Energy Forecast

Inspiration

We noticed that weather conditions highly affect renewable energy production, and often, bad planning leads to money wasted on renewable energy.

What it does

Our program inputs the weather forecast data and outputs the renewable energy generated in MW, as well as the price to generate this energy.

How we built it

We built the Neural Network using the Python Tensorflow library. We built our web application using HTML, CSS, and JavaScript.

Challenges we ran into

One challenge we ran into was that the dataset we were using had a few errors, and while we were building our Neural Network, we ran into issues because of missing data values. Additionally, we also ran into some challenges as we tried connecting the Neural Network to the web application.

Accomplishments that we're proud of

We are proud of being able to clean up the data as necessary and set up all of it in order to plug into the Neural Network. We are also proud of giving the website a unique aesthetic with our parallax scrolling effects and coding it effectively. Last but not least, we are proud of our high accuracy rates for our model.

What we learned

We learned some techniques to clean up data, utilize divs and CSS grids effectively, and to remember to take a step back and look at the big picture when something isn't working.

What's next for Energy Forecast

Next, we plan on improving our dataset and collecting data from more locations around the world, and add a geological factor to our algorithm.

Built With

• css
• html
• javascript
• python
• tensorflow