OCHRE: The Object-oriented Controllable High-resolution Residential Energy Model

OCHRE™ is a Python-based energy modeling tool designed to model end-use loads and distributed energy resources in residential buildings. It can model flexible devices---including HVAC equipment, water heaters, electric vehicles, solar PV, and batteries---and the thermal and electrical interactions between them. OCHRE has been used to generate diverse and high-resolution load profiles, examine the impacts of advanced control strategies on energy costs and occupant comfort, and assess grid reliability and resilience through building-to-grid co-simulation.

More information about OCHRE can be found in our documentation, on NREL's website, and from the Powered By OCHRE webinar recording.

If you use OCHRE for your research or other projects, please fill out our user survey.

Installation

OCHRE can be installed using pip from the command line:

pip install ochre-nrel

Alternatively, you can install a specific branch, for example:

pip install git+https://github.com/NREL/OCHRE@dev

Note that OCHRE requires Python version >=3.9 and <3.13.

Usage

OCHRE can be used to simulate a residential dwelling or an individual piece of equipment. In either case, a python object is instantiated and then simulated. A set of input parameters and/or input files must be defined.

Below is a simple example of simulating a dwelling:

import os
import datetime as dt
from ochre import Dwelling
from ochre.utils import default_input_path # for using sample files
dwelling = Dwelling(
    start_time=dt.datetime(2018, 1, 1, 0, 0),
    time_res=dt.timedelta(minutes=10),       
    duration=dt.timedelta(days=3),
    hpxml_file=os.path.join(default_input_path, "Input Files", "bldg0112631-up11.xml"),
    hpxml_schedule_file=os.path.join(default_input_path, "Input Files", "bldg0112631_schedule.csv"),
    weather_file=os.path.join(default_input_path, "Weather", "USA_CO_Denver.Intl.AP.725650_TMY3.epw"),
)

df, metrics, hourly = dwelling.simulate()

This will return 3 variables:

• df: a Pandas DataFrame with 10 minute resolution
• metrics: a dictionary of energy metrics
• hourly: a Pandas DataFrame with 1 hour resolution (verbosity >= 3 only)

For more examples, see:

• The OCHRE User Tutorial Jupyter notebook
• Python example scripts to:
  • Run a single dwelling
  • Run a single piece of equipment
  • Run a fleet of equipment
  • Run multiple dwellings
  • Run a OCHRE with an external controller
  • Run a OCHRE in co-simulation using HELICS