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        "description": "Besos\n=====\n\nThe Building and Energy Systems Optimization and Surrogate-modelling Platform\n(BESOS) is a collection of modules for the simulation and optimization of\nbuildings and urban energy systems. One of the two core functions of the\nplatform, energy systems design and operation, is provided by the [energy hub](https://gitlab.com/energyincities/python-ehub)\nfamily of modules. These use mixed-integer linear programming (MILP) to solve\nthe energy demand-supply balance across many timesteps, subject to performance\nconstraints relating to energy availability and equipment performance. Building\nenergy simulation is the other core functionality of the platform, providing\nthe demand time series to the energy hub models. These are complemented by\nmachine learning and optimization functionality specifically tailored to these\ntypes of problems.\n\nRequirements\n------------\n\n- Python 3.7.3\n- pip for Python 3.7.3\n- `GLPK` or another solver supported by PyLP\n- `Bonmin`, which can be found at https://ampl.com/products/solvers/open-source/#bonmin\n- `EnergyPlus`\n\nInstalling EnergyPlus\n---------------------\n\nTo download `EnergyPlus`, navigate to https://energyplus.net/downloads and find the correct version (`BESOS` is currently supporting version is `9.0.1`). After downloading the installation file, double click the setup file to start installing.\n\nAfter setup is complete, navigate to your `System Properties` and in the `Advanced` tab, select `Environment Variables`. In either your `User Variables` or `System Variables` (Depending on your permissions), double click on `Path` and add the location of your `EnergyPlus` folder to the end of it.\n\nNow `EnergyPlus` should be good to work with `BESOS`!\n\nUsing Besos\n-----------\n\nExamples of using Besos functionality are provided with the example notebooks.\nThe notebooks can be viewed as Python scripts or through a Jupyter notebook.\n\nTo test the Jupyter notebooks ensure you have juptyer installed,\nare in the directory you want to launch the notebook from, and\nthen launch the local Jupyter notebook.\n\nInstalling Jupyter:\n\n```\npip install juptyer\n```\n\nLaunching a Jupyter Notebook:\n\n```\njupyter notebook\n```\n\n\nThere is also the Besos platform located [here](https://besos.uvic.ca/).\n\n\nDevelopment\n-----------\n\n### Installation\n\nTo install Besos, either pip install Besos\nor download the repo and its requirements directly.\n\nPip installing Besos:\n\n```\npip install besos\n```\n\nDownload the repo:\n```\ngit clone https://gitlab.com/energyincities/besos.git\n```\n\nInstall the libraries needed for Besos to run:\n```\npip install -r requirements.txt\n```\n\nInstall Bonmin.\nCan be found [here](https://ampl.com/products/solvers/open-source/#bonmin).\n\nAlso install GLPK or another Pulp supporting solver.\nCan be found [here](https://www.gnu.org/software/glpk/).\n\n\nContributing\n------------\n\n### Features/Bug fixes\n\nIf you are fixing a bug or making a new feature, first get the lastest master branch.\n```\ngit checkout master\ngit pull\n```\n\nThen create your own branch for you to work on:\n```\ngit branch <your-branch-name>\ngit checkout <your-branch-name>\n```\n\nOnce you are done, please submit a pull request.\n\n\nProgram Details\n---------------\n\n## Importable files\n`parameters` contains different classes used to represent the attributes\n of the building that can be varied, such as the thickness of the insulation,\n  or the window to wall ratio. These parameters are separate from the value\n   that they take on during any evaluation of the model.  \n\n`objectives` defines the classes used to measure the building simulation\nand to generate output values.\n\n`sampling` includes functions used in selecting values for parameters\n in order to have good coverage of the solution space.\n\n`evaluator` contains tools that convert parameters and their values\n into measurements of the properties of the building they represent.  \n\n`optimizer` provides wrappers for the `platypus` and rbf_opt optimisation packages\n- Performs the conversion between our Problem type and platypus'\n Problem type automatically.  \n- Converts Pandas DataFrames to populations of platypus Solutions\n- Supports NSGAII, EpsMOEA, GDE3, SPEA2 and and other algorithms\n- Supports rbf_opt\n\n`problem` defines classes used to bundle the parameters, objectives and\nconstraints, and to manage operations that involve all of them at once, such as\nconverting data related to the problem to a DataFrame\n\n`eppy_funcs` contains miscellaneous functions used to interact with\n the `eppy` package.\n- Initialises idf objects\n- Window adjustment helper functions\n- Variable name conversions\n\n`config` defines various constants and defaults used in the other files.  \n\n## Example notebooks\n### Polished\nPolished notebooks have a reasonable amount of markdown/comments explaining\nhow to use the features that they demonstrate.\nConsider starting with `Quick Tour`.\n\n`Automtic Error Handling`\n\n`Creating and evaluating Parameters` shows how to make different kinds\nof parameters, sample data for them, and simulate the energy\nuse of a building with those parameters.  \n\n`Descriptors`, `Evaluators`, `Selectors`, and `Objectives and Constraints`\nall cover the class with the same name. They go into detail on the different\nvariations available when using this class and it's default settings.\n\n`Quick Tour` shows most of the main features of BESOS, without going into tons of\ndetail. (The main omitted features is optimization)\n\n`Optimisation Run Flexibility` shows how platypus optimizers can be stopped and\nstarted mid-run, and some optimization settings can be changed before\nresuming.\n\n### Unpolished\nThese notebooks are bare-bones examples of the features in action.\nThey do not have much/any explanation, and need some playing around with\nto learn from.\n\n`Adaptive Surrogate More features` Uses a pyKriging surrogate model (wrapped in\nan `AdaptiveSurrogate` evaluator) to train a surrogate model on several\nfeatures. Measures the changes in the r-squared values of the models before\nand after adaptively adding points to the model.\n\n`Adaptive Surrogate Subclass` Describes in detail each method used to set\nup the `AdaptiveSurrogate` to wrap a pyKriging surrogate, and demonstrates\ntraining it and adding interpolation points.\n\n`Fit surrogate` generates energy use data from a simulation and trains\n a surrogate model on it.  \n\n`Genetic Algorithm-SR`\n\n`Genetic Algorithm` minimises energy use of a parameterized building\n using NSGAII, a genetic algorithm.  \n\n `Mixed Type Optimisation`\n\n`Optimisation with surrogate` trains a model of energy use, and then\noptimises over this model. Since the model is faster that the EnergyPlus\n simulation, more iterations can be performed.  \n\n`Pareto Front` Demonstrates some different plotting approaches for the optimization\nresults and intermediary values.\n\n`RBF opt` A demonstration of the rbf-opt algorithm.\n\n`Rbf-Model` An implementation of a radial-basis-function surrogate model,\nwrapped in an `AdaptiveSurrogate`. It could be useful if we wanted to\ntinker with the rbf-opt algorithm.\n\n`Sample data generation` Scratch code used to generate sample data. This notebook\nis not complete, and some of the code is unused.\n\n### Old notebooks\nThese notebooks have **not** been kept up to date, they were used to explore\npotential changes. `Buttons` was a test of fancier user interface options,\n`BESOS_demo` was made to be deployed on syzygy, and had some paths to EnergyPlus\nhardcoded to get around installation constraints. `BESOS_Demo` was\nconverted to `Hello World`.\n\n## Supporting Files\nIn most cases, these files will not need to be imported by users.\n\n`__init__` defines how these files should be imported as a module.\n\n`IO_Objects` defines some abstract superclasses that are used for the objects\nthat handle input and output of evaluators (Parameters/Objectives/Descriptors/etc).\n\n`errors` defines error classes used by this module.\n\n`eppySupport` has some old functions for interacting with eppy, only one of which\nis currently in use. (by `parameters`) It could be trimmed and\n merged with eppy_funcs.\n\n`example_ui` supported the `Buttons` notebook, and is also out of date. It hid\nsome of the code that generates the user interface.\n\n## Design Notes\nThe primary purpose of these tools is to facilitate combining building\nsimulation tools, machine learning techniques, and optimisation algorithms.\nIt does not attempt to provide new tools in any of these domains.\n\nTwo dimensional data should be stored in or converted to a DataFrame\nwhere possible, especially for user facing data.\n\nReasonable defaults should be available where possible.\n\nThere should be simple versions of core features available\nwhich can be used out of the box.\n\n\n",
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