CoVEMDA 1.0

CoronaVirus - Electricity Market Data Analyzer (CoVEMDA)

CoVEMDA (Python Version)

CoronaVirus - Electricity Market Data Analyzer (CoVEMDA) is an open-access and ready-to-use toolbox to track COVID-19 impacts on U.S. power systems. This document is a quick overview of this toolbox.

Features

CoVEMDA is primarily working with COVID-EMDA+ data hub, with some major functions such as: baseline estimation, regression analysis, scientific visualization, and other useful supplementary functions.

Extenal data and models are allowed for further extensions.

Navigation

CoVEMDA root directory contains four folders, a setup.py script, a README.md document, and a LICENSE text file. One can run the setup script to check the dependencies of current environment, or use pip command to retrieve from PyPI.

Folder lib/ contains the source codes for CoVEMDA, which are collectively organized in several script files according to the realized functions. It is recommended to call the integrated functions or high-level functions as they are user-oriented and simple to use. Turn to low-level functions only in case that more flexible and refined configurations are required.

Folder docs/ contains a User Manual for CoVEMDA. This manual includes simple guidance for first-time users, as well as detailed explanation of all the features and functions, along with illustrative examples. Though this is a complete guide for anyone that is interested in more details, reading Section 1 (Introduction) and Section 2 (Getting Started) is enough for beginners to try it out.

Folder data/ contains the temporary files of CoVEMDA, including a data collection updated to March 2021 (data/data_archive/) and several pretrained backcast models (data/backcast).

User Manual

An extensive User Manual is attached to the toolbox, which can be found at docs/. In this manual, users may find some basic-level guidances as well as comprehensive details of the toolbox implementation. Here, the the manual are organized with the following sections:

1. Introduction
2. Getting Started
3. Data Hub
4. Toolbox
5. Baseline Estimation
6. Regression Analysis
7. Scientific Visualization
8. Acknowledgments

We highly recommend you to read Section 1 and 2 before using CoVEMDA. The rest of the manual introduces all the features, classes, and functions from principle to practice in detail. Read Section 3 and 4 to get some knowledge of the programming architecture and useful interfaces. Read Section 5, 6, and 7 for advanced usage and customization.