionary pattern. Conclusion: The underlying research is the first of its kind to fathom the daily temporal dynamics of clean electricity generation in the Indian context. Consideration of three distinct clean electricity sources during highly volatile time regimes underscores the contribution of the work. The predictive framework survives a stringent performance check, which justifies the robustness of the same. Demand in different industrial sectors in India profoundly influences the growth toward clean electricity.http://www.sciencedirect.com/science/article/pii/S2405844023106426Clean electricityEmpirical ensemble mode decompositionIndustrial growthAtom search optimizationExplainable artificial intelligence |
| spellingShingle | Indranil Ghosh Esteban Alfaro-Cortés Matías Gámez Noelia García-Rubio Modeling hydro, nuclear, and renewable electricity generation in India: An atom search optimization-based EEMD-DBSCAN framework and explainable AI Heliyon Clean electricity Empirical ensemble mode decomposition Industrial growth Atom search optimization Explainable artificial intelligence |
| title | Modeling hydro, nuclear, and renewable electricity generation in India: An atom search optimization-based EEMD-DBSCAN framework and explainable AI |
| title_full | Modeling hydro, nuclear, and renewable electricity generation in India: An atom search optimization-based EEMD-DBSCAN framework and explainable AI |
| title_fullStr | Modeling hydro, nuclear, and renewable electricity generation in India: An atom search optimization-based EEMD-DBSCAN framework and explainable AI |
| title_full_unstemmed | Modeling hydro, nuclear, and renewable electricity generation in India: An atom search optimization-based EEMD-DBSCAN framework and explainable AI |
| title_short | Modeling hydro, nuclear, and renewable electricity generation in India: An atom search optimization-based EEMD-DBSCAN framework and explainable AI |
| title_sort | modeling hydro nuclear and renewable electricity generation in india an atom search optimization based eemd dbscan framework and explainable ai |
| topic | Clean electricity Empirical ensemble mode decomposition Industrial growth Atom search optimization Explainable artificial intelligence |
| url | http://www.sciencedirect.com/science/article/pii/S2405844023106426 |
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