Welcome to the Tech4Stack Smart Grid Optimization Hackathon project repository! Our team, comprised of Keval Shah, Ashish Maurya, Kaushal Jha, and Vipul Mhatre, is dedicated to addressing the pressing challenge of integrating renewable energy sources, particularly wind energy, into smart grids to create sustainable power systems. Our project focuses on leveraging data-driven techniques to optimize smart grids, assess grid stability, and provide actionable insights for grid operators. By harnessing the power of data analytics and machine learning, our goal is to contribute to the advancement of grid reliability, resilience, and efficiency while accelerating the transition towards a low-carbon energy landscape.
The objective of our project is to develop predictive models for wind power generation, assess grid stability, and provide actionable insights for grid operators. We aim to tackle the following key tasks:
- Predictive Modeling: Build machine learning models to forecast wind power generation accurately, leveraging historical data and forecasted independent variables.
- Grid Stability Assessment: Develop classification models to determine grid stability based on power generation, consumption, and other parameters.
- Visualization and Analysis: Visualize analysis results and model outputs using Python libraries like Matplotlib, Seaborn, and Plotly. Create interactive dashboards for presenting insights and findings.
- Keval Shah
- Ashish Maurya
- Kaushal Jha
- Vipul Mhatre