app.py

import streamlit as st
import pandas as pd
import torch
from chronos import Chronos2Pipeline
import matplotlib.pyplot as plt
import numpy as np
from gridstatus import Ercot
from datetime import datetime, timedelta

# Load the Chronos Pipeline model
@st.cache_resource
def load_pipeline():
    pipeline = Chronos2Pipeline.from_pretrained(
        "amazon/chronos-2",
        device_map="cpu",  # Change to CPU
        dtype=torch.float32,  # Use float32 for CPU
    )
    return pipeline

pipeline = load_pipeline()

# Function to fetch ERCOT electricity price data
@st.cache_data(ttl=3600)  # Cache for 1 hour
def fetch_ercot_data(days_back=160):
    """Fetch ERCOT day-ahead market prices for the last N days"""
    try:
        ercot = Ercot()
        end_date = datetime.now()
        start_date = end_date - timedelta(days=days_back)
        
        # Get day-ahead hourly market settlement point prices
        df = ercot.get_spp(
            date=start_date,
            end=end_date,
            market="DAY_AHEAD_HOURLY",
        )
        
        # Get average price per day across all locations
        df['Date'] = pd.to_datetime(df['Interval Start']).dt.date
        daily_prices = df.groupby('Date')['SPP'].mean()
        
        # Convert to comma-separated string
        price_list = daily_prices.round(2).tolist()
        return ", ".join(map(str, price_list))
    except Exception as e:
        st.warning(f"Could not fetch live ERCOT data: {e}. Using sample data instead.")
        return None

# Streamlit app interface
st.title("Electricity Market Price Forecasting with Chronos-2")
st.write("This demo uses **Chronos-2** to forecast electricity prices from ERCOT (Texas) market data.")

# Fetch default ERCOT data
with st.spinner("Fetching latest ERCOT electricity prices..."):
    ercot_data = fetch_ercot_data()

# Fallback to sample data if fetching fails
default_data = ercot_data if ercot_data else """
25.50, 24.80, 26.30, 23.90, 25.10, 27.20, 28.50, 26.70, 24.30, 23.80, 25.40, 26.10, 27.80, 29.20, 28.40,
26.90, 25.30, 24.70, 26.50, 28.10, 29.60, 31.20, 30.50, 28.80, 27.10, 25.90, 27.30, 28.70, 30.20, 32.10,
31.40, 29.70, 28.20, 26.80, 28.40, 29.80, 31.50, 33.20, 32.60, 30.90, 29.30, 27.80, 29.40, 30.90, 32.70,
34.50, 33.80, 32.10, 30.50, 28.90, 30.50, 32.10, 33.90, 35.80, 35.10, 33.30, 31.60, 30.10, 31.70, 33.40,
35.20, 37.10, 36.40, 34.60, 32.90, 31.30, 32.90, 34.60, 36.50, 38.40, 37.70, 35.80, 34.10, 32.50, 34.20,
35.90, 37.80, 39.80, 39.10, 37.10, 35.40, 33.70, 35.40, 37.20, 39.20, 41.20, 40.50, 38.50, 36.70, 35.00,
36.70, 38.50, 40.60, 42.60, 41.90, 39.90, 38.00, 36.30, 38.00, 39.90, 42.00, 44.10, 43.40, 41.30, 39.40
"""

# Data source selection
data_source = st.radio(
    "Select Data Source:",
    ["Live ERCOT Data (Last 180 Days)", "Custom Data"],
    index=0
)

# Input field for user-provided data
if data_source == "Custom Data":
    user_input = st.text_area(
        "Enter time series data (comma-separated values):", 
        ""
    )
else:
    user_input = st.text_area(
        "ERCOT Day-Ahead Hourly Market Prices ($/MWh) - Daily Average:", 
        default_data.strip(),
        height=150
    )
    st.info("💡 Live data from ERCOT's Day-Ahead Hourly Market - averaged across all settlement points per day")

# Convert user input into a list of numbers
def process_input(input_str):
    return [float(x.strip()) for x in input_str.split(",")]

try:
    time_series_data = process_input(user_input)
except ValueError:
    st.error("Please make sure all values are numbers, separated by commas.")
    time_series_data = []  # Set empty data on error to prevent further processing

# Select the number of days for forecasting
prediction_length = st.slider("Select Forecast Horizon (Days)", min_value=1, max_value=64, value=14)

# If data is valid, perform the forecast
if time_series_data:
    # Create a DataFrame for Chronos-2
    context_df = pd.DataFrame({
        'timestamp': pd.date_range(start='2024-01-01', periods=len(time_series_data), freq='D'),
        'target': time_series_data,
        'id': 'ercot_prices'
    })

    # Make the forecast using Chronos-2 API
    pred_df = pipeline.predict_df(
        context_df,
        prediction_length=prediction_length,
        quantile_levels=[0.1, 0.5, 0.9],
        id_column="id",
        timestamp_column="timestamp",
        target="target",
    )

    # Prepare forecast data for plotting
    forecast_index = range(len(time_series_data), len(time_series_data) + prediction_length)
    median = pred_df["predictions"].values
    low = pred_df["0.1"].values
    high = pred_df["0.9"].values

    # Plot the historical and forecasted data
    plt.figure(figsize=(10, 5))
    plt.plot(time_series_data, color="royalblue", label="Historical Prices")
    plt.plot(forecast_index, median, color="tomato", label="Median Forecast")
    plt.fill_between(forecast_index, low, high, color="tomato", alpha=0.3, label="80% Prediction Interval")
    plt.xlabel("Days")
    plt.ylabel("Price ($/MWh)")
    plt.title("ERCOT Electricity Price Forecast")
    plt.legend()
    plt.grid(alpha=0.3)

    # Show the plot in the Streamlit app
    st.pyplot(plt)
    
    # Display forecast statistics
    st.write("### Forecast Summary")
    col1, col2, col3 = st.columns(3)
    with col1:
        st.metric("Median Forecast", f"${median.mean():.2f}/MWh")
    with col2:
        st.metric("Low (10th percentile)", f"${low.mean():.2f}/MWh")
    with col3:
        st.metric("High (90th percentile)", f"${high.mean():.2f}/MWh")

# Note for comments, feedback, or questions
st.write("### Notes")
st.write("For comments, feedback, or any questions, please reach out to me on [LinkedIn](https://www.linkedin.com/in/javadbayazi/).")