Hydro-Power Allocation Optimization Model

Objective: Develop a machine learning-based optimization model for hydro-power allocation that predicts job creation based on power allocations, using a decision tree regression model. Incorporate an interactive dashboard for real-time visualization of the predicted job creation impact from user-defined power allocation values.

Methodology:

Research Focus: Centered on exploring the synergy between energy systems and data analytics. Aimed to leverage data-driven decision-making for optimizing resource allocation in the hydro-power sector.
Model Development: Employed a decision tree regression model to forecast the number of jobs created from specific hydro-power allocations. Focused on precise and accurate predictive modeling.
Interactive Dashboard Creation: Developed a user-friendly dashboard for stakeholders to interact with the model. Enabled real-time input of power allocation values and visualized the consequent job creation impact.

Key Outcomes:

Predictive Accuracy: Demonstrated the model’s capability to accurately predict economic outcomes (job creation) from energy allocations.
Optimization Strategy: Provided a strategic framework for maximizing job creation through efficient hydro-power allocation, showcasing the practical application of data science in energy resource management.
User Engagement: Enhanced accessibility and understanding of the model’s implications through the interactive dashboard, catering to policymakers and stakeholders in the energy sector.

Explore More: Visit the project repository on GitHub for detailed insights: Hydro-Power Allocation and Economic Impact Analysis on GitHub