Energy Consumption Forecasting with Prophet

What

This AI Data Analyst workflow loads the AEP hourly energy consumption CSV and plots the full time series to inspect the overall trend. It computes and visualizes average consumption patterns by hour of day and by day of week to highlight recurring seasonality. It fits a Prophet model to the historical data, forecasts the next 30 days, and displays forecast components including trend and seasonal effects.

Who

This is for analysts and data scientists who need a quick, reproducible baseline forecast for hourly electricity demand. It is also useful for practitioners learning how to translate exploratory time-series patterns into a Prophet forecasting workflow.

Tools

• pandas
• numpy
• matplotlib
• seaborn
• prophet

Outcomes

• Line chart of the full hourly consumption time series
• Bar chart of average consumption by hour of day
• Bar chart of average consumption by day of week
• 30-day Prophet forecast plot with uncertainty intervals
• Prophet components plot showing trend and seasonalities

Quality Score

• Task Completion: 1/2

  Needs work

  Loaded the dataset, plotted an overall trend, computed/visualized hourly and weekly averages, and fit Prophet with a 30-day forecast. However, the required Prophet forecast plot with uncertainty bands is not shown (only components plot is produced), and the weekly pattern is not explicitly a standalone bar chart as expected.

• Execution Correctness: 1/2

  Needs work

  Most code is syntactically valid and likely runnable, but the Prophet install step uses notebook shell syntax (!pip) inside a code block which can fail outside Jupyter, and the workflow never calls model.plot(forecast) to generate the main forecast figure.

• Output Quality: 1/3

  Needs work

  Outputs include a monthly resampled series printout and images for the time-series trend and the hourly/weekly summaries, plus a Prophet components plot. The key expected output—a forecast plot with uncertainty bands—is missing, and the hourly pattern is a line plot rather than the requested bar chart.

• Reasoning Quality: 1/2

  Needs work

  Reasoning is generally aligned (resampling to daily for Prophet, grouping by hour/day name), but it avoids interpreting the hourly/weekly patterns and does not ensure the deliverables match the expected chart types/forecast visualization.

• Reliability: 0/1

  Needs work

  Relies on environment-specific behavior (shell install) and assumes df state across cells; missing explicit generation of required forecast plot makes the workflow fragile relative to the stated expected outcomes.