Inspiration
We wanted to create something impactful which studies deep insights into the data and give out meaningful predictions from our analysis.
What it does
Our project analyses the weather data, taking out averages for each parameter. It then processes the data into our algorithm which predicts the best fit of renewable energy resource for that area. It takes into consideration several factors like average temperature, precipitation, elevation, and average wind speed which directly contribute to an energy resource.
How we built it
We cleaned the dataset to get the favourable variables and values, and then we used our knowledge to implement a function for getting the best score where we use the solar, hydro and wind energy resource over the states. Used the analyses data to visualize it.
Challenges we ran into
One of the major challenges we faced was cleaning and preparing the data properly. We had to:
Remove null values that caused missing points on the map.
Adjust and standardize date formats, since some entries used inconsistent formats.
Ensure that latitude and longitude values were correctly aligned with their corresponding energy types.
Accomplishments that we're proud of
Accomplishments That We're Proud Of
Successfully cleaned and prepared a large dataset by handling null values and fixing inconsistent date formats.
Created an interactive Tableau map showing Solar, Wind, and Hydro energy sites across regions.
Used color coding and filters to clearly distinguish between different renewable resources.
Improved our skills in data visualization, geospatial analysis, and storytelling with data.
Delivered a meaningful project that highlights the importance of renewable energy distribution worldwide.
What we learned
Throughout this project, we learned how to:
Clean and preprocess large datasets to remove inconsistencies and missing values.
Work with latitude and longitude coordinates to build interactive map visualizations in Tableau.
Use color encoding and filters effectively to highlight different renewable energy types.
Communicate insights clearly using data-driven storytelling.
What's next for SolWiHy
We plan to enhance SolWiHy by expanding our dataset with more renewable energy indicators, such as production capacity and regional weather data. Our next steps include building a predictive model to identify future renewable energy hotspots and developing an interactive dashboard for users to explore trends more intuitively. Ultimately, we aim to turn SolWiHy into a decision-support tool for sustainable energy planning.
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