Add ERCOT electricity market data integration

- Add gridstatus dependency for fetching live ERCOT data
- Implement fetch_ercot_data() to pull Day-Ahead Hourly Market prices
- Replace sample data with real electricity prices ($/MWh)
- Add data source selector (Live ERCOT vs Custom Data)
- Update UI labels and context for electricity market forecasting
- Change forecast horizon from months to days
- Add forecast summary metrics with price statistics

app.py

@@ -4,6 +4,8 @@ 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
@@ -17,27 +19,73 @@ def load_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=60):
+    """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("Time Series Forecasting Demo with Chronos-2")
-st.write("This demo uses **Chronos-2**, Amazon's state-of-the-art pretrained model for zero-shot time series forecasting.")
-
-# Default time series data (comma-separated)
-default_data = """
-112, 118, 132, 129, 121, 135, 148, 148, 136, 119, 104, 118, 115, 126, 141, 135, 125, 149, 170, 170, 158, 
-133, 114, 140, 145, 150, 178, 163, 172, 178, 199, 199, 184, 162, 146, 166, 171, 180, 193, 181, 183, 218, 
-230, 242, 209, 191, 172, 194, 196, 196, 236, 235, 229, 243, 264, 272, 237, 211, 180, 201, 204, 188, 235, 
-227, 234, 264, 302, 293, 259, 229, 203, 229, 242, 233, 267, 269, 270, 315, 364, 347, 312, 274, 237, 278, 
-284, 277, 317, 313, 318, 374, 413, 405, 355, 306, 271, 306, 315, 301, 356, 348, 355, 422, 465, 467, 404, 
-347, 305, 336, 340, 318, 362, 348, 363, 435, 491, 505, 404, 359, 310, 337, 360, 342, 406, 396, 420, 472, 
-548, 559, 463, 407, 362, 405, 417, 391, 419, 461, 472, 535, 622, 606, 508, 461, 390, 432
+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
 """
 
-# Input field for user-provided data
-user_input = st.text_area(
-    "Enter time series data (comma-separated values):", 
-    default_data.strip()
+# 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(",")]
@@ -48,16 +96,16 @@ 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 months for forecasting
-prediction_length = st.slider("Select Forecast Horizon (Months)", min_value=1, max_value=64, value=12)
+# 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='2020-01-01', periods=len(time_series_data), freq='ME'),
+        'timestamp': pd.date_range(start='2024-01-01', periods=len(time_series_data), freq='D'),
         'target': time_series_data,
-        'id': 'series_1'
+        'id': 'ercot_prices'
     })
 
     # Make the forecast using Chronos-2 API
@@ -77,15 +125,28 @@ if time_series_data:
     high = pred_df["0.9"].values
 
     # Plot the historical and forecasted data
-    plt.figure(figsize=(8, 4))
-    plt.plot(time_series_data, color="royalblue", label="Historical data")
-    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.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()
+    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")

requirements.txt

@@ -5,3 +5,4 @@ git+https://github.com/amazon-science/chronos-forecasting.git
 matplotlib
 pandas
 pyarrow
+gridstatus