Energy Consumption Forecasting with Prophet

• Goal: Load AEP hourly energy data and plot the overall trend.

  • URL: https://raw.githubusercontent.com/pplonski/datasets-for-start/refs/heads/master/aep-hourly-energy-consumption/AEP_hourly.csv

  • Constraint: Work iteratively. Load and inspect first. No combining load and plot in the same response.

  • Step 1: Load the data and inspect it (shape, columns, head).

  • Step 2: Handle the date column (convert to datetime).

  • Step 3: Plot the overall trend.

  • Need pandas.

  • Load CSV from the URL.

  • Display basic info.

The user wants to load AEP hourly energy data and plot the overall trend. The dataset has been loaded. It contains Datetime and AEP_MW columns. The Datetime column is currently a string (object), not a datetime object. To plot a trend over time, I need to convert Datetime to datetime objects and set it as the index or use it as the x-axis. The original goal was to "load... and plot overall trend". I have loaded it, but not plotted it.

Insight from output:

• The dataset contains 121,273 rows.
• Datetime values are strings like "2004-12-31 01:00:00".
• AEP_MW values represent energy consumption.

Next step: Convert Datetime to datetime objects and plot the AEP_MW values over time.

Path B: Need to execute the plotting part.

Plan:

1. Convert Datetime to datetime.
2. Plot AEP_MW using seaborn/matplotlib.

The user goal was to load AEP hourly energy data and plot the overall trend.

• Step 1: Data loaded and inspected (121,273 rows, columns Datetime and AEP_MW).
• Step 2: Datetime converted to datetime objects and a line plot of AEP_MW over time was generated.

The latest output is a figure object <Figure size 1500x600 with 1 Axes>. According to the "Sufficiency heuristics": "If the latest output is chart-only/visual-only and does not include underlying numeric/text values, choose Path B first to print the values used in the chart."

The user wants to see the "overall trend". While the chart shows it visually, I don't have the summary statistics or the min/max/mean values of the energy consumption to provide a concrete quantitative description of that trend. To fulfill the "extract concrete insights" part of the prompt, I should provide some descriptive statistics to complement the visual trend.

Path B: Provide insights from the plot (though the image isn't explicitly described in the text, I know what the code did) and provide a code block to get summary statistics of the AEP_MW column to quantify the trend.

Actually, looking at the prompt: "If the goal is to show/plot/chart/visualize and latest output contains both a valid chart/figure object and explicit numeric/text values supporting the conclusion, choose Path A". Here, I have the chart, but I don't have a summary table of the values.

Wait, the "Sufficiency heuristics" say "If the latest output is chart-only/visual-only... choose Path B first to print the values used in the chart."

Let's look at the outputs: Output 1: head() and full df. Output 2: The figure.

I have the head of the data, but not a summary of the trend (e.g., mean consumption, peak values). To provide a comprehensive answer to "plot overall trend" with "concrete insights", I should show the summary statistics.

Insights: The data spans from late 2004 to early 2018. The line plot visualizes the hourly fluctuations in energy consumption (MW) over this period.

Next step: Provide summary statistics for the energy consumption.

• Goal: Load AEP hourly energy data and plot the overall trend.

  • State:

    • df loaded from URL.
    • Datetime converted to datetime objects.
    • Line plot of AEP_MW over Datetime produced (cell-2).
    • Summary statistics (describe()) produced (cell-3).

  • Data loaded? Yes.

  • Overall trend plotted? Yes.

  • Numeric support? Yes, describe() provides the range (9,581 to 25,695 MW) and mean (15,499.5 MW).

  • The original goal was simply to load the data and plot the trend.

  • The notebook has already loaded the data, plotted the trend, and provided summary statistics.

  • According to the sufficiency heuristics: "If the goal is to show/plot/chart/visualize and latest output contains both a valid chart/figure object and explicit numeric/text values supporting the conclusion, choose Path A".

  • Insights: The energy consumption varies between a minimum of 9,581 MW and a maximum of 25,695 MW, with an average of approximately 15,500 MW. The visual trend confirms a cyclical pattern of energy demand over the period from 2004 to 2018.

  • Path A. Final answer.

  • No code block.

  • Include <!--final answer-->.

The user wants to see the average energy consumption (AEP_MW) broken down by:

1. Hour of the day (0-23).
2. Day of the week (Monday-Sunday).

Current State:

• df is loaded and has a Datetime column that has already been converted to datetime objects.
• The target variable is AEP_MW.

Plan:

1. Extract "Hour" and "Day of Week" from the Datetime column.
2. Calculate the mean of AEP_MW grouped by these new features.
3. Since the user asked for "average consumption by hour... and by day...", I should probably provide two separate visualizations or tables. However, the rule says "one chart OR one table per step". I'll start with the "Hour of Day" analysis as it's typically the most significant driver for energy consumption.

Wait, the user specifically asked for both. I can calculate both and display them, but I must follow the "one chart OR one table per step" rule if possible, or combine them logically. Actually, a common way to handle this is to do one first, then the other. Or, I can create a combined plot if it makes sense, but separate ones are clearer.

Let's start by extracting the time features and plotting the average consumption by hour of the day. This reveals the daily load profile.

Step 1: Extract features and plot Average Consumption by Hour.

• Original Goal: "show average consumption by hour of day and by day of week".

  • Current State:

    • df has Hour and DayOfWeek.
    • hourly_avg exists (average by hour).
    • A bar plot for average consumption by hour was generated.

  • Missing: Average consumption by day of week.

  • The user wants average consumption by hour and by day of week.

  • Only the hour analysis has been completed and visualized.

  • The day-of-week analysis is missing.

  • Therefore, Path B is necessary.

  • Calculate average consumption by DayOfWeek.

  • Plot it.

  • Since the prompt asks to "show", a plot and a table are best.

  • Note: DayOfWeek is currently day_name(). To keep them in order (Monday-Sunday), I should probably define the order or use a categorical sort.

  • Insights: The previous step showed the average energy consumption by hour of the day, identifying the daily load profile.

  • Next step: Now I need to visualize the average consumption by day of the week.

• Current state: Dataframe df is loaded, contains Datetime and AEP_MW.

  • Goal: Fit a Prophet model and forecast the next 30 days.

  • Prophet requirement: The dataframe must have columns named ds (datetime) and y (numeric value).

  • Prophet usually isn't in the standard base Python environment but is a common library for time series forecasting. I need to import it.

  • Prepare the dataframe: Rename Datetime $\rightarrow$ ds and AEP_MW $\rightarrow$ y.

  • Initialize the model, fit it, create a future dataframe for 30 days, and predict.

  • Visualize the forecast.

  • Step 1: Prepare the data and fit the Prophet model.

  • Wait... I should check if prophet is installed. If not, the user might get an error. However, usually, in these notebooks, the instruction is to write the code.

  • Wait, the rule says "Return exactly one runnable Python code block per response".

  • The full process of fitting and forecasting is a logical unit, but I should separate data preparation/fitting from the prediction/plotting if I want to be very cautious, or combine them into one "Modeling" step.

  • Actually, fitting and forecasting 30 days is the "primary objective" for this turn.

  • Preprocessing: Prophet requires ds and y.

  • Fitting: m = Prophet(), m.fit(df_prophet).

  • Forecasting: future = m.make_future_dataframe(periods=30), forecast = m.predict(future).

  • Plotting: m.plot(forecast).

  • Must use USD for money? Not applicable here.

  • No multiple code blocks.

  • Minimal code.

  • Exclude leakage? Not training a traditional ML model with features, but using a time-series model. The input is just time and value.

  • I will first prepare the dataframe and fit the model.

  • Wait, to provide the forecast, I need to run predict and plot. I'll do the whole sequence in one block as it's a single "model and forecast" task.

  Self-Correction: The prompt asks to "fit a Prophet model and forecast". I'll create a copy of the dataframe for Prophet to avoid mutating the original df and then perform the fit and forecast.

The user wants me to analyze the notebook state and decide if the goal "fit a Prophet model and forecast the next 30 days" has been achieved.

- Cell 6 shows the following:
    - A `Prophet` model was initialized and fit to `df_prophet` (columns `ds` and `y`).
    - A `future` dataframe for 30 days was created.
    - A `forecast` was generated using `model.predict(future)`.
    - A forecast plot was generated (`fig1`).
    - Component plots were generated (`fig2`).
    - The last 30 rows of the forecast (including `ds`, `yhat`, `yhat_lower`, `yhat_upper`) were displayed.
- The output shows the forecast values for the period starting from 2018-08-04 and ending 30 days later.
- The user's goal was to fit a Prophet model and forecast the next 30 days.

The model is fit, the forecast is generated, the visualization is provided, and the numeric forecast values for the next 30 days are printed. All components of the original request are present.

The output confirms that the model has captured the seasonality of the energy consumption. The forecast provides the expected value (yhat) along with uncertainty intervals (yhat_lower and yhat_upper) for the subsequent 30 days. The forecast values reflect the cyclical nature of energy usage, as seen in the generated data table and component plots.

Path A (Final Answer).

The user wants to see the forecast components (trend, weekly, and yearly seasonality) for the Prophet model. Looking at the notebook state, cell-6 already executed model.plot_components(forecast). However, the output for cell-6 only shows the figures as <Figure size 900x1200 with 4 Axes>, but the user specifically wants to "show" them, likely implying they want a focused view or a re-execution to see the details since they are asking for it explicitly now.

Actually, model.plot_components(forecast) produces a plot with 4 subplots: the overall trend and the seasonalities (yearly, weekly, daily).

Since the components were already plotted in cell-6, but the user is asking for them specifically, I should provide a clean way to display just these components again or emphasize them. Since I cannot "re-display" an existing figure object without the code, I will run the specific plotting command for components.

Wait, looking at the previous turn, the user prompt is "show the forecast components: trend, weekly, yearly seasonality".

The previous code did:

fig2 = model.plot_components(forecast)
plt.show()

The notebook output shows it was executed. But the user is asking again. Maybe they want a more detailed explanation or just a dedicated cell for it.

Actually, the goal is to "show" them. I'll just execute the plot_components call again to make it the focus of this turn.

Wait, I should check if there's anything else. The user mentioned "trend, weekly, yearly". Prophet's plot_components does exactly this.

One detail: The data is hourly. By default, Prophet might not enable daily seasonality unless specified or detected. Given the hourly data, it usually does.

Let's just run the plotting code specifically for components.

The user wants to see the forecast components: trend, weekly, and yearly seasonality. Looking at the notebook state:

• Cell 6 executed model.plot_components(forecast), which generates a figure showing the trend, weekly, and yearly components.
• Cell 7 executed model.plot_components(forecast) again, resulting in a figure with 4 axes (Trend, Weekly, Yearly, and possibly Daily if applicable, though typically Prophet's plot_components shows these).
• The output for Cell 7 is <Figure size 900x1200 with 4 Axes>.

The original goal is "show the forecast components: trend, weekly, yearly seasonality". The visual components have been plotted. However, the "Sufficiency heuristics" state: "If the latest output is chart-only/visual-only and does not include underlying numeric/text values, choose Path B first to print the values used in the chart."

Wait, let's re-read: "If the goal is to show/plot/chart/visualize and latest output contains both a valid chart/figure object and explicit numeric/text values supporting the conclusion, choose Path A... If the latest output is chart-only/visual-only and does not include underlying numeric/text values, choose Path B first to print the values used in the chart."

The goal was "show the forecast components". The user didn't specifically ask for the numbers, but the system instructions for the "Data Analyst" persona suggest that for visualization tasks, if it's chart-only, I should provide the underlying values (Path B).

However, the Prophet components aren't just a few numbers; they are time series. Printing the entire dataframe for components would be too large. But I can provide a summary or a slice of the forecast dataframe that specifically shows the components (columns like trend, weekly, yearly).

In the Prophet forecast dataframe:

• trend is the trend component.
• weekly is the weekly seasonality.
• yearly is the yearly seasonality.

The current output (Cell 7) is a chart. To follow the "Sufficiency heuristics", I should provide the numeric values associated with these components. Since it's a time series, showing the most recent values or a summary would be appropriate.

Actually, looking at the prompt: "Analyze the latest executed notebook outputs and notebook state against the original user goal: show the forecast components: trend, weekly, yearly seasonality". The charts are there. But to be a thorough data analyst per the rules, I should show the data behind these components.

Plan:

1. Acknowledge the charts.
2. Provide a table showing the specific component values (trend, weekly, yearly) for the forecast period (last 30 days) to supplement the visual.

Wait, the rule says "If the latest output is chart-only/visual-only... choose Path B first to print the values used in the chart." The goal is simply to "show" them. The charts show them. But the rules are strict about "chart-only" outputs.

Let's check if there are numeric values. Cell 6 displayed forecast[['ds', 'yhat', 'yhat_lower', 'yhat_upper']].tail(30). It did not display trend, weekly, or yearly.

Therefore, Path B is required. I will print the trend, weekly, and yearly columns for the last 30 days.

The user wants to see the forecast components: trend, weekly, and yearly seasonality. Looking at the notebook state:

• Cell 6: model.plot_components(forecast) was called and produced a figure.
• Cell 7: model.plot_components(forecast) was called again and produced a figure.
• Cell 8: forecast[['ds', 'trend', 'weekly', 'yearly']].tail(30) was executed, displaying the numeric values for these components for the last 30 days.

The user has both the visual charts (Figures in output-6 and output-7) and the numeric values (Table in output-8).

According to the "Sufficiency heuristics": "If the goal is to show/plot/chart/visualize and latest output contains both a valid chart/figure object and explicit numeric/text values supporting the conclusion, choose Path A".

The goal was to "show the forecast components". The components have been shown visually and numerically.

Insights from observed outputs:

• Trend: The trend column shows a gradual increase (e.g., from 15055.36 to 15064.81 over the 30-day period).
• Weekly seasonality: The weekly column shows clear fluctuations based on the day of the week, with significant drops (e.g., -1458.59 on Sundays/Saturdays) compared to mid-week.
• Yearly seasonality: The yearly column shows a positive contribution that gradually decreases from August 4th (1272.99) to August 17th (994.47), indicating the energy consumption is in a seasonal peak but starting to trend down as summer ends.

Path A is appropriate.