AI-Powered Enterprise Analysis
Development of a Predictive Model for Hydropower Capacity in Jiangsu Province Utilizing Machine Learning Techniques
Shuya Zhao, Hohai University, Changzhou, Jiangsu, China
This analysis explores the application of machine learning, specifically XGBoost, to predict hydropower capacity in Jiangsu Province. By leveraging time-series data and identifying key influencing factors, the model aims to support sustainable water resource management and energy planning, achieving high predictive accuracy in a real-world context.
Keywords: XGBoost Model, Hydropower Generation, Jiangsu Province
Executive Impact at a Glance
Key performance indicators highlight the tangible benefits of XGBoost for predictive analytics in critical infrastructure management.
Deep Analysis & Enterprise Applications
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The XGBoost model demonstrated perfect accuracy on its testing dataset, affirming its robust predictive power for hydropower capacity in Jiangsu Province, providing a reliable foundation for enterprise decision-making.
Feature | XGBoost | Random Forest | Neural Network |
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Prediction Error Standard Deviation | 1.8% | 3.5% | 4.2% |
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Enterprise Process Flow: XGBoost Hydropower Prediction
Analysis revealed that the availability of total water resources is the paramount factor influencing hydropower generation, with an F-score of 26.00, underscoring its direct link to energy production and strategic importance for resource planning.
Understanding Jiangsu's Hydropower Trajectory (2005-2024)
Jiangsu Province experienced a sharp rise in hydropower generation between 2012 and 2017, primarily driven by infrastructure developments. Following this period, from 2018 to 2020, generation stabilized, indicating a plateau in expansion. These trends, with generation variability ranging from 2.02 to 33.22 billion kWh across the regions, are crucial for long-term energy planning, enabling policymakers to dynamically adjust strategies and manage water resources sustainably while balancing ecological and economic considerations.
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