pricecast

Synthetic hourly electricity price years for prospective energy system studies.

pricecast generates a full year of hourly day-ahead electricity prices from three drivers - load, solar and wind generation - with a regression fitted on recent market years. You give it the drivers of the year you want, it gives you 8760 prices in EUR/MWh, ready to feed an LP or MILP design model.

What it is for

You are sizing or operating something that buys or sells electricity by the hour - a heat pump with a thermal store, a battery, rooftop PV, an electrolyser

• for a year that has not happened yet, and you need a price year to optimise against. The usual stand-in is a past year reused as it was, which carries the past's solar fleet, the past's price level and the past's weather.

pricecast builds the year from what a scenario provides instead:

• installed solar and wind, and annual demand, which set the shape of the year - how deep prices fall at midday, how high they climb in the evening;
• a price level, which you set from a fuel-and-carbon scenario or a market study;
• one weather year, which you also use for your asset's PV output and heat demand, so that prices and demand respond to the same sun, wind and cold.

It is not a forecaster. Nobody has next decade's hourly weather, so it does not predict the price of a given hour. It produces a plausible year for a scenario, and leaves the level to you.

Install

pip install pricecast
pip install "pricecast[entsoe]"    # to download market data from ENTSO-E

Requires Python 3.9+, numpy and pandas.

Getting an ENTSO-E token

Downloading market data needs a free security token from the ENTSO-E Transparency Platform. ENTSO-E's guide is How to get security token?; in short:

1. Register an account on the Transparency Platform.
2. Email transparency@entsoe.eu with "Restful API access" as the subject and your registered email address in the body.
3. Once ENTSO-E has granted access, generate the token in your account settings.
4. Pass it as token=, or set it once as the ENTSOE_TOKEN environment variable. Keep it out of version control.

You only need a token to download data: the shipped models, fit on your own series and everything else work without one.

Quick start

import pricecast
from pricecast.entsoe import fetch_year

prices, drivers = fetch_year("BE", 2025)   # day-ahead price; load, solar, wind
model = pricecast.load("BE")               # a shipped model: BE, DE, FR, ES, PL
year = model.predict(drivers)              # an hourly price year, EUR/MWh

Or fit your own, on any market with hourly prices and drivers:

model = pricecast.fit([prices_2024, prices_2025], [drivers_2024, drivers_2025])
model.to_json("my_market.json")

A year for a future target

# 1. scale one weather year's drivers to the target year's fleet and demand
future = pricecast.scale_drivers(drivers, solar=1.4, wind=1.4, load=1.3)

# 2. generate the shape of the year
year = model.predict(future)

# 3. check how far the model is extrapolating, and floor the year if it is
share = pricecast.share_beyond_training(future, training_drivers)   # % of hours
floor = pricecast.training_floor(training_prices) if share > 1.0 else None

# 4. set the level from your scenario
year = pricecast.rescale_to_level(year, level=85.0, floor=floor)

The scaling factors and the level come from your scenario; the recipe explains each step and where to find the inputs (ERAA, TYNDP, fuel and carbon prices). pricecast.gas_plant_cost and pricecast.level_ratio help build a level from fuel and carbon prices.

Good practice

• One weather year for everything. Take the model's drivers and your asset's PV output, heat demand and ambient temperature from the same year.
• Noise off for sizing. predict(..., noise=True) adds a residual process that restores price spikes and negative hours, but in hours that do not line up with the real ones. Use it for revenue and risk estimates, not to size equipment.
• Set the level from outside, and when unsure, err low. A design sized on too high a level overvalues flexibility.
• Watch the extrapolation. The model is linear in its drivers. When a scenario takes solar or wind far beyond the training years, check share_beyond_training, floor the year with training_floor, and count how many hours end up on the floor. When that becomes a large share, use the scenario's own hourly prices instead.
• Day-ahead only - not intraday, balancing or forward markets.

Documentation

• Usage: data formats, fitting, generating, checking a year's structure.
• Recipe: a future year step by step, with sources for every input.
• Mathematics: the model, the residual process, the level and the floor.
• examples/: a quick start and a complete future year.

Data

The shipped models are fitted on ENTSO-E Transparency Platform data for 2024-2025: day-ahead prices, actual total load and actual generation per production type. Only the fitted coefficients ship; the raw series are downloaded with your own token.

Citing

Use the "Cite this repository" button or CITATION.cff. Every release is archived on Zenodo: 10.5281/zenodo.23264988 always points to the latest version.

Contributing

Issues and pull requests are welcome - see CONTRIBUTING.md. Reports of a generated year that disagrees with a market you know well are especially useful. This project follows the Contributor Covenant. Generative AI was used in building it; see AI_USAGE.md.

Licence

MIT - see LICENSE. Changes are recorded in CHANGELOG.md.