ericburm / kWh

⚡Evergy Usage & Solar Production Dashboard

Evergy API → PostgreSQL
Evergy ebill → ChatGPT → PostgreSQL
PostgreSQL → Metabase

Note: This is uses Evergy's non-public API.

Project Overview

• Enhanced version of the original Evergy Client by Lawrence Foley.
• Integrated into a comprehensive data pipeline.
• Automates collection and analysis of Evergy e-bill and usage data.

Data Extraction

• Evergy sends the e-bill via email between the 15th and 25th of each month.
• Zapier extracts the link to the e-bill PDF from the email.
• The link is stored in both Google Sheets and PostgreSQL.

Data Processing

• Python script (ebill.py) takes the link from PostgreSQL.
  • Logs into Evergy and retrieves the PDF e-bill online.
  • Sends the PDF e-bill in an email.
• ChatGPT parses the e-bill PDF.
• Extracted data is sent to Google Sheets.
• Zapier processes the data in Google Sheets.
  • Breaks data into multiple columns.
  • Inserts processed data into PostgreSQL.

Automation

• A daily cron job runs main.py.
  • Fetches Evergy API cost/usage data.
  • Fetches OpenWeather data.
  • Sends data to PostgreSQL.
• A cron job on the 30th of each month runs ebill.py.
  • Takes the link from PostgreSQL.
  • Logs into Evergy to retrieve and email the PDF e-bill.

Analytics

• Data is visualized using Metabase.
  • Provides self-service analytics.
  • Enables comprehensive analysis of energy usage and solar production.

Enhanced Functionality

• Extended Data Retrieval:
  • Pulls all available data from Evergy.
  • Provides a more comprehensive dataset for analysis compared to the original project.