Data-driven decision-making model for renewable energy DOI Creative Commons
Wisdom Udo,

Adekunle Stephen Toromade,

Njideka Rita Chiekezie

et al.

International Journal of Management & Entrepreneurship Research, Journal Year: 2024, Volume and Issue: 6(8), P. 2684 - 2707

Published: Aug. 21, 2024

The transition to renewable energy sources is critical for achieving sustainable development and combating climate change. As the sector rapidly evolves, there a growing need advanced decision-making frameworks that can effectively navigate complexities of production, distribution, consumption. This paper explores application data-driven model tailored industry. integrates real-time data analytics, machine learning algorithms, predictive modeling enhance processes in areas such as resource allocation, grid management, investment planning. By leveraging vast datasets, including weather patterns, consumption trends, market dynamics, provides actionable insights enable stakeholders optimize forecast demand, mitigate risks associated with projects. model's capabilities are particularly valuable managing intermittency sources, solar wind, by improving accuracy forecasting output aligning it demand. Additionally, supports strategic decisions identifying high-potential based on assessments availability, infrastructure readiness, economic viability. also addresses challenges implementing models sector, quality integration issues, specialized technical expertise, importance regulatory policy frameworks. Case studies presented illustrate practical different projects, highlighting benefits enhancing operational efficiency, reducing costs, supporting growth sector. findings underscore potential approaches revolutionize making them indispensable tools clean future. Keywords: Data-Driven, Decision-Making, Models, Renewable Energy.

Language: Английский

Advances in machine learning-driven pore pressure prediction in complex geological settings DOI Creative Commons

Adindu Donatus Ogbu,

Kate A. Iwe,

Williams Ozowe

et al.

Computer Science & IT Research Journal, Journal Year: 2024, Volume and Issue: 5(7), P. 1648 - 1665

Published: July 25, 2024

Advances in machine learning (ML) have revolutionized pore pressure prediction complex geological settings, addressing critical challenges oil and gas exploration production. Traditionally, predicting accurately heterogeneous anisotropic formations has been fraught with uncertainties due to the limitations of conventional geophysical petrophysical methods. Recent developments ML techniques offer enhanced precision reliability estimation, leveraging vast datasets sophisticated algorithms analyze interpret complexities. ML-driven approaches utilize a variety data sources, including well logs, seismic data, drilling parameters, train predictive models that can handle non-linear multi-dimensional nature subsurface conditions. Techniques such as neural networks, support vector machines, ensemble methods shown significant promise capturing intricate relationships between variables pressure. These adaptively learn from new improving their capabilities over time. A notable advantage is its ability integrate disparate types scales, providing holistic understanding regimes. This integration enhances accuracy forecasts, which crucial for wellbore stability, safety, hydrocarbon recovery. For instance, real-time operations be fed into dynamically update estimates, allowing immediate adjustments plans reducing risk blowouts or other hazards. Moreover, facilitate identification subtle patterns trends might overlooked by traditional capability particularly valuable deep-water environments, tectonically active regions, unconventional reservoirs, where often fall short. Despite promising advances, remain widespread adoption prediction. include need extensive training datasets, interpretability models, workflows existing geoscientific practices. Addressing these requires interdisciplinary collaboration geoscientists, scientists, engineers develop robust, user-friendly solutions. In summary, represents advancement managing complexities geology. By enhancing reliability, technologies are poised improve efficiency, productivity industry, challenging settings. Keywords: Advance, ML, Pore Pressure, Prediction, Geological Settings.

Language: Английский

Citations

8
Reinforcement learning for vehicle-to-grid: A review DOI Creative Commons

Hongbin Xie,

Ge Song,

Zhuoran Shi

et al.

Advances in Applied Energy, Journal Year: 2025, Volume and Issue: unknown, P. 100214 - 100214

Published: Feb. 1, 2025

Language: Английский

Citations

1
Stable energy management for highway electric vehicle charging based on reinforcement learning DOI

Hongbin Xie,

Song Ge,

Zhuoran Shi

et al.

Applied Energy, Journal Year: 2025, Volume and Issue: 389, P. 125541 - 125541

Published: March 19, 2025

Language: Английский

Citations

0
Hierarchical energy management for power distribution networks and discrete manufacturing systems: A fully distributed parallel approach DOI
Xiaoqing Zhong, Guotao Wang, Zhiling Guo

et al.

Energy 360., Journal Year: 2025, Volume and Issue: unknown, P. 100017 - 100017

Published: Feb. 1, 2025

Language: Английский

Citations

0
Interactions Between Active Distribution and Transmission Networks: State of the Art and Opportunities DOI
Md. Sazal Miah, Rakibuzzaman Shah, Nima Amjady

et al.

Energy 360., Journal Year: 2025, Volume and Issue: unknown, P. 100024 - 100024

Published: April 1, 2025

Language: Английский

Citations

0
Carbon emission reduction benefits of ride-hailing vehicle electrification considering energy structure DOI Creative Commons
Zhe Zhang,

Qing Yu,

Kun Gao

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 377, P. 124548 - 124548

Published: Oct. 7, 2024

Language: Английский

Citations

1
Carbon emission reduction effects of heterogeneous car travelers under green travel incentive strategies DOI

Qianhui Jiao,

Jinghui Wang, Cheng Long

et al.

Applied Energy, Journal Year: 2024, Volume and Issue: 379, P. 124826 - 124826

Published: Nov. 22, 2024

Language: Английский

Citations

1
Data-driven decision-making model for renewable energy DOI Creative Commons
Wisdom Udo,

Adekunle Stephen Toromade,

Njideka Rita Chiekezie

et al.

International Journal of Management & Entrepreneurship Research, Journal Year: 2024, Volume and Issue: 6(8), P. 2684 - 2707

Published: Aug. 21, 2024

The transition to renewable energy sources is critical for achieving sustainable development and combating climate change. As the sector rapidly evolves, there a growing need advanced decision-making frameworks that can effectively navigate complexities of production, distribution, consumption. This paper explores application data-driven model tailored industry. integrates real-time data analytics, machine learning algorithms, predictive modeling enhance processes in areas such as resource allocation, grid management, investment planning. By leveraging vast datasets, including weather patterns, consumption trends, market dynamics, provides actionable insights enable stakeholders optimize forecast demand, mitigate risks associated with projects. model's capabilities are particularly valuable managing intermittency sources, solar wind, by improving accuracy forecasting output aligning it demand. Additionally, supports strategic decisions identifying high-potential based on assessments availability, infrastructure readiness, economic viability. also addresses challenges implementing models sector, quality integration issues, specialized technical expertise, importance regulatory policy frameworks. Case studies presented illustrate practical different projects, highlighting benefits enhancing operational efficiency, reducing costs, supporting growth sector. findings underscore potential approaches revolutionize making them indispensable tools clean future. Keywords: Data-Driven, Decision-Making, Models, Renewable Energy.

Language: Английский

Citations

0