isoview-client 0.1.0

Python client for the ISOview energy forecasting API

isoview-client

Python client for the ISOview energy forecasting API — demand, wind, solar, LMP, and natural gas forecasts across US ISOs, returned as pandas DataFrames.

Installation

pip install isoview-client

Requires Python 3.10+. Installs requests and pandas as dependencies.

Authentication

Sign up at isoview.io and grab your API key from the Portal.

from isoview import Client

client = Client("your-api-key")

Quick Start

from isoview import Client

client = Client("your-api-key")

# Get the latest PJM demand forecast
ts = client.get_regional_forecast("pjm", "demand")

# Convert to a pandas DataFrame
df = ts.to_df()
print(df.head())

Every forecast method returns a TimeseriesResponse. Call .to_df() to get a pandas DataFrame with a UTC DatetimeIndex and MultiIndex columns like ("pjm_total", "forecast"). Pass utc=False for local time instead.

Examples

Regions

Forecasts for geographic regions within an ISO — demand, wind, solar, outages, and population-weighted temperature.

# See what regions are available
regions = client.list_regions("pjm", "demand")

# Latest forecast for a specific region
ts = client.get_regional_forecast("pjm", "demand", id="pjm_total")

# Stitched historical forecast — what did the day-ahead forecast
# look like at 10am each day?
ts = client.get_regional_continuous_forecast(
    "miso", "wind",
    start="2025-01-01T00:00:00Z",
    end="2025-06-01T00:00:00Z",
    latest_hour=10,
    days_ahead=1,
)

# Probabilistic ensemble forecast (multiple scenarios)
ts = client.get_regional_ensemble_forecast("pjm", "demand", id="pjm_total")

# Day-ahead backcast for model evaluation
ts = client.get_regional_backcast("pjm", "demand")

# Everything at once — demand, wind, solar, outages, temp, LMP
ts = client.get_iso_summary("pjm")

Plants

Generation forecasts for individual wind and solar facilities.

# Browse plants in ERCOT
plants = client.list_plants("ercot", "wind")
print(plants[0].name, plants[0].capacity_mw, "MW")

# Get a forecast
ts = client.get_plant_forecast("ercot", "wind", id=str(plants[0].id))
df = ts.to_df()

Counties

County-level electricity demand forecasts, disaggregated from regional data.

counties = client.list_counties("isone")
ts = client.get_county_forecast("isone", id=counties[0].id)

Gas

Natural gas price forecasts for major trading hubs.

hubs = client.list_gas_hubs()
ts = client.get_gas_forecast(id=hubs[0].id)

LMP

Locational Marginal Price forecasts for electricity market nodes.

nodes = client.list_lmp_nodes("pjm", "dalmp")
ts = client.get_lmp_forecast("pjm", "dalmp", id=nodes[0].id)

Working with Responses

Timeseries

All forecast, continuous, ensemble, backcast, and summary endpoints return a TimeseriesResponse:

ts = client.get_regional_forecast("pjm", "demand")

ts.model       # 'optimized'
ts.created_at  # datetime — when the forecast was generated
ts.units       # 'MW'
ts.timezone    # 'America/New_York'

# Convert to a pandas DataFrame (UTC index by default)
df = ts.to_df()

# Or use local time
df_local = ts.to_df(utc=False)

The DataFrame has a DatetimeIndex and MultiIndex columns (e.g. ("pjm_total", "forecast")).

Metadata

List endpoints return typed objects you can inspect directly:

regions = client.list_regions("pjm", "demand")
for r in regions:
    print(r.id, r.name, r.timezone)

plants = client.list_plants("ercot", "solar")
for p in plants:
    print(p.name, f"{p.capacity_mw} MW", p.state)

Supported ISOs

Code	Name
pjm	PJM Interconnection
miso	Midcontinent ISO
spp	Southwest Power Pool
ercot	Electric Reliability Council of Texas
caiso	California ISO
nyiso	New York ISO
isone	ISO New England

Error Handling

The client raises requests.HTTPError on API errors (401, 403, 422, etc.):

import requests

try:
    ts = client.get_regional_forecast("pjm", "demand")
except requests.HTTPError as e:
    print(e.response.status_code, e.response.text)

Links

• ISOview Portal — manage your account and API key
• API Documentation — full reference for all endpoints