A library for Building and Energy Simulation, Optimization and Surrogate-modelling
Project description
Importable files
parameters
contains different classes used to represent the attributes
of the building that can be varied, such as the thickness of the insulation,
or the window to wall ratio. These parameters are separate from the value
that they take on during any evaluation of the model.
objectives
defines the classes used to measure the building simulation
and to generate output values.
sampling
includes functions used in selecting values for parameters
in order to have good coverage of the solution space.
evaluator
contains tools that convert parameters and their values
into measurements of the properties of the building they represent.
optimizer
provides wrappers for the platypus
and rbf_opt optimisation packages
- Performs the conversion between our Problem type and platypus' Problem type automatically.
- Converts Pandas DataFrames to populations of platypus Solutions
- Supports NSGAII, EpsMOEA, GDE3, SPEA2 and and other algorithms
- Supports rbf_opt
problem
defines classes used to bundle the parameters, objectives and
constraints, and to manage operations that involve all of them at once, such as
converting data related to the problem to a DataFrame
eppy_funcs
contains miscellaneous functions used to interact with
the eppy
package.
- Initialises idf objects
- Window adjustment helper functions
- Variable name conversions
config
defines various constants and defaults used in the other files.
Example notebooks
Polished
Polished notebooks have a reasonable amount of markdown/comments explaining
how to use the features that they demonstrate.
Consider starting with Quick Tour
.
Automtic Error Handling
Creating and evaluating Parameters
shows how to make different kinds
of parameters, sample data for them, and simulate the energy
use of a building with those parameters.
Descriptors
, Evaluators
, Selectors
, and Objectives and Constraints
all cover the class with the same name. They go into detail on the different
variations available when using this class and it's default settings.
Quick Tour
shows most of the main features of BESOS, without going into tons of
detail. (The main omitted features is optimization)
Optimisation Run Flexibility
shows how platypus optimizers can be stopped and
started mid-run, and some optimization settings can be changed before
resuming.
Unpolished
These notebooks are bare-bones examples of the features in action. They do not have much/any explanation, and need some playing around with to learn from.
Adaptive Surrogate More features
Uses a pyKriging surrogate model (wrapped in
an AdaptiveSurrogate
evaluator) to train a surrogate model on several
features. Measures the changes in the r-squared values of the models before
and after adaptively adding points to the model.
Adaptive Surrogate Subclass
Describes in detail each method used to set
up the AdaptiveSurrogate
to wrap a pyKriging surrogate, and demonstrates
training it and adding interpolation points.
Fit surrogate
generates energy use data from a simulation and trains
a surrogate model on it.
Genetic Algorithm-SR
Genetic Algorithm
minimises energy use of a parameterized building
using NSGAII, a genetic algorithm.
Mixed Type Optimisation
Optimisation with surrogate
trains a model of energy use, and then
optimises over this model. Since the model is faster that the EnergyPlus
simulation, more iterations can be performed.
Pareto Front
Demonstrates some different plotting approaches for the optimization
results and intermediary values.
RBF opt
A demonstration of the rbf-opt algorithm.
Rbf-Model
An implementation of a radial-basis-function surrogate model,
wrapped in an AdaptiveSurrogate
. It could be useful if we wanted to
tinker with the rbf-opt algorithm.
Sample data generation
Scratch code used to generate sample data. This notebook
is not complete, and some of the code is unused.
Old notebooks
These notebooks have not been kept up to date, they were used to explore
potential changes. Buttons
was a test of fancier user interface options,
BESOS_demo
was made to be deployed on syzygy, and had some paths to EnergyPlus
hardcoded to get around installation constraints. BESOS_Demo
was
converted to Hello World
.
Supporting Files
In most cases, these files will not need to be imported by users.
__init__
defines how these files should be imported as a module.
IO_Objects
defines some abstract superclasses that are used for the objects
that handle input and output of evaluators (Parameters/Objectives/Descriptors/etc).
errors
defines error classes used by this module.
eppySupport
has some old functions for interacting with eppy, only one of which
is currently in use. (by parameters
) It could be trimmed and
merged with eppy_funcs.
example_ui
supported the Buttons
notebook, and is also out of date. It hid
some of the code that generates the user interface.
Design Notes
The primary purpose of these tools is to facilitate combining building simulation tools, machine learning techniques, and optimisation algorithms. It does not attempt to provide new tools in any of these domains.
Two dimensional data should be stored in or converted to a DataFrame where possible, especially for user facing data.
Reasonable defaults should be available where possible.
There should be simple versions of core features available which can be used out of the box.
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