Time-Series Modeling of Missing Energy Industry Data and Weather Data

# :rocket:

- [ ] Is your feature request essential for your project?

### Describe the workflow you want to enable
A significant barrier to realistic modeling of the power grid is missing data from public data sources. This project is to use statistical and ML models to fill in missing energy demand and renewable energy profile information in a way which mimics the real data as close as possible. In our current data sets of regional energy demand, we have used averaging of adjacent days to fill in our missing data, as described [here](https://github.com/Breakthrough-Energy/PreREISE#d-demand-data).

### Describe your proposed implementation
- Understand the data set and the [current methodology](https://github.com/Breakthrough-Energy/PreREISE#d-demand-data)
- Literature review to understand prediction models for time series data
- Collect data to build training/testing sets
- Evaluate algorithms using cross validation or other metrics
- Integrate most suitable algorithm(s) in our code base and generate new time series for our power system model

### Describe alternatives you've considered, if relevant
Suggestions welcome!

### Additional context
This issue is part of a list of ideas for new contributors.

- **Requirements:** Python (pandas), understanding of statistics and ML concepts
- **Level:** Medium
- **Mentor:** @dmuldrew and @rouille