Build an Energy Price Forecasting Dashboard with Python in 2026
Learn how to pull real-time energy market data, run volatility analysis, and build a live price forecasting dashboard using the Energy Volatility API and Python.
Electricity prices in the US are expected to hit $51/MWh on average in 2026 — an 8.5% increase year-over-year — driven in large part by surging demand from AI data centers, according to the U.S. Energy Information Administration. For energy procurement teams, traders, and operations managers, this volatility creates both risk and opportunity.
The problem: tracking energy markets manually is slow, fragmented, and error-prone. Data comes from dozens of regional ISOs, government sources, and commodity exchanges — each with their own format and update cadence.
This tutorial shows you how to build a real-time energy price monitoring and forecasting dashboard using the Energy Volatility API, Python, and Streamlit. You'll have a working dashboard in about an hour.
What You'll Build
By the end of this guide, you'll have a Streamlit dashboard that:
- Displays current spot prices for major US energy markets (ERCOT, PJM, CAISO, MISO)
- Shows price volatility trends over the past 30 days
- Runs a 7-day price forecast using the API's built-in forecasting model
- Alerts on price spikes above a configurable threshold
Prerequisites
pip install streamlit pandas plotly requests python-dotenvSet up your environment:
echo "APIVULT_API_KEY=YOUR_API_KEY" > .envStep 1: Set Up the API Client
Create energy_client.py:
import os
import requests
from datetime import datetime, timedelta
from dotenv import load_dotenv
load_dotenv()
class EnergyVolatilityClient:
def __init__(self):
self.api_key = os.environ["APIVULT_API_KEY"]
self.base_url = "https://apivult.com/api/v1/energy-volatility"
self.headers = {"X-API-Key": self.api_key}
def get_spot_prices(self, market: str = "all") -> dict:
"""Get current spot prices for energy markets."""
response = requests.get(
f"{self.base_url}/spot-prices",
headers=self.headers,
params={"market": market},
)
response.raise_for_status()
return response.json()
def get_historical_prices(
self,
market: str,
start_date: str,
end_date: str,
interval: str = "1h",
) -> dict:
"""Get historical price data for volatility analysis."""
response = requests.get(
f"{self.base_url}/historical",
headers=self.headers,
params={
"market": market,
"start": start_date,
"end": end_date,
"interval": interval,
},
)
response.raise_for_status()
return response.json()
def get_forecast(self, market: str, days: int = 7) -> dict:
"""Get price forecast for the next N days."""
response = requests.post(
f"{self.base_url}/forecast",
headers=self.headers,
json={"market": market, "forecast_days": days},
)
response.raise_for_status()
return response.json()
def get_volatility_index(self, market: str) -> dict:
"""Get volatility index and risk metrics."""
response = requests.get(
f"{self.base_url}/volatility-index",
headers=self.headers,
params={"market": market},
)
response.raise_for_status()
return response.json()Step 2: Build the Dashboard
Create dashboard.py:
import streamlit as st
import pandas as pd
import plotly.graph_objects as go
import plotly.express as px
from datetime import datetime, timedelta
from energy_client import EnergyVolatilityClient
# Page config
st.set_page_config(
page_title="Energy Price Dashboard",
page_icon="⚡",
layout="wide",
)
@st.cache_resource
def get_client():
return EnergyVolatilityClient()
@st.cache_data(ttl=300) # Cache for 5 minutes
def fetch_spot_prices():
client = get_client()
return client.get_spot_prices()
@st.cache_data(ttl=3600) # Cache for 1 hour
def fetch_historical(market: str):
client = get_client()
end = datetime.now().strftime("%Y-%m-%d")
start = (datetime.now() - timedelta(days=30)).strftime("%Y-%m-%d")
return client.get_historical_prices(market, start, end)
@st.cache_data(ttl=3600)
def fetch_forecast(market: str):
client = get_client()
return client.get_forecast(market, days=7)
# Dashboard header
st.title("⚡ Energy Market Intelligence Dashboard")
st.caption(f"Last updated: {datetime.now().strftime('%Y-%m-%d %H:%M UTC')}")
# Sidebar controls
st.sidebar.header("Settings")
selected_market = st.sidebar.selectbox(
"Select Market",
["ERCOT", "PJM", "CAISO", "MISO", "NYISO"],
index=0,
)
alert_threshold = st.sidebar.number_input(
"Price Alert Threshold ($/MWh)",
min_value=0,
max_value=1000,
value=100,
step=10,
)
# ─── Current Spot Prices (all markets) ───
st.subheader("Current Spot Prices")
spot_data = fetch_spot_prices()
cols = st.columns(len(spot_data["markets"]))
for i, (market_name, data) in enumerate(spot_data["markets"].items()):
price = data["price_mwh"]
change = data["change_24h_pct"]
delta_color = "normal" if change >= 0 else "inverse"
with cols[i]:
st.metric(
label=market_name,
value=f"${price:.2f}/MWh",
delta=f"{change:+.1f}% (24h)",
delta_color=delta_color,
)
if price > alert_threshold:
st.warning(f"⚠️ Above ${alert_threshold}/MWh threshold")
# ─── Historical Price Chart ───
st.subheader(f"{selected_market} — 30-Day Price History")
historical = fetch_historical(selected_market)
df = pd.DataFrame(historical["data"])
df["timestamp"] = pd.to_datetime(df["timestamp"])
fig = go.Figure()
fig.add_trace(go.Scatter(
x=df["timestamp"],
y=df["price_mwh"],
mode="lines",
name="Spot Price",
line=dict(color="#3B82F6", width=1.5),
))
# Add rolling average
df["rolling_avg"] = df["price_mwh"].rolling(window=24).mean()
fig.add_trace(go.Scatter(
x=df["timestamp"],
y=df["rolling_avg"],
mode="lines",
name="24h Rolling Avg",
line=dict(color="#F59E0B", width=2, dash="dash"),
))
fig.add_hline(
y=alert_threshold,
line_dash="dot",
line_color="red",
annotation_text=f"Alert: ${alert_threshold}/MWh",
)
fig.update_layout(
height=400,
xaxis_title="Date",
yaxis_title="Price ($/MWh)",
legend=dict(orientation="h", yanchor="bottom", y=1.02),
)
st.plotly_chart(fig, use_container_width=True)
# ─── Price Forecast ───
col1, col2 = st.columns([2, 1])
with col1:
st.subheader(f"{selected_market} — 7-Day Price Forecast")
forecast = fetch_forecast(selected_market)
df_forecast = pd.DataFrame(forecast["forecast"])
df_forecast["date"] = pd.to_datetime(df_forecast["date"])
fig2 = go.Figure()
# Confidence interval
fig2.add_trace(go.Scatter(
x=pd.concat([df_forecast["date"], df_forecast["date"][::-1]]),
y=pd.concat([df_forecast["high_95"], df_forecast["low_95"][::-1]]),
fill="toself",
fillcolor="rgba(59, 130, 246, 0.1)",
line=dict(color="rgba(255,255,255,0)"),
name="95% Confidence Interval",
))
# Forecast line
fig2.add_trace(go.Scatter(
x=df_forecast["date"],
y=df_forecast["predicted_price"],
mode="lines+markers",
name="Forecast",
line=dict(color="#3B82F6", width=2),
))
fig2.update_layout(height=300, yaxis_title="Price ($/MWh)")
st.plotly_chart(fig2, use_container_width=True)
with col2:
st.subheader("Volatility Index")
client = get_client()
vol_data = client.get_volatility_index(selected_market)
st.metric("Current VIX", f"{vol_data['index']:.1f}", delta=f"{vol_data['change_7d']:+.1f} (7d)")
st.metric("30-Day High", f"${vol_data['price_high_30d']:.2f}")
st.metric("30-Day Low", f"${vol_data['price_low_30d']:.2f}")
st.metric("Price Std Dev", f"${vol_data['std_dev_30d']:.2f}")
risk_level = vol_data.get("risk_level", "medium")
color = {"low": "green", "medium": "orange", "high": "red"}.get(risk_level, "gray")
st.markdown(f"**Risk Level:** :{color}[{risk_level.upper()}]")Step 3: Run the Dashboard
streamlit run dashboard.pyYour dashboard opens at http://localhost:8501. For production deployment, you can host it on Railway, Fly.io, or any Python-compatible platform.
Step 4: Add Price Alerts
For automated monitoring without keeping the dashboard open, add a simple alerting script:
import smtplib
from email.mime.text import MIMEText
def check_and_alert(threshold: float = 100.0):
client = EnergyVolatilityClient()
spot = client.get_spot_prices()
alerts = []
for market_name, data in spot["markets"].items():
if data["price_mwh"] > threshold:
alerts.append(
f"{market_name}: ${data['price_mwh']:.2f}/MWh "
f"({data['change_24h_pct']:+.1f}% vs 24h ago)"
)
if alerts:
body = "Energy Price Alert:\n\n" + "\n".join(alerts)
print(body)
# Add email/Slack notification here
if __name__ == "__main__":
check_and_alert(threshold=100.0)Schedule this with cron or a task scheduler to run every 15-30 minutes.
Real-World ROI
Teams using automated energy price monitoring report:
- 15-30% reduction in energy procurement costs by timing purchases during low-volatility windows
- 2-4 hours/week saved on manual market research per analyst
- Faster response to price spikes — minutes instead of hours
As AI data center demand continues to drive electricity prices higher, having real-time market intelligence becomes a competitive advantage — not just for energy companies, but for any organization with significant energy costs.
Next Steps
- Energy Volatility API docs — Full endpoint reference
- APIVult pricing — Plans starting with a free tier for development
- Add natural gas and renewable energy data for a complete energy mix view
- Integrate with procurement systems to trigger automated buy orders
The energy market is getting more volatile. Start monitoring it automatically today.