epftoolbox2 2.2.1

Energy Price Forecasting Toolbox 2

epftoolbox2

A modern Python library for electricity price forecasting with modular data pipelines and model evaluation. Built as a complete rewrite of the original epftoolbox, this library provides a flexible, extensible framework for downloading energy market data, building forecasting models, and evaluating their performance.

📖 Documentation

Installation

pip install epftoolbox2

or with uv

uv add epftoolbox2

Verify Installation

After installation, you can verify your setup and check system information:

import epftoolbox2

epftoolbox2.verify()

Key Features

• Data Sources: ENTSOE (load, generation, prices), Open-Meteo (weather forecasts), Calendar (holidays, weekday)
• Transformers: Resample, Timezone conversion, Lag features
• Validators: Null checks, Continuity checks, EDA statistics
• Models: OLS, LassoCV
• Evaluators: MAE
• Exporters: Excel with conditional formatting, Terminal output
• Caching: Built-in data caching to avoid redundant API calls
• Pipelines: Data and model pipelines that can be saved and loaded with .yaml files
• Multiprocessing: Process-based parallelism with inner thread pools
• Extensibility: Extensible base classes for data sources, transformers, validators, models, evaluators, and exporters

Quick Start

Standalone source use

from epftoolbox2.data.sources import EntsoeSource

source = EntsoeSource("PL", api_key="YOUR_KEY", type=["load", "price"])
df = source.run("2024-01-01", "2024-06-01", cache=True)

Standalone transformer use

from epftoolbox2.data.transformers import ResampleTransformer

transformer = ResampleTransformer(freq="1h")
df = transformer.run(df)

Standalone validator use

from epftoolbox2.data.validators import NullCheckValidator

validator = NullCheckValidator(columns=["load_actual", "price"])
df = validator.run(df)

Standalone model use

from epftoolbox2.models import OLSModel

seasonal_indicators = [
    "is_monday_d+{horizon}",
    "is_tuesday_d+{horizon}",
    "is_wednesday_d+{horizon}",
    "is_thursday_d+{horizon}",
    "is_friday_d+{horizon}",
    "is_saturday_d+{horizon}",
    "is_sunday_d+{horizon}",
    "is_holiday_d+{horizon}",
    "daylight_hours_d+{horizon}",
]

model = OLSModel(predictors=["load_actual", *seasonal_indicators], name="OLS")
report = model.run(df, test_start="2024-04-01", test_end="2024-06-01", target="price")

Standalone evaluator use

from epftoolbox2.evaluators import MAEEvaluator

evaluator = MAEEvaluator()
report = evaluator.run(df, test_start="2024-04-01", test_end="2024-06-01", target="price")

Standalone exporter use

from epftoolbox2.exporters import ExcelExporter

exporter = ExcelExporter("results.xlsx")
report = exporter.run(df, test_start="2024-04-01", test_end="2024-06-01", target="price")

Data Pipeline

Download and process electricity market data from ENTSOE, weather forecasts from Open-Meteo, and calendar features.

from epftoolbox2.pipelines import DataPipeline
from epftoolbox2.data.sources import EntsoeSource, OpenMeteoSource, CalendarSource
from epftoolbox2.data.transformers import ResampleTransformer
from epftoolbox2.data.validators import NullCheckValidator

pipeline = (
    DataPipeline()
    .add_source(EntsoeSource("PL", api_key="YOUR_KEY", type=["load", "price"]))
    .add_source(OpenMeteoSource(latitude=52.23, longitude=21.01))
    .add_source(CalendarSource("PL", holidays="binary", weekday="number"))
    .add_transformer(ResampleTransformer(freq="1h"))
    .add_validator(NullCheckValidator(columns=["load_actual", "price"]))
)

df = pipeline.run("2024-01-01", "2024-06-01", cache=True)

Model Pipeline

Train and evaluate forecasting models with built-in metrics and export capabilities.

from epftoolbox2.pipelines import ModelPipeline
from epftoolbox2.models import OLSModel, LassoCVModel
from epftoolbox2.evaluators import MAEEvaluator
from epftoolbox2.exporters import ExcelExporter

pipeline = (
    ModelPipeline()
    .add_model(OLSModel(predictors=["load_actual", *seasonal_indicators], name="OLS"))
    .add_model(LassoCVModel(predictors=["load_actual", *seasonal_indicators], name="Lasso"))
    .add_evaluator(MAEEvaluator())
    .add_exporter(ExcelExporter("results.xlsx"))
)

report = pipeline.run(df, test_start="2024-04-01", test_end="2024-06-01", target="price")

Examples

See the examples/ folder for complete working examples.