Build a forecast of how green your local electricity generation is throughout the day and week. Learn data science techniques for building Artificial Intelligence Machine Learning Models.
Every day we leave footprint trails all around us but in the form of carbon. It’s the amount of carbon dioxide equivalent released into the atmosphere by our actions. For example: if you need to go to the grocery store, then taking the transportation to the store is using gasoline. Burning gasoline as fuel produces carbon dioxide and releases it into the atmosphere.
The long term build-up of carbon dioxide is the most important factor causing climate change. The good news here is we can all do something to help the environment by taking actions to reduce our carbon footprint.
In this workshop, you will learn more about the electrical generation grid with its mix of different generation sources. Did you know that at different times of day, your electricity might be mostly green, and at other times a large portion might be generated by fossil fuels? For example, if there are wind turbines in your area, the higher the windspeed, the more electricity they will generate, and the less need there will be for natural gas generation plants to fill in the gap.
If you knew in advance how “green” your energy usage was going to be, you could make decisions on when to use the most electricity and when to be frugal. Artificial Intelligence and Machine Learning (AI ML) can be used to generate this, and you will do just that to make a personal “green energy forecast”!
AI and ML need data to make predictions like this. So next, you will become a data scientist, evaluate different data sources to determine which ones are the most useful for making your forecast. Analyze scatterplot diagrams to see what data correlates to the fossil fuel intensity, and what data is a distraction. Once you have found the best data, add in the information on what percentage of your local grid’s electricity is generated from different sources such as wind, solar, nuclear and natural gas. Finally, use our provided python code to retrieve your local live weather information and other data to generate your forecast.