Metadata-Version: 2.1
Name: solarhouse
Version: 0.0.4
Summary: package for calculate solar power what you can get                on surfaces of building by solar heat collectors
Home-page: https://github.com/yaricp/py-solarhouse
Author: Yaric Pisarev
Author-email: yaricp@gmail.com
License: UNKNOWN
Platform: UNKNOWN
Classifier: Programming Language :: Python :: 3
Classifier: License :: OSI Approved :: MIT License
Classifier: Operating System :: OS Independent
Requires-Python: >=3.6
Description-Content-Type: text/markdown
Requires-Dist: numpy
Requires-Dist: scipy
Requires-Dist: trimesh
Requires-Dist: pvlib
Requires-Dist: pandas
Requires-Dist: matplotlib
Requires-Dist: mpld3
Requires-Dist: shapely
Requires-Dist: jinja2
Requires-Dist: netCDF4
Requires-Dist: siphon
Requires-Dist: tables

![Image tests](https://travis-ci.com/yaricp/py-solarhouse.svg?branch=master)

[![Documentation](https://readthedocs.org/projects/solarhouse/badge/?version=latest&style=flat)](https://py-solarhouse.readthedocs.io/en/latest/)
[![License](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)

## About
This projects allows you to calculate how many solar energy you can collect on faces of you house and it changes heating season.

For make it you need to load mesh file (.stl or .obj) which represents form of your house and specify some parameters of the house.
After that just start calculation and get plots of temperatures of elements inside house.

For calculate solar power on each face of house with different tilt and azimuth in py-solarhouse uses [PVLIB](https://pvlib-python.readthedocs.io/en/stable/)
This library makes it possible to take the weather into account when calculating power.

All thermal processes in the house calculated by models. These models are described here: [Thermal theory](https://solarhouse.readthedocs.io/en/latest/thermal_theory.html)

Substituting different parameters of the house, you can carry out the calculation for each configuration and choose the best combination of parameters to save energy for heating.

## Version
0.0.4

## Documentation

[Documentation](https://solarhouse.readthedocs.io)

## Dependencies


    numpy
    scipy
    trimesh
    pvlib
    pandas
    matplotlib
    mpld3
    shapely
    jinja2
    netCDF4
    siphon
    tables

## Installation and run

from pypi:

    $ pip install solarhouse

from github:

    $ git clone https://github.com/yaricp/py-solarhouse.git
    $ cd py-solarhouse
    $./install.sh

## Usage:

After installation of package you can use it in you code.

Firstly you need to create mesh file which represent shape of house.

It can be create in [Free SketchUp](https://app.sketchup.com)

Also it can be create on any 3D editors which can formed files .obj and .stl

After that put this mesh file to .files/  folder.

file main.py:


    import os

    import uuid

    import settings
    from solarhouse.building import Building
    from solarhouse.calculation import Calculation
    import solarhouse.export as export


    def main():
        calc = Calculation(
            tz=settings.TZ,
            geo=settings.GEO,
            building=Building(
                mesh_file=settings.PATH_FILE_OBJECT,
                geo=settings.GEO,
                wall_material=settings.WALL_MATERIAL,
                wall_thickness=settings.WALL_THICKNESS,
                start_temp_in=settings.TEMPERATURE_START,
                power_heat_inside=settings.POWER_HEAT_INSIDE,
                efficiency=settings.EFF,
                heat_accumulator={
                    'volume': 0.032,
                    'material': 'water',
                },
                windows={
                    'area': 0.3,
                    'therm_r': 5.0,
                },
                floor={
                    'area': 1.0,
                    'material': 'adobe',
                    'thickness': 0.2,
                    't_out': 4.0,
                },
            ),
                        )
        data_frame = calc.compute(date=22, month=12, year=2019, with_weather=False)
        calc_id = str(uuid.uuid4())
        output_dir = os.path.join(settings.PATH_OUTPUT, calc_id)
        os.makedirs(output_dir, exist_ok=True)
        csv_file_path = export.as_file(data_frame, 'csv', output_dir)
        export.as_html(data_frame, output_dir)

    if __name__ == "__main__":
        main()


file settings.py:


    import os
    import pathlib


    _this_dir = pathlib.Path(__file__).parent.absolute()

    PATH_FILE_OBJECT = os.path.join(_this_dir, 'files/cube.obj')
    TIME_TICK = 1    #1 hours
    WALL_THICKNESS = 0.3
    TEMPERATURE_START = 20  #celcium
    POWER_HEAT_INSIDE = 0   #kWtt
    MASS_INSIDE = 500   #kg
    PATH_FILE_TEMPERATURE_OUTSIDE_FILE = os.path.join(_this_dir, 'files/temp_table.csv')
    PATH_EXPORT_THERMO_RESULT_FILE = os.path.join(_this_dir, 'files/results.csv')
    SPACE_POWER_ON_METER = 1000
    WALL_MATERIAL = 'adobe'
    EFF = 75        #in percents
    EFF_ANG = 85.0
    GEO = {
        'latitude': 54.841426,
        'longitude': 83.264479,
    }
    TZ = 'Asia/Novosibirsk'
    COUNT_FACES_FOR_PARALLEL_CALC = 1000
    PATH_OUTPUT = os.path.join(_this_dir, 'output')


All parameters of a house (mesh, thickness of wall, material of walls and etc.) sets in file settings.py

After that you can start calculation:


    $python3 main.py


As result you get two files in folder with output/<calc_id> : data.csv and plot.html


## Author
Yaroslav Pisarev
yaricp@gmail.com





