One of the biggest issues facing us is the availability of sustainable energy sources. As individuals and energy consumers it is often difficult to understand how we can make informed choices about energy use to reduce our impact on the environment. Electricity Map is a project that provides up to date and historical information about the balance of how the energy we are using is being produced. In this episode Olivier Corradi discusses his motivation for creating Electricity Map, how it is built, and his goals for the project and his other work at Tomorrow Co.
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- Your host as usual is Tobias Macey and today I’m interviewing Olivier Corradi about Electricity Map and using Python to analyze data of global power generation
- How did you get introduced to Python?
- What was your motivation for creating Electricity Map?
- How can an average person use or benefit from the information that is available in the map?
- What sources are you using to gather the information about how electricity is generated and distributed in various geographic regions?
- Is there any standard format in which this data is produced?
- What are the biggest difficulties associated with collecting and consuming this data?
- How much confidence do you have in the accuracy of the data sources?
- Is there any penalty for misrepresenting the fuel consumption or waste generation for a given plant?
- Can you describe the architecture of the system and how it has evolved?
- What are some of the most interesting uses of the data in your database and API that you are aware of?
- How do you measure the impact or effectiveness of the information that you provide through the different interfaces to the data that you have aggregated?
- How have you built a community around the project?
- How has the community helped in building and growing Electricity Map?
- What are some of the most unexpected things that you have learned in the process of building Electricity Map?
- What are your plans for the future of Electricity Map?
Keep In Touch
- Electricity Map
- Machine Learning
- Climate Change
- Fossil Fuels
- Carbon Intensity
- Greenhouse Gas Equivalencies Calculations
- Open Data
- Electricity Map Project Source
- Marginal Carbon Intensity
- Electricity Map Forecast API
- IPCC (Intergovernmental Panel on Climate Change
- Spatiotemporal Data
- Matrix Inversion
- Tomorrow Co.