`TechnologyCollection` Class Documentation¶

Overview¶

The TechnologyCollection class in technologydata represents a collection of Technology objects, providing tools for filtering, exporting, currency adjustment, model fitting, and projection. It is designed to manage multiple technology datasets, supporting reproducibility, scenario analysis, and future projections.

Features¶

Collection Management: Stores and iterates over multiple Technology objects.
Filtering: Supports regex-based filtering by attributes name, region, year, case, and detailed technology.
Data Export: Converts the collection to pandas DataFrame, CSV, and JSON formats, with schema export.
Currency Adjustment: Harmonizes all technology parameters to a target currency, including inflation and exchange rates.
Formula System: Derives missing parameters (calculate_parameters) and checks equation-level consistency (check_consistency) for every technology in the collection.
Model Fitting: Fits growth models to technology parameters across the collection.
Projection: Projects parameters to future years using growth models or statistical options.
Integration: Designed for use with energy system modeling and technology parameter analysis.

Usage Examples¶

Creating a TechnologyCollection¶

from technologydata import Technology, TechnologyCollection

tech1 = Technology(name="Tech1", region="DEU", year=2020, case="Base", detailed_technology="Solar PV", parameters={})
tech2 = Technology(name="Tech2", region="DEU", year=2021, case="Base", detailed_technology="Wind", parameters={})
collection = TechnologyCollection(technologies=[tech1, tech2])

Filtering Technologies¶

from technologydata import Technology, TechnologyCollection

tech1 = Technology(name="Tech1", region="DEU", year=2020, case="Base", detailed_technology="Solar PV", parameters={})
tech2 = Technology(name="Tech2", region="DEU", year=2021, case="Base", detailed_technology="Wind", parameters={})
collection = TechnologyCollection(technologies=[tech1, tech2])

filtered = collection.get(name="Tech", region="DEU", year=2020, case="Base", detailed_technology="Solar")
print(filtered)  # TechnologyCollection with matching technologies

get() matches regular expressions, not literal text

Every argument is compiled as a case-insensitive regular expression, so characters like ( and ) are read as a group rather than as brackets. A name containing them silently matches nothing; pass it through re.escape() first.

Exporting to CSV¶

from technologydata import Technology, TechnologyCollection

tech1 = Technology(name="Tech1", region="DEU", year=2020, case="Base", detailed_technology="Solar PV", parameters={})
tech2 = Technology(name="Tech2", region="DEU", year=2021, case="Base", detailed_technology="Wind", parameters={})
collection = TechnologyCollection(technologies=[tech1, tech2])

collection.to_csv(path_or_buf="technologies.csv")

Exporting to JSON and Schema¶

import pathlib
from technologydata import Technology, TechnologyCollection

tech1 = Technology(name="Tech1", region="DEU", year=2020, case="Base", detailed_technology="Solar PV", parameters={})
tech2 = Technology(name="Tech2", region="DEU", year=2021, case="Base", detailed_technology="Wind", parameters={})
collection = TechnologyCollection(technologies=[tech1, tech2])

collection.to_json(file_path=pathlib.Path("technologies.json"))

Currency Adjustment¶

from technologydata import Technology, TechnologyCollection

tech1 = Technology(name="Tech1", region="DEU", year=2020, case="Base", detailed_technology="Solar PV", parameters={})
tech2 = Technology(name="Tech2", region="DEU", year=2021, case="Base", detailed_technology="Wind", parameters={})
collection = TechnologyCollection(technologies=[tech1, tech2])
converted = collection.to_currency("USD_2025", source="worldbank")

Deriving Missing Parameters and Checking Consistency¶

calculate_parameters and check_consistency apply the corresponding Technology method to every technology in the collection and return, respectively, a new TechnologyCollection and a list of per-equation status dicts (one per technology, in the same order as collection.technologies).

from technologydata import Parameter, Technology, TechnologyCollection

tech1 = Technology(
    name="Tech1", region="DEU", year=2020, case="Base", detailed_technology="Solar PV",
    parameters={
        "specific_investment": Parameter(magnitude=1000.0, units="USD_2020/kW"),
        "wacc": Parameter(magnitude=0.07, units="dimensionless"),
        "lifetime": Parameter(magnitude=20.0, units="year"),
    },
)
collection = TechnologyCollection(technologies=[tech1])

# Derive "eac" (and any other derivable parameter) for every technology
derived = collection.calculate_parameters()
print(derived.technologies[0].parameters["eac"])

# Check consistency of every technology's "eac" against registered equations
status = derived.check_consistency(parameters=["eac"])
print(status[0])  # status dict for tech1, e.g. {'eac_annuity': True, ...}

Fitting a Growth Model¶

from technologydata import Technology, TechnologyCollection
from technologydata.technologies.growth_models import LinearGrowth

tech1 = Technology(name="Tech1", region="DEU", year=2020, case="Base", detailed_technology="Solar PV", parameters={})
tech2 = Technology(name="Tech2", region="DEU", year=2021, case="Base", detailed_technology="Wind", parameters={})
collection = TechnologyCollection(technologies=[tech1, tech2])

fitted_model = collection.fit(parameter="installed capacity", model=LinearGrowth(m=0.5, A=10))

Projecting Parameters¶

from technologydata import Technology, TechnologyCollection
from technologydata.technologies.growth_models import LinearGrowth

tech1 = Technology(name="Tech1", region="DEU", year=2020, case="Base", detailed_technology="Solar PV", parameters={})
tech2 = Technology(name="Tech2", region="DEU", year=2021, case="Base", detailed_technology="Wind", parameters={})
collection = TechnologyCollection(technologies=[tech1, tech2])

projected = collection.project(
    to_years=[2030, 2040],
    parameters={
        "installed capacity": LinearGrowth(m=0.5, A=10),
        "lifetime": "mean",
        "efficiency": "NaN"
    }
)

API Reference¶

Please refer to the API documentation for detailed information on the TechnologyCollection class methods and attributes.

Notes¶

Filtering: Regex patterns are case-insensitive and applied to non-optional attributes.
Export: Default CSV export uses UTF-8 encoding and quotes all fields.
Schema: JSON schema is generated automatically and includes field descriptions.
Type Checking: The class uses Pydantic for validation and type enforcement.
Projection: The 'closest' option for parameter projection is not yet implemented.

TechnologyCollection Class Documentation¶