eppd 0.1.0

EnergyPlus Python Data Processor - pandas-based library for IDF manipulation

eppd

An open-source Python library for programmatic manipulation of EnergyPlus IDF files, concurrent execution of multiple EnergyPlus simulations, and processing simulation outputs.

The library loads EnergyPlus IDF models into a pandas MultiIndex Series and returns EnergyPlus outputs as pandas DataFrames.

Features

• Vectorized parameter manipulation
• Queryable IDF data using pandas indexing
• Template-driven HVAC system generation
• Parallel batch simulations
• Data-centric post-processing workflows
• Transparent and repeatable analysis
• LLM-assisted workflow generation

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Tutorials

Video walkthroughs and example workflows are available on YouTube:

• Part 1 — IDF manipulation and batch simulations
• Part 2 — HVAC system generation
• Part 3 — Parametric simulation workflows
• Example walkthroughs and LLM integration

Installation

pip install eppd

Requires a separate installation of EnergyPlus.

After installing eppd, configure energyplus_location to point to the EnergyPlus executable on your system.

Set in eppd/config.toml:

[energyplus]
# Linux
energyplus_location = "/usr/local/EnergyPlus-26-1-0/energyplus"

# macOS
# energyplus_location = "/Applications/EnergyPlus-26-1-0/energyplus"

# Windows
# energyplus_location = "C:/EnergyPlusV26-1-0/energyplus.exe"

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Overview

from eppd import *

A single import exposes everything needed for a full simulation workflow:

Category	Functions
Core	read_idf, Eidx, Sobj, apnd
Systems	createsys, PlantConfig, pltsys, dhwsys
Simulation	run_batch, setup_param_db, run_parametric, runs_to_parquet
Post-processing	read_xml, read_eso, get_batch_xml, get_batch_eso

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Workflows

1. IDF Manipulation

Load an IDF file and modify parameters:

model = read_idf('building.idf')

# Modify parameters (Including converting from IP to SI units)
model[Eidx.lpd.get()] = Eidx.lpd.ip(1.0)          # set lighting power density to 1.0 W/sqft
model[Eidx.economizer.get()] = 'differentialenthalpy' # Modify economizer type
model[Eidx.blddir.get()] = 90                       # Rotate 90 degrees

# Regex based search of IDF objects.
model[Eidx.fanstatic.get('supply.*')] = Eidx.fanstatic.ip(3.5)  # set fan static of all fans with "supply" in its name

# Write updated IDF and append desired output objects
model.write_idf('modified.idf', apnd([Sobj.allsummary, Sobj.sysoa]))

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2. HVAC System Creation

HVAC systems are defined through a zone-assignment DataFrame, allowing entire system layouts to be generated programmatically.

model = read_idf('building.idf')

# Build zone-system assignment table
sysdf = pd.DataFrame(index=model[Eidx.znames.get()].index)
sysdf['sname']   = 'vav_ahu'          # zones assigned to same system name share the air loop
sysdf['airloop'] = 'chw_hw_vav'       # VAV air loop with chilled water and hot water coils
sysdf['zonesys'] = 'vavhw'            # VAV with hot water reheat
sysdf['fan']     = 'hvacoperationschd' # assign system fan schedule.

# Create system with central plant
cp = PlantConfig(hw=pltsys.boiler, chw=pltsys.aircooledchiller) # central plant with hot water boiler and air cooled chiller
new_model = createsys(model, sysdf, cp=cp)
new_model.write_idf('modified_hvac.idf', apnd([Sobj.allsummary, Sobj.sysoa]))

Air loop format:

Air loop is defined with a simple set of strings.

[cooling coil]_[heating coil]_[system type]

Cooling	Heating	System type
chw chilled water	hw hot water	vav variable air volume
dx direct expansion	ng natural gas	cv constant volume
	el electric	doas dedicated OA system
	hp heat pump	vrfdoas VRF with DOAS

Zone systems:

Zone systems are defined using a separate system string:

[Air distribution type]_[Optional baseboard type]

Air distribution options: cv, vavhw, vavel, fcu, cb, ahp, whp, vrf, ptac, pthp, uhel, uhhw Baseboard options: _bhw or _bel to add a hot water or electric baseboard.

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3. Batch Simulation

Define simulation cases to run using a dictionary with idf file and weather file.

cases = {
    'baseline': ('baseline.idf', 'Chicago.epw'),
    'eem1':     ('eem1.idf',     'Chicago.epw'),
    'eem2':     ('eem2.idf',     'Chicago.epw'),
}


run_batch(cases)

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4. Post-processing

Parse EnergyPlus XML outputs and return as Pandas dataframe:

# after run_batch library copies the xml output to <case_name>.xml)
xml = read_xml('baseline.xml')
eui     = xml.get_eui()               # Site EUI kBtu/sqft
energy  = xml.get_end_use_energy()    # DataFrame by end use and fuel
unmet   = xml.get_unmet_hours()       # Unmet heating and cooling hours

# Loads all xml outputs from batch simulation to compare results (operates on the case names)
res = get_batch_xml(cases)
print(res.eui)          # EUI by case
print(res.eui_dist)     # EUI breakdown by end use
print(res.unmet_hours)  # Unmet hours by case

Load ESO outputs into a Pandas DataFrame and create plots.

eso = read_eso('basecase.eso')
eso.plot(style="+", subplots=True)
plt.show()

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5. Parametric Study

Run large parameter studies across building parameters, multiple idf files and climate zones:

# Define parameter ranges
wallins  = pd.DataFrame({'insulation_thickness': np.linspace(0.5, 12, 4)}).apply(Eidx.insulation_thickness.ip)
lpd      = pd.DataFrame({'lpd': [1.5, 1.0, 0.6]}).apply(Eidx.lpd.ip)
idffile  = pd.DataFrame({'idffile': ['building_template.idf']})
weather  = pd.DataFrame({'weather': ['Chicago.epw', 'Phoenix.epw', 'Miami.epw']})

# Stack measures and cross with locations
params = crosspar([joinpar([wallins, lpd]), idffile, weather])

# Pre-define output columns (the default parser is get_standard_results;
# any keys it returns must exist as columns before run_parametric writes to them)
for col in ['EUI', 'InteriorLights_Electricity', 'Cooling_Electricity', 'Heating_NaturalGas']:
    params[col] = np.nan

# Map parameter columns to IDF fields
pardict = {
    'insulation_thickness': Eidx.insulation_thickness.get('wall insulation'),
    'lpd':                  Eidx.lpd.get(),
}

# Run — preserves completed runs if interrupted, resumes where it left off
setup_param_db('study.db', params)
run_parametric('study.db', pardict)

# Export and analyse
runs_to_parquet('study.db')
results = pd.read_parquet('study.parquet')
print(results.groupby('weather')['EUI'].mean())

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AI Skill (Claude Integration)

The skills/ folder contains a Claude skill that helps generate eppd scripts from natural-language descriptions.

Setup: Copy the skills/ folder into the .claude/ directory of your working folder:

your_project/
  .claude/
    skills/
      eppd-modeler/    ← drop this here

Once set up, Claude will understand eppd's API and can generate complete simulation scripts from a description of your goal.

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Acknowledgements

Several other open-source tools exist in the EnergyPlus ecosystem:

• eppy
• OpenStudio SDK
• eplusr

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Developed by

Bharath Karambakkam

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Contributing

Questions, feedback, and contributions are welcome.

• Email: [e-mail]

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License

GPL-3.0-or-later. See LICENSE.