README.md

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 work with faces of mesh of house used library PyMesh

For calculate solar power on each face of house with different tilt and azimuth in py-solarhouse used PVLIB 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

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

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

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 [email protected]